Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 318 appraisals
The American journal of pathology
An Integrated Machine Learning and Genomic Framework for Precise Detection of Gastric Cancer
This study presents a novel integrative approach for the analysis of high-dimensional gene expression data, leveraging the complementary strengths of unsupervised clustering and supervised classification. Using K-means clustering, the data set is stratified into three distinct clusters, revealing intrinsic biological patterns and relationships. The resulting cluster assignments are subsequently used as pseudolabels to train machine learning models, including support vector machines, random forest, and a stacking ensemble classifier. To validate and enhance the robustness of clustering, complementary methods, such as hierarchical clustering and density-based spatial clustering of applications with noise (DBSCAN), are used, with results visualized through principal component analysis-driven dimensionality reduction. The high predictive accuracy achieved by the classifiers underlines the separability and reliability of the identified clusters. Furthermore, feature importance analysis highlighted key genetic determinants within each cluster, offering actionable insights into potential biomarkers and critical genomic features. This framework bridges the gap between exploratory unsupervised learning and predictive supervised modeling, providing a scalable and interpretable method for analyzing complex genomic data sets. Its applicability extends to biomarker discovery, patient stratification, and other precision medicine applications, emphasizing its utility in advancing genomic research and clinical practice.
3 Aug 2026
Read appraisal →Current opinion in chemical biology
Prediction of protein-protein interactions and co-complex models with deep learning
Protein-protein interactions (PPIs) are fundamental to cellular processes, and essential for understanding biological function and disease mechanisms. In this review, we emphasize recent deep learning-based methods for protein interaction study. Focusing on three closely related tasks of proteome-wide PPI prediction, PPI interface prediction, and PPI co-complex structure prediction, we discuss how emerging concepts and computation approaches have evolved to shape these fields We categorize recent approaches according to their methodological paradigms, summarize their strengths and limitations, and further explore diverse biological and biomedical applications, highlighting how computational methods in PPI prediction, PPI interface prediction, and PPI structure prediction jointly contribute to understanding of complex biological systems.
3 Aug 2026
Read appraisal →European journal of radiology
A CT-based deep learning model to differentiate between benign and malignant adrenal lesions
OBJECTIVE: To develop a deep learning model to differentiate benign from malignant adrenal lesions. MATERIALS AND METHODS: A total of 380 patients with 385 pathologically confirmed adrenal lesions (101 malignant, 284 benign) were retrospectively included. Adrenal lesions were manually segmented on CT images and analyzed in a deep learning pipeline aimed at differentiating benign from malignant lesions. Four predictive models were developed that incorporated combinations of radiological data (tumor size, and spontaneous attenuation) and non-radiological data (i.e., medical history and laboratory results). Data of 267 patients were used as a training set and those of 113 patients for the test set. The diagnostic capabilities of the four models were estimated using sensitivity, specificity, accuracy, and areas under the receiver operating characteristic curves (AUC) using histopathological findings as the gold standard. The reproducibility of manual segmentation was estimated using the Dice similarity coefficient after blinded resegmentation of 40 adrenal lesions by an independent radiologist. RESULTS: Segmentation reproducibility achieved a mean Dice similarity coefficient of 0.92 ± 0.03 (range: 0.72-0.97). The most accurate model, which combined clinical, biological, and radiological data, achieved 84.2% accuracy (95% confidence interval: 79.9, 88.6) and an AUC of 0.93 (95% confidence interval: 89.9, 97.9) in the test set for diagnosis of malignant adrenal lesion. CONCLUSION: A deep learning model integrating preoperative clinical, biological, and radiological features demonstrates high capabilities in differentiating benign from malignant adrenal lesions on initial CT examination.
3 Aug 2026
Read appraisal →CPT: pharmacometrics & systems pharmacology
Structured Schemas for Provenance-Rich, LLM-Assisted QSP Model Calibration
Quantitative systems pharmacology (QSP) models require calibration data from literature, yet manual curation is inconsistently documented and large language model (LLM) extraction can hallucinate values and fabricate citations. We present MAPLE (Model-Aware Parameterization from Literature Evidence), which uses structured validation schemas as a collaboration interface between LLMs and modelers. Two schemas span two scales: the SubmodelTarget schema for isolated experiments constraining individual parameters, and the CalibrationTarget schema for clinical and in vivo endpoints constraining the full model. Both separate data extraction from modeling decisions, recording every value with full provenance. Targeted validators catch characteristic LLM errors by matching values to source snippets, resolving DOIs, and executing code. For a pancreatic ductal adenocarcinoma QSP model, we used MAPLE to extract and curate 37 SubmodelTargets and 45 CalibrationTargets. Before any human review, the validators triggered 50 automated retries; every value carries a direct quote from its source and a verified citation; and 11 of 19 parameters are supported by more than one independent source. The LLM drafted usable forward models and code from context, while the modeler supplied the context and scientific judgment it cannot infer, revising forward-model choices in 65% of SubmodelTargets, priors in 46%, and source relevance in all files. This evaluation covers one model in one disease area, by a single group, so it characterizes the framework rather than establishing how broadly it generalizes. MAPLE records the modeler's reasoning in a form that can be re-run and independently checked, so it is not lost when the modeling team changes.
2 Aug 2026
Read appraisal →Medical physics
Reference-Free large language model agents for physician-guided radiotherapy treatment planning
BACKGROUND: Large language models (LLMs) have recently demonstrated exceptional capabilities, offering the potential to streamline workflows and enhance efficiency across diverse tasks. However, their application in domain-specific areas, such as radiotherapy treatment planning, remains challenging due to the lack of publicly available, specialized knowledge required for these tasks. PURPOSE: To investigate the critical architectural and functional components necessary to adapt an off-the-shelf large language model into a clinically viable planning agent for inverse treatment planning in intensity-modulated radiation therapy (IMRT). MATERIALS/METHODS: Twenty head-and-neck (HN) cancer patients who received IMRT at our institution were retrospectively collected under IRB approval. The LLM agent was implemented to directly interact with the clinical treatment planning system (TPS) to iteratively extract intermediate plan states and propose new constraint values to guide inverse optimization. Its decision-making was informed by real-time plan evaluations and prior optimization outcomes, enabling adaptive refinement of planning strategies across iterations. The agent was equipped with three core capabilities: comprehension of clinical planning objectives, contextual understanding of the optimization environment, and arithmetic proficiency for quantitative reasoning. Treatment planning was conducted in a reference-free inference setting, wherein the LLM operated without prior exposure to manually generated treatment plans and without any fine-tuning or task-specific training. LLM-generated treatment plans were compared with clinically approved plans created by certified dosimetrists. Key dosimetric endpoints were evaluated and statistically analyzed. Paired comparisons between LLM-generated and clinical plans were performed using the Wilcoxon signed-rank test. RESULTS: LLM-generated plans achieved comparable organ-at-risk (OAR) sparing relative to clinical plans, while demonstrating improved hot spot control (Dmax: 106.5% vs. 108.8%, p < 0.05) and superior conformity (conformity index: 1.18 vs. 1.39, p < 0.05 for boost PTV; 1.82 vs. 1.88, p = 0.47 for primary PTV). CONCLUSIONS: This study demonstrates the feasibility of a reference-free, LLM-driven workflow for automated IMRT treatment planning in a commercial TPS. The proposed approach provides a generalizable solution that could reduce planning variability and support broader adoption of AI-based planning strategies.
2 Aug 2026
Read appraisal →Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico
Research progress of machine learning applications in gastric cancer diagnosis and therapy
Gastric cancer (GC), a malignant neoplasm originating from the gastric mucosal epithelium, represents one of the most prevalent cancers worldwide. Early detection is critical for improving treatment outcomes and patient prognosis. Recent advances in artificial intelligence (AI), particularly in machine learning, have introduced powerful computational and analytical capabilities that are increasingly being applied in GC research. Machine learning algorithms have shown considerable promise in enhancing the accuracy of GC diagnosis and optimizing therapeutic strategies. This review provides a concise overview of progress in machine learning applications within oncology, examines their current role and clinical utility in GC diagnosis and treatment, and highlights the transformative potential of machine learning in advancing GC management and patient care.
2 Aug 2026
Read appraisal →Journal of the American Medical Informatics Association : JAMIA
Explainable machine learning in healthcare: methods, interpretation, and applications for clinical research
OBJECTIVES: To provide a practical and methodologically grounded overview of explainable machine learning (XML) approaches in healthcare, with emphasis on their interpretation and application in clinical research and decision support. By moving beyond traditional predictive models, this primer aims to foster trust, transparency, and informed clinical decision-making, ultimately bridging the gap between data science and medical practice. MATERIALS AND METHODS: We present a structured review of commonly used XML methodologies, including global and local interpretability tools such as SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), Partial Dependence Plots (PDP), and Individual Conditional Expectation (ICE) plots. For each method, we explain the underlying mechanism at a high level, visualize representative outputs, and provide structured guidance on interpretation, appropriate use, and limitations, illustrated using the publicly available Heart Disease dataset. RESULTS: XML techniques provided intuitive visual and quantitative insights into how predictors influence model predictions. Global methods characterized population-level feature effects, whereas local methods revealed patient-level contributions useful for individualized interpretation. Our worked examples demonstrate how XML outputs can identify nonlinear relationships, detect interaction effects, and reveal heterogeneity in predicted risk across patients, addressing key challenges in translating ML predictions into interpretable outputs for clinical research. DISCUSSION AND CONCLUSION: XML tools offer valuable interpretability for ML models and support more transparent and accountable ML applications in clinical research. By providing a methodologically grounded overview alongside practical implementation examples and structured guidance on each method's strengths and limitations, this primer helps bridge the gap between advanced ML methodology and clinical applicability. Thoughtful adoption of XML approaches may facilitate better understanding, communication, and critical evaluation of ML predictions in healthcare research, ultimately supporting evidence-based clinical decision-making.
2 Aug 2026
Read appraisal →Journal of pharmacokinetics and pharmacodynamics
Diffusion models for virtual populations and pharmacometric simulations
To evaluate diffusion model-based artificial intelligence approaches for generating virtual populations with physiological determinants of drug dosing (PDODD) and pharmacokinetic (PK) profiles. A denoising diffusion probabilistic model (DDPM) was applied to a 31-variable dataset of PDODD covariates (18 continuous, 13 binary) from the National Health and Nutrition Examination Survey and compared to a tabular variational autoencoder (TVAE). For nivolumab PK data (12,000 patients, 13 time points, 5 covariates), sequence-based diffusion model (SDM) and a time-aware diffusion model (TDM) with temporal self-attention were evaluated. The predictive performance of the TDM was evaluated by imputing masked time points. All models were trained and tested on 80%:20% partitions of the data using univariate, bivariate, and multivariate distributional similarity metrics. The diffusion model satisfactorily approximated the univariate distributions of continuous PDODD biomarkers (mean Kolmogorov-Smirnov D-statistic, KSD = 0.014), disease status frequencies (mean absolute error, MAE = 0.31%), and preserved bivariate correlations (MAE = 0.033). DDPM outperformed TVAE for categorical variables (0.31% vs. 1.07% MAE) and correlation (0.033 vs. 0.091 MAE). For nivolumab PK, SDM has KSD of 0.047 and a relative error of 1.36%. TDM accurately imputed missing PK timepoints (KSD = 0.014), reconstructing masked Day 1, Peak concentration (Cmax) Dose-9, and Terminal phase concentrations with MAE of 0.43%, 0.25%, and 0.76%, and correlations ≥ 0.999. Diffusion models demonstrated strong performance in generating cross-sectional PK covariate data and longitudinal PK profiles, capturing complex distributional and temporal dependencies. Diffusion-based approaches provide a flexible and robust framework for virtual simulations in pharmacometrics.
1 Aug 2026
Read appraisal →Journal of chemical information and modeling
HyperDC: A Non-Uniform Hypergraph Framework for Dual- and Higher-Order Drug Combination Recommendation Across Diverse Complex Diseases
Complex diseases are commonly characterized by dysregulation across multiple targets and pathways, making drug combinations an important therapeutic strategy. This creates a growing need for computational methods to prioritize candidate combinations, yet current methods face several practical challenges, including cross-disease application, unified prediction of dual- and multidrug combinations, lack of reliable negative labels, and prediction for low-resource diseases. In this study, we proposed HyperDC, a unified cross-disease drug combination recommendation framework. HyperDC constructs a nonuniform hypergraph based on clinical and knowledge-driven drug-disease association data, representing single drugs and dual- and multidrug combinations in a unified modeling space. It further integrates knowledge graph pretraining and adversarial negative sampling to enhance model discrimination across tasks with different difficulty levels. In the unified dual-drug benchmark and disease-specific prediction tasks, HyperDC outperformed representative methods by up to 12.8 and 30.0 percentage points, respectively. In fixed-anchor clinical ranking tasks, HyperDC showed superior performance across multiple top-ranked recall settings and preferentially recalled FDA-approved dual-drug combinations and clinically supported three-drug combinations. In the data-sparse metabolic dysfunction-associated steatohepatitis (MASH) scenario, HyperDC completed a full workflow from anchor-drug identification and combination partner prioritization to in vitro experimental validation: 70% of the top 10 single-drug candidates were supported by recent literature, and 4 of 6 tested combinations showed synergistic lipid-lowering and anti-inflammatory effects, yielding a hit rate of 66.7%. Overall, HyperDC may narrow the screening space, improve prioritization efficiency, and provide methodological support for the development of combination therapy strategies for complex diseases.
29 July 2026
Read appraisal →Journal of chemical information and modeling
Functionally Guided Graph Learning for Robust Cross-Patient Cell-Type Annotation in Single-Cell RNA Sequencing
Cross-patient cell-type annotation in single-cell RNA sequencing (scRNA-seq) remains challenging due to pronounced interpatient heterogeneity and distribution shifts across patient-specific cellular contexts. Conventional annotation approaches often rely on proximity-driven graph construction or expression similarity, which may introduce spurious cell-cell connections and lead to unstable knowledge transfer across patients. To address this limitation, we propose PathoGraph, a functionally guided graph learning framework for robust cross-patient cell-type annotation. The proposed method integrates KEGG-7-based biosemantic graph structure learning with cross-patient representation adaptation. Specifically, pathway-derived functional semantic profiles are incorporated to refine patient-specific cell graphs, encouraging biologically coherent neighborhoods and suppressing noise introduced by purely expression-based similarity. Based on the refined graphs, a cross-patient representation adaptation mechanism further aligns embeddings between labeled reference patients and unlabeled query patients to facilitate reliable annotation transfer. Experiments on three cross-patient scRNA-seq data sets, including leukemia, breast invasive carcinoma, and colorectal cancer data sets, demonstrate that PathoGraph achieves stable annotation performance across 32 directed reference-to-query transfer tasks. Across all tasks, PathoGraph obtained an average ACC of 84.28% and an F1-score of 84.08%, showing competitive and stable performance compared with representative marker-based, correlation-based, and model-based annotation methods. Ablation studies further show that removing the biosemantic graph learning module reduces the average accuracy to 83.48%, highlighting the importance of functional-guided graph refinement. In addition, post hoc functional relevance analyses in immune-cell and cancer-associated contexts suggest that the learned cell-cell graphs capture biologically relevant neighborhood structures beyond expression-driven proximity. The source code and processed data are publicly available at: https://github.com/LiYuechao1998/PathoGraph.
29 July 2026
Read appraisal →Histochemistry and cell biology
Molecular plasticity of LAMA3 across the disease spectrum: pathogenic mechanisms and clinical translation.
The laminin α3 chain, encoded by LAMA3, constitutes a principal component of laminin-332 (LN-332), an essential extracellular matrix (ECM) glycoprotein governing cell adhesion, proliferation, and tissue homeostasis. This systematic review consolidates current evidence on the molecular features and regulatory mechanisms of LAMA3, including its roles in PI3K/Akt, epithelial-mesenchymal transition (EMT), and Hippo-YAP signaling, as well as epigenetic and post-transcriptional modulation. Its context-dependent functions across distinct pathological states are delineated. In malignancies, including colorectal and ovarian cancers, LAMA3 functions as an oncogenic determinant that enhances invasion, metastatic dissemination, and chemotherapeutic resistance. In contrast, in hereditary diseases such as junctional epidermolysis bullosa (JEB) and chronic disorders such as idiopathic pulmonary fibrosis (IPF), LAMA3 deficiency or dysfunction constitutes a structural basis of tissue pathology. From a translational standpoint, elevated LAMA3 expression has been recognized as an independent prognostic indicator in pancreatic ductal adenocarcinoma, whereas LAMA3 promoter methylation is a candidate biomarker for platinum resistance in ovarian cancer. LAMA3-directed gene therapy for JEB has progressed to clinical evaluation. Current limitations in the field are critically examined, and emerging therapeutic approaches, including proteolysis-targeting chimeras (PROTACs), are discussed. Collectively, an integrated framework that connects LAMA3 biology with clinical applications is presented to inform future investigations and precision therapeutic strategies targeting this multifunctional molecule.
29 July 2026
Read appraisal →Biomedical physics & engineering express
BMSNet: a boundary-guided multi-scale polyp segmentation network
Accurate polyp segmentation in colonoscopy images is essential for computer-aided diagnosis and early colorectal cancer screening. However, this task remains challenging due to large variations in polyp size, shape, and texture, as well as blurred boundaries and low contrast between polyps and surrounding mucosa. To address these challenges, we propose boundary-guided multi-scale polyp segmentation network (BMSNet), a boundary-guided multi-scale network for accurate polyp segmentation. First, an Scale-Aware Modulation Meets Transformer-T backbone initialized with officially released pretrained weights is adopted to extract multi-level features efficiently. Then, a boundary prediction module is introduced to generate preliminary boundary priors through bidirectional feature interaction and differentiable Canny-based auxiliary supervision. Based on the generated boundary information, we design a boundary-guided feature enhancement module to refine feature representations. This module combines frequency-domain enhancement, multi-scale feature interaction, and boundary-guided modulation. Finally, a feature fusion unit is introduced to progressively integrate multi-scale features and generate the final segmentation prediction. Extensive experiments on five benchmark datasets demonstrate the effectiveness of the proposed method. On ClinicDB, Kvasir, ColonDB, ETIS, and CVC-300, BMSNet achieves Dice scores of 0.943, 0.920, 0.828, 0.825, and 0.913, respectively. These results indicate that BMSNet achieves competitive segmentation performance with moderate computational complexity under the evaluated experimental setting.
28 July 2026
Read appraisal →Journal of chemical information and modeling
Unveiling Large-Scale Kinase-Centric Protein-Protein Interactions through a Knowledge-Informed Workflow
Protein phosphorylation regulates signaling, yet atomic-level substrate specificity remains elusive due to sparse structural data and phosphorylation-site-insensitive deep-learning predictors. Here we present a pipeline reformulating kinase-substrate modeling as a Bayesian inference problem. By integrating curated data sets and literature evidence parsed by Large Language Models, we converted diverse biological knowledge into structural restraints for the restraint-guided deep-learning model GRASP. For EGFR, BRAF and JNK1, we obtained 336 new phosphorylation-site-specific structure candidates refined by molecular dynamics. These models recapitulate known features, such as JNK1's hydrophobic docking groove, and enabled a Virtual Position Scanning Peptide Array (V-PSPA) to map recognition patches and derive sequence preferences. Cross-referencing predicted interfaces with AlphaMissense pathogenicity scores reveal that the interaction types and distances to the catalytic pocket significantly influence pathogenicity scores. A comparison with clinical mutation data sets further connects pathogenic mutations to the kinase-substrate interface. This high-resolution, high-throughput pipeline can be broadly applicable to kinase specificity studies and general drug discovery.
28 July 2026
Read appraisal →Journal of chemical information and modeling
Machine Learning for RNA-Targeting Drug Design
Targeting RNA with small molecules offers significant therapeutic potential. Machine learning could substantially accelerate preclinical drug discovery, from hit identification to lead optimization. Yet a limitation emerges: drug design machine learning models, designed for proteins, are not readily applicable to RNAs because of fundamental differences between RNAs and proteins in both structural characteristics and interactions with small molecules. RNA-specific approaches have consequently emerged, primarily focusing on binding site identification and virtual screening. In this review, we comprehensively compare machine learning tools for RNA-targeting drug design according to the tasks they address, their methodology and their relevance in RNA-specific contexts. As open challenges will catalyze new method development, we emphasize the need for standardized, drug design-specific evaluation approaches. We provide clear guidelines to establish these standards and present a benchmark assessing the ability of current machine learning models to predict specific drug-RNA interactions.
28 July 2026
Read appraisal →Medicine
Performance of DeepSeek V3 and ChatGPT-4o in answering esophageal cancer-related questions
Esophageal cancer remains a significant global health issue. ChatGPT-4o and DeepSeek V3 can provide the public with health-related knowledge about esophageal cancer. This study aimed to evaluate the accuracy of DeepSeek V3 and ChatGPT-4o in responding to health knowledge questions related to esophageal cancer. Fifty-two questions related to esophageal cancer were classified into themes of basic knowledge, diagnosis and molecular biology, management of local and locoregional diseases, management of advanced and metastatic diseases, clinical case analysis and patient frequently asked questions (FAQs). These questions were entered into DeepSeek V3 and ChatGPT-4o to obtain responses, and 2 experienced gastroenterologists independently evaluated the accuracy and temporal stability of each response. Overall, the scores of DeepSeek V3 and ChatGPT-4o on all questions were 4 (3-4), and there was no statistically significant difference between the 2 groups. The final scores of DeepSeek V3 in basic knowledge, diagnosis and molecular biology, management of local and locoregional diseases, management of advanced and metastatic diseases, clinical case analysis, and FAQs were 4 (3-4), 4 (3-4), 4 (3-4), 3 (3-4), 4 (4-4), and 4 (4-4), respectively, while the scores of ChatGPT-4o were 4 (3-4), 3 (2-4), 4 (3-4), 3 (3-4), 4 (4-4), and 4 (4-4), respectively. For temporal stability across 2 independent test runs, DeepSeek V3 presented inconsistent responses on 2 questions, and ChatGPT-4o on 1 question; no statistically significant differences were found in overall and subgroup scores between the 2 runs for both models (all P > .05). ChatGPT-4o and DeepSeek V3 showed favorable accuracy and comprehensive responses to most of the 52 esophageal cancer-related questions in this study, but our findings do not confirm their general reliability for esophageal cancer health information in routine clinical or public use.
27 July 2026
Read appraisal →Scandinavian journal of pain
Multimodal deep learning fusion for automatic pain detection in cancer patients
INTRODUCTION: Since pain is a multidimensional and subjective experience, pain assessment remains challenging. With advances in artificial intelligence (AI), automatic pain assessment (APA) systems offer a valuable opportunity for objective pain evaluation. However, most approaches focus on a single modality. In this proof-of-concept study, exploring multimodal fusion strategies in a controlled experimental setting, we present a deep learning framework for multimodal fusion that combines facial, acoustic, and textual information to improve APA in cancer patients. METHODS: A multimodal dataset was created from video-recorded interviews with oncologic patients. In Phase I, audio, video, and transcripts were segmented at the sentence level and temporally aligned using the Eudico Linguistic Annotator (ELAN) to ensure frame-level correspondence across modalities. In Phase II, modality-specific features were extracted: Facial Action Units from OpenFace, acoustic descriptors (MFCCs, chroma, spectral contrast, and Mel-spectrogram) from a dedicated speech-processing pipeline, and sentence-level textual embeddings from ITA-BERT. During training, the most effective analytical strategy was chosen through knowledge transfer approaches. The ELAN-assisted annotation pipeline streamlined expert labeling. Two architectures were implemented and compared: bimodal autoencoder fusion models and a transformer-based model with pairwise cross-modal attention. These models were trained and evaluated using subject-independent and stratified 5-fold cross-validation. To address the lack of independence between segments, a strictly subject-independent cross-validation strategy was adopted. RESULTS: Knowledge transfer using pretrained large-scale models outperformed traditional feature-based approaches and was applied to multimodal pain detection. Multimodal models achieved performance comparable to the strongest unimodal modality (text), while showing improved balance across modalities, suggesting potential complementary effects. Both multimodal architectures demonstrated high accuracy in distinguishing between pain and non-pain classes. The bimodal autoencoder achieved stable results across folds, with a mean accuracy of about 80 % and balanced error distribution. The pairwise transformer with cross-modal attention achieved similar performance, with smooth training and validation loss curves. No evident divergence between training and validation loss curves was observed across folds, suggesting stable behavior within the cross-validation setting. However, subject-level overfitting cannot be excluded given the limited sample size. CONCLUSION: Multimodal fusion enhances system robustness by integrating complementary signals. Despite limitations and the need for improvement, multimodal deep learning strategies can support the detection of observable pain-related expressions.
26 July 2026
Read appraisal →Journal of medical ethics
AI interventions in cancer screening: balancing equity and cost-effectiveness
This paper examines the integration of artificial intelligence (AI) into cancer screening programmes, focusing on the associated equity challenges and resource allocation implications. While AI technologies promise significant benefits-such as improved diagnostic accuracy, shorter waiting times, reduced reliance on radiographers, and overall productivity gains and cost-effectiveness-current interventions disproportionately favour those already engaged in screening. This neglect of non-attenders, who face the worst cancer outcomes, exacerbates existing health disparities and undermines the core objectives of screening programmes.Using breast cancer screening as a case study, we argue that AI interventions must not only improve health outcomes and demonstrate cost-effectiveness but also address inequities by prioritising non-attenders. To this end, we advocate for the design and implementation of cost-saving AI interventions. Such interventions could enable reinvestment into strategies specifically aimed at increasing engagement among non-attenders, thereby reducing disparities in cancer outcomes. Decision modelling is presented as a practical method to identify and evaluate these cost-saving interventions. Furthermore, the paper calls for greater transparency in decision-making, urging policymakers to explicitly account for the equity implications and opportunity costs associated with AI investments. Only then will they be able to balance the promise of technological innovation with the ethical imperative to improve health outcomes for all, particularly underserved populations. Methods such as distributional cost-effectiveness analysis are recommended to quantify and address disparities, ensuring more equitable healthcare delivery.
25 July 2026
Read appraisal →mAbs
Optimization strategies for monoclonal antibody production: advances in simulation and artificial intelligence in bioprocessing
The rapid growth of monoclonal antibody (mAb) therapies has increased the need for efficient, scalable, and affordable manufacturing processes. However, mAb production remains complex because of nonlinear upstream cell culture behavior, expensive downstream purification, especially Protein-A chromatography, and plant-level bottlenecks that can increase cost, cycle time, and manuacturing uncertainty. This review examines recent developments in mAb manufacturing with focus on process simulation, mathematical optimization, and artificial intelligence/machine learning (AI/ML) across upstream processing (USP), downstream processing (DSP), and integrated plant-level operation. In USP, media optimization, dynamic feeding, high-density cultures, and continuous perfusion bioreactors are discussed in relation to productivity and critical quality attributes (CQAs). In DSP, alternative and intensified purification strategies are reviewed with a focus on recovery, impurity clearance, scalability, cost, and technology maturity. AI/ML applications are also discussed from early-stage development and cell-line screening to upstream control, CQA prediction, chromatography optimization, and downstream decision support. Despite these advancements, challenges such as data heterogeneity, limited standardized datasets, model transferability, and regulatory constraints remain important barriers to implementation. Overall, this review uniquely connects simulation and AI/ML approaches to practical optimization across the full mAb manufacturing workflow, including design, scheduling, debottlenecking, purification, monitoring, and quality prediction. The combination of process simulation, continuous bioprocessing, and AI/ML-based decision support may enable more flexible, reliable, and cost-effective mAb manufacturing. However, these benefits depend on validation through robust models, process-specific case studies, and technoeconomic analysis.
25 July 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Initial development and usability testing of a digital fertility preservation navigator for newly diagnosed young adult cancer survivors
PURPOSE: Young adults with cancer (YAs) are rarely provided the necessary resources to make informed decisions about fertility preservation (FP) prior to beginning treatment. Digital decision aids are a promising strategy to ensure YAs receive timely, guideline-concordant information. We developed an alpha version Fertilit-e, a digital FP navigator, and conducted usability testing to obtain feedback from YAs. METHODS: We designed an alpha version of Fertilit-e that drew from existing evidence-based FP resources. Feedback was obtained from two waves of participants using a think-aloud framework, and modifications were made to the decision aid. RESULTS: Across waves, participants (n = 12) understood the purpose of Fertilit-e and thought modules were comprehensive and easy to understand. The usability of Fertilit-e was rated as excellent across waves and devices (iPad and computer). Participants suggested reducing the use of and better defining medical terminology and adding text to videos to enhance comprehensibility. CONCLUSIONS: YAs reported that Fertilit-e provided important information regarding FP in an understandable manner. Participants made suggestions for how to improve comprehensibility and usability that will guide future iterations of Fertilit-e. For example, tailoring algorithms and reorganization may direct users to the most relevant information. IMPLICATIONS FOR CANCER SURVIVORS: Digital decision aids may be a feasible and useful strategy for helping YAs navigate decisions regarding whether to pursue FP at the time of diagnosis. Additional research is needed to refine and evaluate such tools before integration into routine care.
24 July 2026
Read appraisal →Cell reports. Medicine
Toward generalizable prediction of cancer signal using a cell-free DNA language model.
Cell-free DNA can be used for early cancer detection, minimal residual disease monitoring, and post-treatment risk stratification. However, current assays are often designed for a single purpose and rely on deep or broad sequencing panels that capture only a small fraction of tumor-derived signals, limiting transferability, increasing cost, and reducing scalability. Fragmentia-AI is an artificial intelligence language model that learns fragment-level sequence patterns in tumor-derived cell-free DNA. Instead of focusing on mutations, it uses the structure of cell-free DNA to detect cancer signals in a partially panel-agnostic manner from ultra-low sequencing input, approximately 0.1%-1% of conventional depth. The model performs well across cancer types and clinical settings, including monitoring after surgery or immunotherapy, and in samples with low variant allele frequencies or no detected mutations. Fragment-level analyses identify shorter fragments and tumor-derived sequence patterns across panels of different sizes and ultra-low-pass whole-genome sequencing in multiple cohorts.
23 July 2026
Read appraisal →Journal of robotic surgery
Reliability and readability of AI chatbot responses to patient questions about robot-assisted radical cystectomy
Robot-assisted radical cystectomy (RARC) is a complex procedure that requires patients to understand surgical indications, urinary diversion, perioperative treatment, complications, recovery, and long-term functional outcomes. Although artificial intelligence (AI) chatbots are increasingly used to obtain medical information, their suitability for RARC patient education remains unclear. We conducted a cross-sectional comparative evaluation of four contemporary AI chatbots: ChatGPT-5, DeepSeek-V4, Claude Sonnet 4.6, and Gemini 3.5 Pro. A set of 20 core patient-education questions on RARC was developed by three senior urologic experts. Chatbot responses were assessed using DISCERN, the Ensuring Quality Information for Patients tool, the Global Quality Scale, and JAMA benchmark criteria. Readability was evaluated using the Automated Readability Index, Coleman-Liau Index, Flesch-Kincaid Grade Level, Flesch Reading Ease, Gunning Fog Index, and SMOG. Reliability scores differed significantly across models for DISCERN, EQIP, and GQS, while JAMA benchmark criteria were summarized descriptively as transparency signals. DeepSeek-V4 achieved the highest mean scores for DISCERN, EQIP, and GQS, while ChatGPT-5 and DeepSeek-V4 showed the strongest JAMA benchmark performance. Gemini 3.5 Pro generally had the lowest reliability and transparency scores. Readability also varied across models. DeepSeek-V4 produced the most readable responses overall, whereas Gemini 3.5 Pro generated the most complex text. However, all models exceeded the recommended sixth-grade reading level, and FRES scores remained below the recommended threshold. Contemporary AI chatbots generated responses with variable presentation quality, transparency, and readability for common RARC patient-education questions. Because factual accuracy was not directly assessed, these tools should not be interpreted as validated sources of clinical guidance and should not replace individualized counseling by urologists.
23 July 2026
Read appraisal →Journal of materials chemistry. B
Multimodal health monitoring and theranostics based on functionalized hydrogels and artificial intelligence
Functionalized hydrogels are ideal flexible interfaces for multimodal health monitoring and integrated diagnosis-therapy systems, owing to their tissue-like mechanical properties, programmable biochemical functions, and hierarchical pores. However, practical applications are often limited by several material bottlenecks: mechanical fatigue and conductivity loss under cyclic stress, the mismatch between degradation rate and functional lifespan, and the trade-off between sensitivity and biocompatibility. To address these challenges, artificial intelligence (AI) has been applied to accelerate structural optimization and property prediction through molecular network engineering and inverse design. Meanwhile, during the collection of coupled mechanical and biochemical signals, these interfaces usually suffer from high background noise, data variability, and baseline drift. Machine learning and deep learning can process these complex datasets through noise filtering, automated feature extraction, and pattern recognition, enabling continuous monitoring and adaptive health management. This review summarizes the recent material design strategies of functionalized hydrogels, AI-driven data analysis methods, and their progress and challenges in integrated diagnosis and therapy.
23 July 2026
Read appraisal →Cell reports methods
Data modeling the interplay between single-cell shape, single-cell protein expression, and tissue state.
While cell shape fundamentally governs tissue function, the underlying links between single-cell shape and protein expression have been difficult to resolve due to limitations in imaging multiplexing and population averaging. Here, we use multiplexed imaging and machine learning to investigate the coupling of cell shape and protein expression in heterogeneous human tissues. Our analysis establishes a universal, bi-directional link between a cell's shape and its protein expression across cell types, diseases, and disease states, amplified for cell state markers. Machine learning interpretability shows that shape features can potentially generate hypotheses of protein functions. Screening all protein-cell type pairs identified a subpopulation of large, p53-positive tumor cells across two cancers. Shape properties further enhanced graph neural network disease state prediction. Our results open the door to unraveling the intricate connections between protein expression, cell shape, tissue organization, and tissue state in a physiological context.
23 July 2026
Read appraisal →JMIR cancer
Multiturn Large Language Model-Based Conversational Agents for Patients With Cancer and Caregivers: Scoping Review
BACKGROUND: Large language model (LLM)-based conversational agents are increasingly used in health care, yet their capacity to support genuine multiturn dialogue remains underexplored. In oncology, where patients and caregivers experience complex informational and emotional needs throughout the disease trajectory, conversational agents may support information provision, symptom consultation, and emotional assistance. However, research specifically examining multiturn conversational agents designed for patients with cancer and informal caregivers remains limited. OBJECTIVE: This scoping review aimed to map the research landscape of LLM-based multiturn conversational chatbots developed for patients with cancer and informal caregivers, focusing on system design, intervention purposes, evaluation approaches, safety considerations, and transparency of LLM-related components. METHODS: This scoping review followed the Joanna Briggs Institute methodology and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. Six databases-PubMed, Embase, Scopus, Web of Science, CINAHL, and PsycINFO-were searched for studies published between January 2022 and January 2026, with supplementary searches conducted in IEEE Xplore Digital Library and ACM Digital Library in May 2026. Studies were included if they described LLM-based chatbots designed for patients with cancer or informal caregivers that supported multiturn conversational interaction. Two reviewers independently conducted the study selection and data extraction. RESULTS: Eight studies met the inclusion criteria. Most studies focused on prototype development, with limited research evaluating clinical outcomes. ChatGPT-based models were the most commonly used LLMs, and retrieval-augmented generation techniques were applied in several studies. Chatbots were primarily designed for emotional support or information provision. Evaluation approaches varied widely, including response quality, psychological outcomes, and user experience. However, no studies evaluated interaction-level characteristics such as conversational continuity or context retention, and only 2 studies reported any conversational memory mechanism. Reporting on safety risks, mitigation strategies, prompt design, model parameters, and adherence to LLM reporting guidelines was often limited or absent. CONCLUSIONS: This scoping review identified only 8 studies on LLM-based multiturn conversational chatbots for patients with cancer and informal caregivers. The field remains at an early stage, characterized by prototype-oriented development, heterogeneous design and evaluation approaches, and inconsistent safety and transparency reporting. Future development should prioritize genuine conversational capability, safety management, and transparent reporting.
22 July 2026
Read appraisal →Journal of robotic surgery
Perioperative outcomes and nodal station assessment after robotic-assisted versus video-assisted thoracoscopic segmentectomy for early-stage NSCLC: a systematic review and meta-analysis
The relative benefits of Robotic-assisted thoracoscopic segmentectomy (RATS) versus video-assisted thoracoscopic segmentectomy (VATS) for early-stage NSCLC remain uncertain. This meta-analysis compared perioperative outcomes, lymph node assessment, and postoperative safety between the two approaches. PubMed, Cochrane Library, Embase, and Web of Science were searched from inception to April 2026. Comparative studies of RATS versus VATS segmentectomy for early-stage NSCLC were included. Outcomes were pooled as weighted mean differences or odds ratios with 95% confidence intervals. Random-effects models were used for substantial heterogeneity (I² >50% or P < 0.05); otherwise, fixed-effects models were applied. Leave-one-out sensitivity plots are provided in the supplementary materials. Nine retrospective comparative studies involving 19,805 patients were included. Robotic-assisted segmentectomy was associated with a greater number of lymph node stations examined (WMD = 1.16, 95% CI: 0.51, 1.81, P < 0.001) and a shorter length of hospital stay (WMD = - 0.75, 95% CI: -1.36, - 0.15, P = 0.015). No significant differences were observed in operative time (WMD = 1.86, 95% CI: -8.80, 12.52, P = 0.732), duration of drainage (WMD = - 0.37, 95% CI: -0.79, 0.06, P = 0.090), overall complications (OR = 0.94, 95% CI: 0.67, 1.34, P = 0.745), air leak (OR = 1.02, 95% CI: 0.54, 1.91, P = 0.949), pneumonia (OR = 1.33, 95% CI: 0.89, 1.99, P = 0.170), or 30-day readmission (OR = 1.09, 95% CI: 0.94, 1.26, P = 0.257). Current retrospective evidence suggests that RATS may offer more examined lymph node stations and shorter hospital stay without increased morbidity, but these findings are limited by risk of bias and should not be interpreted as evidence of oncologic superiority.
22 July 2026
Read appraisal →BMJ open
Endoscopic biopsy strategies for gastric ulcers: an updated systematic review
OBJECTIVE: To systematically summarise recent evidence on endoscopic biopsy for gastric ulcer. DESIGN: Systematic review of clinical studies. DATA SOURCES: PubMed, Embase, Cochrane Library, Web of Science, CNKI and CiNii Research were searched for studies published from 2015 to May 2025. ELIGIBILITY CRITERIA: Original studies involving adults with endoscopically diagnosed gastric ulcers or ulcerative gastric lesions directly informing biopsy strategy were eligible if they evaluated biopsy timing, number, site or related diagnostic outcomes. DATA EXTRACTION AND SYNTHESIS: Two reviewers independently screened studies, extracted data and assessed risk of bias using the Newcastle-Ottawa Scale. Owing to substantial clinical and methodological heterogeneity, findings were synthesised narratively. RESULTS: Seven studies were included and grouped into four domains: biopsy timing, biopsy number, biopsy site and safety. Available data suggested that most malignancies may be identifiable at the initial endoscopy examination although repeat biopsy during ulcer healing or scarring might still detect some missed cancers. Evidence on the biopsy site remained limited and inconsistent, and findings from healed-phase ulcers further suggested that the anatomical site alone may be insufficient to guide biopsy strategy. Available studies also indicated that four well-targeted biopsies may be adequate for some depressed or polypoid lesions whereas more extensive sampling might be needed for infiltrative or otherwise suspicious lesions. Limited safety data did not show a clear increase in adverse events with broader biopsy strategies or the biopsy of stabilised bleeding ulcers. CONCLUSIONS: Contemporary evidence on endoscopic biopsy for gastric ulcers remains limited, heterogeneous and largely observational. Available data suggest that biopsy at initial endoscopy and adequate, lesion-targeted sampling may improve diagnostic yield, while follow-up and intensive sampling should be individualised according to endoscopic appearance, histological findings and clinical suspicion. PROSPERO REGISTRATION NUMBER: CRD420251157851.
21 July 2026
Read appraisal →Journal of robotic surgery
Technical advancements in robot-assisted radical prostatectomy: systematic review and bibliometric analysis
Robot-assisted radical prostatectomy has transformed the surgical management of localized prostate cancer, yet its research landscape remains incompletely mapped. We conducted a systematic review and bibliometric analysis of 578 articles and conference papers from Scopus and Web of Science (2005-2026), following PRISMA guidelines, and analyzed publication trends, geographic and institutional distribution, author networks, journal output, keyword co-occurrence, and thematic evolution. The results reveal exponential growth in research, with an inflection around 2015-2016 and accelerated momentum after 2020, reflecting the widespread adoption and maturation of robotic programs. The United States leads in publication volume, followed by Italy and the rapidly growing contribution of China, while The University of Turin (Italy) emerges as the most productive institution due to its pioneering Retzius-sparing approach; the Journal of Robotic Surgery remains the leading publication venue. Thematic analysis demonstrates a progression from early feasibility studies to the standardization of nerve-sparing and continence-preserving techniques, advancing toward single-port surgery, augmented reality navigation, and AI-assisted intraoperative decision-making. Emerging technologies such as augmented reality, the NeuroSAFE protocol, and deep learning models for outcome prediction are particularly transformative, and geographic disparities are highlighted as critical priorities for future research.
21 July 2026
Read appraisal →Science advances
Multistage microrobots with pH-responsive release of platelet membrane-coated nanoparticles
Targeted drug delivery in the gastrointestinal tract remains challenging because therapeutics must overcome multiple hierarchical barriers before reaching diseased tissue. Here, we present a multistage delivery platform that integrates magnetic microrobots, a pH-responsive protective coating, and platelet membrane-coated nanoparticles (PNPs) in one platform. A fillable design enables the formation of an internal magnetic layer for microrobot actuation, while the pH-responsive coating protects the cargo during transit and selectively degrades upon pH change, releasing cancer cell-targeting PNPs. In an in vitro colon cancer model that reproduces key gastrointestinal features, including flow, pH variation, and villi-like structures, this strategy increased nanoparticle retention and enhanced cancer cell cytotoxicity compared to nanoparticles administered alone. Ex vivo studies in porcine stomach and intestine further demonstrated robust locomotion on compliant and folded tissue surfaces. These results establish an environment-responsive hierarchical delivery strategy for more precise oral delivery in complex gastrointestinal settings.
20 July 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
A machine learning approach to profiling anxiety risk among cancer patients on treatment
PURPOSE: Previous literature has identified multiple risk factors for anxiety among individuals with cancer. However, the relative importance across many interrelated variables is not clear. Further, it is unknown how combined psychosocial factors and demographic/clinical characteristics increase an individual's vulnerability to anxiety. The aims of this study are to systematically test the following: (1) the best predictors of anxiety, (2) the best-performing classifiers for distinguishing high versus low anxiety, and (3) the most informative combinations of psychosocial features and demographic/clinical characteristics that heighten an individual's vulnerability to anxiety. METHODS: Machine learning (ML) models were used. For Aim 1, we tested the predictors of anxiety using the models of regularized regressions, GAMS, and best subset selection. For Aim 2, models tested included support vector machines (SVM), random forest, nearest neighbors, and XGBoost as the classifiers to predict high versus low anxiety. For Aim 3, gradient-boosted decision tree models were trained to examine the most important combinations of candidate predictors. We used SHapley Additive exPlanations (SHAP) to improve the interpretability of our ML models. RESULTS: Across five imputed datasets, using linear regression as the primary benchmark (MSE = 69.945 ± 8.131; MAE = 6.636 ± 0.361), elastic net showed the lowest average prediction error (MSE = 65.486 ± 8.323; MAE = 6.504 ± 0.357), although differences among predictor-based models were modest. For classifiers, random forest achieved the highest mean accuracy (0.751 ± 0.015), kNN achieved the highest PR-AUC (0.797 ± 0.016), and logistic regression achieved the highest ROC-AUC (0.835 ± 0.005). Participation self-efficacy was the most consistent key predictor of anxiety across models. The most important risk combinations of demographic and psychosocial/symptom variables included age-symptom communication barriers, age-cancer coping self-efficacy, ECOG performance status-depressive symptoms, the type of helper-psychological well-being, and education-psychological well-being. CONCLUSION: Our findings may inform risk stratification in clinical settings and facilitate personalized intervention designs aiming at relieving anxiety among cancer patients.
19 July 2026
Read appraisal →Antonie van Leeuwenhoek
Advancements in the study of gut microbiome in disease diagnosis
This review summarizes disease-associated changes in gut microbial composition and evaluates the diagnostic performance of models constructed with different machine-learning algorithms. The review seeks to answer questions related to the relationship between the human gut microbiome and disease progression, how different machine learning algorithms affect disease diagnosis using gut microbiome data, and how disease-specific microbial communities impact diagnostic models. Multiple studies report that gut microbiome dysbiosis is commonly observed in many diseases, though patterns vary between conditions and cohorts. Large-scale computational analyses are increasingly applied to identify microbial signatures and to build diagnostic models; however, model performance often depends on data source, preprocessing and choice of algorithm. Overall, evidence indicates disease-associated shifts in gut microbial composition, and that diagnostic model accuracy is sensitive to cohort, sequencing and modeling choices. While certain taxa recur across studies for some diseases, heterogeneity between cohorts limits immediate clinical translation; thus, harmonized study designs and external validation are required. Future work should prioritize reproducible multi-cohort analyses, transparent reporting (e.g., PRISMA for reviews) and prospective validation before clinical deployment.
18 July 2026
Read appraisal →Cell systems
BulkFormer: A large-scale foundation model for bulk transcriptomes
Foundation models are transforming transcriptome analysis, yet most RNA sequencing (RNA-seq) foundation models are pretrained on sparse single-cell RNA-seq data, which typically captures only ∼3,000 genes per cell. This creates a need for models tailored to bulk transcriptomes, a modality that profiles ∼16,000 genes per sample and supports clinical and tissue-level analyses. Here, we present BulkFormer, a foundation model for bulk transcriptome analysis. BulkFormer contains ∼150 million parameters, covers 20,010 protein-coding genes, and is pretrained on 581,503 human bulk RNA-seq profiles. Its hybrid encoder combines a graph neural network to model explicit gene-gene relationships with a Performer module to capture global expression dependencies. Across five downstream tasks, BulkFormer outperformed existing single-cell foundation models while requiring substantially lower training cost. These results identify pretraining data modality as a key determinant of foundation model performance and establish BulkFormer as a framework for bulk transcriptome modeling. A record of this paper's transparent peer review process is included in the supplemental information.
17 July 2026
Read appraisal →Analytical methods : advancing methods and applications
Emerging trends in amino acid detection: wearable devices and machine learning-assisted signal processing
As critical metabolic biomarkers, amino acids exert essential physiological functions, and their abnormal levels are closely associated with a range of diseases such as cancer, neurodegenerative disorders, and cardiovascular conditions. In recent years, amino acid analysis technologies have achieved remarkable advancements in the field of personalized healthcare. This review navigates the latest progress in amino acid analysis, with a focus on two prominent emerging trends. The first trend encompasses the development of flexible and wearable sensing devices for non-invasive, continuous amino acid monitoring in diverse biofluids, such as blood, sweat, and interstitial fluid. Particular attention is given to their design principles, operational mechanisms, practical applications, and key performance metrics. The second trend involves the application of machine learning (ML) for processing and interpreting complex response signals. Specifically, this review discusses how various ML approaches, including classical chemometric linear regression models, deep learning models, support vector machines (SVMs), ensemble learning, and tree-based models, address common challenges in complex environments, such as signal interference and nonlinear drift. Furthermore, this review outlines the current challenges and proposes future research directions, aiming to advance amino acid analysis toward more intelligent, integrated, and personalized health monitoring systems.
17 July 2026
Read appraisal →ACS applied materials & interfaces
The Evolving Landscape of Terahertz Biosensing: From Sensitivity to Precision
Terahertz (THz) spectroscopy has recently gained significant attention as a powerful tool for biomacromolecule detection due to its exceptional sensitivity in capturing molecular fingerprints, attributed to its unique wavelength range. THz biosensing platforms offer distinct advantages in identifying molecular rotational and vibrational states, making them effective for high-sensitivity, high-precision biomacromolecule analysis. This review highlights recent advancements in THz biosensing platforms, focusing on their spectroscopy interpretation and practical applications. We begin by introducing commonly used THz equipment and various metamaterials, followed by an overview of the key spectroscopy interpretation strategies for THz spectral analysis, including refractive index sensing and molecular fingerprint recognition. We also summarize the machine learning (ML) methods applied to enhance spectral analysis in THz biosensing platforms. Lastly, we provide an integrated perspective on the spectroscopy interpretation, clinical applications, and recent technological advancements in THz biosensing platforms, with an emphasis on how to construct a standardized THz-based biosensing workflow. We believe THz biosensing platforms hold immense potential to revolutionize molecular detection by enabling precise, label-free, and highly sensitive analysis of biomacromolecule interactions, paving the way for breakthroughs in medical diagnostics, environmental monitoring, and biochemical research.
17 July 2026
Read appraisal →ACS biomaterials science & engineering
AI-Driven Design and Clinical Translation of Nucleotide-Peptide and Peptide-Drug Conjugates
While nucleotide-peptide conjugates and peptide-drug conjugates demonstrate a considerable evolution as next-generation therapeutics, the successful translation of these complex agents relies on advances in bioconjugation strategies. By anchoring nucleotides or pharmaceutical payloads to multifaceted peptide scaffolds, researchers have engineered molecular platforms with dual mechanisms of action. However, to fully realize the potential of these hybrid systems, further refinements are required to ensure both in vivo stability and retention of the desired biological activities over time. This comprehensive review examines ongoing progress toward optimizing conjugation chemistry for the development of stable and efficacious NPC and PDC candidates. Through evaluation of fundamental design principles and their clinical relevance, it aims to accelerate the journey of these multilayered biomolecular constructs from theory to practice. It details various strategies to mitigate challenges such as enzymatic degradation and rapid systemic clearance, including covalent linkages (e.g., aminoacylation and thiol oxidation) and noncovalent interactions (e.g., ligand-receptor binding). Central to this emerging field is the integration of artificial intelligence and deep learning, which are rapidly transforming the design and development of NPCs and PDCs. AI-driven tools enable predictive modeling of peptide-drug interactions, intelligent linker optimization, and in silico screening of bioactive scaffolds-thus redefining the pipeline for precision therapeutics with improved selectivity and translatability. The potential of hybrid nanostructures formed by peptide-DNA conjugates for advanced drug delivery and diagnostic applications is also discussed. Furthermore, the clinical translation of PDCs demonstrates their promise in treating conditions such as cancer, neurodegenerative diseases, and inflammatory disorders, alongside the ongoing challenges regarding peptide availability, linker optimization, and scalable synthesis. The review concludes by discussing future directions of NPCs and PDCs, highlighting the role of interdisciplinary approaches in advancing precision medicine and addressing complex therapeutic needs.
14 July 2026
Read appraisal →Archives of microbiology
SigMine and OPathDb: a literature-mining pipeline and database of potential opportunistic pathogens
Conversion of unstructured biomedical literature into structured knowledge for identifying cross-domain associations between biological entities remains a challenging task. SigMine is an automated pipeline constructed to mine biomedical literature to identify significantly associated biological entities. SigMine performs biomedical entity recognition from PMC articles using the EuropePMC Annotation API. Advanced entity recognition was performed using Python scripting, NCBI E-Utilities, and an n-gram algorithm followed by extensive data cleaning and mapping against standard databases. Statistical evaluation identified significantly co-occurring entities. The entire workflow was automated through a modular framework developed in Python v3.13 with a Tkinter-based Graphical User Interface. SigMine enhances usability while retaining the flexibility to use new dictionaries for annotation. SigMine was used to construct a literature-derived potential human Opportunistic Pathogens Database (OPathDb), housing 5,626 potential opportunistic pathogens significantly co-occurring with 1440 diseases and 7121 genes mined from 25,000 PMC articles. Additional annotation of 598 significantly co-occurring metabolites and 30 affected tissues is available for 3204 and 227 pathogens, respectively. OpathDb has a user-friendly query interface searchable by organism, disease, tissue, gene, protein and metabolite available at https://www.opathdb.cbsblab-nsut.in . Organism-entity associations can be visualized as weighted networks, with color-coded nodes and significance-scaled edges. Significant associations of opportunistic pathogens like Akkermansia mucinifila with colorectal cancer and Segatella copri with glucose intolerance can be identified through OpathDb. Through this database, the SigMine framework demonstrates conversion of unstructured text in vast and heterogenous corpora into standardized and well-organized information. Statistically inferred associations in OPathDb are potential candidates for clinical and experimental validation.
13 July 2026
Read appraisal →Nucleic acids research
DeepKinomeWeb: a quantitative, panel-level platform for kinase inhibitor screening and selectivity profiling
Protein kinases are central targets in drug discovery, yet early-stage development of potent and selective inhibitors remains challenging due to high experimental costs and limited interpretability of large-scale screening data. Here, we present DeepKinomeWeb, an integrated web-based platform that transforms competition-based high-throughput screening data into actionable insights for kinase inhibitor prioritization. Built upon our previously validated deep learning regression model, DeepKinome, the platform enables quantitative prediction of kinase-inhibitor binding affinities and provides panel-level visualization of selectivity landscapes, selectivity metric calculations, and integrated structural and physicochemical analyses. Through its user-friendly interface, DeepKinomeWeb supports rational, data-driven decision-making for biologists and medicinal chemists, lowering the barrier to systematic selectivity assessment in kinase inhibitor discovery. DeepKinomeWeb is freely available to all users without any login requirement at https://str.kribb.re.kr/deepkinome.
13 July 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Psychosocial interventions for families with minor children affected by parental cancer: An umbrella review
PURPOSE: Research on psychosocial interventions for families with minor children affected by parental cancer has expanded over recent decades; however, the evidence remains fragmented across multiple systematic reviews. This umbrella review aims to gather and synthesize the existing systematic reviews, with a particular focus on intervention characteristics, implementation, effectiveness, methodological limitations, and implications for future research and clinical practice. METHODS: A comprehensive search of CINAHL, MEDLINE, PubMed, Web of Science, Scopus, PsycINFO, PsycArticles, Epistemonikos, and the Cochrane Database of Systematic Reviews was conducted in September 2025, including gray literature. Eligible reviews were published between 1990 and 2025 in English, Portuguese, or Spanish and had full-text availability. Methodological quality was assessed using AMSTAR-2. The review was registered in PROSPERO (ID 1139397). RESULTS: Eight systematic reviews met inclusion criteria and were synthesized narratively due to substantial clinical and methodological heterogeneity. Across these reviews, 46 distinct psychosocial interventions were identified. Overall, interventions were associated with preliminary beneficial outcomes for children, adolescents, parents, and families, particularly in improving children's understanding of parental illness, enhancing family communication and functioning, strengthening parenting skills, and promoting psychological well-being. However, confidence in these findings was limited by the generally low methodological quality of the included reviews and the high overlap of primary studies. CONCLUSIONS: Current evidence suggests that psychosocial interventions may support families with minor children affected by parental cancer, but conclusions remain tentative due to methodological limitations. There is a clear need for the development and rigorous evaluation of developmentally and disease-stage-sensitive interventions, supported by high-quality research and institutional collaboration. Strengthening the evidence base in this area is essential to inform clinical practice, guide supportive care services, and support policy development in psycho-oncology.
13 July 2026
Read appraisal →Physics in medicine and biology
Four-dimensional on-beam computed tomography reconstruction using projection-difference images
Purpose.Accurate verification of delivered dose requires anatomical information during the treatment beam delivery. However, conventional cone-beam computed tomography (CBCT) is typically acquired prior to beam delivery and fails to account for intrafractional tumor shift/drift. This study aims to develop a 4D on-beam computed tomography (4D OBCT) framework to capture anatomical states during irradiation.Materials and methods.A thoracic phantom with rigid tumor motion was used to develop a conformal arc treatment plan. During delivery, the target was displaced by 1 cm superior to its planned position to simulate a baseline shift, and megavoltage transmission data were acquired using an electronic portal imaging device. Transmission data corresponding to the planned tumor position were obtained from the planning CT (pCT) via Monte Carlo simulation. Volumetric difference images were reconstructed from the differences between the measured and simulated transmission data for each phase. Subsequently, the images were superimposed onto the corresponding phases of the 4D pCT to obtain 4D OBCT. For evaluation, we compared 4D OBCT with 4D pCT (ground-truth) and 4D CBCT which were acquired after the target displacement. A dosimetric performance was evaluated by recalculating 4D dose distributions on the three images and comparing in dose-volume histograms and gamma indices.Results.4D OBCT achieved lower root mean square error than 4D CBCT in both the lung (58.46 HU vs 74.37 HU) and chest wall regions (23.78 HU vs 66.84 HU). This quantitative accuracy was also reflected in the dosimetric comparison. Under 2%/2 mm and 1%/1 mm criteria, 4D OBCT achieved gamma pass rates of 99.76% and 99.08%, respectively, whereas 4D CBCT yielded 72.50% and 57.77%, respectively.Conclusions.The proposed 4D OBCT framework successfully reconstructed accurate phase-resolved images of the target during irradiation. By providing high-resolution volumetric images that reflect intrafractional motion, 4D OBCT could provide a robust solution for precise post-delivery dosimetric verification and adaptation.
12 July 2026
Read appraisal →BMJ open
Association of wearable device-based perioperative physical activity with recovery outcomes in patients undergoing lung cancer surgery: a systematic review and meta-analysis protocol.
INTRODUCTION: Lung cancer surgery is a cornerstone of curative treatment for resectable disease, but postoperative recovery is often prolonged and may be complicated by impaired pulmonary function, reduced physical activity, symptom burden and delayed functional restoration. Wearable devices may support perioperative physical activity through objective monitoring and activity promotion; however, existing evidence remains fragmented and has not been synthesised in a focused review. This protocol describes a systematic review and meta-analysis to evaluate the association of wearable device-based perioperative physical activity with recovery outcomes in patients undergoing lung cancer surgery. METHODS AND ANALYSIS: We will include randomised controlled trials, non-randomised interventional studies and prospective or retrospective cohort studies involving adults undergoing surgery for suspected or confirmed primary lung cancer. Eligible studies must examine wearable device-based perioperative physical activity during the preoperative period, postoperative period or both. The primary outcomes will be postoperative pulmonary complications, length of hospital stay and physical function or exercise capacity. Secondary outcomes will include overall postoperative complications, pulmonary function, symptom burden, health-related quality of life, chest tube duration, readmission and postoperative mortality. We will search PubMed, EMBASE, Web of Science, CINAHL and the Cochrane Library from inception to the final search date, supplemented by reference screening, forward citation tracking, grey literature and trial registry searches. Two reviewers will independently perform study selection, data extraction and risk-of-bias assessment. Where appropriate, meta-analysis will be performed using random-effects models; otherwise, findings will be synthesised narratively. ETHICS AND DISSEMINATION: Ethical approval is not required because this review will use data from published studies and publicly accessible sources only and will not involve individual participants or identifiable personal data. The findings will be disseminated through publication in a peer-reviewed journal and presentation at relevant academic or professional conferences. PROSPERO REGISTRATION NUMBER: CRD420261375254.
11 July 2026
Read appraisal →BMJ open
Determinants of cervical screening in Ghana: a systematic review
BACKGROUND: Cervical cancer remains a leading cause of morbidity and mortality among women in Ghana, yet screening coverage is extremely low and organised national programmes are limited. Although several studies have examined determinants of screening, no comprehensive synthesis has summarised the evidence to guide effective policy and intervention design. OBJECTIVE: To synthesise existing evidence on determinants of cervical screening in Ghana and identify opportunities to strengthen national screening strategies. DESIGN: This is a systematic review conducted following Aromataris and Pearson's methodology and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Searches across PubMed Central, CINAHL, Web of Science, Scopus and grey literature (January 2000-July 2025) identified studies reporting barriers and facilitators of cervical screening in Ghana. Risk of bias was assessed using standard appraisal tools. SETTING: This study was conducted in the context of Ghana. RESULTS: Of 7833 records screened, 32 studies met inclusion criteria. Barriers to cervical screening spanned individual, interpersonal, health system, socio-cultural and structural factors, including poor knowledge, fear, limited access, financial constraints and social norms. Facilitators included knowledge and awareness, provider recommendation, social support, positive health perceptions, healthcare access and cost-related enablers. Overall, 23 studies were rated low risk of bias, while nine were rated moderate risk of bias. CONCLUSION: Cervical screening behaviour in Ghana is influenced by structural, cultural, psychological and informational factors. The reviewed evidence suggests that these determinants do not operate in isolation but interact in ways that may vary across contexts and populations. OSF REGISTRATION NUMBER: https://doi.org/10.17605/OSF.IO/M4XKT.
11 July 2026
Read appraisal →Current treatment options in oncology
Telemedicine and Virtual Reality Technologies in Pain Management for Cancer Patients: A Structured Narrative Review
Cancer-related pain remains a complex and persistent clinical challenge due to its multidimensional nature and the limitations of pharmacological treatments alone. In our view, telemedicine and virtual reality (VR) technologies represent complementary and increasingly relevant approaches that address different yet interconnected dimensions of pain management in oncology care. Telemedicine should be considered a key component of contemporary cancer care, particularly for continuous symptom monitoring, early detection of pain exacerbation, and timely clinical intervention. Its ability to enhance communication between patients and healthcare providers and to support continuity of care makes it especially valuable in the long-term management of cancer-related symptoms. In contrast, VR interventions offer a targeted, non-pharmacological strategy for modulating pain perception through attentional distraction, emotional regulation, and immersive engagement. Based on current evidence, VR appears particularly useful in the management of procedure-related pain and in patients experiencing persistent pain that is insufficiently controlled with standard approaches. We propose that the integration of telemedicine and VR should be considered within a coordinated, patient-centered care model, where telemedicine facilitates ongoing assessment and identification of patient needs, and VR is applied as an adjunctive intervention tailored to individual symptom profiles. Such an approach has the potential to enhance both symptom control and psychological well-being while supporting treatment adherence. However, despite their promise, the implementation of these technologies requires careful consideration of clinical context, patient characteristics, and system-level factors, including digital literacy and technological infrastructure. Future research should prioritize the development of standardized protocols and the evaluation of long-term outcomes to support the sustainable integration of these digital health strategies into routine oncology practice.
11 July 2026
Read appraisal →BMJ open
Development and acceptability of gist-based decision aids for prostate and breast cancer screening: a two-phase qualitative interview study in an online setting.
BACKGROUND: Traditional approaches to designing decision aids have focused on providing completeness of information and quantitative detail. An alternative approach based on psychological research emphasises understanding the essence or 'gist' of the decision. Few gist-based decision aids exist. The objective of this study was to develop novel gist-based decision aids for prostate and breast cancer screening and evaluate their acceptability. METHODS: Men aged 40-60 years and women aged 40-49 years eligible for prostate or breast cancer screening were recruited from ResearchMatch (National Institutes of Health) and a public hospital located in New York City. Three rounds of semistructured interviews were performed to determine self-reported comprehensibility and acceptability, with iterative modifications after each phase, including: Phase 1: initial feedback; Phase 2, Round 1: evaluating our modifications; and Phase 2, Round 2: confirming final content. RESULTS: A total of 80 participants were involved in the two-phase qualitative interview process; Phase 1 and Phase 2 included 32 and 48 participants, respectively. Racial distribution was 50% white, 30% black and 10% Asian; 9% of participants were Hispanic or Latino. Most participants were highly educated. Interview themes established content validity of the final decision aids. In a quantitative analysis of the final decision aids using a questionnaire with fixed response options, acceptability was high, with two-thirds finding the length and amount of information of the decision aid to be optimal. The majority found the decision aid visually appealing, easy to read and easy to get through, and indicated that it held their interest and did not require much mental effort to read. One-quarter said the tool made them feel somewhat nervous, and three-quarters not at all. Most participants could understand and relate to the images, graphs and patient stories in the decision aid. After Phase 2, 45 of the 48 participants (94%) said they would find the decision aid helpful when making a decision. CONCLUSION: This study describes the iterative development and pilot testing of a novel gist-based decision aid for cancer screening, demonstrating high acceptability. Our findings provide a foundation for randomised trials comparing gist-based and traditional tools.
10 July 2026
Read appraisal →BMC research notes
From manual entry to machine precision: challenges and evolution of metadata schema development in collaborative research centers
OBJECTIVE: Metadata standardization in collaborative biomedical research must balance interoperability with domain-specific detail. We describe a parent-template approach in which a baseline schema from the nephrology-focused CRC 1453 NephGen was adapted for the tumor-immunology CRC OncoEscape and the perinatal-immunology CRC Pilot. RESULTS: The derivation process produced three structurally compatible yet vocabulary-divergent schemas. Pilot required the highest granularity (324 levels), followed by NephGen (287) and OncoEscape (283). Vocabulary reuse from the NephGen baseline was limited: 134 of 283 OncoEscape levels (47%) and 113 of 324 Pilot levels (35%) were retained unchanged. The main adaptations were not only expanded level lists, such as cell lines and mouse lines, but also new CRC-specific query dimensions, including "Oncogenes" in OncoEscape and "Timeline" in Pilot. In the context of AI-assisted extraction, we use the term instruction set to denote a schema that specifies target fields, expected granularity, example values, and validation resources for each metadata dimension, rather than a simple drop-down form or a free-text prompt template.
8 July 2026
Read appraisal →Journal of medical Internet research
Magnetic Resonance Imaging-Based Artificial Intelligence in Predicting Prostate Cancer Biochemical Recurrence: Systematic Review and Meta-Analysis
BACKGROUND: Artificial intelligence (AI) has emerged as a promising tool for prostate cancer (PCa) risk stratification and outcome prediction. However, current studies often lack multicenter external validation, have limited sample sizes, present significant intermodel variability, and face overfitting concerns. OBJECTIVE: This study aimed to comprehensively evaluate the diagnostic performance of magnetic resonance imaging (MRI)-based AI models in predicting biochemical recurrence (BCR) of PCa. METHODS: Systematic searches were conducted in the PubMed, Embase, Web of Science, and Cochrane Library databases up to January 13, 2026. Studies were included that involved participants diagnosed with PCa, used MRI-based AI for predicting BCR, and had clearly defined reference standards. The quality of the included studies was assessed using the PROBAST+AI tool. A bivariate random effects model was used to pool sensitivity, specificity, and area under the curve (AUC) statistics. RESULTS: A total of 28 studies were included, with 2623 patients in internal validation and 1134 patients in external validation. Diagnostic contingency tables were reconstructed from published performance metrics for most studies, while others were extracted from receiver operating characteristic curves due to the lack of direct reporting. In the internal validation set, pooled sensitivity was 0.80 (95% CI 0.73-0.86; prediction interval [PI] 0.48-0.99), specificity was 0.83 (95% CI 0.77-0.89; PI 0.49-1.00), and AUC was 0.86 (95% CI 0.83-0.89; PI 0.74-0.99). In the external validation set, pooled sensitivity was 0.82 (95% CI 0.72-0.91; PI 0.54-0.99), specificity was 0.83 (95% CI 0.71-0.92; PI 0.49-1.00), and AUC was 0.84 (95% CI 0.79-0.90; PI 0.70-0.98). No statistically significant differences were observed between internal and external validation in sensitivity (P=.73), specificity (P>.99), AUC (P=.53), or diagnostic odds ratio (P=.98). Medical Net and Extreme Gradient Boosting achieved the highest sensitivity and AUC, whereas multiple kernel learning and support vector machine had the highest specificity. Subgroup and meta-regression analyses suggested that AI method, model type, timing of MRI acquisition, and treatment modality may contribute to heterogeneity. CONCLUSIONS: This meta-analysis innovatively realizes the quantitative direct comparison of MRI-based AI model performance between internal and external validation cohorts for PCa BCR prediction. It comprehensively evaluates AI performance across diverse PCa treatment modalities and integrates machine learning and deep learning approaches. For the field, it identifies key sources of performance heterogeneity (eg, MRI acquisition timing and treatment modality) and quantifies the sensitivity-specificity trade-off in integrated radiomic-clinical models, advancing the systematic understanding of MRI-based AI for BCR prediction. In real-world practice, it provides actionable guidance to prioritize pretreatment MRI for AI model development and clinical BCR assessment and underscores the urgent need for standardized imaging protocols and prospective multicenter studies, laying a foundation for the safe clinical translation of these AI tools as adjunctive decision support instruments.
8 July 2026
Read appraisal →Nanoscale horizons
Multidimensional additive manufacturing micro/nanorobots: from elaborate design to smart cargo delivery
Micro/nanorobots have attracted much attention because of their potential to perform complex tasks with high precision under controlled actuation within the human body, such as targeted cargo delivery, lesion exploration, and minimally invasive surgery. Among the various emerging fabrication technologies, multidimensional additive manufacturing (MAM) technology can enable the design and fabrication of complex structures with multifunctional characteristics. Compared with traditional manufacturing methods, MAM significantly reduces production complexity and time while enhancing design flexibility and customization. This review provides a comprehensive overview of MAM technologies for constructing micro/nanorobots, along with their applications and associated challenges in the biomedical field. In addition, emerging MAM approaches, including 4D, 5D, and 6D printing assisted by physical intelligence, machine learning, and artificial intelligence show great potential for designing and fabricating more sophisticated and intelligent micro/nanorobots, thereby advancing their clinical translation in the near future.
8 July 2026
Read appraisal →Journal of medical Internet research
Exploring the Narratives of Patients With Cancer Using Large Language Models: Topic Modeling and Social Network Analysis
BACKGROUND: Patients with cancer often experience diverse psychosocial stressors that profoundly affect disease trajectories, treatment adherence, and overall quality of life. Understanding how patients experience and articulate these issues is critical for designing patient-centered interventions. Conventional data collection methods, such as surveys and interviews, provide depth but are constrained by recall bias and scalability and may overlook sensitive or underreported concerns. Patient-authored narratives in online health communities present a valuable opportunity to identify prevalent and underserved issues. However, critical analytic challenges remain in generating coherent and interpretable insights due to their unstructured and large-scale nature. OBJECTIVE: This study aims to leverage TopicGPT, a prompt-based topic modeling framework powered by large language models (LLMs), in combination with network analysis for interpretable topic discovery and interrelationship analysis in the narratives of patients with cancer. METHODS: Patient-authored posts describing psychosocial challenges about cancer experience were collected from 4 online health communities. Eligible posts were preprocessed and analyzed using TopicGPT, wherein topics were generated hierarchically and mapped at the sentence level. Comparison analyses were conducted among 3 state-of-the-art LLMs through cosine similarity and manual evaluation. Results from the best-performing LLM were further compared with 2 conventional topic models through topic diversity and were used to construct the network subsequently. Topic co-occurrence was examined using the pointwise mutual information algorithm and centrality metrics to reveal influential topics and thematic interconnections across narratives. RESULTS: A total of 11,306 posts were collected from Reddit, Macmillan, Mijian, and Douban between December 6, 2006, and September 24, 2025. Of these, 3169 posts were retained for topic modeling and network analysis. DeepSeek-V3.2 consistently outperformed Gemini-2.5-Flash and GPT-4o, with similarity scores of 0.6295, 0.5342, and 0.5247, respectively. TopicGPT maintained consistently high topic diversity across languages. "Fear of cancer recurrence" and "Psychological distress" emerged as both most frequent and bridging topics across a hierarchy comprising 42 top-level and 58 subtopics. Strong connections were observed among "Sexual health concerns," "Reproductive concerns," and "Quality of life impact"; "Family communication concerns" frequently co-occurred with "Employment concerns," "Diagnostic delays and misdiagnosis," and "Social support." CONCLUSIONS: This study demonstrates the potential of LLM-based topic modeling for large-scale, context-sensitive analysis of patient-authored narratives. The proposed integrated, domain-adaptable pipeline enables the identification of high-fidelity topics and their interrelationships, offering a scalable and interpretable approach to qualitative data in health care. Importantly, our findings reveal substantial concerns and unmet needs among patients with cancer, with potential to support patient-centered research and inform future clinical assessment and supportive care strategies.
7 July 2026
Read appraisal →Biomedical physics & engineering express
A verification-oriented review of reaction-enhanced radiotherapy: linking nuclear data, transport modeling, and measurable observables
Reaction-enhanced radiotherapy (RER) aims to improve the therapeutic ratio of particle therapy by exploiting isotope-specific nuclear reactions that generate localized high-linear energy transfer secondary radiation. Absorbed dose remains the central metric for radiotherapy planning and evaluation; however, RER introduces additional verification requirements related to nuclear-reaction data, mixed-field transport, isotope delivery, and measurable secondary-field observables. This review frames RER as a verification-oriented physics problem built on three interdependent pillars: covariance-aware nuclear data, high-fidelity Monte Carlo transport, and reaction-specific secondary-field monitoring. Boron neutron capture therapy (BNCT) is treated as the clinically mature benchmark for RER, whereas PBCT and the emerging15N(p,α)12C-based proton-carbon-alpha therapy (Proton-CAT) are discussed as early-stage comparative cases. BNCT has the strongest clinical evidence base, while PBCT remains constrained by narrow resonances, low reaction yield, and limited observability. Proton-CAT provides a candidate 4.439 MeV prompt-γobservable whose verification value still requires detector-level validation and delivery/selectivity assessment. Overall, clinically credible RER requires coordinated progress in uncertainty-aware nuclear data, detector-integrated transport modeling, isotope delivery and tumor-to-normal biodistribution assessment, and high-sensitivity radiation monitoring for therapy verification.
7 July 2026
Read appraisal →Molecular pharmaceutics
Platelets as Programmable Drug Depots: Toward Predictive and Mechanistic Drug Loading
Efficient drug delivery remains a major challenge in pharmaceutical science, with synthetic nanocarriers often facing limitations in real biological systems. While blood platelets have been explored as biomimetic carriers, their intrinsic capacity for molecular storage and stimulus-triggered release remains largely underutilized. Here, we propose that platelet granules can be viewed as programmable drug depots, whose loading can be rationally controlled using physicochemical principles and endogenous transport mechanisms. In contrast to empirical approaches, we outline a framework based on two complementary pathways: passive accumulation driven by ion trapping of weakly basic compounds in acidic granules, and active uptake mediated by vesicular transporters. Building on recent advances in structural biology and computational modeling, we further argue that drug loading into platelets can be predicted and optimized using molecular descriptors, docking approaches, and machine learning models to estimate transporter compatibility and accumulation efficiency. This perspective introduces a conceptual design loop linking prediction and molecular optimization─i.e., enabling both selection of suitable drug candidates and rational modification of their structures to enhance platelet compatibility. Together, these concepts position platelets not simply as biomimetic carriers but as model-guided and engineerable platforms for drug delivery, bridging biological systems with rational design strategies.
6 July 2026
Read appraisal →Briefings in bioinformatics
Literature-informed gene extraction and ranking for multimodal data fusion.
Published biomedical experiments provide an increasingly rich collection of results and identify genes potentially involved in diverse biological mechanisms. However, individual studies are often confined to narrow experimental contexts and are restricted to single omics layers. Cross-study knowledge aggregation can broaden this perspective and enable the construction of global, context-aware gene rankings. Recent developments in natural language processing have made large-scale literature mining increasingly feasible. This enables the systematic extraction and fusion of symbolic knowledge from published experiments. We present pathXcite, a software that extracts genes associated with specific contexts, such as diseases or biological mechanisms from the literature, and ranks them by contextual relevance. These relevance-based gene rankings can compress a scientific context into a symbolic representation. This representation enables diverse downstream analyses, including cross-context comparisons, network-based analysis, enrichment analysis, and integration with experimental omics data. In multiple use cases, we show how our extraction and fusion strategy can be applied to uncover hidden aspects in biological data.
6 July 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Interventions to improve the psychosocial outcomes of individuals with lymphedema: a systematic review
PURPOSE: Lymphedema is a common and enduring consequence of cancer treatment with substantial physical and psychosocial morbidity, yet research predominantly focuses on physical outcomes. This systematic review examines psychosocial interventions for individuals with lymphedema, describing the intervention characteristics, methodological quality, and strength of the evidence. METHODS: A systematic literature search was conducted of studies published through November 2025. Empirical interventions with psychosocial components and outcomes for individuals with lymphedema were eligible. Methodological quality was assessed. Strength of the evidence was determined by combining quality with intervention effectiveness. RESULTS: Ten studies met inclusion criteria, including mind-body (n = 3), education and support (n = 3), and physical activity (n = 4) intervention models, and most delivered following completion of decongestive therapy. Psychosocial outcomes included mental health (n = 8) and quality of life (n = 6). Methodological rigor was mixed, with MQRS scores ranging from 5-13 (of 14). Interventions delivered in combined in-clinic and remote settings demonstrated stronger evidence for improving mental health outcomes than either setting alone. CONCLUSION: Few empirical evaluations exist targeting the psychosocial needs of individuals with lymphedema, particularly during intensive phases of decongestive therapy. Heterogeneity in intervention models, outcome measures, and treatment timing limits cross-study comparisons; however, hybrid delivery approaches show promise for improving psychosocial outcomes. Greater integration of psychosocial care across the lymphatic care continuum, supported by standardized outcome measures, may strengthen supportive care for individuals living with lymphedema.
5 July 2026
Read appraisal →PloS one
Delayed health-seeking behavior and its associated factors among cancer patients in Ethiopia: A systematic review and meta-analysis, 2025
BACKGROUND: Delayed Health-seeking behavior among cancer patients is a major contributor to late diagnosis, poor prognosis, and high mortality, particularly in low-resource settings like Ethiopia. However, evidence on the magnitude and determinants of delayed care-seeking remains fragmented. OBJECTIVE: This systematic review and meta-analysis aimed to estimate the pooled prevalence of delayed Health-seeking behavior among cancer patients in Ethiopia and to identify associated factors influencing delays. METHODS: This study employed a systematic review and meta-analysis design to assess delayed Health-seeking behavior and its influencing factors among cancer patients in Ethiopia. A systematic search was conducted in PubMed, Scopus, Web of Science, CINAHL, AJOL, Google Scholar, and Ethiopian University repositories until April 27, 2025. The data were extracted from March 10-20 and analyzed from March 21-30, with report generation till April 27, 2025, using R software. Meta-analysis was performed using a random-effects model, with forest plots illustrating pooled prevalence and associated factors. Heterogeneity was assessed using the I² statistic, and study quality was evaluated using a validated tool. RESULTS: Seven studies conducted across multiple regions of Ethiopia were included in the final analysis with a total of 2,641 participants. The pooled prevalence of delayed Health-seeking behavior among cancer patients was 54% (95% CI: 39%-68%). Meta-analysis of associated factors showed that rural residence was significantly associated with delayed Health-seeking behavior, with patients residing in rural areas having more than threefold higher odds of delay (AOR = 3; 95% CI: 1.81-4.19), poor knowledge about cancer was strongly associated with delay, with nearly seven times higher odds among patients with poor knowledge compared to those with adequate knowledge (AOR = 6.63; 95% CI: 2.21-11.05), lack of cancer awareness was also a significant predictor of delayed Health-seeking behavior (AOR = 2.63; 95% CI: 1.75-3.51), and patients without pain were over three times more likely to delay Healthcare(AOR = 3.38; 95% CI: 2.44-4.67) were factors associated with delayed Health-seeking behavior. CONCLUSIONS: Our review showed that half of the cancer patients in Ethiopia experienced delayed health-seeking behavior. Delayed care-seeking was associated with rural residence, poor knowledge, limited awareness of cancer, and absence of pain symptoms. Targeted interventions, including public awareness campaigns, expansion of healthcare services in rural areas, and financial support initiatives, are urgently needed to reduce delays and improve early cancer diagnosis and outcomes. PROSPERO REGISTRATION NUMBER: CRD420251037845.
4 July 2026
Read appraisal →Journal of hematology & oncology
Large language model-guided CAR-T in silico platform for cytokine optimization in liver cancer with low antigen density
CAR-T cell therapy has shown remarkable success in hematologic malignancies but remains limited in solid tumors such as liver cancer due to antigen heterogeneity, low target antigen density, and an immunosuppressive tumor microenvironment (TME). Cytokine engineering can enhance CAR-T persistence and effector function; however, the optimal cytokine payload may vary depending on tumor type, target antigen expression level, and microenvironmental context, making systematic experimental comparison time-consuming and labor-intensive. Here, we applied a large language model (LLM)-based CAR-T in silico platform to systematically evaluate cytokine engineering strategies, including IL-2, IL-7, IL-12, IL-15, and IL-18, in glypican-3 (GPC3)-targeted CAR-T cells for liver cancer. We used cytokine selection as a biologically grounded benchmark to test whether the platform could recover known CAR-T cell-relevant cytokine biology and support future novel predictions. Computational predictions identified IL-15 as the most effective enhancer, particularly against tumor cells with low GPC3 expression. Guided by these results, we generated cytokine-armored GPC3 CAR-T cells and performed in vitro and in vivo validation. IL-15-engineered CAR-T cells exhibited superior proliferation, persistence, and serial cytotoxicity against GPC3-low liver cancer cells. In human liver cancer xenograft models, IL-15-enhanced CAR-T cells achieved improved tumor control compared with conventional and other cytokine-engineered CAR-T cells. The recovery of IL-15 served as a positive benchmark supporting the validity of the LLM-guided CAR-T in silico workflow. Collectively, this study establishes an LLM-guided framework, schema-constrained for rational cytokine selection in CAR-T engineering and identifies IL-15 as a potent enhancer for targeting antigen-low liver cancers.
3 July 2026
Read appraisal →Nature communications
A deep learning framework for efficient pathology image analysis
Artificial intelligence has transformed digital pathology by enabling biomarker prediction from high-resolution whole-slide images. However, current methods are computationally inefficient, processing thousands of redundant tiles per slide and requiring complex aggregation models. We introduce EAGLE (Efficient Approach for Guided Local Examination), a deep learning framework that emulates pathologists by selectively analyzing informative regions. EAGLE combines task-agnostic tile selection with detailed feature extraction and is benchmarked against leading slide- and tile-level foundation models across 43 tasks from nine cancer types spanning morphology, biomarker prediction, treatment response and prognosis. EAGLE outperforms patch aggregation methods by up to 23% and achieves the highest overall classification performance. It processes one slide in 2.27 s, reducing computational time by more than 99% compared with existing models. This efficiency supports rapid and auditable workflows by enabling review of the exact tiles used for each prediction and reducing dependence on high-performance computing. By reliably identifying informative regions and minimizing artifacts, EAGLE provides robust and auditable outputs, supported by systematic negative controls and attention concentration analyses. Its unified embedding enables rapid slide search, integration into multi-omics pipelines and emerging clinical foundation models.
2 July 2026
Read appraisal →Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
Integrating Artificial Intelligence into Prostate MR Imaging: Technical Foundations, Clinical Applications, and Workflow Implications
Prostate MRI has become a cornerstone of contemporary prostate cancer diagnosis, enabling improved detection of clinically significant disease while reducing unnecessary biopsies and overtreatment. However, prostate MRI remains technically demanding, time-consuming, and subject to inter-reader variability, particularly as healthcare systems move toward abbreviated protocols such as non-contrast MRI (biparametric MRI). In this context, artificial intelligence (AI) has emerged as a promising tool to enhance image quality, diagnostic consistency, and workflow efficiency across the prostate MRI pathway. This non-systematic narrative review provides a comprehensive overview of the technical foundations, clinical applications, and workflow implications of AI integration into prostate MRI. It summarizes key concepts in machine learning and deep learning relevant to prostate imaging and reviews current evidence supporting AI-based solutions for image quality assessment and reconstruction, automated prostate segmentation, lesion detection, and risk stratification. Particular attention is given to human-AI collaboration models, the role of AI in supporting equivocal lesions, and the integration of imaging with clinical variables for personalized risk estimation. In addition, it discusses the impact of AI on reporting efficiency, training, and standardization, as well as the current landscape of commercially available AI tools. Despite encouraging results from large multicenter studies, important challenges remain, including heterogeneity in study design, limited prospective validation, generalizability across institutions, and ethical and regulatory considerations. Overall, AI should be regarded as a complementary decision-support technology rather than a replacement for radiologists. Thoughtful implementation, robust validation, and appropriate user training are essential to ensure that AI meaningfully enhances the quality, efficiency, and reliability of prostate MRI-based care.
2 July 2026
Read appraisal →Open biology
Deep learning in tumour genomics: from multi-omics integration to precision oncology
Cancer remains a leading cause of death globally, with nearly 10 million deaths in 2020. Advances in genomic technologies have revolutionized cancer research, shifting focus towards precision medicine based on comprehensive tumour genomic profiling. Concurrently, deep learning (DL) has emerged as a powerful paradigm for complex biological data. This review critically assesses recent advances in DL applications for tumour genomics, emphasizing four key domains: DNA sequencing analysis for mutation detection, gene expression profiling for cancer subtype classification, methylation function prediction for epigenetic characterization and integrative multi-omics approaches for comprehensive tumour profiling. We systematically analyse how different DL architectures-including convolutional neural networks, recurrent neural networks, graph neural networks, autoencoders and transformers-address specific challenges in cancer genomics. Our review highlights how these approaches significantly enhance detection sensitivity for genomic alterations, improve cancer subtype stratification, identify novel biomarkers and optimize therapeutic target selection. We examine technical challenges in DL implementation, including model interpretability, data scarcity, computational requirements and integration issues, alongside emerging solutions such as explainable AI, federated learning, and multi-modal frameworks. By synthesizing methodological innovations and identifying research directions, this review provides bioinformaticians and cancer researchers with a roadmap for leveraging DL to advance precision oncology.
2 July 2026
Read appraisal →Journal of medical Internet research
Large Language Model Versus Multidisciplinary Team: Feasibility Study of Pancreatic Cancer Management Recommendations
Of 269 pancreatic cancer multidisciplinary team cases, 125 complete structured cases were evaluated. The chat interface model labeled ChatGPT-5.2 matched the multidisciplinary team's broad initial management category in 119 cases (95.2%, 95% CI 89.8%-98.2%). The end point was coarse, and the model was not version controlled, so findings only support supervised exploratory use.
2 July 2026
Read appraisal →Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Advancing radiotherapy with deep learning: A review of dose prediction models
BACKGROUND: Artificial intelligence (AI), especially deep learning algorithms, holds significant promise in transforming radiation therapy by enabling more precise, personalized, and efficient treatment planning. In particular, deep learning models have demonstrated their utility in critical tasks such as dose prediction, helping to streamline complex processes and improve patient outcomes. METHODS: This paper provides a comprehensive review of recent research on deep learning applications for radiotherapy dose prediction, highlighting the architectures, datasets, and methodologies used. RESULTS: The review highlights the advancements in accuracy, efficiency, and robustness achieved by deep learning-based dose prediction models. These approaches help reduce treatment planning time and minimize human error, thereby improving clinical workflows. CONCLUSION: Deep learning offers promising solutions to enhance radiotherapy dose delivery. Future research should focus on developing the generalized dose prediction models, multi-institutional validation frameworks, explainable AI approaches for clinical trust, real-time adaptation capabilities, uncertainty quantification methods, and strategies for handling rare anatomical variations.
1 July 2026
Read appraisal →CPT: pharmacometrics & systems pharmacology
A Narrative Review of Artificial Intelligence for Drug Repurposing: Lessons From COVID-19 and Oncology (2020-2025)
Drug repurposing presents a cost-effective and time-efficient strategy to identify new therapeutic applications for existing drugs. Recent advances in artificial intelligence, including machine learning, deep learning, knowledge graphs, and natural language processing, have revolutionized this field by enabling automated discovery of drug-disease associations. This review examines the role of artificial intelligence in drug repurposing, drawing insights from two critical case areas: Coronavirus disease 2019 and oncology. We explore current trends, methodological frameworks, and technological innovations in artificial intelligence-driven drug repurposing, as well as challenges and emerging future directions. The findings of this paper underscore the transformative potential of artificial intelligence in biomedical research and justify its continued integration in pharmaceutical pipelines.
1 July 2026
Read appraisal →BMJ supportive & palliative care
Efficacy and safety of acupuncture for breast cancer-related insomnia: a systematic review and meta-analysis
Breast cancer-related insomnia (BCRI) is a common and burdensome sequela. Acupuncture is frequently used, but robust evidence is limited, and previous systematic reviews included few trials yielding imprecise estimates. This updated systematic review and meta-analysis evaluated the efficacy and safety of acupuncture for BCRI. We systematically searched eight databases for randomized controlled trials (RCTs) up to July 1, 2025. RCTs comparing acupuncture with any control for BCRI were eligible. Methodological quality was assessed using the Cochrane Risk of Bias tool (RoB 2.0) and the modified Jadad scale. Meta-analyses employed a random-effects model. Subgroup analyses explored heterogeneity sources, and the GRADE framework rated evidence certainty. Twenty-seven RCTs (n=2,025) were included. Acupuncture significantly reduced Pittsburgh Sleep Quality Index (PSQI) scores (MD= -2.74, 95% CI [-3.92, -1.55], P<0.00001) and increased the overall efficacy rate (RR=1.51, 95% CI [1.27, 1.81], P<0.00001) compared to control, but did not significantly improve Insomnia Severity Index (ISI) scores. Subgroup analyses identified control intervention type and treatment frequency as significant sources of heterogeneity. Adverse events were infrequent and typically mild. GRADE assessments rated the certainty of evidence as low or very low, primarily due to risk of bias and substantial heterogeneity. Current evidence suggests acupuncture may improve sleep in patients with BCRI, with effects moderated by comparator type and treatment frequency. However, conclusions are limited by methodological weaknesses and heterogeneity. Future rigorous RCTs using validated sham controls, standardized protocols, and objective outcomes are needed to confirm efficacy and guide practice. PROSPERO Registration number (ID: CRD42024559828).
1 July 2026
Read appraisal →BMJ supportive & palliative care
Ethical dilemmas for palliative care nurses: systematic review
BACKGROUND: Nurses play a unique and critical role in palliative care, and it is noteworthy that nurses often encounter ethical dilemmas in this field. OBJECTIVE: This review aims to conduct a summarised synthesis of the latest research on the ethical considerations nurses faced in palliative care. METHODS: We conducted a rigorous systematic review of relevant existing studies published in high-quality English peer-reviewed journals from January 2017 to July 2023. We identified a total of 4492 articles (1029 in Web of Science, 1570 in PubMed and 1893 in Science Direct). Out of these, only 13 studies met the inclusion criteria. RESULTS: Following the thematic analysis, the ethical considerations reported in these 13 studies were grouped into three main themes and four subthemes: ethical issues in communication (ethical issues in communication with patients, ethical issues in communication with families), ethical issues in decision-making (autonomy, dignity) and moral distress in palliative care. CONCLUSION: This study elaborated on the ethical challenges faced by nurses in their communication with patients and families as well as decision-making and analysed the causes and effects of ethical distress, hoping to give a hand to ethical issues for nurses' work in palliative care.
1 July 2026
Read appraisal →BMJ open
Comparative diagnostic accuracy of image-enhanced endoscopy modalities and artificial intelligence-assisted endoscopy for gastric intestinal metaplasia and gastric dysplasia: protocol for a systematic review and diagnostic test network meta-analysis.
INTRODUCTION: Gastric intestinal metaplasia (GIM) and gastric dysplasia are critical precursors in the Correa cascade of gastric carcinogenesis. Although multiple image-enhanced endoscopy (IEE) modalities and artificial intelligence (AI)-assisted systems have been developed to improve their detection, the comparative diagnostic accuracy of these technologies remains unclear. Direct comparative studies are limited and conventional pairwise meta-analyses do not provide a coherent framework for comparing multiple diagnostic technologies. METHODS AND ANALYSIS: This protocol follows the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) and PRISMA-Network Meta-Analysis guidelines. We will search MEDLINE (Ovid), Embase (Ovid), the Cochrane Central Register of Controlled Trials (CENTRAL; Cochrane Library/Wiley), Web of Science Core Collection (Clarivate), Scopus (Elsevier), ClinicalTrials.gov and the WHO International Clinical Trials Registry Platform from inception, without date or language restrictions. Eligible studies will be diagnostic accuracy studies evaluating IEE modalities (including narrow-band imaging with or without magnification, blue laser imaging, linked colour imaging and iScan) or AI-assisted endoscopy against histology. Comparative studies will form the primary evidence base for the diagnostic test network meta-analysis; single-arm studies will be summarised separately and will not contribute to the primary network estimates. The coprimary diagnostic parameters will be sensitivity and specificity for GIM and dysplasia. These will be analysed jointly using a hierarchical bivariate diagnostic test network meta-analysis with binomial likelihoods and correlated random effects. Diagnostic OR and area under the receiver operating characteristic curve will be treated as secondary summary measures. Two reviewers will independently screen, extract data and apply Quality Assessment of Diagnostic Accuracy Studies 2. Heterogeneity, transitivity and inconsistency will be assessed using prespecified clinical and methodological effect modifiers, model-based diagnostics and sensitivity analyses. The certainty of evidence will be assessed using Confidence in Network Meta-Analysis, with interpretation adapted to diagnostic accuracy outcomes. ETHICS AND DISSEMINATION: This study does not require ethical approval as it relies on secondary analysis of published literature. Findings will be disseminated through peer-reviewed publication and conference presentations. PROSPERO REGISTRATION NUMBER: CRD420261363078.
29 June 2026
Read appraisal →JMIR cancer
Large Language Models for Breast and Cervical Cancers Communication: Mixed Methods Evaluation Study Assessing Linguistic Quality, Safety, and Accessibility
BACKGROUND: Effective communication about breast and cervical cancers remains a public health challenge, with widespread misinformation and barriers to cancer-related language understanding. Large language models (LLMs) offer potential for scalable health communication, yet trade-offs between quality, safety, and accessibility of general-purpose and medical-domain LLMs remain underexplored. OBJECTIVE: This study aimed to propose a comprehensive evaluation framework and systematically assess the performance of LLMs in generating breast and cervical cancer information, with a focus on linguistic quality, safety and trustworthiness, and communication accessibility and affectiveness. METHODS: This mixed methods evaluation study assessed outputs from 5 general-purpose and 3 medical LLMs using real-world breast and cervical cancer-related questions curated from publicly available medical datasets. LLM-generated responses were evaluated in a controlled offline setting. Primary outcomes included linguistic quality (fluency, coherence, and accuracy), safety and trustworthiness (toxicity, bias, and harm potential), and communication accessibility and affectiveness (readability, empathy, and clarity). Qualitative ratings were performed by domain experts, while quantitative metrics were compared across models. Statistical analyses included Welch ANOVA to detect differences in metric scores, Games-Howell tests for pairwise comparisons, and Hedges g to assess effect sizes. RESULTS: General-purpose LLMs, particularly Llama 3 and Gemma, demonstrated superior linguistic quality and affectiveness but often produced complex outputs that may limit accessibility. In contrast, medical LLMs (eg, MedAlpaca and BioMistral) generated simpler content suitable for broader audiences but scored lower in safety and empathy due to higher levels of hallucination, bias, and toxicity. CONCLUSIONS: While LLMs show promise for improving digital cancer communication, our findings reveal a trade-off between domain specialization and overall communication quality and safety. Future development of health-focused LLMs should prioritize hybrid modeling strategies to enhance trust, clarity, and clinical relevance in patient-facing tools.
29 June 2026
Read appraisal →Journal of medical Internet research
Physical Activity Interventions Using Digital Health Interventions for Cancer-Related Fatigue in People With a History of Cancer: Scoping Review
BACKGROUND: Although exercise has been proven effective in alleviating cancer-related fatigue (CRF), traditional face-to-face programs may not be accessible due to physical, temporal, or geographical barriers. Digital health interventions (DHIs) offer scalable alternatives for promoting physical activity; however, evidence synthesizing DHI-based physical activity interventions specifically targeting CRF and their intervention characteristics remains limited. OBJECTIVE: This scoping review aimed to map the types of digital health-based physical activity interventions for managing CRF, to summarize the key characteristics of DHI modalities and CRF outcomes, and to identify knowledge gaps for future research. METHODS: A systematic literature search was conducted across 4 databases (PubMed, EMBASE, Cochrane, and PsycINFO) up to December 2025. Inclusion criteria comprised experimental studies involving adults with a history of cancer, digital exercise interventions, a control group, and fatigue outcomes. Screening and data extraction followed the Joanna Briggs Institute Manual for Evidence Synthesis and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Review) standards. The protocol was registered in PROSPERO (International Prospective Register of Systematic Reviews; CRD42022304285). Interventions were classified using the Evidence Standards Framework (ESF). RESULTS: Thirty-three studies comprising 3443 participants were included, representing 32 interventions. Most were randomized controlled trials (n=30, 91%). Interventions were delivered primarily via mobile apps (11/32, 34%) and wearable devices (8/32, 25%), followed by web-based platforms, videoconferencing, exergaming, and augmented reality. Eighteen interventions (reported in 19 studies) demonstrated statistically significant between-group CRF reductions, predominantly at immediate postintervention assessments. Evidence for sustained benefit beyond 12 months was limited, and only one study evaluated ultra-long-term outcomes, which did not demonstrate maintained improvement. Populations with breast cancer accounted for the largest proportion of participants. Fatigue measurement tools varied substantially, potentially contributing to heterogeneity in effect estimates. Most interventions were classified as ESF tier C, indicating a predominant focus on clinical outcome improvement rather than individual-level self-management and system-level implementation. Wearable device-based interventions showed the highest proportion of significant CRF improvement, whereas mobile app-based interventions reported both significant and nonsignificant results. DHIs lasting more than 12 weeks appeared to be associated with more effective CRF outcomes compared to shorter programs. Direct comparisons between in-person and digital delivery were scarce. CONCLUSIONS: Digital health-based physical activity interventions can reduce CRF in people with a history of cancer, with wearable device and longer-duration interventions showing the most favorable outcomes. However, the current evidence is concentrated in populations with breast cancer, and system-level integration remains unexplored. Future research should prioritize diverse populations with cancer, longer follow-up periods, standardized fatigue measurement, and systematic examination of specific intervention components that contribute to CRF reduction. Ultimately, advancing from individual efficacy trials toward scalable, workflow-integrated digital solutions will be key to sustainable CRF management across diverse oncology settings.
29 June 2026
Read appraisal →Journal of medical Internet research
Effectiveness of a Home-Based and Group-Based Tele-Exercise Program for Breast Cancer Survivors: Pilot Randomized Controlled Trial
BACKGROUND: Breast cancer remains the most prevalent cancer among women globally. Adjuvant therapies can cause adverse effects that compromise physical and mental health. Exercise may mitigate these effects; however, many breast cancer survivors remain insufficiently active. OBJECTIVE: This pilot study aimed to test the effectiveness of a 7-week theory-informed tele-exercise intervention for breast cancer survivors in Hong Kong. METHODS: We developed a 12-week theory-informed tele-exercise intervention for breast cancer survivors in Hong Kong; this pilot tested a 7-week abbreviated version. In this 2-group randomized controlled pilot trial, 34 individuals were assessed for eligibility, and 27 were randomized; 24 completed baseline and were included in the modified intention-to-treat analyses (12 per group). The intervention comprised a progressively supervised group-based tele-exercise intervention transitioning to unsupervised sessions, combined with psychological counseling. Outcomes were guided by the RE-AIM (reach, effectiveness, adoption, implementation, and maintenance) framework, with emphasis on feasibility, acceptability, and preliminary effects. RESULTS: Recruitment was 79.4% (27/34) and baseline-to-post retention was 100% (24/24), with satisfactory attendance (87.7%) and compliance (85.3%) in the intervention group. Acceptability was high. Preliminary signals of improvement were observed in cardiorespiratory fitness (primary outcome), lower-extremity strength, balance, affected-side shoulder range of motion, and health-related quality of life. CONCLUSIONS: This pilot supports the feasibility and acceptability of tele-exercise for breast cancer rehabilitation and provides preliminary evidence to justify a larger trial with longer follow-up to assess sustained effects and broader applicability.
29 June 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
The effectiveness of non-pharmacological interventions on sleep quality in lung cancer patients: a systematic review and meta-analysis (2015-2025)
PURPOSE: Sleep disorders are highly prevalent among lung cancer patients and seriously affect their quality of life and mental health. This systematic review and meta-analysis aimed to assess the effects of non-pharmacological interventions on sleep quality, quality of life, and anxiety in lung cancer patients. METHODS: PubMed, Web of Science, Cochrane Library, CINAHL, Embase, CNKI, Wanfang Data, and VIP databases for studies published between 2015 and February 2025. The primary outcome was sleep quality, with quality of life and anxiety levels as secondary outcomes. We assessed the risk of bias using the Cochrane tool and synthesized data using meta-analysis. RESULTS: Twenty-four studies involving 2604 patients were included. Non-pharmacological interventions were compared with usual care, placebo, or standard oncology care. After excluding studies with inappropriate content and methods, 20 studies evaluated sleep quality, and 12 studies evaluated quality of life and anxiety levels, respectively. The quality of the evidence was low to moderate; the promotion results should be interpreted with caution, but they may have a positive effect on the sleep quality, quality of life, and anxiety levels of lung cancer patients. CONCLUSIONS: Diverse non-pharmacological interventions can improve sleep quality, quality of life, and anxiety in lung cancer patients. However, high-quality, rigorously designed randomized controlled trials are still needed to confirm these findings. Future research should focus on personalized interventions tailored to lung cancer patients' characteristics and integrate digital health interventions to better support clinical decision-making.
28 June 2026
Read appraisal →Public health research & practice
Optimising SMS content for bowel cancer screening participation in Australia: cross-sectional national survey findings.
OBJECTIVES AND IMPORTANCE OF STUDY: This study aimed to identify short message service (SMS) reminder content perceived as most likely to prompt bowel cancer screening, examine differences across sociodemographic subgroups, and explore preferences for timing and frequency. METHODS: Australian residents (N = 1016) aged 50-74 years completed an online survey rating five SMS reminders presented in random order. Outcomes included perceived usefulness, likelihood of encouraging kit return, likelihood of irritation and clarity. Preferences for timing and frequency were also assessed. Bayesian multilevel modelling (cumulative probit) compared ratings across SMS types, with effect sizes expressed as standard deviation (s.d.) differences in perceived likelihood that each SMS would encourage kit return compared with a reminder-only message. Interactions with age, gender, socioeconomic status and screening history were explored. RESULTS: Compared with a 'reminder-only' message, SMS content that encouraged storing the kit near the toilet (s.d. 0.44), conveyed general practitioner endorsement (s.d. 0.32) and gave instructions (s.d. 0.22) was more likely to prompt kit return. Responses varied slightly by age and area-level socioeconomic status. Most participants preferred two to three SMS reminders (mean 2.89, s.d. 6.73). CONCLUSIONS: SMS reminders using behavioural prompts and clear, concise content may support improved screening participation. Tailored SMS messages that reflect public preferences may increase kit return rates, support national screening goals and reduce bowel cancer mortality.
27 June 2026
Read appraisal →Familial cancer
When screentime fails: initiative to improve completion of hereditary cancer genetic testing after telemedicine counseling.
Telemedicine has broadened access to genetic counseling while maintaining high levels of patient satisfaction. However, emerging evidence suggests lower completion rates of genetic testing following virtual visits compared with in-person encounters. This study evaluated genetic testing completion rates among patients who underwent cancer risk assessment via telemedicine and assessed the effect of a structured follow-up telephone reminder on genetic testing completion. We conducted a retrospective review of a quality improvement initiative - Genetic Engagement via Navigation and Encouragement (GENE CALL) - designed to address incomplete genetic testing following telemedicine counseling. The study included all patients who underwent virtual cancer risk assessment between September 2023 and July 2024 at a single urban academic cancer genetics program. Patients who expressed interest in genetic testing but had not scheduled specimen collection were contacted by telephone as a reminder. During the call, patients were asked about ongoing interest in testing and barriers to scheduling; they were also offered assistance completing specimen collection. Among 647 patients evaluated via telemedicine, 469 (72.5%) expressed interest in genetic testing. Of these, 290 (61.8%) had scheduled or completed testing, while 179 (38.2%) had not. Among those who had not scheduled testing, 120 (67.0%) were successfully reached by telephone, and 98 (81.7%) confirmed ongoing interest in completing testing. At 3-month follow-up, 48 (40.0%) of the 120 contacted patients had completed testing, compared with 13 (22.0%) of 59 patients who were not reached (p = 0.017). Telephone follow-up is an effective strategy to improve genetic testing completion after virtual counseling.
27 June 2026
Read appraisal →Genome biology
MLMarker: a machine learning framework for tissue inference and biomarker discovery
MLMarker is a machine learning tool that computes continuous tissue similarity scores for proteomics data, addressing the challenge of interpreting complex or sparse datasets. Trained on 34 healthy tissues, its Random Forest model generates probabilistic predictions with SHAP-based protein-level explanations. A penalty factor corrects for missing proteins, improving robustness for low-coverage samples. Across three public datasets, MLMarker revealed brain-like signatures in cerebral melanoma metastases, achieved high accuracy in a pan-cancer cohort, and identified brain and pituitary origins in biofluids. MLMarker provides an interpretable framework for tissue inference and hypothesis generation, available as a Python package and Streamlit app.
26 June 2026
Read appraisal →Pulmonology
Association between smoking and prognosis in idiopathic pulmonary fibrosis: A systematic review and meta-analysis
BACKGROUND: Although smoking is a well-established risk factor for idiopathic pulmonary fibrosis (IPF) development, its impact on clinical outcomes remains unclear. RESEARCH QUESTION: Is smoking associated with clinical outcomes of IPF? METHODS: A systematic search of PubMed, Embase, and the Cochrane Library was conducted to identify studies reporting associations between smoking status and IPF outcomes, including mortality, acute exacerbation (AE), lung cancer development, and baseline lung function. Summary estimates with 95% confidence intervals (CIs) were pooled using random-effects model. Subgroup analyses were conducted by study region. RESULTS: Forty-nine studies comprising 32 974 patients were included. Overall mortality did not differ by smoking status. However, significant regional differences were observed, with ever smokers demonstrating higher mortality in non - East Asian studies (Hazard ratio [HR] 1.17, 95% CI: 1.03-1.33) but lower mortality in East Asian studies (HR 0.75, 95% CI: 0.62-0.91, p-for-interaction < 0.001) compared with never smokers. Current smokers had a higher risk of developing lung cancer compared with non-current smokers (HR 1.88, 95% CI: 1.24-2.87). No significant associations were observed between smoking and the risk of AEs or baseline forced vital capacity. CONCLUSION: Smoking was not associated with overall mortality in IPF, although significant regional differences were observed.
26 June 2026
Read appraisal →World journal of surgical oncology
Robotic-assisted versus conventional nipple-sparing mastectomy with immediate implant-based breast reconstruction: a GRADE-assessed systematic review and meta-analysis of prospective studies
BACKGROUND: Robotic nipple-sparing mastectomy with immediate implant-based reconstruction is increasingly adopted to improve cosmetic outcomes and patient experience; however, its comparative perioperative value against conventional or open nipple-sparing mastectomy remains uncertain when restricted to prospective evidence. METHODS: We performed a PRISMA-aligned systematic review and random-effects meta-analysis of prospective comparative studies and randomized trials comparing robotic versus conventional or open nipple-sparing mastectomy with immediate implant-based reconstruction. The primary outcome was the total operative time, and the secondary outcomes were major complications, nipple-areolar complex ischemia or necrosis, and length of hospital stay. The risk of bias was assessed using RoB 2 for randomized data and ROBINS-I for nonrandomized data. Leave-one-out sensitivity analyses were conducted for outcomes with substantial heterogeneity, and the certainty of the evidence was graded using GRADE. RESULTS: Three prospective studies were included. Robotic surgery was associated with a significantly longer total operative time (mean difference 64.01 min, 95% confidence interval 8.54-119.47). Major complications did not differ between the approaches (risk ratio 0.63, 95% confidence interval 0.27-1.47), nor did nipple areolar complex ischemia or necrosis (risk ratio 0.56, 95% confidence interval 0.28-1.11) or length of hospital stay (mean difference 0.22 days, 95% confidence interval - 0.26 to 0.70). Heterogeneity was high for operative time and moderate to substantial for length of stay. The certainty of the evidence was low overall, driven by imprecision, heterogeneity, and limitations of the non-randomized design. CONCLUSIONS: Robotic nipple-sparing mastectomy is associated with a longer operative time than conventional or open approaches. No statistically significant differences were observed in major complications or nipple-areolar complex viability; however, the current prospective evidence base is limited, heterogeneous, and imprecise, and clinically important differences cannot be excluded. In experienced centers, robotic nipple-sparing mastectomy may be considered for carefully selected patients who prioritize scar concealment and minimally invasive access; however, its broader role in routine practice remains uncertain pending stronger prospective data on safety, patient-reported outcomes, and resource use.
26 June 2026
Read appraisal →La Medicina del lavoro
Cadmium Exposure and Risk of Pancreatic Cancer: A Systematic Review and Meta-Analysis
INTRODUCTION: Pancreatic cancer is a highly lethal malignancy with poor prognosis and limited treatment options. Environmental exposures, particularly to heavy metals such as cadmium, may contribute to its etiology. This systematic review and meta-analysis evaluated the association between cadmium exposure from different sources and pancreatic cancer incidence or mortality. METHODS: Following PRISMA guidelines, we searched PubMed, EMBASE, and Scopus. Eighteen studies comprising 20 risk estimates were included. Random-effects meta-analyses were conducted overall and stratified by exposure source, gender, region, study design, and outcome. Dose-response relationships were assessed using meta-regression of cadmium exposure measures. Publication bias was evaluated using funnel plots and Egger's test. RESULTS: Overall, cadmium exposure was associated with an increased risk of pancreatic cancer [relative risk (RR) = 1.69, 95% confidence interval (CI): 1.28-2.22]. Occupational exposure showed the most consistent association (RR = 1.38, 95% CI: 1.19-1.61), followed by urinary and blood/serum biomarkers. Risk was higher in men than in women, and in case-control than in cohort studies. Dose-response analysis did not reveal a linear trend. There was limited evidence of publication bias overall, though some asymmetry was observed for urinary cadmium studies (P = 0.045). CONCLUSION: Cadmium exposure was associated with pancreatic cancer risk, particularly in occupational and biomarker-based studies. While findings support a potential causal link, heterogeneity, residual confounding, and limited dose-response data necessitate cautious interpretation.
26 June 2026
Read appraisal →Nucleic acids research
SpliceSelectNet: a hierarchical Transformer-based deep learning model for splice site prediction
Accurate RNA splicing is essential for gene expression and protein function, yet the mechanisms governing splice site recognition remain incompletely understood. Aberrant splicing caused by mutations can lead to severe diseases, including cancer and genetic disorders, underscoring the need for accurate computational tools to predict splice sites and detect disruptions. Existing methods have made significant advances in splice site prediction but are often limited in handling long-range dependencies due to high computational costs, a factor critical to splicing regulation. Moreover, many models lack interpretability, hindering efforts to elucidate the underlying biological mechanisms. Here, we present SpliceSelectNet (SSNet), a hierarchical Transformer-based deep learning model that predicts splice sites from DNA sequences spanning up to 100 kb. By integrating local and global attention mechanisms, SSNet efficiently captures both proximal and distal regulatory signals while maintaining single-nucleotide resolution. Across multiple benchmark datasets, SSNet achieves state-of-the-art performance in splice site prediction and aberrant splicing detection. Systematic in silico mutagenesis demonstrates that attention scores reflect functional sequence importance, supporting their biological relevance. Long-range sequence perturbation experiments further show that SSNet captures distal regulatory effects beyond conventional receptive fields. Together, these results establish SSNet as a biologically interpretable framework for modeling long-range splicing regulation from genomic sequence.
23 June 2026
Read appraisal →Journal of chemical information and modeling
A Reproducible Hierarchical Virtual Screening Framework Integrating Scaffold-Aware Machine Learning, Ensemble Docking, and Molecular Dynamics: Application to IDO1
Indoleamine 2,3-dioxygenase 1 (IDO1) is a heme-containing enzyme implicated in cancer immune escape and remains an attractive therapeutic target despite recent clinical setbacks. We report a fully reproducible hierarchical virtual screening framework integrating scaffold-aware machine learning, ensemble docking, consensus scoring, and molecular dynamics simulations for robust prioritization of IDO1 inhibitors. A curated ChEMBL data set of IDO1 inhibitors was subjected to strict standardization, duplicate removal, and activity binarization at pChEMBL ≥6. Models were trained using scaffold-based splitting and nested cross-validation to prevent chemical series leakage. An ensemble of Random Forest, XGBoost and SVM classifiers achieved balanced predictive performance (ROC-AUC ≈0.88-0.89) with applicability domain filtering to ensure reliability. Prospective screening of FDA-approved drugs yielded 39 compounds within the applicability domain predicted as active. These were further evaluated through ensemble docking against multiple IDO1 crystal structures using GNINA with CNN rescoring. Consensus strategies were systematically benchmarked, demonstrating that best-Z-score aggregation outperformed mean, rank-based, and weighted methods in enrichment factor (EF) metrics. Two top-ranked candidates were subjected to 300 ns molecular dynamics simulations, revealing stable binding modes and persistent interactions with key catalytic residues. This study demonstrates that hierarchical integration of scaffold-aware machine learning with structure-based ensemble strategies enhances robustness and reduces false positives in virtual screening campaigns. The proposed workflow is generalizable and supports reproducible candidate prioritization in computational drug discovery. The complete implementation, including data processing, model training, and analysis steps, is provided as a fully executable Jupyter notebook available at https://github.com/rocco-b/IDO1-inhibitors-ML-and-docking-data.
23 June 2026
Read appraisal →Journal of chemical information and modeling
SSPSPredictor: A Sequence- and Structure-Based Deep Learning Model for Predicting Phase-Separating Proteins
Liquid-liquid phase separation (LLPS) underlies the formation of biomolecular liquid condensates (also termed membraneless organelles, MLOs), which are essential for spatially organizing various biochemical processes within cells. Proteins that play a key role in driving condensate formation are termed phase-separating proteins (PSPs). Given that experimental identification of PSPs remains labor-intensive and time-consuming, multiple computational tools have been developed based on empirical features or deep learning. In this study, we propose SSPSPredictor, a novel multimodal predictive model for PSPs with folded or intrinsically disordered structures, leveraging the fusion of sequence information from a protein language model ESM-2 and structural insights from a graph neural network GVP. Compared with existing tools, SSPSPredictor achieves a balanced performance in identifying PSPs, predicting relative LLPS propensities, and recognizing key regions that drive LLPS. More importantly, SSPSPredictor exhibits good interpretability in identifying driving regions along protein sequences, although no relevant supervision was provided during training. Further predictive analysis of the human proteome using SSPSPredictor reveals that the proportion of intrinsically disordered proteins (IDPs) undergoing LLPS is significantly higher than that of the folded proteins. In addition, pathogenic variants, especially those located in disordered regions, exhibit a higher LLPS propensity than other mutations, uncovering a link between LLPS and diseases at the amino acid level. An online Web server for SSPSPredictor (https://bio-comp.ucas.ac.cn/SSPSPredictor/) and its source code (https://github.com/ucas-biocomp/SSPSPredictor) are available.
23 June 2026
Read appraisal →Journal of robotic surgery
A focused systematic review and meta-analysis of robot-assisted inguinal lymphadenectomy versus open inguinal lymph node dissection in penile cancer
This focused systematic review and meta-analysis evaluated robot-assisted inguinal lymphadenectomy (RAIL) versus open inguinal lymph node dissection (OILND) in penile cancer, aiming to provide robot-specific comparative estimates rather than a broad minimally invasive synthesis. PubMed, EMBASE, Web of Science, and the Cochrane Library were searched through December 2025 for comparative studies. All meta-analyses were conducted using STATA 18, employing random-effects modeling. Binary outcomes were summarized using odds ratios (ORs), and continuous outcomes were presented as weighted mean differences (WMDs), with all estimates accompanied by 95% confidence intervals (CIs). Risk of bias in the included non-randomized comparative studies was assessed using the ROBINS-I tool. Four retrospective comparative studies involving 276 patients were included, and no randomized controlled trials were identified. A lower odds of total postoperative complications was observed with RAIL (OR = 0.52, 95% CI: 0.27, 0.97; P = 0.047), but this borderline finding should be interpreted cautiously given the small number of retrospective studies and potential residual confounding. Across individual studies, operative time generally tended to be longer with RAIL, whereas findings for estimated blood loss and drainage-related outcomes were inconsistent. Lymph node yield, skin-related complications, minor complications, and groins with positive nodes did not differ significantly between approaches. According to the GRADE assessment, the certainty of evidence was low for most pooled outcomes and very low for skin-related complications and lower-limb edema/lymphedema. RAIL may be associated with lower overall postoperative complications than open surgery, but the evidence remains low certainty and insufficient to establish a definitive morbidity-reduction benefit. In addition, several continuous perioperative variables showed extreme between-study heterogeneity and were not clinically interpretable as reliable pooled effects.
22 June 2026
Read appraisal →Biogerontology
RNA-binding proteins in aging and age-related diseases: roles, mechanisms, and a large language model analysis
Aging is a progressive and irreversible biological process that contributes to the pathogenesis of numerous age-related diseases. Elucidating the molecular mechanisms of aging is crucial for promoting healthy aging and extending healthspan. RNA-binding proteins (RBPs) are pivotal regulators of post-transcriptional gene expression and play essential roles in diverse biological processes. RBPs interact with both coding and non-coding RNAs to regulate RNA metabolism, stability, localization, and translation. Dysregulated RBP-RNA interactions have been closely associated with aging and age-related diseases. This review systematically summarizes the structural characteristics of RBPs and the evolution of methods used to study them. We focus on the molecular mechanisms of six key RBPs, namely HuR, AUF1, TTP, IGF2BP2, QKI, and LARP7, in the context of aging and age-related diseases. In addition, we discuss the regulatory functions of post-translational modifications of RBPs. Furthermore, we provide a multidimensional overview of RBP involvement in aging and age-related diseases through large language model (LLM)-based text-mining analysis. Our study provides a foundation for the comprehensive characterization of RBPs in aging and age-related diseases.
22 June 2026
Read appraisal →European radiology experimental
Digital twin technologies in prostate cancer as a frontier for precision medicine
Prostate cancer (PCa) is the most frequently diagnosed malignancy among men and presents major clinical and socioeconomic challenges worldwide. Despite advances in early detection, imaging, and therapy, managing PCa remains complex due to disease heterogeneity, risks of overdiagnosis, and overtreatment. Digital twin (DT) technologies might represent an emerging conceptual framework aimed at supporting dynamic, patient-specific virtual modeling for personalized clinical decision-making. By integrating multimodal clinical, imaging, molecular, and physiological data, DTs can simulate disease progression, predict treatment responses, and support proactive, adaptive care. This perspective explores the conceptual framework for DT ecosystems in PCa, highlighting potential clinical impacts, infrastructural requirements, and barriers to implementation. Harnessing DTs could impact PCa management into a truly predictive, personalized, and participatory approach, improving outcomes and optimizing healthcare resource utilization globally. RELEVANCE STATEMENT: DT technologies may enable personalized, predictive PCa management by integrating multimodal patient data to guide diagnosis, treatment selection, and monitoring, with the potential to improve outcomes, reduce overtreatment, and optimize healthcare resource utilization KEY POINTS: DTs create virtual patient models to personalize PCa care. They integrate imaging, clinical, and molecular data into one system. This approach may reduce overdiagnosis and unnecessary treatments.
20 June 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Photobiomodulation for oral mucositis in cancer care: prevention, clinical management, and evidence from randomized controlled trials.
PURPOSE: This systematic review aimed to evaluate the efficacy of photobiomodulation (PBM) for the prevention and clinical management of oral mucositis (OM) in adult cancer patients, with particular emphasis on protocol characteristics potentially associated with favorable clinical outcomes. METHODS: A systematic search of PubMed, Web of Science, and Scopus was conducted up to March 31, 2026, in accordance with PRISMA guidelines. Randomized controlled trials in adult cancer patients comparing PBM with sham interventions were included. Outcomes of interest included OM incidence, severity, pain, and related clinical outcomes. The review protocol was prospectively registered in PROSPERO. RESULTS: Fifteen randomized controlled trials involving 868 adult patients were included. Most studies reported favorable effects of PBM, including reductions in severe OM incidence, pain intensity, analgesic requirements, and nutritional support needs compared with controls. Preventive intraoral PBM protocols were generally associated with more favorable outcomes, although variability in treatment parameters, delivery methods, and outcome reporting limited comparability. Most included studies demonstrated acceptable methodological quality. CONCLUSIONS: PBM appears to be a promising supportive care intervention for reducing the burden of OM in adult cancer patients, particularly in preventive settings. However, variability across protocols precludes definitive conclusions regarding optimal treatment parameters. Further high-quality randomized trials with standardized reporting are needed to refine protocol recommendations and expand evidence in therapeutic settings.
20 June 2026
Read appraisal →Progress in biomedical engineering (Bristol, England)
Advancement of deep learning models with whole slide image in diagnosis, subtyping and prognosis for glioma.
Gliomas are heterogeneous primary central nervous system tumors with diverse molecular subtypes and variable prognosis. A paradigm shift in histopathology is underway as samples are digitized into whole-slide images (WSIs). Deep learning (DL) shows great potential for glioma diagnosis, subtyping, grading, and prognosis. This review summarizes recent advances in WSI-based DL models, covering data preprocessing, model architectures, and translational performance. Model evolution has progressed from CNNs (e.g. ResNet, capturing local features via convolution) to Transformers (e.g. Vision transformer, modeling global dependencies via self-attention), hybrid architectures, and large language model. Typical pipelines include quality control, stain normalization, patch extraction, feature integration, and prediction. Public datasets such as The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium serve as key resources. On various datasets (public and institutional), a glioma-specific model ResNet-50 achieved AUC 0.983 for subtype classification; a Vision Transformer model reached AUC 0.960 for molecular typing; a hybrid model, ROAM, attained AUC 0.990; and the pan-cancer hybrid model called CHIEF achieved AUC 0.9397 for diagnosis. Attention heatmaps and Shapley Additive exPlanations provide interpretability by linking model outputs to histologic regions. Novel multimodal fusion integrates genomic, proteomic, radiologic, and clinical data to enhance prediction and uncover biological relevance. Future research may focus on enhancing model generalizability and predictive accuracy via advanced architectures, developing lightweight models for resource-limited settings to facilitate clinical translation.
20 June 2026
Read appraisal →International journal of medical informatics
Automated Flow and local LLM-Driven clinical Context Engineering: Precision colorectal cancer recurrence registry
OBJECTIVE: This study develops and validates a deployable, privacy-preserving automated tool to address the labor-intensive nature of colorectal cancer (CRC) recurrence registration. Our tool utilizes a reproducible, two-stage 'Clinical Context Engineering' workflow to mimic expert clinical reasoning and overcome the limitations of handling longitudinal clinical data ambiguity. METHODS: We retrospectively studied 3053 CRC patients (2010-2018). A local Large Language Model (LLM) (Qwen3:14B) analyzed ∼ 19,900 pathology and ∼ 43,900 imaging reports using iterative, patch-based analysis ("raw LLM"). Clinical validation rules were applied to generate "rule-based LLM" output, enhancing explainability and trustworthiness. Both automated methods and the manual Taiwan Cancer Registry (TCR) database were compared against a 20% manual reference standard (N = 602). Full prompts and validation code are provided for complete reproducibility. RESULTS: Under a strict 60-day temporal tolerance, the Rule-Based LLM achieved 90.7% accuracy, comparable to standard TCR processes (92.0%), and 77.2% sensitivity. The application of clinical validation rules significantly improved specificity from 87.7% (Raw LLM) to 93.9% (Rule-Based LLM). In time window analysis, the Rule-Based LLM identified 87.1% of recurrences within 60 days of the reference date. CONCLUSION: Our locally deployed, privacy-preserving, and explainable Clinical Context Engineering framework offers a viable, non-inferior alternative to standard TCR processes, reducing workload while maintaining data quality and fostering trust in AI-assisted cancer registry automation.
16 June 2026
Read appraisal →Analytica chimica acta
Rapid detection of drug-resistant leukemia cell using an optofluidic chip and machine learning
BACKGROUND: Rapid detection of drug-resistant leukemia played a crucial role in formulating appropriate treatment plans for patients and improving their prognosis. In this research, an integrated optofluidic platform was developed to detect and analyze leukemia and drug-resistant leukemia cell. The detection technique was designed by embedding optical fibers coupled with photosensors and a laser source into optofluidic chip. The scattered light signals were detected when the cells pass through the detecting area. RESULTS: The platform was first validated by classifying 1 μm polystyrene microparticles and 1 μm polystyrene microparticles coated with spherical 10 nm Fe3O4 nanoparticles. After validation, the method was applied to classify leukemia and drug-resistant leukemia cells by injecting the testing sample and obtaining. The SVM classifier demonstrated the highest classification accuracy of 91.1% compared with LR, RF, and KNN classifiers for analyzing leukemia cells and drug-resistant leukemia cells. The proposed method can perform detection within 10 min with a total experimental timeframe of 20 min. SIGNIFICANCE: The presented results demonstrate the feasibility of applying microfluidics and machine learning approaches to detect and classify biological entities with slight variations based on scattered light signals. This platform holds significant potential for clinical diagnostics, offering a rapid, cost-effective, and efficient method for detecting drug-resistant leukemia cells, potentially aiding in personalized treatment strategies.
16 June 2026
Read appraisal →Sexual health
A systematic review of barriers to cervical cancer screening among Thai women based on the social ecological model: a multi-level analysis of local evidence
BACKGROUND: Cervical cancer is a significant public health issue in Thailand. Despite the implementation of a national screening program, its coverage remains suboptimal. This study aims to systematically review local Thai evidence and comprehensively analyze the multi-level barriers affecting women's participation in cervical cancer screening using the social ecological model (SEM). METHODS: Systematic searches were conducted in PubMed, Scopus, CINAHL and ThaiJO databases from inception to 2025. Literature was screened according to pre-defined inclusion and exclusion criteria, resulting in the inclusion of 11 studies. The Newcastle-Ottawa scale and a mixed-methods assessment tool were used for rigorous quality appraisal. Thematic synthesis was used to categorize screening barriers into five levels: individual, interpersonal, community, health system and public policy. RESULTS: The key obstacles identified include: at the individual level, lack of knowledge, fear, shame and insufficient risk perception; at the interpersonal level, the influence of family and social norms; at the community level, cultural, religious and regional factors; at the health system level, poor service accessibility and system inefficiencies; and at the public policy level, incomplete health insurance coverage and insufficient awareness campaigns. Many of these obstacles show distinct characteristics of Thai society and culture. CONCLUSION: Barriers to cervical cancer screening among Thai women constitute a complex, multi-level system. Future intervention strategies must move beyond single-level health education, and should develop cross-level, culturally-appropriate, comprehensive measures. These include promoting HPV self-sampling, conducting community empowerment education, optimizing the health service system and strengthening policy support to systematically increase screening participation.
16 June 2026
Read appraisal →Cancer
Alcohol consumers' receptivity to artificial intelligence-generated alcohol-cancer risk messages: An experimental study.
BACKGROUND: Alcohol is a group 1 carcinogen linked to seven cancers, yet awareness of this risk remains low in the United State. Identifying effective alcohol-cancer communication strategies is a public health priority. The objective of this study was to test the effects of message specificity (general vs. cancer-specific) and visual intensity (text-only, neutral pictorial, graphic pictorial) on message receptivity (attention, emotional reactions) and precursors of behavior (harm perception; intentions to reduce, limit, or stop drinking) among moderate and heavy alcohol consumers. METHODS: Eight evidence-based text messages were created across two topics: general and cancer-specific harm. Artificial intelligence was used to generate pictorial versions at two intensities: neutral (symbolic, no visible disease) and graphic (explicit health consequences). In a 2025 online within-subject/between-subject crossover experiment, 639 US adult consumers (aged 21 years and older; 49.3% female) each viewed two randomly selected messages (one general, one cancer-specific) presented in three formats (text-only, neutral, and graphic; for six total formats), with counterbalanced order. Linear mixed-effects models were used to estimate differences by intensity, specificity, and drinking levels, reporting regression coefficients (β, 95% confidence intervals). RESULTS: Cancer-specific messages produced greater attention, emotional reactions, and intentions to reduce drinking than general messages (β = 0.10-0.19; p < .05). Graphic pictorials outperformed neutral images on emotional and behavioral outcomes (β = 0.19-0.22; p < .05). Moderate consumers showed stronger perceived harm and message responsiveness than heavy consumers (β = 0.29-0.77; p < .05). CONCLUSIONS: Artificial intelligence-generated alcohol-cancer messages are feasible and effective in strengthening precursors to behavior change. Cancer-specific content and higher visual intensity enhance impact, particularly among moderate consumers, highlighting the importance of tailoring alcohol-cancer communication strategies to different audience characteristics. Many people in the United States are unaware that drinking alcohol increases the risk of several cancers. This study tested different ways of communicating the alcohol–cancer link, including text‐only messages, messages with artificial intelligence–generated images (neutral or graphic), and general versus cancer‐specific content; in addition, the authors examined whether responses differed between moderate drinkers and heavy drinkers. The authors observed that messages with images were more effective than those with text alone, especially when the images clearly showed health harm. Messages that named specific cancers (such as breast or colon cancer) were also more effective than general statements about cancer; however, heavy drinkers, who face the highest cancer risk, were less responsive overall. The findings suggest that effective alcohol–cancer communication should carefully consider what the message says, how it is presented, and who the audience is. Nonstigmatizing, harm‐reduction messages that encourage small, realistic changes and are delivered in supportive settings, such as conversations with health care providers, may work better for heavy drinkers. The study also highlights the promise of artificial intelligence as a tool for developing and tailoring health messages. Future research should explore how different messages influence behavior over time.
15 June 2026
Read appraisal →Mammalian genome : official journal of the International Mammalian Genome Society
A novel biological function-based method for mining core genes in rare disease with limited cases
Conventional high - throughput sequencing data analysis methods, relying on large cohorts, are unsuitable for rare diseases with few cases and poor at explaining gene biological functions. This study proposes a biological-function-based method for mining core genes under limited-sample conditions. Using colorectal cancer (CRC) and its rare subtype signet-ring cell carcinoma (SRCC) as examples, differential expression analysis was performed separately for each SRCC sample against CRC samples. Shared genes obtained through progressive intersections were defined as the initial core gene set (List 1). Non-core genes were subjected to functional enrichment analysis, and pathway-associated genes were integrated to construct the non-core gene set. Two independent SRCC samples were subsequently used for secondary mining to obtain the final biologically significant core gene set (List 2). In addition, RankProd (RP), Robust Rank Aggregation (RRA), and an external mucinous adenocarcinoma dataset (GSE281917) were used for validation. First-round mining identified 246 core genes in SRCC, while secondary mining generated 66 and 65 biologically significant core genes from two validation samples with a high degree of overlap. RP identified 1,850 candidate genes and RRA identified 601 candidate genes, among which 64 genes overlapped with the 65 genes identified by this method. In the external mucinous adenocarcinoma dataset, first-round mining identified 144 core genes, while secondary mining generated 122 and 119 core genes with high overlap between validation samples. This method enables the identification of biologically significant core genes in rare diseases under limited-sample conditions, improves the biological interpretability of gene-mining results, demonstrates stable performance in both SRCC and external mucinous adenocarcinoma datasets, and provides a reliable experimental basis for the advancement of precision medicine in rare disease research.
15 June 2026
Read appraisal →JMIR cancer
Identification of Early Signs of Mental Health Disorders in Older Survivors of Cancer Using Patient-Generated Health Data: Observational Study
BACKGROUND: Older survivors of cancer face heightened risk of depression and anxiety related to cancer experiences, fear of recurrence, and aging-related difficulties. Conventional mental health monitoring approaches, such as clinical assessments and even electronic patient-reported outcomes, are limited by recall bias, patient burden, and infrequent data collection. Emerging patient-generated health data from wearables and smart home devices offer passive, low-burden, continuous monitoring, but their ability to capture mental health risks in older survivors of cancer remains unclear. OBJECTIVE: This study aims to explore whether patient-generated health data collected in the wild, either passively or actively, can classify older survivors of cancer as having or not having signs of anxiety and depression based on Patient Health Questionnaire-4 (PHQ-4) scores and to assess the potential added value of passive monitoring modalities, such as smart plugs. METHODS: This study recruited 41 older survivors of cancer (mean age 72.3, SD 6.81 years) from the LifeChamps project. Over a 12-week period, participants were monitored using an activity tracker to measure physical activity, sleep, and physiological metrics; a smart scale to capture weight and body composition; and a smart plug to track television use as a proxy for sedentary television viewing. Mental health status was self-reported via the PHQ-4 questionnaire in a mobile app. Machine learning models were trained to classify mental health risk based on features derived from each sensor modality, both independently and in combination. RESULTS: Tree-based gradient boosting models showed good performance in classifying PHQ-4-defined mental health risk. The best-performing configuration, combining smart plug and smart scale features, achieved a mean F1-score of 0.77 (SD 0.15) and a mean area under the receiver operating characteristic curve (AUC) of 0.85 (SD 0.10) across 3 repeated train-test splits. Standalone smart plug models, based solely on passive television use patterns, achieved a mean F1-score of 0.66 (SD 0.04) and a mean AUC of 0.71 (SD 0.06), outperforming models that relied only on activity tracker data (mean F1 0.59, SD 0.2). Multimodal combinations tended to improve average performance but did not consistently yield large gains over the strongest single-modality configurations, likely reflecting adherence-related data loss for wearables and scales. Crucially, passive monitoring of television use patterns emerged as a promising behavioral proxy measure of mental health states. CONCLUSIONS: This study pioneers the use of passively collected data (eg, smart plugs) for mental health monitoring in older survivors of cancer, demonstrating their potential. Smart plugs capture behavioral patterns without user burden, with reasonable standalone performance (mean AUC 0.71, SD 0.06), positioning them as a promising low-burden modality. Future work should validate findings in larger independent cohorts and in prospective clinical workflows. Such technologies could transform monitoring for vulnerable populations, enabling scalable, inclusive care while reducing health care burdens.
14 June 2026
Read appraisal →Journal of medical systems
MEL-IA: An Interoperable AI System for Multimodal Skin Lesion Classification in Hospital Settings
UNLABELLED: Skin cancer represents a growing healthcare challenge, motivating the development of AI sytems that can support lesion assessment while integrating efficiently into clinical workflows. This study presents MEL-IA (MobilE skin Lesion dIAgnosis), an interoperable AI system designed for automated skin lesion classification and full integration with hospital information systems. METHODS: The system combines dermatoscopic images and structured clinical metadata using an EfficientNet-B4 - based multimodal model trained on the ISIC 2019 BCN_20000 and MSK subsets. Internal robustness was assessed through stratified 5-fold cross-validation, and external generalization was evaluated on the independent HAM10000 dataset. Interoperability and operational performance were validated through deployment in a real hospital environment using HL7, DICOM, PACS, and HIS/RIS systems. RESULTS: We evaluated internal robustness using stratified five‑fold cross‑validation, with global accuracy of 0.86, macro F1-score of 0.85, and AUC values above 0.97. The external evaluation showed that the model generalizes well under different acquisition conditions, achieving an accuracy of 0.84 and balanced accuracy of 0.65, with sensitivities of 0.60 for melanoma 0.72 for basal cell carcinoma. The multimodal model outperformed the image model baseline across all classes. Technical deployment confirmed full interoperability, > 99% successful study integration, and near real time processing. CONCLUSIONS: MEL-IA provides multimodal skin lesion classification capabilities and integrates smoothly into hospital infrastructures based on standards. The results demonstrate technical feasibility, interoperability, and operational viability for deployment within dermatology oriented clinical workflows. Further clinical validation studies are warranted to assess the system's impact on diagnostic decision-making and patient outcomes.
14 June 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Feasibility and acceptability of ExerciseGuideUK for those living with and beyond lung cancer: a mixed methods study
PURPOSE: Digital health interventions offer promise for addressing unmet needs in individuals living with and beyond lung cancer (LWBLC). ExerciseGuideUK, a personalised, web-based exercise platform, was developed for those LWBLC. This study aimed to evaluate ExerciseGuideUK. METHODS: Participants were recruited from Hull University Teaching Hospital and used ExerciseGuideUK over eight weeks. Feasibility and acceptability were assessed using quantitative measures, including the System Usability Scale, structured questionnaires, and qualitative interviews (guided by the Theoretical Framework of Acceptability). A novel mixed-methods integration approach called the Pillar Integration process (PIP) was used to integrate quantitative and qualitative findings on feasibility, engagement, barriers, and adaptation potential. RESULTS: Eighteen participants (mean age 65 ± 14.42y) enrolled. Recruitment and retention rates were 30.5% and 77%, respectively. Notably, 25.4% of those screened were excluded due to digital access issues, including some participants who owned smartphones, suggesting limited digital literacy. Among those who participated, acceptability, usability, and engagement were high. Valued features included goal-setting tools, breathlessness management strategies, and personalised exercise prescriptions. Barriers included treatment side effects, work responsibilities, and low confidence with technology. CONCLUSION: ExerciseGuideUK showed moderate feasibility and high acceptability among users. This is the first known application of PIP in the context of lung cancer, exercise, and digital health. The PIP facilitated a comprehensive understanding of feasibility and acceptability, guiding informed adaptations by users. The PIP warrants further testing and refinement in relation to optimising digital intervention design that promotes equitable, patient-centred care. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT05121259.
14 June 2026
Read appraisal →BMJ open
Difficult intravenous access (DIVA) in the adult population: an umbrella review.
OBJECTIVES: To synthesise evidence from systematic reviews on difficult intravenous access (DIVA) in adults, focusing on definitions, diagnostic criteria, risk factors and clinical interventions to guide clinical practice and health policy. DESIGN: Umbrella review of systematic reviews and meta-analyses. SETTING: Any clinical setting involving adult patients requiring peripheral venous access (including hospital, emergency and outpatient care). DATA SOURCES: A systematic search was performed in PubMed, CINAHL, Cochrane, Scopus and Web of Science in July 2025. ELIGIBILITY CRITERIA: Systematic reviews and meta-analyses published from 2014 to 2025 that addressed DIVA in the adult population were included. Primary studies and protocols were excluded. DATA EXTRACTION AND SYNTHESIS: Methodological quality was assessed using the Risk Of Bias In Systematic Reviews tool. Data extraction followed the Joanna Briggs Institute methodology for overviews and the Preferred Reporting Items for Overviews of Reviews reporting guideline. RESULTS: Seven reviews (six systematic reviews and one meta-analysis) were included. Three analytical dimensions emerged: (1) the conceptual and operational definition of DIVA, identifying common elements such as ≥2 failed attempts, lack of visible or palpable veins and a documented history of difficult access; (2) risk factors and clinical assessment, highlighting obesity, chronic diseases, prior chemotherapy, venous invisibility or non-palpability and the limited validation of diagnostic tools and (3) interventions, including organisational strategies (escalation protocols, specialised teams), technological resources (ultrasound guidance) and clinical measures (pain management and technique optimisation). CONCLUSIONS: DIVA is a multifactorial challenge that requires a standardised definition to improve clinical identification. Effective management relies on a combination of specialised training, the use of ultrasound technology and the implementation of escalation protocols to ensure patient safety and efficiency. PROSPERO REGISTRATION NUMBER: CRD420251084947.
14 June 2026
Read appraisal →JMIR formative research
Automated Machine Learning Frameworks for Radiomics: Comparative Evaluation Study
BACKGROUND: Automated machine learning (AutoML) frameworks can lower technical barriers for predictive and prognostic model development in radiomics by enabling researchers without programming expertise to build models. However, their effectiveness in addressing radiomics-specific challenges remains unclear. OBJECTIVE: This study aimed to evaluate the performance, efficiency, and accessibility of general-purpose and radiomics-specific AutoML frameworks on diverse radiomics classification tasks, thereby guiding researchers and highlighting development needs for radiomics. METHODS: A total of 10 public and private radiomics datasets with varied imaging modalities (computed tomography and magnetic resonance imaging), sizes, anatomies, and end points were used. Six general-purpose and 5 radiomics-specific frameworks were tested with predefined parameters using standardized cross-validation. Evaluation metrics included area under the receiver operating characteristic curve, runtime, and qualitative aspects related to software status, accessibility, and interpretability. RESULTS: Simplatab, a radiomics-specific tool with a no-code interface, achieved the best overall balance between performance and computational efficiency, recording the highest average test area under the receiver operating characteristic curve (mean 78.46%, SD 12.22%) with a moderate runtime (1.1 h). However, its performance was not statistically superior to the most intensive general-purpose solutions. Most radiomics-specific frameworks were excluded from the performance analysis due to obsolescence, extensive programming requirements, or computational inefficiency. Conversely, general-purpose frameworks demonstrated higher accessibility and ease of implementation. CONCLUSIONS: While no single framework demonstrated absolute predictive superiority, Simplatab provides an effective balance of performance, efficiency, and accessibility for radiomics classification problems. However, continued efforts are needed to further mature AutoML solutions in the radiomics domain.
14 June 2026
Read appraisal →Scientific reports
Performance of GPT-based large language models in hepatocellular carcinoma stratification: liver function assessment, BCLC staging, and treatment recommendations.
Large language models (LLMs) like GPT have been proposed to support complex clinical decision-making. This study evaluated the performance of GPT-based LLM in analyzing clinical, radiological, and laboratory data from patients with hepatocellular carcinoma (HCC) to assess liver function, assign BCLC stage, and recommend treatment. Data from 106 HCC patients (82% male, median age 65 [22-86]) were compiled into anonymized integrated reports. Four GPT-versions (4, o1, o3, 5.4) were prompted-using both short and long instructions-to calculate MELD, ALBI, and Child-Pugh scores, assign BCLC stage, and generate treatment recommendations based on current guidelines. Outputs were compared to expert consensus and tumor board decisions. Errors were categorized by type and source. Time and cost analyses compared GPT to clinical staff. All GPT versions achieved high accuracy (> 85%) in liver function assessment, with MELD calculation being the most error-prone. BCLC staging accuracy ranged from 46.2% (version 4) to 84.0% (o3), with misclassification of radiological reports as the main error source. Reasoning-optimized models (o1, o3) performed best for treatment recommendations, achieving an overall accuracy (correct suggestions and acceptable alternatives) of up to 90.6%. In 9-14% of cases, GPT suggestions were retrospectively more guideline-concordant than tumor board decisions. GPT processing was significantly faster and reduced costs by approximately 300- to 1300-fold compared to clinical staff. GPT-based LLMs show potential as decision-support tools for liver function assessment, BCLC staging, and treatment guidance in HCC. Particularly with reasoning-optimized models and detailed prompting, LLMs may serve as valuable adjuncts in multidisciplinary HCC workflows. However, a non-negligible error rate requires expert oversight and further model refinement.
14 June 2026
Read appraisal →Journal of medicinal chemistry
Integrating AI in Medicinal Chemistry for Accelerated Drug Discovery: A Comprehensive SAR (CSAR) Optimization Strategy and Discovery of Potent ALDH3A1 Inhibitors
Developing potent, selective small-molecule inhibitors remains a major challenge in drug discovery. ALDH3A1, a detoxifying aldehyde dehydrogenase isoform implicated in cancer and neurodegeneration, is a promising yet underexplored therapeutic target. To accelerate inhibitor optimization, we developed an AI-guided, reaction-based hit-to-lead workflow combining sequential reaction enumeration, pharmacophore-informed docking, and predictive modeling to support scalable SAR expansion. Applied to ALDH3A1, two rounds of enumeration using Enamine building blocks generated about 250,000 virtual analogues. Combined deep learning and docking-based triage prioritized 150 compounds for synthesis, leading to a roughly 1,000-fold improvement in biochemical potency from 1.41 μM to 1 nM for NCATS-SM0707, together with 4 nM cellular activity for NCATS-SM0708. Crucially, this methodology can be expanded by applying various chemical reactions at different positions. This study highlights CSAR as a scalable, generalizable complementary strategy to accelerate hit optimization through reaction-based enumeration and AI-guided prioritization of larger synthetically accessible chemical space.
13 June 2026
Read appraisal →Blood
Fixed-duration VenO vs FCR/BR in fit patients with untreated CLL: primary analysis of the phase 3 CRISTALLO trial
The phase 3 CRISTALLO trial compared first-line fixed-duration venetoclax-obinutuzumab (VenO) vs fludarabine, cyclophosphamide, and rituximab (FCR)/bendamustine-rituximab (BR) in patients with chronic lymphocytic leukemia (CLL), using undetectable minimal residual disease (uMRD) as the sole primary end point. Previously untreated patients with a cumulative illness rating scale score ≤6 and creatinine clearance ≥70 mL/min without del(17p)/TP53 mutations were randomized 1:1 to VenO or FCR/BR. The primary end point was uMRD (<10-4) in peripheral blood (PB) using next-generation sequencing at month 15. Key secondary end points included uMRD (<10-4) in PB and bone marrow (BM) at end of treatment (EOT) and progression-free survival (PFS). uMRD at deeper cutoffs were explored. At data cutoff (19 March 2024), 80 patients received VenO, and 86 received FCR/BR. Baseline characteristics were generally balanced across arms. The primary end point was met: 81.3% (VenO) and 54.7% (FCR/BR) achieved uMRD (<10-4) in PB at month 15 (P = .0004). uMRD (<10-4) in PB and BM at EOT was also higher with VenO vs FCR/BR. Short follow-up precluded evaluation of PFS at the first planned interim analysis; however, fewer patients progressed/died with VenO vs FCR/BR (7 vs 13). At month 15, 65.0% (VenO) and 25.6% (FCR/BR) achieved uMRD (<10-6) in PB. The overall safety profile was consistent with the known safety profile of each drug. No patient in the VenO arm was deemed high risk for tumor lysis syndrome (TLS) after obinutuzumab debulking; no clinical TLS occurred. These results confirm and extend the findings from the GAIA-CLL13 trial, validating increased depth of response with VenO vs chemoimmunotherapies. This trial was registered at www.clinicaltrials.gov as NCT04285567.
13 June 2026
Read appraisal →Biomedical physics & engineering express
Enhancing brain tumor classification with a simplified CNN through hyperparameter optimization
Brain tumors remain a major public health challenge because of their high mortality rate and the need for timely and accurate diagnosis. Magnetic resonance imaging (MRI) is widely used for brain tumor assessment, yet conventional diagnostic workflows still depend largely on the subjective interpretation of radiologists, which may lead to inter-observer variability. In this context, deep learning approaches-particularly convolutional neural networks (CNNs)-have gained increasing attention for automated medical image analysis, as they are capable of learning discriminative features directly from imaging data and improving classification accuracy. Nevertheless, the performance of CNN-based models is strongly influenced by the choice of hyperparameters, whose optimization remains a non-trivial task due to complex and non-linear interactions within the network.This study investigates the impact of different hyperparameter optimization strategies on the performance of a simplified CNN model for brain tumor classification from MRI images. Three widely used optimization methods, Genetic Algorithms (GA), Bayesian Optimization (BO), and HyperBand-are considered and systematically compared. To emphasize efficiency and practical applicability, the CNN architecture is deliberately kept lightweight, aiming to reduce computational complexity while preserving sufficient modeling capacity. Experiments are carried out on a publicly available brain MRI dataset that includes glioma, meningioma, pituitary tumor, and healthy subjects.To account for performance variability and ensure statistical reliability, each optimized model is evaluated over ten independent runs using different random initializations. The reported results therefore include both the mean performance values and the corresponding standard deviations. The experimental analysis shows that the GA-based approach achieves a mean accuracy of 96.41%±0.36%, while BO improves this performance to 97.37%±0.30%. Among the evaluated strategies, HyperBand yields the highest average accuracy of 97.73%±0.48%, indicating its ability to consistently identify effective hyperparameter configurations for the proposed CNN architecture.
13 June 2026
Read appraisal →Physics in medicine and biology
Unsupervised multimodal deformable medical image registration based on feature perceptual contrast learning
Objective.Multimodal medical image registration has extensive applications in clinical diagnosis and is fundamental for a series of medical analysis tasks. However, the presence of modality differences makes the registration process challenging. Existing methods often employ modality-independent feature descriptors that are sensitive to noise, or attempt to bridge differences within networks, which typically results in translation inaccuracies and misaligned anatomical information.Approach.In this paper, we propose a novel unsupervised approach utilizing a Feature Perceptual Contrast Learning Network (FP-net) to learn descriptors that bridge modality differences while accurately capturing common details. We unify the feature representation of anatomical information under homogeneous and heterogeneous intensity distributions through local sampling-based feature perceptual contrast learning and image reconstruction learning. The trained FP-net is subsequently employed to drive an unsupervised registration framework without requiring ground-truth deformation fields.Main results.We extensively evaluated our method on two public benchmarks: the BraTS 2021 dataset for brain T2-T1 and T1-T1ce registration, and the Learn2Reg 2021 dataset for challenging abdominal CT-MR registration. By passing multimodal image pairs with shape differences through the fixed FP-net, we generate optimization gradients that successfully update the registration network. Quantitative evaluations demonstrate our method's superiority over state-of-the-art baselines. Specifically, our model achieved a dice similarity coefficient (DSC) of 76.3% and 77.7% in tumor-bearing T2-T1 and T1-T1ce tasks, respectively. Furthermore, in the complex abdominal CT-MR task, it reached a DSC of 50.1%, significantly improving structural alignment.Significance.Our method effectively shifts the burden of bridging the modality gap away from the registration network, enabling standard U-Net architectures to achieve state-of-the-art deformable registration. This provides a robust, accurate, and easily deployable unsupervised solution for complex clinical multimodal image analysis.
12 June 2026
Read appraisal →Biochemical and biophysical research communications
Efferocytosis-related biomarkers and immune infiltration in pancreatic ductal adenocarcinoma based on single-cell sequencing and machine learning: a comprehensive bioinformatics analysis and experimental validation
BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is a highly invasive and lethal malignancy, with its complex tumor microenvironment (TME) severely limiting therapeutic efficacy. Efferocytosis, a key process by which macrophages clear apoptotic cells, has been demonstrated to influence tumor initiation and progression by modulating immune responses, promoting M2 polarization, and facilitating immune evasion. Yet, the function and predictive significance of genes linked to efferocytosis in PDAC are still not fully understood. METHODS: This study integrated TCGA-PDAC, GTEx, and GEO datasets with single-cell transcriptomics to screen and analyze expression patterns and functions of efferocytosis-related genes (ERGs) in PDAC. Differential analysis, single-cell clustering, cell communication, and pseudo-time analysis revealed heterogeneity in macrophage efferocytosis. Subsequently, a six-gene prognostic model (CD36, ITGAV, PANX1, PPARG, S100A4, THBS1) was constructed and validated using Cox and LASSO regression. Concurrently, GSEA pathway analysis, immune infiltration assessment, and immunotherapy response prediction were conducted. Key gene functions were validated through molecular docking, RT-qPCR, western blotting, and functional experiments. RESULTS: Single-cell analysis revealed that macrophages exhibit the highest efferocytic activity within the PDAC microenvironment, demonstrating significant subpopulation heterogeneity. Differential analysis identified 23 key intersecting genes, ultimately yielding a risk model comprising six core genes. The model exhibited strong predictive accuracy in both the training and validation groups (notable AUC). Additional examination showed that patients at high risk showed a heightened presence of pathways linked to the reprogramming of energy metabolism and inflammatory reactions, along with heightened genomic instability and immunosuppressive TME traits, suggesting a grim prognosis. Experimental validation confirmed that knockdown of THBS1 significantly inhibited the proliferation, migration, and invasion of PDAC cells. CONCLUSION: The research clarifies the diverse nature of macrophage efferocytosis in PDAC and its crucial function in the restructuring of TME. It establishes and validates a six-gene risk prediction model, clarifying the functional significance of core genes, including THBS1. These findings provide novel molecular tools for PDAC prognosis assessment and identify potential targets for immunotherapy and personalized treatment strategies.
11 June 2026
Read appraisal →JMIR formative research
User Perspectives on a Clinical Decision Tool to Support Individualized Exercise Prescriptions for Breast Cancer Survivors Not Meeting Exercise Guidelines: Cross-Sectional Survey
BACKGROUND: More than 80% of breast cancer survivors do not meet the recommended levels of exercise, and <50% of health care providers promote exercise as part of survivorship care. Patient-provider communication may enhance exercise engagement by increasing patients' understanding of exercise benefits and linking patients to resources, such as rehabilitation and exercise programs. OBJECTIVE: This study aimed to explore perspectives on a novel clinical decision tool designed to support individualized exercise discussions and prescriptions among breast cancer survivors who do not meet exercise guidelines and health care providers who primarily treat such survivors. METHODS: We conducted a cross-sectional online survey among US breast cancer survivors and health care providers. Participants were (1) female breast cancer survivors aged ≥35 years engaging in ≤150 minutes/week of moderate-intensity aerobic exercise or ≤2 days/week of muscle-strengthening exercise and (2) health care providers who had cared for breast cancer survivors within the past 12 months and reported below-average guideline adherence among their patients. Respondents reviewed a paper draft of a web-based clinical decision prototype tool for supporting individualized exercise discussions and prescriptions based on patients' demographic, clinical, and contextual characteristics. We assessed perceived usefulness, potential uses (eg, counseling), preferred timing of access within clinical encounters, and preferences for tool characteristics (inputs/outputs). RESULTS: The analytic sample comprised 26 breast cancer survivors and 69 health care providers. The survivors' median age was 48 (IQR 37-65) years. Providers included patient navigators/social workers/nurses (29/69, 42.0%), breast oncologists (13/69, 18.8%), and occupational/physical therapists (12/69, 17.4%). The majority of providers (62/69, 89.9%, 95% CI 80.2%-95.8%) and survivors (23/26, 88.5%, 95% CI 69.8%-97.6%) reported that they would find the tool useful. Similarly, 85.5% of providers (59/69, 95% CI 75.0%-92.8%) and 84.6% of survivors (22/26, 95% CI 65.1%-95.6%) reported that the tool would increase their confidence to discuss exercise in a clinical setting. Both groups preferred that survivors access the tool with staff after a medical appointment (survivors: 20/26, 76.9%, 95% CI 56.4%-91.0%; providers: 58/67, 86.6%, 95% CI 76.0%-93.7%). Both groups also endorsed treatment history and readiness to exercise to consider as key inputs and improved quality of life and reduced treatment-related side effects as exercise benefits to communicate as tool outputs. CONCLUSIONS: The prototype tool concept was well received, with high endorsement of individual characteristics to consider and clinical benefits of exercise to communicate. Findings will inform refinement of the tool and future implementation testing in an understudied population of breast cancer survivors.
11 June 2026
Read appraisal →Gene
Based on WGCNA and machine learning studies, SMURF2 drives NSCLC malignant transformation, ferroptosis, and macrophage polarization by ubiquitinating SPP1
BACKGROUND: Non-small cell lung cancer (NSCLC) is a common type of lung cancer with poor prognosis and high mortality in advanced stages. Although secreted phosphoprotein 1 (SPP1) is associated with the progression of NSCLC, its specific mechanisms remain to be explored. METHODS: Through integrated bioinformatics analysis combining weighted gene co-expression network analysis (WGCNA), differential expression analysis, gene enrichment analysis with the Kyoto Encyclopedia of Genes and Genomes (KEGG)/Gene Ontology (GO), and machine learning techniques incorporating database predictions, core genes were identified. Experimental validation was conducted using Western blot, co-immunoprecipitation (Co-IP), cycloheximide (CHX) chase assay, Ubiquitin Immunoprecipitation (Ub-IP), colony formation, flow cytometry, transwell assay, reactive oxygen species (ROS) detection, and quantitative real-time PCR (qRT-PCR). The functional impact of SPP1 on tumor growth was further confirmed through mouse models and immunohistochemistry (IHC). RESULTS: Based on WGCNA and machine learning, SPP1 was identified as a core gene. SMURF2 ubiquitinated and degraded SPP1. Rescue experiments confirmed that SMURF2 inhibited proliferation and invasion, and promoted apoptosis in NSCLC cells, which were reversed by SPP1 overexpression. Furthermore, SMURF2 enhanced ferroptosis and modulated macrophage polarization via the same ubiquitination mechanism. Animal studies verified that SMURF2-mediated ubiquitination and degradation of SPP1 suppressed tumor growth. CONCLUSION: This study aims to investigate the role of SMURF2-mediated ubiquitination of SPP1 in inhibiting malignant progression, promoting ferroptosis, and modulating macrophage polarization in NSCLC, thereby providing a theoretical foundation and new insights for understanding and targeting NSCLC.
10 June 2026
Read appraisal →Blood advances
Final phase 2 study results of acalabrutinib in treatment-naive and relapsed/refractory chronic lymphocytic leukemia
Acalabrutinib is a selective, covalent Bruton tyrosine kinase inhibitor approved for marketing in chronic lymphocytic leukemia (CLL)/small lymphocytic lymphoma (SLL). We report final, long-term phase 1/2 study results in 99 patients with treatment-naive (TN) and 134 with relapsed/refractory (R/R) CLL/SLL. At final data cutoff, 71% and 31% of patients in the TN and R/R cohorts, respectively, remained on acalabrutinib treatment (median follow-up of 73.7 and 52.6 months). Among the events of clinical interest (any grade) in the TN and R/R cohorts, atrial fibrillation was reported in 6.1% and 9.0%, hypertension in 29.3% and 23.1%, other malignancies (excluding nonmelanoma skin cancer) in 14.1% and 17.2%, and major bleeding in 8.1% and 8.2% of patients, respectively. The incidence of the most common adverse events decreased over time. Overall response rates were 97.0% and 94.8% in the TN and R/R cohorts, respectively, with similar response findings among patients with standard and high-risk genomic features. In the TN cohort, median progression-free survival (PFS) was not reached and the 72-month PFS rate was 86.7% (95% confidence interval [CI], 77.0-92.5). For the R/R cohort, median PFS was 66.1 months (range, 0.4-87.8) and the 72-month PFS rate was 45.1% (95% CI, 35.6-54.1). This final analysis extends the duration of benefit observed with acalabrutinib, demonstrates that no new safety signals are apparent with longer follow-up, and confirms the safety and tolerability of acalabrutinib monotherapy for patients with CLL/SLL. This trial was registered at www.clinicaltrials.gov as #NCT02029443.
9 June 2026
Read appraisal →Gene
Utilization of next generation sequencing from glioma patient liquid biopsy to determine survival prognostics: A systematic review
Glioma is a malignant cancer that affects the central nervous system. Early detection of glioma is still difficult due to the hard-to-reach location of the cancer. The use of next generation sequencing is one of the new developments that can be used for diagnosis, however the use of this modality for prognosis is still rarely discussed. This systematic review aims to determine the benefits of using next generation sequencing (NGS) on the assessment of ctDNA or cfDNA concentration, progression free survival (PFS), and overall survival (OS). This systematic review was made using data from six databases, namely PubMed, PMC, ProQuest, Google Schoolar, ScienceDirect, and SCOPUS, and was compiled based on guidelines from PRISMA. Fourteen studies were included in this systematic review with a total of 663 glioma patients. The results of ctDNA measurements generated through the NGS method with liquid biopsy samples show that the higher the grading and sampling when the tumor progresses can increase the ctDNA value. When assessing ctDNA concentrations at baseline when samples were taken, most studies showed a worse prognosis for PFS and OS survival time. However, the grouping of patients in the current studies is still highly variable so further studies with standardized ctDNA cut off values are needed for more precise determination of prognosis.
7 June 2026
Read appraisal →Journal of theoretical biology
Subtypes estimation in single-cell multi-omics data via S-multi-SNE
Single-cell RNA sequencing (scRNA-seq) has rapidly become an essential method in modern biology, as it allows global characterisation of transcriptomics in individual cells from different tissues, or organisms. scRNA-seq data are collected from a single individual organism and they are composed as mRNA count matrices with rows representing the cells and columns the genes. New technologies further allow multi-omics data to be obtained on the same set of single cells. In this paper, multi-SNE and S-multi-SNE, two multi-view dimensionality reduction approaches, are adapted for visualization and classification of cellular subtypes, two important challenges in the analysis of scRNA-seq data. S-multi-SNE has been adapted for cell subtype estimation of scRNA-seq data. In a series of experiments we illustrate that it is possible to identify cell subtypes by leveraging information from individuals from the same species, from different species, and when data are generated by different sequencing technologies. In the conducted analyses, we show that S-multi-SNE consistently outperformed several other machine learning techniques and, in most comparisons, surpassed existing reference-based single-cell classification algorithms. We further illustrate how two single-cell multi-omics datasets, scATAC-seq and scRNA-seq, can be integrated together through S-multi-SNE for improved cell subtype identification.
7 June 2026
Read appraisal →Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Multivariate feature analysis of early-stage laryngeal cancer serum components using surface-enhanced Raman spectroscopy
Laryngeal cancer is a common head-and-neck malignant tumor with geographically variable incidence. Its lack of specific early clinical symptoms often causes missed diagnosis, leading to most cases being identified at intermediate/advanced stages and thus poor treatment efficacy and prognosis. Early, accurate screening for high-risk populations is therefore critical to improving survival. This study systematically investigates the clinical application value of surface-enhanced Raman spectroscopy (SERS) combined with deep learning models for serum screening in early laryngeal cancer. It specifically examines the discriminative performance of one-dimensional convolutional neural network (1D-CNN) models, including CNN-attention with a fused attention mechanism and CNN-baseline without an attention mechanism, and compares these with traditional machine learning models.Serum samples were collected from three groups: Early laryngeal cancer, vocal fold polyps, and healthy controls. SERS was used to obtain serum molecular vibration fingerprint spectra. After spectral preprocessing, five models (CNN-attention, CNN-baseline, SVM, AdaBoost, KNN) were established to discriminate the three groups and compare performance. Leave-one-patient-out cross-validation (LOPO-CV) results indicate that the CNN-attention and CNN-baseline models significantly outperform other models in discrimination efficacy. Specifically, the CNN-attention model achieves a patient-level accuracy of 89.33%, while the CNN-baseline model reaches 88.67%. SVM, KNN, and AdaBoost had inferior performance. This study confirms that serum SERS-based 1D-CNN provides an efficient, accurate method for early laryngeal cancer screening, laying a foundation for clinical translation and offering new technical support for early screening.
7 June 2026
Read appraisal →Acta microbiologica et immunologica Hungarica
Characteristics of fungaemia caused by Trichosporon species in South-Eastern Europe
The incidence of fungaemia caused by rare yeasts, particularly Trichosporon species, is rising globally, posing diagnostic and therapeutic challenges-especially in immunocompromised patients. This systematic review aims to analyse the epidemiology, diagnostic approaches, antifungal susceptibility, and clinical outcomes of Trichosporon fungaemia (TF) in South-Eastern Europe, including Turkey. A comprehensive search was conducted in PubMed and Scopus in August 2025. A total of 59 cases from 12 studies were identified, with Trichosporon asahii being the most prevalent species (86.4%). Cases originated from Turkey (79.7%), Greece (18.6%), and Croatia (1.7%). Diagnostic techniques varied, with phenotypic methods still widely used. MALDI-TOF MS and DNA sequencing were mainly applied as confirmatory methods. Haematologic disorders were the most frequently reported underlying conditions among the patients. Sixteen breakthrough fungaemia cases-occurring despite empirical or prophylactic antifungal therapy-were identified. Although rare, TF represents a severe infection with significant mortality in South-Eastern Europe. Early and accurate species identification-facilitated by advanced diagnostic tools-is crucial for effective management. In addition to diagnostic difficulties, treatment is also challenging. Voriconazole appears to be the preferred antifungal agent, even in breakthrough fungaemia cases. Enhanced awareness, routine use of molecular diagnostics, and ongoing epidemiological monitoring are essential to improve patient outcomes.
1 June 2026
Read appraisal →Techniques in coloproctology
Robotic versus laparoscopic TME for rectal cancer: meta-analysis of pathological quality indicators
BACKGROUND: Total mesorectal excision (TME) quality is a key determinant of oncological outcomes in rectal cancer. While robotic surgery offers technical advantages over laparoscopy in the confined pelvis, its superiority regarding pathological outcomes remains debated. We conducted a meta-analysis comparing robotic and laparoscopic TME focusing on quality indicators of TME and risk factors for incomplete TME. METHODS: A PROSPERO-registered systematic search of PubMed and EMBASE up to May 2025. Comparative studies reporting pathological outcomes of robotic versus laparoscopic TME were included. Primary endpoints were TME completeness, circumferential resection margin (CRM) positivity and distal resection margin (DRM) positivity. Secondary analysis included baseline characteristics (male gender, BMI, bulky tumours, distance to anal verge, neoadjuvant radiotherapy) and local recurrence rate. RESULTS: Fifty-six studies (27,648 patients; robotic 10,629, laparoscopic 17,019) were included. Robotic surgery was associated with significantly more complete TME specimens (OR 1.50, 95% CI 1.23-1.82, p < 0.001) and fewer positive DRMs (OR 0.68, 95% CI 0.48-0.97, p = 0.031). CRM positivity was comparable between groups (OR 0.93, 95% CI 0.77-1.12, p = 0.44). In random-effects analysis, there was a non-significant trend towards fewer local recurrences after robotic TME (OR 0.75, 95% CI 0.54-1.05, p = 0.09). Robotic cohorts more frequently included male patients, distal tumours and neoadjuvant chemoradiotherapy, suggesting preferential selection of technically challenging cases. CONCLUSIONS: Robotic TME is associated with higher specimen completeness and lower DRM positivity compared with laparoscopic TME, while CRM positivity and local recurrence rates appear broadly similar. These data support the use of robotics as a primary minimally invasive option for mid-low rectal cancer; however, as a result of low certainty of evidence, these findings should be interpreted cautiously. SYNOPSIS: This meta-analysis provides the most up-to-date synthesis of pathological outcomes comparing robotic and laparoscopic TME for rectal cancer, incorporating data from 56 studies including the recent REAL and COLRAR randomized trials. Unlike previous reviews, it exclusively analyses total mesorectal excision procedures, excluding partial or high anterior resections, thereby eliminating a major source of heterogeneity. The findings demonstrate that robotic TME is associated with higher specimen completeness and fewer positive distal margins, even in technically demanding mid- and low-rectal cancers, supporting the role of robotics as the preferred minimally invasive approach in mid-low, difficult tumours.
1 June 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Cancer patient distress and health service use is linked with carer distress: evidence from a systematic review and meta-analysis
BACKGROUND: Family carers (caregivers) of cancer patients often have poor mental health, which may adversely affect patient wellbeing and health service use. We examined the evidence for this using a systematic review and meta-analyses. METHODS: We systematically searched online databases for studies reporting associations between carer psychological health and either: (a) psychological health of patients with cancer they cared for; or (b) health service use in carers or patients. Studies' risk of bias was assessed using the Joanna Briggs Institute Critical Appraisal Tool. We undertook meta-analyses to estimate pooled correlations for the most commonly reported associations between carer and patient psychological health. For associations between carer psychological health and carer and/or patient health service use, we conducted a narrative synthesis. RESULTS: Our search identified 11,911 records, 169 of which were eligible. The majority of studies were of high-to-moderate quality. Carers who were distressed, depressed, anxious, or had poor scores in the mental component of quality of life were significantly more likely to be caring for patients with these same outcomes (pooled effect sizes ranged from 0.28 to 0.42; all p < 0.001). Subgroup analyses by gender, disease stage, and study quality revealed no substantial differences. Carers with poor psychological health used general practice, mental healthcare, and hospital services more frequently than those who were psychologically healthy, and the patients they cared for used more medications and had more frequent emergency presentations. CONCLUSION: The inclusion of carers alongside patients in early psychosocial care may improve family outcomes and reduce health service use.
1 June 2026
Read appraisal →JMIR research protocols
Creation of an mHealth Infrastructure to Support the Development and Delivery of mHealth Interventions: Protocol for Demonstration Projects Addressing Smoking Cessation in Cancer Care
BACKGROUND: Cancer remains a leading cause of morbidity worldwide. To reduce this burden, scalable, effective approaches are needed to address modifiable risk factors for cancer and support behavioral self-management. With smartphone ownership now nearly ubiquitous, mobile health (mHealth) interventions offer a powerful means to extend the reach, accessibility, and sustainability of evidence-based treatments for a variety of modifiable risk factors (eg, excessive alcohol use, physical inactivity, poor diet, and smoking). Moreover, the flexibility of mHealth platforms enables efficient delivery of novel interventions, supports innovative study designs, and facilitates real-time data collection to advance public health research. OBJECTIVE: Despite the great potential of mHealth interventions, developing high-quality mHealth tools is complex, time-consuming, and resource-intensive. To address these challenges, we are developing a coordinated, accessible, research-grade infrastructure for mHealth app development, testing, and dissemination. METHODS: The mHealth Florida infrastructure (mFLi) will provide a comprehensive, low-code software platform that enables researchers to build apps compatible with major mobile operating systems, namely, Apple iOS and Google Android. Through a modular interface, users will select from a menu of prebuilt features to tailor functionality to specific study needs. The platform will include 3 integrated environments (development, testing, and production), allowing researchers to prototype, evaluate, and deploy mHealth interventions. This infrastructure will be developed and maintained by a multidisciplinary team, ensuring that the platform is technically robust and usable and adheres to institutional and regulatory standards. To demonstrate the platform's functionality, utility, and adaptability, a multisite study comprising three initial projects focused on smoking cessation among patients with cancer is being conducted: (1) participant screening and enrollment, (2) randomization and treatment delivery, and (3) data processing using machine learning methods with on-device and cloud-based approaches. RESULTS: This study was funded in May 2023, and ethics approval was obtained from all involved sites' institutional review boards between February 2024 and October 2025. Recruitment began in March 2025 and enrollment is ongoing. As of January 2026, 41% (37/90) of the target sample have been enrolled and 21% (19/90) have completed their 6-month assessment. Data collection will be completed once the final participant completes their 6-month assessment (expected May 2027), with analyses commencing thereafter. Study findings are anticipated to be published in a peer-reviewed journal in 2027. CONCLUSIONS: Collectively, these projects will illustrate how mFLi can streamline app development, facilitate rapid translation of research into practice, and reduce barriers for researchers and developers. Ultimately, mFLi is designed to accelerate innovation in mHealth research, enhance access to behavioral interventions, and improve health outcomes among diverse populations. TRIAL REGISTRATION: ClinicalTrials.gov NCT06909357; https://clinicaltrials.gov/study/NCT06909357. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/92288.
1 June 2026
Read appraisal →The New Zealand medical journal
Cancer risk and screening in transgender and gender-diverse individuals on gender-affirming hormone therapy: a review article for the Australasian context
AIM: We aimed to synthesise current evidence on the association between gender-affirming hormone therapy (GAHT) and cancer risk in transgender and gender-diverse (TGD) individuals and to provide evidence-based cancer screening recommendations for Australasian general practitioners. METHODS: A systematic review of epidemiological studies, case reports and clinical guidelines was conducted in accordance with PRISMA 2020 guidelines. Searches were performed across PubMed/MEDLINE, Embase, Scopus and the Cochrane Library for literature published up to December 2025. Key evidence sources were narratively synthesised to develop practical recommendations tailored to the Australasian context. RESULTS: The available evidence, although limited by short follow-up durations and small cohort sizes, does not demonstrate a consistent, statistically significant increase in common cancers among TGD individuals receiving GAHT. However, emerging case reports highlight novel clinical concerns, including malignancies in surgically constructed tissues (neophallus and neovagina). Breast cancer risk in trans women appears to increase with the duration of oestrogen therapy, though it remains lower than in cisgender women. Prostate cancer may present at a higher grade in trans women receiving oestrogen. Screening participation is consistently lower in TGD populations, often resulting in later-stage diagnoses. CONCLUSION: Cancer screening for TGD individuals should be guided by an organ-inventory approach, based on the organs a person has rather than their gender identity. Clinicians should maintain a low threshold for investigating suspicious lesions, particularly in surgically altered tissues. The development of culturally safe and inclusive primary care environments is paramount to improving screening uptake and ensuring equitable health outcomes for TGD communities in Australia and New Zealand.
31 May 2026
Read appraisal →Journal of robotic surgery
Comparative efficacy and safety of open, laparoscopic, and robot-assisted radical cystectomy: a systematic review and network meta-analysis
The most appropriate surgical strategy for radical cystectomy in bladder cancer remains to be established. This study performed a network meta-analysis of randomized controlled trials to evaluate open, laparoscopic, and robot-assisted approaches. Outcomes were systematically classified into intraoperative metrics, postoperative recovery, perioperative safety, and oncological efficacy. A random effects model was employed, and treatment hierarchy was determined using the surface under the cumulative ranking curve. Eleven studies comprising 1270 patients met the inclusion criteria. Compared with open surgery, minimally invasive techniques were associated with reduced intraoperative blood loss and lower transfusion rates, although they required longer operative time. Among these, laparoscopic surgery was associated with a potentially greater likelihood of enhanced postoperative recovery and fewer complications, although these findings should be interpreted with caution given the sparse network and substantial heterogeneity in some outcomes. Robot-assisted procedures did not demonstrate consistent superiority over laparoscopy in perioperative measures. Oncological outcomes were similar across all modalities. Overall, minimally invasive approaches confer perioperative benefits and appear comparable to open surgery with respect to short-term surrogate oncological endpoints, although long-term outcomes remain to be determined.
31 May 2026
Read appraisal →BMJ open
The association of the Healthy Lifestyle Index (HLI) and HLI trajectories with risk of all-cause and cause-specific mortality: a GRADE-assessed dose-response systematic review and meta-analysis.
BACKGROUND: The Healthy Lifestyle Index (HLI) integrates key behaviours to assess their cumulative impact on health. While higher HLI adherence is linked to lower disease and mortality risk, its long-term trajectory association remains understudied. This study aims to examine the dose-response relationship and long-term association of HLI on mortality risks. DESIGN: Systematic review and dose-response meta-analysis using the Grading of Recommendation, Assessment, Development, and Evaluation (GRADE) approach. DATA SOURCES: PubMed, Scopus and Web of Science were searched until June 2024. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: We included observational cohort studies that assessed the relationship of HLI or its trajectories with all-cause, cardiovascular disease (CVD)-cause or cancer-cause of mortality. RESULT: Analysis of 13.7 million participants demonstrated that higher adherence to the HLI is linked to lower risk of all-cause (HR: 0.48; 95% CI 0.46 to 0.53; GRADE: moderate), CVD-cause (HR: 0.49; 95% CI 0.44 to 0.51; GRADE: moderate) and cancer-cause mortality (HR: 0.55; 95% CI 0.49 to 0.61; GRADE: low). These associations were further confirmed in a dose-response manner. Moreover, compared with maintaining an unhealthy lifestyle, a decline in HLI adherence was associated with a 14% higher risk of all-cause and a 19% higher risk of cancer-related mortality. In contrast, an improvement in HLI adherence was linked to a 20% lower risk of all-cause and a 13% lower risk of cancer-related mortality. CONCLUSION: Adherence to HLI and its long-term patterns are associated with lower mortality risk. These findings emphasise the importance of lifestyle-based prevention and intervention strategies in reducing mortality. PROSPERO REGISTRATION NUMBER: CRD42024500538.
31 May 2026
Read appraisal →Journal of neuro-oncology
Intramedullary or invasive characteristics of spinal melanocytomas: a PRISMA compliant systematic review and pooled case analysis
PURPOSE: Primary meningeal melanocytomas (MMs) of the spinal cord are rare lesions. Even rarer are intramedullary and invasive variants. These intramedullary lesions may exhibit more aggressive behavior than their "benign" classification appreciates. The role of adjuvant radiotherapy (aRT) in this context remains uncertain. METHODS: This study is a PRISMA-compliant systematic review with a pooled-case analysis, including two recent institutional cases included as illustrative examples. RESULTS: Sixty-three reports were identified of primary intramedullary or invasive MMs. Among 56 patients with available recurrence data, 8 of 30 patients (26.7%) experienced recurrence after gross-total resection (GTR) and in 8 of 22 (36.4%) after near-total or subtotal resection (NTR/STR), with no significant difference between groups (two-sided Fisher's exact, P = 0.548). No recurrence was observed among three patients who received aRT upfront post GTR, compared with 8 of 27 (29.6%) who did not receive radiation (two-sided Fisher's exact, P = 0.545); however, this subgroup was markedly underpowered and should be interpreted as hypothesis-generating only. Nine patients developed metastatic disease or progression, including one post biopsy. Of the eight who underwent resection at index surgery, 2 (25%) had undergone GTR and 6 (75%) STR. CONCLUSIONS: These findings suggest that the designation of MMs as benign lesions may underestimate the oncologic potential of intramedullary or invasive disease. Consideration of aRT on an individualized basis for patients with intramedullary or invasive disease is reasonable, regardless of extent of resection.
30 May 2026
Read appraisal →Science translational medicine
Efficacy and safety of durcabtagene autoleucel in a phase 1 trial for patients with relapsed/refractory multiple myeloma
Traditional manufacturing of B cell maturation antigen (BCMA)-targeted chimeric antigen receptor (CAR) T cell therapies is prolonged, leading to reduced patient access, T cell exhaustion, and consequently limiting therapeutic efficacy. To address this issue, we developed durcabtagene autoleucel, a BCMA-directed CAR T cell therapy manufactured using a rapid platform aimed at preserving T cell stemness. Here, we present the primary findings of part A of a phase 1 study (NCT04318327) of durcabtagene autoleucel in patients with relapsed/refractory multiple myeloma (r/r MM). The primary objective was safety; secondary objectives included response rates, cellular kinetics, immunogenicity, and manufacturing feasibility. Durcabtagene autoleucel was successfully manufactured for all 55 patients (median vein-to-vein time, 24 days) and given as a single infusion at one of four flat target doses (2.5 × 106 to 20 × 106 CAR T cells). Among all patients, the overall response rate was 98%, and the stringent complete response rate was 55%. Eighty percent of evaluable patients (35 of 44) achieved minimal residual disease negativity. There were no unexpected safety findings and no reports of delayed neurotoxicity. Immunophenotyping and transcriptomic analyses confirmed preservation of a stem-like phenotype in the manufactured final product. On the basis of the safety, efficacy, and cellular expansion results from the phase 1 trial, a phase 2 trial (NCT05172596) was initiated to further explore the efficacy and safety of durcabtagene autoleucel in heavily pretreated patients with aggressive r/r MM.
30 May 2026
Read appraisal →Biomedical physics & engineering express
Biwt-UNet: lung nodule segmentation via wavelet transform and multi-scale feature fusion
Lung nodule segmentation is a crucial step in computer-aided diagnosis of lung diseases and is of great significance for early detection and diagnosis of lung cancer. Traditional segmentation methods face many challenges, such as blurred nodule boundaries that are difficult to distinguish effectively, and the diversity of nodule sizes and shapes, which together increases the difficulty of segmenting lung nodules. Existing approaches often fail to address these issues adequately, highlighting the need for more effective solutions. To meet these challenges, this paper proposes a hybrid lung nodule segmentation approach-Biwt-UNet that integrates improved Haar wavelet transform and multi-scale feature information from the multi-scale fusion module and bi-encoder fusion module proposed in this study. The approach enables accurate segmentation of lung nodules of varying sizes, diverse shapes and, in particular, indistinct boundaries. Experimental results on the public LIDC-IDRI dataset demonstrate that Biwt-UNet achieves excellent segmentation performance. It significantly surpasses other state-of-the-art methods with an average dice similarity coefficient of 90.18%, an average intersection over union of 82.67%, and an average normalized surface dice of 96.39%, fully validating the effectiveness and accuracy of the proposed model in CT-based lung nodule analysis. Moreover, ablation studies further confirm the individual contributions of each architectural component and show that the best performance is achieved within an acceptable parameter budget. This study provides a new technical perspective for automatic lung nodule segmentation and is expected to assist physicians in more efficient diagnosis.
29 May 2026
Read appraisal →Neurosurgical review
Modified tubular retractor for deep brain tumour resection: enhanced surgical outcomes in resource-constrained settings
Tubular retractors permit minimally invasive access to deep-seated intracranial tumours; however, limitations in visualisation and manoeuvrability reduce their suitability for lesions ≥ 3 cm, particularly in resource-limited centres. We evaluated a modified, low-cost tubular retractor incorporating enhanced visualisation and ergonomic features, comparing its performance with Leyla spatula-based retraction and traditional syringe-derived tubular systems. A prospective comparative cohort of 105 patients with deep brain tumours ≥ 3 cm was analysed. Patients underwent Leyla retraction (Group 1, n = 38), conventional tubular systems (Group 2, n = 32), or a modified tubular retractor (Group 3, n = 35). Outcomes included intraoperative visualisation, retractor stability, manoeuvrability, extent of resection, complications, and three-month Glasgow Outcome Scores. Owing to non-normal distribution, continuous variables were analysed using non-parametric tests and binary logistic regression. The modified tubular retractor demonstrated greater stability, broader visualisation, and enhanced instrument handling compared with conventional tubular systems. Gross total resection was achieved in 91.4% with the modified device, 78.9% with Leyla-based retraction, and 28.9% with traditional tubular systems. Logistic modelling identified retractor type (OR 2.59, p = 0.035), intraoperative visualisation (OR 2.01, p = 0.036), and histopathology (OR 1.41, p = 0.036) as independent predictors of residual tumour. The modified retractor group exhibited fewer complications, shorter ICU stays, and superior functional outcomes (GOS ≥ 4 in 91%). The modified tubular retractor demonstrated favourable operative handling and outcome trends for deep-seated tumours ≥ 3 cm, with improved visualisation, stability, and ergonomic access compared with conventional systems. However, limitations related to non-randomised design, learning-curve effects, advanced mapping, fluorescence integration, and supramaximal resection warrant cautious interpretation pending multicentre long-term validation.
29 May 2026
Read appraisal →European respiratory review : an official journal of the European Respiratory Society
Nonpharmacological dyspnoea-relief strategies in lung cancer: a systematic review and network meta-analysis
BACKGROUND: Dyspnoea is a common and distressing symptom in lung cancer patients. While various dyspnoea-relief strategies show promise, their comparative effectiveness remains unclear. METHODS: We conducted a systematic review and network meta-analysis of randomised controlled trials on nonpharmacological dyspnoea-relief strategies in lung cancer patients, using studies published until 12 June 2025 from the databases PubMed, Embase, Web of Science, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature, ClinicalTrials.gov, and the World Health Organization International Clinical Trials Registry Platform. The primary outcome was dyspnoea score post-intervention. RESULTS: 4138 participants from 46 randomised controlled trials were included in the analysis. Among 10 nonpharmacological dyspnoea-relief strategies in lung cancer patients, acupressure (surface under the cumulative ranking curve (SUCRA) 98.7%, Hedges' g -1.96, 95% CI -2.92 to -0.99) demonstrated the highest efficacy, followed by fan (SUCRA 83.7%, Hedges' g -1.11, 95% CI -1.72 to -0.50), and multicomponent interventions (SUCRA 80.7%, Hedges' g -1.01, 95% CI -1.49 to -0.52). In post-operative lung cancer patients, multicomponent interventions (SUCRA 97.4%, Hedges' g -1.55, 95% CI -2.16 to -0.94) were the most effective, followed by cognitive behavioural therapy (SUCRA 74.8%, Hedges' g -0.90, 95% CI -1.75 to -0.05). For patients with pre-existing dyspnoea, acupressure (SUCRA 90.9%, Hedges' g -1.72, 95% CI -3.01 to -0.42) and fan therapy (SUCRA 77.3%, Hedges' g -1.18, 95% CI -2.10 to -0.27) were the top interventions. CONCLUSIONS: Acupressure is the primary strategy for alleviating dyspnoea in lung cancer patients, including those with pre-existing dyspnoea, followed by fan therapy, while multicomponent interventions are recommended for post-operative patients.
29 May 2026
Read appraisal →JMIR medical informatics
Multimodal Prediction of Renal Tumor Malignancy From Radiology Reports and Structured Electronic Health Records: Retrospective Cohort Study
BACKGROUND: Accurate preoperative prediction of renal tumor malignancy is critical for guiding decisions and reducing overtreatment, as a substantial proportion of renal masses prove benign. Although radiology assessments and structured electronic health record (EHR) data are routinely used, many tumor-specific descriptors remain embedded in free-text radiology reports and are underused due to extraction challenges. OBJECTIVE: This study aimed to develop and evaluate a multimodal pipeline that integrates structured EHR variables with natural language processing features from computed tomography (CT) radiology reports, including large language model (LLM)-extracted abnormality characteristics and transformer-based report embeddings, to improve malignancy prediction. METHODS: We conducted a retrospective cohort study using University of Florida Health Integrated Data Repository Observational Medical Outcomes Partnership-mapped EHR data from December 2011 to August 2024. Adults with renal tumors were included if they had longitudinal diagnostic documentation consistent with a renal mass and at least 1 preoperative renal CT report; final benign or malignant status served as the outcome. Structured features included demographics, comorbidities, medications, vital signs, and laboratory measurements. From the recent preindex CT report, an on-premises LLM isolated kidney-specific findings and extracted abnormality characteristics. Four locally deployed LLMs were evaluated against manual annotations of 500 reports. Kidney-specific text was encoded using pretrained biomedical transformer models, including radiology Bidirectional Encoder Representations from Transformers (BERT) variants. We evaluated unimodal baselines and multimodal early, middle, and late fusion strategies. Model development used 5-fold cross-validation within the 80% training partition; each fold-specific model was evaluated on the same independent 20% held-out test set, with performance reported as mean and SD across the 5 held-out test evaluations. The primary metric was area under the receiver operating characteristic curve (AUC). RESULTS: The final cohort included 967 patients (n=712, 73.6% malignant). In extraction evaluation, Qwen2.5-32B achieved 88.3% overall accuracy with a 100% extraction success rate and was selected for downstream feature generation. Among unimodal models, the structured clinical variable model achieved an AUC of 0.758 (SD 0.012), kidney-specific text with radiology BERT achieved an AUC of 0.746 (SD 0.058), and abnormality characteristics alone achieved an AUC of 0.716 (SD 0.015). Multimodal fusion models achieved higher descriptive performance than unimodal models. Early fusion achieved the highest AUC (mean 0.813, SD 0.008), and F1-score (mean 0.809, SD 0.030), while late fusion achieved an AUC of 0.805 (SD 0.016). Ablation and interpretability analyses suggested complementary predictive information from structured clinical variables and kidney-specific text embeddings. CONCLUSIONS: Integrating unstructured radiology report text with structured EHR variables achieved higher mean predictive performance than unimodal approaches in descriptive comparisons. Multimodal fusion, particularly early fusion incorporating radiology BERT-derived kidney-specific text embeddings, achieved the strongest discrimination, suggesting potential value of natural language processing-enabled multimodal EHR pipelines for informing preoperative risk stratification.
28 May 2026
Read appraisal →PloS one
One out of four patients with pancreatic cancer experience psychological symptoms: A systematic review and meta-analysis
The death rate from pancreatic cancer is one of the highest of all cancers, with increasing incidence every year over the past decades. As the prognosis remains extremely poor, this trend brings not only a growing clinical challenge, but also a significant psychological toll for patients. In comparison to other types of cancer, our understanding of the psychological burden of such a deadly disease is still limited. Recognizing and addressing the psychological aspects of pancreatic cancer are crucial for comprehensive patient care. We, therefore, aimed to quantify the psychological burden of patients with pancreatic cancer. We conducted a systematic search (PROSPERO: CRD42022288805) of MEDLINE, Cochrane, and Embase databases to identify articles reporting on the prevalence of psychological symptoms among patients with pancreatic cancer. The symptoms examined were depression, anxiety, distress, fatigue, sleep disturbances. Study quality for the included studies was assessed using the Joanna Briggs Institute tool. Relevant data were extracted and exported for quantitative synthesis. The meta-analysis of the proportion of symptoms with a 95% confidence interval (CI) was conducted using frequentist random-effects models. Subset analyses were performed based on the severity of depression. A total of 28 articles were included in the meta-analysis and 31 in the systematic review. Our results showed that the proportion of depression (19 studies; 78,930 patients) was 0.27 (95% CI: 0.18-0.39). The proportion for anxiety (18 studies; 26,538 patients) was 0.29 (95% CI: 0.16-0.46; for distress (6 studies; 2746 patients) 0.43 (95% CI: 0.25-0.63); for fatigue (7 studies; 11 338 patients) 0.40 (95% CI: 0.21-0.63); and for sleep disturbances (3 studies; 10,645) 0.31 (95% CI: 0.06-0.77). These findings highlight the urgent need to integrate psychological care into pancreatic cancer treatment. Future research should explore sources of heterogeneity to better identify which approaches provide the most psychological benefit and tailor interventions to patient needs accordingly.
28 May 2026
Read appraisal →BMJ open
Assessment of patient preferences in digital health trials: procedure development and implementation in a preference-based trial
OBJECTIVES: To develop and evaluate a standardised, fully automated assessment procedure for patient treatment preferences suitable for use in randomised controlled trials (RCTs) with remote or digital health interventions. DESIGN: Multiphase developmental study followed by implementation in a fully randomised preference trial (FRPT). SETTING: University and outpatient cancer care settings in Switzerland; implementation in RCTs with a remote digital health intervention. PARTICIPANTS: 50 medical students for pilot testing; 11 patients with cancer for qualitative testing; 124 adult patients with cancer experiencing fatigue or sleep problems in an FRPT with a remote digital health intervention. PRIMARY AND SECONDARY OUTCOME MEASURES: Primary outcome: feasibility and applicability of a standardised digital preference assessment procedure. SECONDARY OUTCOMES: distribution and strength of treatment preferences, reasons for preference. RESULTS: A procedure with three items for assessing treatment preferences was developed and refined based on pilot testing, stakeholder meetings and patient interviews. The stepwise procedure ensured that study participants with an initially unclear preference were finally able to report their preference. The majority of students (91.7%) indicated their preference already in the first assessment step. Similar in the RCT phase, 110 of 124 participants (88.7%) indicated their preference in the first step. Preference drivers included interest, perceived ease and conceptual appeal in addition to expected treatment benefits. CONCLUSIONS: Our fully automated standardised preference assessment with three items appears feasible in trials with digital health interventions. Future trials with two active treatment conditions should consider implementation of such a preference assessment to evaluate non-specific treatment effects driven by the match of patients' preferences with the respective treatment. TRIAL REGISTRATION NUMBERS: DRKS00035596; DRKS00035541; SNCTP000006209.
28 May 2026
Read appraisal →Indian journal of ophthalmology
Immune checkpoint inhibitor therapy for treatment of periocular Merkel cell carcinoma: A systematic review of clinical outcomes and safety
Merkel cell carcinoma (MCC) is a rare, aggressive neurocutaneous malignancy associated with ultraviolet damage, immunosuppression, and Merkel cell polyoma virus infection. Periocular MCC is uncommon but has high rates of local recurrence and metastasis and presents unique therapeutic challenges due to functional and cosmetic considerations. Immune checkpoint inhibitor (ICI) therapy has shown efficacy in systemic MCC; however, evidence for periocular use is limited to small case series and reports without consolidated evidence or consensus. Thus, this study summarizes and evaluates the effectiveness and safety of ICIs for periocular MCC. A systematic literature search across PubMed, Embase, and Web of Science was conducted according to PRISMA guidelines. Randomized and nonrandomized studies, case series, and case reports in English describing ICI use for periocular MCC were included. Non-human studies, non-periocular MCC, and reports without ICI treatment were excluded. Extracted data included patient demographics, tumor characteristics, treatment regimen, and patient outcomes. Seven studies reporting ten patients were included. Treatment regimens consisted of pembrolizumab (n = 6) and avelumab (n = 3), or both medications (n = 1). At a mean follow-up of 11.4 ± 9.0 months, nine patients had complete response (90%); only one patient developed subsequent distant progression despite local response. Patients with both localized periocular disease and metastatic involvement demonstrated favorable responses to ICI. Adverse effects were infrequent and typically mild; only one patient discontinued therapy due to adverse effects. ICIs may be an effective and well-tolerated treatment for local and advanced periocular MCC; however, larger, prospective studies are needed.
28 May 2026
Read appraisal →Cancer causes & control : CCC
Built environment (BE) and cancer: a systematic review of the BE's impact during the treatment journey and patient outcomes
The role of the built environment (BE) in cancer research has been extensively explored in prevention, focusing on its influence as a determinant of exposure to carcinogens and a lifestyle shaper, particularly through geographic accessibility to resources like transportation, healthy food, and diagnostic centers. However, the BE's role and influence during the treatment period and its connection with patient outcomes remain comparatively understudied. This systematic review employed the CDC definition of BE, following PRISMA guidelines and AI-driven software (ASReview) for a thorough and efficient literature review. A total of 31 articles were identified (27 database, 4 snowballing) that examined the BE's role during the treatment phase. These studies investigated diverse BE characteristics including urban features (e.g., transportation, green environment, food environment) and architectural features (e.g., design, aesthetics) during the cancer patient treatment journey. Among the included studies, the most analyzed outcomes were Physical Activity (PA) (11 articles), followed by survival and mortality (9 articles). Findings revealed positive associations between specific BE characteristics-such as green spaces, accessibility, transportation options, and aesthetics-and cancer patients' physical activity levels, quality of life, and mental health status; however, findings are inconsistent. These results underscore the potential of integrating urban and architectural BE analysis to enhance the psychosocial dimensions of cancer treatment, improve healthcare delivery, and open new pathways for research strategies.
28 May 2026
Read appraisal →JMIR cancer
Machine Learning-Based Survival Prediction Models for Young Patients With Gastric Cancer: Model Development and Validation Study
BACKGROUND: Despite a global decline in the incidence of gastric cancer (GC), the number of cases diagnosed among younger individuals continues to increase. Several studies have been conducted to develop predictive models of mortality in patients with GC. OBJECTIVE: We developed 3- and 5-year survival prediction models for young patients with GC based on machine learning-based survival modeling approaches. METHODS: Data from 813 young patients (≤50 years) diagnosed with GC between 2013 and 2015 were retrieved from the Gastric Cancer Public Staging Database. Among these 813 patients, data from 569 (70%) and 244 (30%) were allocated to the model training and testing datasets, respectively. Random survival forest, gradient boosting survival analysis, extra survival tree, and the Cox proportional hazards model were applied to predict survival outcomes at the 3- and 5-year time horizons. Model performance was assessed and quantified using the concordance index (C-index) metric. For the machine learning prediction models, the mean C-index values and corresponding 95% CIs were estimated across 100 repeated training iterations. RESULTS: For the random survival forest model, the C-index for predicting 3-year mortality was 95.89% (95% CI 95.80%-95.97%), whereas the C-index for predicting 5-year mortality was 91.82% (95% CI 91.68%-91.96%). In the gradient boosting survival analysis model, the C-index for predicting 3-year mortality was 95.32% (95% CI 95.31%-95.33%), and the C-index for predicting 5-year mortality was 89.98% (95% CI 89.95%-90.01%). For the extra survival tree model, the C-index for predicting 3-year mortality was 95.53% (95% CI 95.46%-95.60%), whereas the C-index for predicting 5-year mortality was 94.60% (95% CI 94.50%-94.70%). In addition, the Cox proportional hazards model showed a C-index of 94.15% for predicting 3-year mortality and 82.26% for predicting 5-year mortality. Tumor stage and tumor size were the primary predictive variables used to train the models for mortality prediction at different time points. Other variables exhibited varying levels of predictive contribution across different time points. CONCLUSIONS: These findings may facilitate the identification of high-risk young patients with GC who may benefit from more aggressive treatment strategies by enabling the prediction of mortality risk at different time points.
28 May 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Psychological distress in cancer survivors: a population-based analysis and machine learning-based risk stratification
PURPOSE: As the number of cancer survivors increases, psychological distress has become an important issue. Using nationally representative data, we evaluated mental health outcomes among Korean cancer survivors compared with cancer-free controls and developed models to identify individuals at risk of psychological distress. METHODS: We analyzed data from the Korea National Health and Nutrition Examination Survey (KNHANES) from 2007 to 2021. Psychological outcomes were assessed using standardized questionnaires, and a composite distress outcome was constructed. Risk stratification models were developed among cancer survivors using logistic regression and machine learning algorithms, including random forest, XGBoost, LightGBM, support vector machines, k-nearest neighbors, and naïve Bayes. RESULTS: A total of 88,061 participants were included, comprising 3733 cancer survivors and 84,328 cancer-free controls. Compared with cancer-free controls, cancer survivors had higher odds of depressed mood (OR 1.33; 95% CI 1.18-1.51), suicidal ideation (OR 1.14; 95% CI 1.00-1.31), suicide planning (OR 1.91; 95% CI 1.37-2.65), and mental health counseling (OR 1.36; 95% CI 1.08-1.71). Among cancer survivors, multiple models were evaluated, with logistic regression showing the highest performance (AUROC 0.689), followed by XGBoost (0.686). In logistic regression, longer working hours, depression history, activity limitation, female sex, smoking, employment, low income, and distorted body image were independently associated with distress. SHAP analysis identified activity limitation, sex, and depression history as key factors. CONCLUSIONS: Cancer survivors experience increased psychological distress across multiple outcomes. Machine learning-based models may help identify individuals at higher risk of psychological distress, supporting risk-based assessment in survivorship care.
28 May 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Effectiveness of eHealth interventions in long-term follow-up care for breast and gynecological cancer survivors: a systematic review
PURPOSE: As survival rates for breast and gynecological cancers improve, many survivors experience persistent long-term challenges, including fatigue, psychological distress, and reduced health-related quality of life (HRQoL). eHealth interventions may offer a scalable and accessible approach to support survivorship care. This systematic review aimed to evaluate the effectiveness of eHealth interventions in long-term follow-up care for breast and gynecological cancer survivors. METHODS: A systematic literature search was conducted across six databases (PubMed, PsycINFO, Web of Science, Cochrane Library, MedRxiv, and PsyArXiv) for studies published between January 2010 and February 2026. Eligible studies included adult breast or gynecological cancer survivors and evaluated eHealth interventions in follow-up care compared with usual care. Outcomes included outcomes such as burden of treatment, HRQoL, self-management, hospital readmissions, complications, infections, fatigue, or malnutrition. Data were extracted, synthesized narratively, and methodological quality was assessed using established risk-of-bias tools. RESULTS: A total of 41 studies were included, predominantly involving breast cancer survivors, with very limited representation of gynecological cancer populations. Overall, methodological quality ranged from low to moderate, with higher risk of bias observed in nonrandomized studies. eHealth interventions were associated with small to moderate improvements in fatigue, anxiety, depression, HRQoL, and treatment adherence, particularly among breast cancer survivors. However, effect sizes were generally smaller in studies with lower risk of bias. Evidence for gynecological cancer survivors was scarce and inconclusive, with only one study exclusively focusing on this population. Long-term outcomes were rarely assessed, and no studies evaluated the defined outcomes infections, malnutrition and unplanned hospital admissions. CONCLUSIONS: eHealth interventions show potential to support long-term survivorship care, particularly in improving psychological outcomes and quality of life among breast cancer survivors. However, the overall evidence base is limited by methodological heterogeneity, short follow-up periods, and the underrepresentation of gynecological cancer populations. Further high-quality, long-term, and cancer-specific research is needed to better establish the effectiveness and generalizability of these interventions in survivorship care. IMPLICATIONS FOR CANCER SURVIVORS: As survivorship care needs continue to increase, eHealth interventions may contribute to more accessible, scalable, and patient-centered models of follow-up care. However, their long-term effectiveness and applicability across diverse cancer populations remain unclear. More rigorous, long-term, and cancer-specific research is required to better define their role in survivorship care.
27 May 2026
Read appraisal →Journal of chemical information and modeling
Integrating QSAR-Machine Learning, Biochemical Assays, and Molecular Dynamics for the Discovery of JAK2 Inhibitors in Cervical Cancer
Cervical cancer remains a major global health challenge, where dysregulated JAK2 signaling constitutes a key molecular driver. Nevertheless, selective small-molecule JAK2 inhibitors for HPV-positive cervical cancer are still limited. Here, we integrated biochemical assays, QSAR-machine learning, and molecular dynamics simulations to identify potent JAK2 inhibitors. A series of naphthalene-based derivatives, including hydroxynaphthalenamide and phosphorylated dihydronaphthylamide analogs, were evaluated for cytotoxicity in HeLa cells and JAK2 kinase inhibition. Several compounds exhibited selective cytotoxicity with minimal activity toward normal fibroblasts, among which 2q and 2s showed low-nanomolar JAK2 inhibition and strong apoptosis induction through suppression of the JAK2/STAT3/STAT5 pro-tumorigenic signaling pathway. To accelerate hit identification, a QSAR-machine learning (QSAR-ML) framework was employed to prioritize 13 newly designed derivatives. Among three ensemble boosting models, the Categorical Boosting (CB) model demonstrated the strongest predictive capability, achieving a high R2 of 0.955 for the training set and a low RMSE of 0.156 for the test set. This model successfully identified five active candidates with strong prediction-experiment agreement (MAPE = 2.6-14.4%), with D4 and D13 meeting drug-likeness criteria and displaying potent nanomolar JAK2 inhibition. Finally, 1-μs molecular dynamics simulations revealed that hydrophobic contacts and hydrogen bonding cooperatively stabilize these inhibitors within the JAK2 ATP-binding pocket. Collectively, these findings establish a QSAR-ML-guided strategy for accelerating JAK2 inhibitor discovery and highlight naphthalene-based scaffolds as promising leads for targeted cervical cancer therapy.
27 May 2026
Read appraisal →Physics in medicine and biology
Lattice peak optimization: a mixed-integer framework for geometry-adaptive lattice radiotherapy
Objective.Lattice radiotherapy (LATTICE) delivers spatially distributed high-dose peaks within the tumor volume while maintaining lower doses in surrounding valley regions. Determining feasible peak locations is typically performed using heuristic or manual approaches, which may limit the number and spatial distribution of deliverable peaks. This work introduces a lattice peak optimization (LPO) framework that jointly optimizes peak placement and dose distribution to identify the maximum number of geometrically feasible peaks within the target.Approach.Proton LATTICE planning is formulated as a mixed-integer optimization problem that selects an optimal subset of peaks from a large set of candidate locations within the target. Binary variables represent peak selection and continuous variables model spot weights. The formulation enforces geometric feasibility between peaks while optimizing dosimetric objectives to improve peak-to-valley dose ratio (PVDR) and reduce organs-at-risk (OAR) dose. The resulting nonconvex problem is solved using iterative convex relaxation within an alternating direction method of multipliers framework.Main results.LPO was evaluated on three clinical cases with 150-400 candidate peak locations, from which 4-13 peaks were selected. Compared with 50-90 randomly generated LATTICE configurations per case, LPO consistently achieved higher PVDR and improved OAR sparing. In an abdominal case, the composite objective value was 2.93 (worst random), 2.40 (median random), 1.90 (best random), and 1.95 (LPO), with similar trends observed across all cases.Significance.A geometry-adaptive, mixed-integer optimization framework for lattice peak placement is presented, demonstrating improved PVDR and OAR sparing relative to manual and random LATTICE approaches. The present study evaluates performance of the proposed framework in the context of proton LATTICE planning; however, the framework is mathematically modality-agnostic and could in principle be applied to other radiation modalities.
27 May 2026
Read appraisal →Journal of radiation research
Deep learning-based dose prediction in proton beam therapy for hepatocellular carcinoma: comparison of network architectures and loss functions
In proton beam therapy (PBT) for hepatocellular carcinoma (HCC), deep learning (DL)-based dose prediction offers clinical value by providing immediate reference dose distributions as a guideline tool for treatment planners and enabling virtual PBT dose assessment at institutions lacking PBT facilities to support clinical decision-making. This study proposes a dose gradient-aware DL training approach and a beam arrangement-free framework that predicts dose distributions from computed tomography (CT) images and target/organs-at-risk structures without requiring beam arrangement inputs. Using data from 172 HCC patients, we systematically compared 20 DL models combining five architectures with four loss functions, including a novel dose gradient-aware loss capturing PBT-specific dose distribution characteristics arising from the Bragg peak. Prediction accuracy was evaluated using dose metrics for clinical target volume, planning target volume, and normal liver, 10% dose region volume assessment, visual examination, Dice similarity coefficient (DSC) for 10% dose region, and dose-volume histogram (DVH) analysis. DL models employing dose gradient-aware loss demonstrated reduced prediction errors in target volume dose metrics, achieved the lowest errors in 10% dose region volume when combined with U-Net (MAE: 35.4 cm3, RMSE: 51.3 cm3), attained the highest DSC (0.88 ± 0.05), showed the closest DVH agreement with clinical plans for both target structures and normal liver. A comprehensive evaluation consistently confirmed the superiority of dose gradient-aware loss across all metrics, demonstrating the importance of physics-informed, dose gradient-aware learning for accurate PBT dose distribution prediction. Particularly, DL models combining simple architectures with dose gradient-aware loss predicted doses that closely approximated clinical doses in PBT.
27 May 2026
Read appraisal →Journal of chemical theory and computation
MolSculptor: An Adaptive Diffusion-Evolution Framework Enabling Generative Drug Design for Multitarget Affinity and Selectivity
The rational design of molecules with tailored activity/selectivity across multiple protein targets is crucial for developing therapies for complex diseases like cancer, yet it remains a formidable challenge. While deep generative models show immense promise, their application to these tasks faces fundamental challenges, as they struggle to incorporate the structural information on multiple distinct protein pockets and require vast multitarget data sets or specialized expert knowledge that are rarely available. Here we introduce MolSculptor, an adaptive diffusion-evolution framework designed to generate inhibitors for any combination of on- and off-targets, circumventing the need for target-specific training data or prior expert knowledge. MolSculptor unifies both de novo design and lead optimization, provides a versatile workflow applicable to different stages of drug discovery, and allows for direct conditioning on key drug-like properties. It integrates a 3D-aware surrogate model to enable flexible guidance for any set of specified on- and off-targets. Furthermore, MolSculptor employs an active learning protocol to adaptively refine this guidance, ensuring high performance even in data-scarce scenarios. We demonstrate MolSculptor on a series of challenging multitarget and selective inhibitor design tasks, where it significantly outperforms state-of-the-art methods in generating high-quality candidates that satisfy all complex constraints. Notably, many of the generated molecules exhibit predicted affinity profiles superior to those of experimentally validated references. Using MolSculptor, we successfully designed and synthesized a novel and potent dual-target inhibitor for castration-resistant prostate cancer (CRPC), whose inhibitory activity was confirmed through wet-lab validation. MolSculptor provides a powerful and generalizable paradigm for designing ligands with complex, multitarget activity profiles, paving the way for data-efficient solutions to complex therapeutic problems.
27 May 2026
Read appraisal →Blood advances
Pediatric-inspired regimens and HSCT for adolescents and young adults with acute lymphoblastic leukemia
The best frontline treatment for acute lymphoblastic leukemia (ALL) in adolescents and young adults (AYAs) is unclear. To support a clinical practice guideline, we systematically reviewed and meta-analyzed evidence on (1) asparaginase-based (pediatric) vs non-asparaginase-based (adult) regimens and (2) allogeneic hematopoietic stem cell transplantation (HSCT) vs no HSCT for AYAs (aged 15-39 years) with ALL in first complete remission. Data sources were PubMed, CINAHL, and PsycINFO from inception to 29 November 2023. Eligible studies compared regimens or the use of HSCT and reported survival, remission, toxicity, or quality of life in AYAs. We included 19 studies (14 comparative, 5 single-group; N = 3607) comparing regimens and 7 studies (N = 7492) comparing HSCT and no HSCT. Studies were mostly of poor quality, with sparse randomized controlled trials (RCTs), yielding low-certainty findings. Pediatric regimens were associated with higher 5-year overall survival (OS; relative risk [RR], 1.40; 95% confidence interval [CI], 1.18-1.65), event-free survival (RR, 1.80; 95% CI, 1.13-2.85), disease-free survival (DFS; RR, 1.55; 95% CI, 1.32-1.82), and lower treatment-related mortality (TRM; RR, 0.29; 95% CI, 0.09-0.90). HSCT was associated with lower 5-year OS (RR, 0.72; 95% CI, 0.61-0.84) and DFS (RR, 0.78; 95% CI, 0.68-0.90) and higher nonrelapse mortality (RR, 2.70; 95% CI, 1.18-6.18) and TRM (hazard ratio, 6.88; 95% CI, 3.02-15.70). Relapse risk varied by time point. In AYAs with Philadelphia chromosome-negative ALL, pediatric regimens may improve survival; toxicity-related evidence remains limited. HSCT may lead to inferior OS and DFS. With a dearth of RCTs, low-certainty evidence highlights the need for high-quality studies and subanalyses of AYAs.
26 May 2026
Read appraisal →Blood advances
Phase 2 study of the lysine-specific demethylase 1 inhibitor bomedemstat for essential thrombocythemia
Novel treatments that can improve outcomes of essential thrombocythemia (ET) are needed. In a phase 2 trial, participants with ET who required cytoreduction and had inadequate response to, or were intolerant of, ≥1 standard therapy received bomedemstat at a starting dose of 0.6 mg/kg per day, titrated to achieve a target platelet count (200 × 109/L to 400 × 109/L). Primary end points were safety and response, defined as a platelet count of ≤400 × 109/L without new thromboembolic events. Of 73 who received bomedemstat, at 24 weeks, 49 of 64 evaluable participants (77%) achieved a response. Durable reductions in platelet count (≤400 × 109/L for ≥12 weeks) were observed in 52 of 72 participants (72%). Durable reduction in white blood cell count (<10 × 109/L for ≥12 weeks) was observed in 61 of 72 participants (85%); of 10 participants with elevated white blood cell count at baseline, 9 had normal white blood cell count (<10 × 109/L) at week 24. Hemoglobin levels remained stable. After 24 weeks of treatment, a decrease in variant allele frequency of CALR, JAK2, or MPL was observed in 39 of 46 (85%) evaluable participants. By week 24, 2 of 73 participants (3%) had experienced ≥1 thrombotic event, and 15 of 73 (21%) experienced ≥1 hemorrhagic event. During overall treatment period, grade 3 or 4 adverse events (AEs) occurred in 34 of 73 participants (47%). AEs led to temporary treatment interruption in 29 participants (40%) and permanent discontinuation in 11 (15%). No participants died due to AEs. Bomedemstat had clinically relevant activity and manageable safety in participants with ET. This trial was registered at www.clinicaltrials.gov as #NCT04254978.
26 May 2026
Read appraisal →Otolaryngologia polska = The Polish otolaryngology
Leveraging large language models to audit perioperative total laryngectomy care: anesthetic, nutritional, and complication profiles in a real‑world cohort
<p><strong>Introduction: </strong>Total laryngectomy (TL) remains the standard treatment for advanced la‑ ryngeal cancer, and its perioperative management requires close collaboration within a multidisciplinary team, including anaesthesiologists and clinical nutritionists.</p><p><strong>Aim:</strong> The aim of this study was to analyse the perioperative course of patients un‑ dergoing TL from both an anaesthesiologist's and a nutritionist's perspective, with a particular focus on the feasibility and performance of structured data extraction from electronic medical records using large language models (LLMs).</p><p><strong>Material and methods:</strong> We performed a single‑centre retrospective analysis of 43 consecutive patients undergoing TL (ICD‑9 codes 30.32, 30.39, 30.41, 30.49) be‑ tween January 2024 and April 2026. Clinical data, including physician notes (n = 845), nursing notes (n = 2578), discharge summaries (n = 173), and consultations (n = 123), were subjected to structured extraction using large language models (gemma-3-31Bit/gemma-4-26B-it). Demographics, tumour stage, length of stay (LOS), periopera‑ tive nutrition, pharmacotherapy, postoperative complications, temporal laboratory trends, and rehabilitation were analysed.</p><p><strong>Results: </strong>The cohort comprised 35 men (81%) and 8 women (19%), with a mean age of 66.4 9.8 years; pT4a (49%) and pN0 (82%) predominated. Median LOS was 15.0 days (IQR 13.1-16.5). Enteral nutrition via nasogastric tube was used in 95% of patients, and oral intake was initiated at a median of postoperative day 13. Multimodal anal‑ gesia based on intravenous paracetamol (60%), ketoprofen (47%), and metamizole (40%) was routinely employed, while strong opioids were used in only 5% of pa‑ tients. Postoperative complications occurred in 22.5% of cases, with pharyngocuta‑ neous fistula (PCF) being the most frequent (12.5%). In‑hospital mortality was 0%, and overall mortality during a median 12‑month follow‑up was 2.3%. CRP peaked on postoperative day 3 (mean 114 mg/L). Pharmacologic VTE prophylaxis was admi‑ nistered in 65% of patients.</p><p><strong>Conclusions:</strong> In TL patients, enteral feeding forms the backbone of nutritional sup‑port, and multimodal analgesia provides effective pain control, whereas VTE pro‑phylaxis and other modifiable elements of perioperative care warrant optimization within prehabilitation and ERAS-based protocols.</p><p>&nbsp;</p>.
26 May 2026
Read appraisal →Journal of robotic surgery
Short-term outcomes of robot-assisted versus conventional minimally invasive esophagectomy for resectable esophageal cancer: a systematic review and meta-analysis
Robot-assisted minimally invasive esophagectomy (RAMIE) has emerged as an alternative to conventional minimally invasive esophagectomy (MIE) for esophageal cancer, but its short-term perioperative advantages remain uncertain. To compare early postoperative outcomes of RAMIE and MIE in patients with resectable esophageal cancer, this systematic review and meta-analysis was conducted according to PRISMA 2020 guidelines. PubMed was searched for randomized controlled trials, propensity score-matched studies, and retrospective comparative studies published between January 2005 and December 2024. Adult patients undergoing RAMIE or conventional MIE for resectable esophageal cancer were included. Random-effects models were used to pool standardized mean differences and odds ratios with 95% confidence intervals. Study quality was assessed using the MINORS criteria and Cochrane RoB 2 tool. In addition, a post hoc GRADE assessment was carried out for key outcomes to evaluate the certainty of evidence. Forty-one studies involving 13,321 patients were included, of whom 4,327 underwent RAMIE and 8,994 underwent MIE. Compared with MIE, RAMIE was associated with lower blood loss, reduced conversion to open surgery, higher total lymph node yield, greater left recurrent laryngeal nerve lymph node harvest, fewer pulmonary complications, and lower overall postoperative morbidity. ICU stay and hospital stay were also e shorter after RAMIE, whereas operative time was longer. No significant differences were observed in R0 resection, anastomotic leak, recurrent laryngeal nerve palsy, cardiac complications, chyle leak, surgical site infection, or 30- and 90-day mortality. Post hoc GRADE assessment showed that the certainty of evidence was very low across key outcomes, mainly because of the predominance of non-randomized studies, substantial heterogeneity across several pooled analyses, and outcome-specific concerns regarding imprecision and publication bias. RAMIE is safe and may offer some short-term perioperative advantages over conventional MIE without clear evidence of increased major morbidity or short-term mortality. However, the certainty of evidence across key outcomes was very low, and the predominance of nonrandomized studies and substantial heterogeneity across several analyses warrant cautious interpretation. Further adequately powered randomized trials are required.
25 May 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Facilitators and barriers to the implementation of traditional Chinese exercises in cancer patients: a mixed-methods systematic review
PURPOSE: To identify barriers and facilitators to the implementation of traditional Chinese exercises in cancer patients. METHODS: This was a mixed-methods systematic review. It searched for published studies from nine different sources. Data synthesis followed a convergent integrated approach according to the Joanna Briggs Institute methodology for mixed-methods systematic reviews. Quantitative data were qualitized using thematic analysis and synthesized with qualitative data using thematic synthesis. RESULTS: Twenty-one studies were included. Forty-three implementation determinants were identified across five domains of the Consolidated Framework for Implementation Research (CFIR), including 25 facilitators and 18 barriers. Key facilitators included perceived benefits, simplicity, social support, and flexible delivery, whereas major barriers involved symptom burden, resource constraints, competing life demands, and limited organizational support. CONCLUSIONS: The implementation of traditional Chinese exercises in cancer patients is shaped by multilevel determinants across CFIR domains. Implementation outcomes depend on the alignment between intervention characteristics, patient capacity, and contextual conditions. Context-sensitive and tailored strategies are required to improve uptake and long-term sustainability.
25 May 2026
Read appraisal →Science advances
Generalizability of an AI-based mammogram risk score (MRS) for breast cancer among diverse populations of women
Conventional prediction models incorporating genetic and clinical factors including breast density underperform in non-European populations. We investigated an artificial intelligence-derived mammogram risk score (MRS), a summary of texture features that captures intrinsic breast-tissue characteristics-the substrate for cancer development. This study leveraged data from two North American screening cohorts totaling >226,000 women, including non-Hispanic white, non-Hispanic Black, East Asian, South Asian, and Indigenous women. MRS distributions showed nonsignificant shifts (and similar SDs) between cohorts and across race and ethnic subgroups. MRS increased with age and was significantly associated with breast cancer risk, with hazard ratios per SD ranging from 2.24 [95% confidence interval (CI), 2.03 to 2.46] to 2.32 (95% CI, 2.25 to 2.39) after age adjustment. Associations remained significant within all subgroups. Calibration was excellent across the racial and ethnic groups and across full-field digital mammograms and tomosynthesis. These findings establish MRS as a strong predictor that is independent of race or ethnicity, demonstrating its potential for broader clinical utility.
24 May 2026
Read appraisal →Medicine
Applying digital technologies for remote care in the real life context: A 3-year experimentation with postoperative lung cancer patients
Surgery remains the main curative treatment for lung cancer patients. However, operation has resulted in some-degree reduction in postoperative pulmonary function. The decreased pulmonary function could be rehabilitated to the preoperative level through restoring exercise, which is related to the ways and exercise time of rehabilitating. The Dignio platform is 1 digital telemedicine system and has been used maturely in Norway, however, it has not yet been widely promoted in China. In the present study, we adopted the telemedicine system, according to the formulated rehabilitation exercise program, to design a good way to recover pulmonary function for postoperative lung cancer patients, promoting early and quick recovery, and reducing the long-term postoperative complications and outpatient visits. This retrospective controlled study enrolled 250 postoperative lung cancer patients using a remote digital care platform and 250 matched controls receiving standard instructions. In the postoperative 1-week follow-up, we found that the forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), forced expiratory volume in 6 seconds, FVC25-75, peak expiratory flow had an obvious upward trend, although there were no significant differences in the measurements on postoperative day 7 compared with day 1. From the long-term data at 1 month/3 months/6 months post-operation, we found that the recovery was better to varying degrees in the experiment group using the remote care digital platform, compared with that of the control, especially the FEV1 at 3 (P = .065) and 6 months post-operation (P = .037), and forced Expiratory Flow between 25% and 75% of forced vital capacity at 3 months post-operation (P = .084). However, most pulmonary values, such as the FEV1/FVC, FVC and peak expiratory flow have no significant difference (all P > .05) in the recovery at 1 month post-operation between the experimental group and control, the second time at 3 months and the third time at 6 months after surgery. Especially in the experimental group, 2 patients with sudden pneumothorax were timely detected after discharge through the Digino platform, the patient returned to the hospital in time, and both patients were out of danger ultimately. The Digino remote care indeed has some benefit in the pulmonary function recovery in the long-term follow up, and reduces postoperative complications obviously.
24 May 2026
Read appraisal →Systematic reviews
Impact of the COVID-19 pandemic on diagnosis, and healthcare utilization, among patients with cancer (lung, breast, and pancreas) and cardiovascular diseases (HF, AF, hypertensive, and chronic ischemic heart disease) in Germany: two systematic reviews.
BACKGROUND: The COVID-19 pandemic and related non-pharmaceutical interventions (NPIs) (e.g., lockdowns and contact restrictions) disrupted routine healthcare delivery. In Germany, these measures affected diagnostic and treatment services for people with cancer and cardiovascular diseases, potentially delaying diagnosis and adversely influencing outcomes. We assessed whether and to what extent diagnosis, health utilization and health outcome among patients with selected cancer and cardiovascular conditions changed in Germany during the pandemic. METHODS: We conducted two systematic reviews of studies from Germany on selected cancers (breast, lung and pancreatic) and cardiovascular conditions (atrial fibrillation/flutter, heart failure, hypertensive and chronic ischemic heart disease). Protocols were registered in PROSPERO and the reviews were reported in accordance with PRISMA. We searched PubMed, Web of Science, Cochrane Library, Scopus, and Embase and screened grey literature. Outcomes included changes in new diagnoses, healthcare utilization, treatment, and disease-specific mortality during the pandemic (2020-2023) compared with the pre-pandemic period (2018-2019). Two reviewers independently screened records, extracted data, and assessed risk of bias using an adapted ROBINS-E tool. Owing to heterogeneity, we synthesized findings narratively. RESULTS: We screened 1991 records for cancer and 4,981 records for cardiovascular diseases, and included 9 cancer studies and 10 cardiovascular studies. For cancer, several studies reported a relative reduction in new breast and lung cancer diagnoses of up to 25% during lockdown periods; hospital admissions decreased by up to 9%. For cardiovascular conditions, hospital admissions for atrial fibrillation/flutter and heart failure decreased by up to 20%, particularly during pandemic peaks. Evidence on treatment delays, changes in treatment, and mortality was limited, and outcomes for other included diagnoses were often not reported. DISCUSSION: The available evidence indicates substantial reductions in hospital admissions and new diagnoses among patients with cancer and cardiovascular disease in Germany during the pandemic, suggesting major disruptions to care delivery. However, heterogeneity and gaps in the evidence base limit a comprehensive assessment of downstream outcomes. More comprehensive, linked data and further research are needed to quantify the full pandemic's impact and to strengthen health-system resilience for future crises.
23 May 2026
Read appraisal →Current environmental health reports
Industrial Pollution and Health in Louisiana: A Systematic Review of Quantitative and Qualitative Studies
PURPOSE OF REVIEW: Louisiana has one of the largest concentrations of petrochemical industry in the USA. Many studies have assessed patterns of industrial pollution and health in Louisiana; we aim to systematically review this evidence. We systematically searched PubMed, Web of Science, Embase, APA PsycInfo, and GreenFILE for peer-reviewed papers published 1999-2024 that reported geographical variation in health or industrial pollution, and/or tested for an association between the two in Louisiana. We used Covidence to support standardized review and extraction. RECENT FINDINGS: We identified 2485 non-duplicate papers in our search; 53 met the inclusion criteria. Most reported quantitative findings. All studies of industrial pollution described air pollution (some also described other pollution). Studies described various health outcomes, including cancer, respiratory health, mortality, and COVID-19. Overall, people who lived closer to industrial activity had higher pollution exposure and worse health. Black and lower-income residents were exposed to more industrial activity than white and higher-income residents. Twenty-one studies assessed statistical associations between industrial pollution and health; many found an association. Twenty-one studies were quantitative and adjusted for confounding, 29 studies did not adjust for confounding (including qualitative studies), and three studies did not adjust for confounding and had authors with industry ties. Evidence suggests that there is a higher burden of air pollution and worse health outcomes in industrialized areas of Louisiana. While there was some evidence of significant associations between industrial pollution and health outcomes, research with larger sample sizes and improved pollution exposure measurements could be informative.
23 May 2026
Read appraisal →JMIR formative research
MyLungHealth, a Patient-Facing Education Tool for Lung Cancer Screening: Qualitative User-Centered Design Study
BACKGROUND: Lung cancer remains the leading cause of cancer-related mortality worldwide, with low-dose computed tomography screening demonstrating an approximately 20% reduction in mortality among high-risk individuals. Despite this benefit, screening prevalence remains suboptimal, with often less than 20% of eligible individuals reported to be up to date on screening. Shared decision-making is essential for effective lung cancer screening (LCS) implementation, with decision aids shown to enhance patient knowledge and engagement. OBJECTIVE: The aim of this study is to identify patient preferences, concerns, and design considerations through qualitative evaluation of MyLungHealth, a personalized patient-facing educational tool for LCS integrated with electronic health records, and to describe how these findings informed iterative design modifications. METHODS: We employed qualitative research methods through focus groups (n=34) and individual interviews (n=18) with individuals who met screening eligibility criteria. Participants were recruited from the University of Utah Health and New York University Langone Health between May and December 2023. Feedback was analyzed using Braun and Clarke's thematic analysis principles. RESULTS: Six themes were organized into three overarching domains. Domain A included interpretation and impact of personalized risk information: theme 1, difficulties interpreting risk information, and theme 2, varied impacts of risk information on motivation. Domain B included autonomy, privacy, and user interface preferences: theme 3, desire for autonomy and control over personal health data, and theme 4, preference for straightforward language and multiple information formats. Domain C included integration with clinical workflows and patient portal systems: theme 5, expectations for integration with health care provider workflows, and theme 6, mixed experiences with personal health record systems. These insights led to key design modifications, including simplified risk presentation, multimodal content delivery options (video and text), and implementation of electronic health record alerts for clinicians. CONCLUSIONS: The user-centered design process for MyLungHealth revealed important considerations for developing effective patient education tools for LCS. The findings highlighted the need for simplified risk presentation, personalized information delivery, and integration with clinical workflows. These findings underscore the importance of balancing comprehensive risk communication with user accessibility.
23 May 2026
Read appraisal →ACS sensors
Urine-Based Noninvasive Detection of Prostate Cancer Using Human Olfactory Receptor-Embedded Nanodiscs
The diagnosis of prostate cancer (PCa) is limited by the low specificity of prostate-specific antigen (PSA) testing, which contributes to overdiagnosis and patient exposure to unnecessary invasive prostate biopsies. Here, we report a urine-based diagnostic platform that combines a six-member human olfactory receptor-embedded nanodisc (OR-ND) sensor array with fluorescence sensing and machine learning to detect PCa-associated volatile organic compounds (VOCs). Six ORs were reconstituted into lipid nanodiscs and evaluated using urine samples from a strictly curated subcohort of 40 PCa patients and 33 healthy controls (n = 73) to establish baseline sensor responsiveness. Subsequently, to ensure diagnostic robustness, machine learning classifiers were developed and cross-validated using an expanded data set of 290 samples, achieving an accuracy of 0.890 and an AUC of 0.964. Analysis of the full receptor data set revealed distinct response patterns, and statistical screening with correlation filtering identified OR2W1, OR51E1, and OR51E2 as the most informative features. Using these selected receptors, a random forest classifier achieved an accuracy of 0.890 and an AUC of 0.964, demonstrating high sensitivity and specificity. OR response patterns showed a closer association with Gleason score than with serum PSA levels, indicating that urinary VOC signatures capture tumor-related metabolic information complementary to conventional biomarkers. These results demonstrate that an OR-ND sensor array coupled with machine learning enables accurate and noninvasive detection and classification of PCa from urine samples. Our study provides a modular framework extensible to other VOC-associated diseases.
23 May 2026
Read appraisal →Occupational and environmental medicine
Lymphoid neoplasms and benzene exposure using a revised classification scheme: systematic review and meta-analysis
Lymphoid neoplasms are a diverse group of cancers derived from lymphocytes, with classification recently updated by the 5th edition of the WHO Classification of Haematolymphoid Tumours (WHO-HAEM5). Benzene, a well-known carcinogen, is widely used in industries and environmental exposures vary; however, its association with specific lymphoid neoplasm subtypes remains unclear due to historical classification challenges. This study aimed to clarify the risk of lymphoid neoplasms related to benzene exposure using the updated WHO-HAEM5 framework.A systematic review and meta-analysis were conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Eligible studies were comparative human studies evaluating benzene exposure and lymphoid neoplasm risk, with sufficient data for risk estimates. Inclusion criteria specified study designs, participants, exposures and outcomes based on clinical diagnoses. Data were independently extracted by multiple reviewers. The main outcome was risk of lymphoid neoplasms and subtypes analysed by random-effects meta-analysis with heterogeneity and bias assessments.From 1488 records, 95 studies met criteria (65 occupational and 30 environmental exposure). Benzene exposure significantly increased overall lymphoid neoplasm risk (RR 1.26; 95% CI 1.18 to 1.35). B-cell neoplasms showed elevated risk (RR 1.26; 95% CI 1.16 to 1.37), including mature B-cell neoplasms, Hodgkin lymphoma and plasma cell neoplasms. T-cell and natural killer cell neoplasm risk was not significantly increased overall. Occupational exposures conferred higher risks with lower heterogeneity than environmental exposures.Benzene exposure is strongly associated with increased risk of lymphoid neoplasms, particularly B-cell subtypes, supported by the refined WHO-HAEM5 classification reducing heterogeneity. This study underscores the importance of biologically informed disease classification and the need for targeted occupational safety and environmental health policies.The review protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO) (Registration ID: CRD420251063844) .
23 May 2026
Read appraisal →JMIR mHealth and uHealth
mHealth-Supported Exercise Rehabilitation to Reverse Frailty After Autologous Transplantation in Multiple Myeloma: Randomized Controlled Trial
BACKGROUND: Frailty is highly prevalent in survivors of multiple myeloma (MM) after autologous hematopoietic cell transplantation and is associated with poor functional recovery and adverse clinical outcomes. Although exercise is known to improve physical function, traditional center-based rehabilitation models are often inaccessible to this population during early posttransplant recovery. Mobile health (mHealth)-supported exercise may offer a scalable alternative; however, evidence in hematologic malignancies remains limited. OBJECTIVE: This study aimed to evaluate the effects of a 16-week mHealth-supported exercise rehabilitation program on frailty phenotype and physical function in survivors of MM within 180 days after autologous hematopoietic cell transplantation. METHODS: In this single-center randomized controlled trial, participants who self-reported as prefrail or frail were randomized 1:1 to an mHealth-supported exercise group (n=16) or usual care control (n=16). Remote assessments were conducted at baseline (week 0), midpoint (week 9), and follow-up (week 17). The intervention consisted of 8 weeks of supervised tele-exercise (3 sessions/week, 50 minutes/session), followed by 8 weeks of independent home-based exercise using the same mHealth platform. Exercise intensity was prescribed using a repetitions-in-reserve-based rating of perceived exertion approach with symptom-guided progression. The primary outcome was change in the 5-component Fried frailty phenotype score (0-5). Secondary outcomes included Short Physical Performance Battery components, chair stand time, gait speed, and handgrip strength. Intention-to-treat analyses were conducted using generalized estimating equations to evaluate between-group differences over time. RESULTS: Participants had a mean age of 64.6 (SD 7.1) years and were enrolled a mean of 136 (SD 36.3) days posttransplant. At baseline, 94% (30/32) of participants were classified as frail. Adherence to the supervised sessions was 85% (326/384 sessions), and adherence during the unsupervised phase was 78% (298/384 sessions). The exercise group demonstrated a significantly greater reduction in frailty score compared with control from baseline to week 17 (P<.001). Between-group difference estimates showed a clinically meaningful improvement favoring exercise at both week 9 and week 17 (P<.001). Chair stand time improved significantly in the exercise group compared with control, with faster completion times observed at week 9 and sustained through week 17 (P=.002). Improvements in other Short Physical Performance Battery components and handgrip strength favored the exercise group but did not reach statistical significance. No serious adverse events occurred. CONCLUSIONS: A 16-week mHealth-supported, progressively prescribed exercise rehabilitation program was feasible, safe, and effective in reversing frailty phenotype and improving functional mobility in survivors of MM early after autologous transplantation. This approach provides a scalable model for delivering structured rehabilitation during a high-risk recovery window. Larger trials incorporating attention-matched controls and longer follow-up are warranted. TRIAL REGISTRATION: ClinicalTrials.gov NCT05142371; https://clinicaltrials.gov/study/NCT05142371.
22 May 2026
Read appraisal →Journal of medical Internet research
Large Language Models in Colorectal Cancer Care and Clinical Decision Support: Systematic Review
BACKGROUND: Colorectal cancer (CRC) is a leading cause of cancer morbidity and mortality worldwide. The complexity of guideline-concordant care and unstructured clinical data has driven demand for decision-support tools. Large language models (LLMs) show promise for processing clinical data and patient-provider communication, yet evidence is fragmented, and a CRC-specific synthesis across the full care continuum is lacking. OBJECTIVE: This systematic review evaluates the current applications, performance determinants, and clinical implications of LLMs across the continuum of CRC care. METHODS: Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), we searched 6 databases (PubMed, Embase, Web of Science, Scopus, CINAHL, Cochrane) through April 1, 2026. Eligible studies were peer-reviewed original investigations of LLMs on CRC tasks with extractable outcomes; reviews, editorials, and abstracts were excluded. Two reviewers assessed quality with QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies-2), PROBAST (prediction model risk of bias assessment tool), and ROBINS-I (Risk of Bias in Nonrandomized Studies - of Interventions). Data on model types, applications, prompts, input/output formats, and outcomes were analyzed descriptively, with narrative synthesis per synthesis without meta-analysis (SWiM) guidelines. RESULTS: Of 8880 records, 37 studies met inclusion criteria (2023-2026), mostly from China and the United States, with GPT series most frequently evaluated. Overall risk of bias was low in 10/37 studies (27.0%), moderate in 14/37 (37.8%), unclear in 7/37 (18.9%), and high or serious in 6/37 (16.2%). Problematic domains included outcome measurement, intervention classification, patient selection, and lack of blinded assessment. LLMs showed utility in automating data extraction from clinical texts, supporting patient education, aiding diagnosis, and assisting clinical decision-making, with emerging visual interpretation and multimodal capacities. Domain-specific and multimodal models showed advantages over general-purpose models in certain tasks. Performance was significantly influenced by prompt design, from zero-shot queries to fine-tuning. Despite efficiency and outcome benefits, challenges persist regarding methodological quality, data privacy, and generalizability. CONCLUSIONS: This review provides an integrative framework synthesizing evidence across study designs and LLM categories in CRC care. Unlike prior reviews addressing gastroenterology broadly or limited to one design, it covers the full CRC continuum and, for the first time, comparatively evaluates general-purpose, domain-specific, and multimodal LLMs, clarifying how prompt engineering and heterogeneous metrics shape outcomes. Although findings support LLMs' clinical potential, results must be interpreted cautiously, given low overall evidence quality. Most studies lacked safeguards against bias-blinded assessment, confounder adjustment, or prospective multicenter validation. Substantial heterogeneity across tasks, LLM types, prompts, reference standards, and outcomes means reported advantages cannot be generalized. Future work should prioritize real-world integration via prospective multicenter validation, robust privacy frameworks, and rigorous human oversight. Amid rising global CRC burden and health care disparities, this review informs clinical translation, equitable scaling, and policy on LLM deployment.
22 May 2026
Read appraisal →Journal of robotic surgery
A randomized controlled trial comparing robotic NOSES versus robotic TME for Mid-rectal cancer: short-term oncological and perioperative outcomes
This randomized study compared perioperative outcomes between robotic natural orifice specimen extraction surgery (R-NOSES) and robotic total mesorectal excision (R-TME) in participants with mid-rectal cancer. After exclusions, 71 participants in the R-TME cohort and 69 in the R-NOSES cohort were included in the final analysis, from an initial randomized cohort of 150 eligible participants (1:1 allocation). The two cohorts had comparable preoperative characteristics. Intraoperative and pathological outcomes, including blood loss, operative duration, and harvested lymph nodes, showed no notable differences. The rates of overall complication were comparable between the two groups (R-NOSES 13.0% vs. R-TME 15.5%, P = 0.679), and anastomotic leakage did not vary significantly. Conversely, the R-TME group experienced a higher rate of wound complications (5.6% vs. 0.0%, P = 0.045). Notably, the R-NOSES group demonstrated significant advantages in postoperative recovery, including shorter time to first flatus (48 vs. 53 h, P = 0.02), earlier resumption of a liquid diet (79 vs. 84 h, P = 0.041), and markedly reduced VAS pain ratings on postoperative days 1 and 3. In conclusion, for selected patients with middle rectal cancer, R-NOSES appears to be a safe and feasible alternative to R-TME, with similar short-term oncological outcomes. It may offer some benefits in terms of faster recovery, less postoperative pain, and fewer wound-related complications.Trial Registration: ClinicalTrials.gov Identifier NCT06454201.
22 May 2026
Read appraisal →Journal of robotic surgery
Oncologic, functional, and economic outcomes of transoral robotic surgery for HPV-associated oropharyngeal squamous cell carcinoma: a systematic review and meta-analysis of pathology and treatment strategies
The incidence of human papillomavirus (HPV)-positive oropharyngeal squamous cell carcinoma (OPSCC) has increased substantially. Transoral robotic surgery (TORS) provides detailed pathological assessment that may support risk-adapted treatment strategies; however, integrated evidence on oncologic, functional, and economic outcomes remains limited. To systematically evaluate oncologic, functional, and economic outcomes of TORS-based treatment strategies in patients with HPV-positive OPSCC. A systematic review and meta-analysis were conducted in accordance with PRISMA 2020 guidelines. PubMed, Wiley Online Library, MDPI, Cureus, and medRxiv were searched for studies published between 2019 and 2025. Pre-specified subgroup meta-analyses (randomized/clinical trial versus observational studies, and primary versus salvage settings) were performed using random-effects models for overall survival (OS), postoperative bleeding, tracheostomy rate, and long-term gastrostomy dependence. Twenty-two studies (20 clinical and 2 economic) were included, with 14 contributing to quantitative synthesis. The pooled 2-year OS was 94% (95% CI: 0.90-0.98), the 3-year OS was 96% (95% CI: 0.92-0.99), and the 5-year OS was 92% (95% CI: 0.90-0.95). In the salvage setting, the pooled 2-year OS was 76% (95% CI: 0.71-0.80), with no observed statistical heterogeneity, although based on a limited number of studies. Subgroup analyses indicated higher survival estimates in randomized/clinical trial settings compared with observational cohorts. Functional outcomes were generally favorable, with pooled postoperative hemorrhage of 7% (95% CI: 0.04-0.11), tracheostomy rate of 9% (95% CI: 0.03-0.14), and long-term gastrostomy dependence of 2% (95% CI: 0.00-0.03).Across included studies, TORS provided detailed pathological information that may support risk stratification and inform adjuvant treatment decisions; however, the extent of treatment de-escalation varied across studies. TORS appears to be an effective treatment option in selected patients with HPV-positive OPSCC, with generally favorable oncologic and functional outcomes. However, interpretation of these findings is limited by heterogeneity in study design and patient populations. Further prospective studies are required to clarify its role in treatment de-escalation and long-term outcomes.Registration: PROSPERO (CRD420251240194).
22 May 2026
Read appraisal →Cell systems
Learning multi-cellular representations of single-cell transcriptomics data enables characterization of patient-level disease states
Single-cell RNA sequencing (scRNA-seq) is a prominent tool for studying human disease biology. The availability of massive scRNA-seq datasets and advanced machine learning has driven the development of single-cell foundation models that provide versatile cell representations based on expression. To understand disease states, we need to account for entire tissue ecosystems, simultaneously considering many different interacting cells. Here, we tackle this challenge by generating patient-level representations derived from multi-cellular expression contexts measured with scRNA-seq of tissues. We develop PaSCient, a machine learning model that employs a multi-level representation learning paradigm and provides importance scores at the cell and gene levels for fine-grained analysis of disease characteristics. We apply PaSCient to a large-scale scRNA-seq atlas of 12.5 million cells from over 2,700 patients. Comprehensive benchmarking demonstrates the superiority of PaSCient in disease classification and multiple downstream applications, including dimensionality reduction, feature prioritization, and patient subgroup discovery.
22 May 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Nutritional prehabilitation in patients with head and neck cancer: an evidence mapping analysis
BACKGROUND: Head and neck cancer patients are often at high risk of malnutrition due to the disease and its treatment, and pretreatment nutritional prehabilitation is an important strategy for improving prognosis; however, the existing evidence still needs to be systematically synthesized. PURPOSE: To systematically describe and evaluate the evidence related to pretreatment nutritional prehabilitation in patients with head and neck cancer through evidence mapping to provide a basis for clinical practice. METHODS: Computerized searches of CNKI, Wan-fang, VIP, SinoMed, PubMed, Web of Science, Cochrane Library, Embase, and CINAHL were performed from the time of database construction to May 2025, to include systematic review/meta-analysis of nutritional prehabilitation of patients with head and neck cancer, randomized controlled trials, and observational studies. The Risk of Bias Tool, Newcastle-Ottawa Scale (NOS), and AMSTAR-2 scale were used to evaluate the methodological quality of the included studies, respectively, and the current status of the studies was demonstrated using a combination of text and graphs. RESULT: Then, 6 systematic evaluations/meta-analyses, 13 randomized controlled trials, and 4 observational studies were finally included. The largest number of studies was published in China, and all study populations consisted exclusively of patients with head and neck cancer. Interventions include pretreatment immunonutritional interventions, nutritional risk screening-guided interventions, standard enteral nutritional support, disease-specific nutritional formula support, nutritional counseling, and pretreatment swallow training. The outcome indicators mainly focused on the patients' nutritional status, complication rates, length of hospital stay, body weight, and BMI, etc. However, the methodological quality of the included studies needs to be improved. The results of the studies all showed that nutritional prehabilitation benefits the prognosis of patients with head and neck cancer. CONCLUSION: Overall evidence suggests that nutritional prehabilitation can improve patients' nutritional status and enhance their prognosis and quality of life, showing beneficial effects. More high-quality, large-sample clinical studies should be conducted in the future to guide the clinical implementation of nutritional prehabilitation.
21 May 2026
Read appraisal →Genome medicine
DNA methylation biomarkers-based pan-cancer classifier: predictive modeling for cancer classification
BACKGROUND: Machine-learning (ML) driven molecular diagnostics based on omics data has a potential to revolutionize personalized medicine. However, implementation of ML into diagnostic protocols is hindered by methodological challenges which often lead to inflated performance assessment of models during development followed by poor performance of these models in implementation phase. Here, we aimed to develop and validate a pan-cancer classification framework based on DNA methylation data, that addresses methodological challenges of omics data powered ML. METHODS: We curated a primary dataset of DNA methylation profiles for 10756 samples, that included 54 healthy and cancer tissue types and validation dataset comprising data for 2306 samples from 28 independent studies. The classification framework was build using custom biomarkers selection strategy based on effect size metric that considers variance and class imbalance. The ML models were trained, tuned and evaluated using nested cross-validation approach. Local outlier factor algorithm was built into the inference pipelines to identify and filter samples displaying technical or biological anomalies. Additionally, for methodological validation of our framework we used methylation profiles for 3905 central nervous system (CNS) tumors. RESULTS: We found that relatively simple ML models outperformed complex algorithms such as deep neural network. A logistic regression classifier achieved a balanced accuracy (BACC) of 0.90 to classify 54 cancer and healthy tissue types using methylation levels at 1208 CpG sites. Similarly, our CNS tumor classifier also based on logistic regression algorithm reached a BACC of 0.94 across 59 CNS tumor subtypes. The anomaly filtering improved performance across all categories of samples tested. CONCLUSIONS: Our study demonstrates that DNA methylation profiling, when combined with carefully controlled ML practices allows for development of robust solutions that might substantially increase the efficacy of oncological diagnosis. Finally, we deployed our inference pipelines for public access via secure web platform - https://opp.pum.edu.pl/ .
21 May 2026
Read appraisal →The British journal of dermatology
Artificial intelligence-based smartphone application for skin cancer detection: a prospective diagnostic accuracy study
BACKGROUND: Smartphone applications ('apps') that use artificial intelligence (AI) for skin cancer diagnosis may help with early detection. However, prospective studies assessing real-world performance are scarce. OBJECTIVES: To evaluate in an independent prospective study the diagnostic accuracy of a widely used skin cancer detection app and the effect of photographic conditions in a user-representative cohort of patients consulting for lesions of concern. METHODS: Between 1 February 2021 and 30 June 2023, participants presenting at an early-access consultation with a lesion of concern were consecutively enrolled. Following dermatological assessment, each lesion was photographed using the app. The app's convolutional neural network (CNN) generates a binary risk output and advice. A teledermatology review was performed in a subset of cases. The diagnostic accuracy of the CNN combined with teledermatology review was compared with final clinical or histopathological diagnosis. Performance was tested under different photographic conditions (angle, lighting, user) and with different smartphone models. The trial was registered with ClinicalTrials.gov (NCT05246163). RESULTS: A total of 1458 participants with 1904 lesions of concern were included. Of these, 185 (9.7%) were skin cancers, with 32 melanomas. Image capture was unsuccessful in 16.6% of lesions (n = 317/1904), despite optimal conditions. For successfully captured lesions, the CNN achieved a sensitivity of 82.5% and specificity of 76.8% for skin cancer detection. In-app teledermatology review was available for 65.7% (n = 1042/1587) of images. Combined CNN and teledermatology review resulted in increased specificity (86.8%; P < 0.001), with a sensitivity of 75.3%. Overall sensitivity in melanocytic lesions was lower than in nonmelanocytic lesions (71.0% vs. 76.3%; P < 0.001), whereas specificity was higher (90.0% vs. 84.9%; P < 0.001). A substudy demonstrated low image-capture success in user hands (28.9%) and variation in diagnostic performance between smartphone models. CONCLUSIONS: This independent, prospective study evaluated diagnostic performance of a widely used skin cancer detection app in a user-representative cohort. The findings highlight the importance of independent clinical validation of AI-based healthcare tools in real-world settings. Skin cancer is one of the most common cancers that affects people worldwide. Finding cancer early is important, especially for melanoma, a serious form of skin cancer. There is potential for smartphone apps to use AI to help people identify skin spots that may need medical attention. In this study, we evaluated a widely used app called SkinVision. People presenting with a worrisome spot at a dermatology appointment were invited to take part in this study. Altogether, 1458 people were included. The skin area of concern was assessed with the app. Using AI, the app produced a risk score. It also gave a final score. The final score was the AI risk score that may be combined with dermatology advice delivered remotely (‘teledermatology’). We compared the app scores with the doctor’s final diagnosis. We also studied the impact of things such as photo angle, lighting, type of smartphone and the person who took the photo. We found that over 16% of worrisome skin areas could not be captured by the app under ideal conditions. If participants were asked to take the photo themselves, this increased to over 70%. The app correctly identified skin cancers in 83% of cases but gave false alarms in 23%. The in-app teledermatology review reduced the number of false alarms, but this increased the number of missed skin cancers. Finally, we found important differences between different smartphones. Our findings highlight the importance of trialling apps like this with real patients in real-life setting before they are fully introduced to the public.
20 May 2026
Read appraisal →Revista do Instituto de Medicina Tropical de Sao Paulo
Molecular biomarkers associated with ATLL and HAM progression in HTLV-1 infection: a systematic review
Human T-lymphotropic virus type 1 (HTLV-1) infects approximately five to 10 million individuals worldwide, although only a minority develop severe outcomes such as adult T-cell leukemia/lymphoma (ATLL) or HTLV-1-associated myelopathy (HAM). Proviral load (PVL), while widely used, shows limited sensitivity and specificity, reinforcing the need for complementary biomarkers. Omics-based approaches have emerged as promising tools to improve risk prediction. We conducted a systematic review following PRISMA guidelines, searching PubMed, Web of Science, Virtual Health Library (BVS), and CAPES Periodicals databases for studies published between May 2020 and May 2025. Eligible studies included original observational designs investigating genomic, proteomic, and metabolic biomarkers associated with progression to ATLL or HAM. Methodological quality was assessed using tools from the National Heart, Lung, and Blood Institute (NHLBI). In total, 35 studies met the inclusion criteria, most conducted in Brazil, Japan, and Iran. A total of 67 biomarkers were identified: 37 genomic, 27 proteomic, and three metabolic with potential clinical applications in risk stratification, prognosis, and therapeutic monitoring of HTLV-1 infection. PVL remained the most frequently investigated marker but lacked predictive power in isolation. Additional candidates with strong potential included IFN-γ, CXCL10, Neopterin, AnxA1, and sTNFR2. This review highlights the potential of integrated multiparametric panels-combining PVL with omics-derived biomarkers-as a promising strategy to improve risk stratification, prognosis, and therapeutic monitoring in people living with HTLV-1 (PLHTLV-1). However, further longitudinal and clinically validated studies are needed to confirm their applicability and support their translation into early intervention strategies, particularly during the asymptomatic phase.
20 May 2026
Read appraisal →The British journal of dermatology
Evaluation and enhancement of the prognostic ability of the eighth edition of TNM staging in cutaneous malignant melanoma: a population-based study, using machine learning, of 111 871 patients
BACKGROUND: Melanoma is the fifth most common cancer in the USA and the UK, with global incidence on the rise. The TNM staging system guides treatment decisions and predicts patients' outcomes. OBJECTIVES: To evaluate the prognostic ability of the eighth edition TNM (TNM-8) staging system to predict overall (OS) and melanoma-specific survival (MSS) in cutaneous malignant melanoma (CMM), and to explore the potential of machine learning (ML) methods to enhance melanoma prognostication. METHODS: The records of adult patients diagnosed with CMM from 2018 to 2022 were extracted from the Surveillance, Epidemiology, and End Results (SEER) Program database. TNM-8 was evaluated using Kaplan-Meier plots and OS and MSS probabilities. Fine-Gray competing risk and accelerated failure time (AFT) models evaluated factors affecting MSS and OS, respectively. ML models were used to evaluate the predictive ability of TNM stages for OS and MSS, compared with that of multiple clinically important prognostic variables. RESULTS: Altogether, 111 871 patients with CMM were included in the analysis, with OS and MSS as survival endpoints. Most T (tumour), N (node), M (metastasis) and TNM-8 stage categories had distinct MSS and OS probabilities, except in the N1 and N2 stages at 12 months and in OS for TNM-8 stages 2 and 3 at 12, 36, 48 and 59 months. Fine-Gray and AFT models showed that age, sex, race/origin, tumour site, histological type, ulceration, Breslow thickness, mitotic rate, number of positive lymph nodes and M stage were important prognostic variables for MSS and OS. ML models showed that TNM had a higher predictive prognostic ability for MSS than OS and that including clinically important prognostic variables in addition to TNM stage has higher discriminative prognostic ability for OS and MSS (testing C-indices range 0.84-0.85 and 0.89-0.92, respectively) than using TNM staging alone (testing C-indices range 0.67-0.72 and 0.82-0.87, respectively). CONCLUSIONS: This proof-of-concept study demonstrated that although TNM staging retains prognostic value, adding important prognostic variables and using ML could improve prognostication for patients with melanoma. ML could be used to develop interactive clinical decision-support tools to improve the prognostication of individuals with melanoma. Cutaneous malignant melanoma is a common type of skin cancer. A guide called the ‘TNM’ is often used to decide what treatment patients should get. It also helps doctors decide how long patients need specialist care for. The guide is also used to help determine what the outlook for a patient might be. Lots of factors can affect the outcome for a person with the disease. These include a person’s age, sex and race. They also include factors related to the tumour, whether lymph glands are involved and whether the cancer has spread to other organs. Not all these factors are included in the current TNM guide. An accurate staging system is important. It will ensure that patients are followed-up and treated appropriately. This will help avoid unnecessary treatment or surveillance. In this study, we used a type of artificial intelligence (AI) to test how well the latest edition of the TNM can determine what the outlook for patients with melanoma is. We also used it to see if adding other important factors could improve its ability to predict the outlook for these patients. We included over 100,000 patients with melanoma from a large database called SEER. We found that using AI in addition to the current TNM guide could enhance the prediction of outcomes for patients with melanoma. The findings suggest that AI could be useful in helping health professionals predict outcomes for patients with melanoma.
20 May 2026
Read appraisal →Analytical chemistry
Adaptive Deep Learning for High-Fidelity Quantitative Single-Molecule Imaging
Accurate quantification of molecular dynamics in live cells is critical for elucidating receptor signaling and guiding therapeutic strategies. Yet current deep learning methods for fluorescence imaging often distort intensity and lack robustness under diverse imaging settings, limiting quantitative utility. We present an adaptive deep learning framework that constructs in situ training data sets from ongoing sequences and integrates a self-feedback algorithm to preserve absolute molecular intensities. This approach achieves ≥ 3.9-fold gains in signal-to-noise and 1.6-fold improvements in localization precision across organic dyes, quantum dots, and fluorescent proteins. By maintaining both spatial and intensity fidelity, it enhances super-resolution reconstruction, stepwise photobleaching analysis, and live-cell single-molecule tracking. Applied to programmed death-ligand 1 (PD-L1), it uncovers density-dependent clustering, with small-molecule inhibitors inducing dimerization and reduced mobility, while antibodies increase diffusivity. Combined treatments exert complementary effects on PD-L1 membrane organization. This adaptive and intensity-preserving framework provides a broadly applicable platform for high-precision quantitative bioimaging across modalities and experimental conditions.
20 May 2026
Read appraisal →Cancer causes & control : CCC
Prediction models for early detection and diagnosis of lung cancer in people who have never smoked: a systematic review and critical appraisal
BACKGROUND: Never-smokers can develop lung cancer, but this possibility is often overlooked by patients and physicians. We conducted a systematic review to identify existing prediction models that could be implemented in primary care or at a population level to facilitate early detection and diagnosis of lung cancer in never-smokers. METHODS: This study was registered on PROSPERO (ref: CRD42023374471). We systematically searched literature on the Medline, Embase, PsycINFO, and CINAHL databases published before 22 January 2025, with additional hand searching. Primary care or population-level data from subjects including never-smokers had to be used for model derivation. Studies involving specialized tests (radiological or genetic) were excluded. We used CHARMS to guide data extraction and critical appraisal, the TRIPOD statement for model evaluation, and PROBAST for risk of bias assessment. RESULTS: Among 2,431 studies retrieved, 31 models were included. Eight models were developed exclusively for never-smokers, but none were at low risk of bias. Among 23 models derived from never- and ever-smokers, five were at low risk of bias. Two were diagnostic models with 1-2 years prediction horizons, and three were prognostic models with 2-10 years prediction horizons. Methodological issues from the included studies were identified, analyzed, and discussed. CONCLUSION: This systematic review critically appraises and summarizes key information from currently available prediction models for lung cancer in never-smokers. The findings can inform future research to improve care and services for this underserved population.
20 May 2026
Read appraisal →Current oncology reports
Mind-Body Therapies for Depression and Anxiety Symptoms in People with Cancer: A Systematic Review with Network Meta-Analysis
INTRO: Mind-body therapies (MBTs) are promising forms of treatment for depression and anxiety symptoms in people with cancer, however, their effectiveness has not been compared. The objective of this study is to rank the effectiveness of MBTs for depression and anxiety symptoms in people with cancer. METHODS: EMBase, PubMed, Cinahl, PsychINFO, IndMED, CSI-NISCAIR, CNKI, Clinicaltrial.gov, ChiCTR, and CTRI were searched until February 2025 for randomised controlled studies in which MBTs were tested in a cancer population. Network meta-analyses were used on the selected studies to rank the reduction in depression and anxiety symptoms compared with usual care. RESULTS: A total of 182 studies which involved 16,835 participants were included. The network meta-analysis showed a statistically significant large effect compared with usual care for biofeedback (standardised mean difference, SMD = -1.18, p = 0.038), spiritual interventions (SMD = -0.98, p = 0.047), and mindfulness-based interventions (MBIs; SMD = -0.87, p < 0.001) for depression symptoms and yoga (SMD = -1.13, p < 0.001), MBIs (SMD = -1.02, p < 0.001), physical relaxation (SMD = -0.93, p < 0.001), qigong/Tai Chi (QTC; SMD = -0.90, p = 0.004) and music therapy (SMD = -0.90, p < 0.001) for anxiety symptoms. CONCLUSIONS: The MBTs that should be prioritised are biofeedback, spiritual interventions, and MBIs for depression symptoms and yoga, MBIs, physical relaxation, QTC, and music therapy for anxiety symptoms in people with cancer.
20 May 2026
Read appraisal →Cell reports methods
A lightweight TcrLM model predicts T cell receptor and epitope binding specificity.
Immune responses depend on specific interactions between T cell receptors (TCRs) and peptides presented by antigen-presenting cells (APCs). The vast diversity of the TCR repertoire makes accurate prediction of TCR-antigen binding specificity highly challenging. Here, we present a lightweight, masked language model, tcrLM, to address this problem. We pretrain tcrLM on a large-scale TCR CDR3 sequence dataset and use the pretrained encoder to extract informative features for pTCR binding prediction. tcrLM achieves competitive performance on hold-out and external test sets and shows comparatively robust zero-shot generalization relative to several baselines on a large, unseen-COVID-19 peptide set. The model effectively captures biochemical properties and positional preferences of amino acids within TCR sequences. In an exploratory melanoma cohort, the predicted TCR-neoantigen binding scores correlate with immunotherapy response and clinical outcomes. These results highlight the potential of tcrLM for advancing immunotherapy and personalized medicine.
20 May 2026
Read appraisal →Journal of robotic surgery
Perioperative and oncological outcomes of robotic versus laparoscopic low anterior resection in younger rectal cancer cohorts: a systematic review and meta-analysis with narrative functional assessment
Robotic low anterior resection (R-LAR) has been proposed to overcome technical limitations of laparoscopy in rectal surgery. However, previous meta-analyses have evaluated mixed-age populations, and no meta-analysis has specifically investigated younger patients (aged < 65 years) undergoing low anterior resection. This study aimed to compare operative, short-term postoperative, and oncological outcomes between robotic and laparoscopic low anterior resection in this cohort. A systematic review and meta-analysis were conducted according to PRISMA 2020 and the Cochrane Handbook, with prospective registration in Prospero. PubMed, Scopus, and the Cochrane Library were searched up to January 2026. Comparative studies evaluating R-LAR versus L-LAR in younger rectal cancer cohorts, defined as studies with mean patient age ≤ 65 years, were included. Random-effects models were used to calculate pooled mean differences (MD), odds ratios (OR), and hazard ratios (HR) with 95% confidence intervals (CI). Risk of bias was assessed using ROBINS-I and RoB2, and certainty of evidence using GRADE. A total of 16 studies were included. Compared with laparoscopy, R-LAR was associated with longer operative duration (MD 24.59 min, 95% CI 3.85-45.33, p = 0.02, I² = 96%), lower conversion to open surgery (OR 0.38, 95% CI 0.27-0.53, p < 0.0001, I² = 38%), lower overall complications (OR 0.84, 95% CI 0.73-0.97, p = 0.02, I² = 0%), lower 30-day mortality (OR 0.59, 95% CI 0.45-0.77, p = 0.006, I² = 0%), lower 30-day reoperation (OR 0.77, 95% CI 0.61-0.98, p = 0.04, I² = 0%), higher complete TME rates (OR 3.30, 95% CI 2.14-5.10, p = 0.003, I² = 0%) and shorter hospital stay (MD -0.82, 95% CI -1.50 to -0.13, p = 0.02, I²=97%). Estimated blood loss (p = 0.05) and major complications (p = 0.05) were borderline significant in favor of R-LAR. Anastomotic leakage (p = 0.17), postoperative ileus (p = 0.49), time to first flatus (p = 0.12), time to diet initiation (p = 0.10), CRM positivity (p = 0.56), lymph node yield (p = 0.09), local recurrence (p = 0.67), loop ileostomy (p = 0.56), disease-free survival (p = 0.53), and overall survival (p = 0.73) were comparable. The certainty of evidence ranged from very low to moderate. Functional outcomes were insufficient for pooling. Robotic low anterior resection in younger rectal cancer cohorts may improve several perioperative and technical outcomes without compromising oncological adequacy. Further high-quality studies are required to confirm these findings.
19 May 2026
Read appraisal →Journal of clinical pathology
Clinicopathological and molecular characterisation of superficial CD34-positive fibroblastic tumour: A systematic review
AIM: This systematic review aims to comprehensively evaluate the clinicopathological and molecular features of superficial CD34-positive fibroblastic tumour (SCD34FT), a recently described intermediate-grade soft-tissue neoplasm recognised in the 5th edition of the WHO classification of soft tissue tumours. METHODS: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, we systematically searched for English-language studies in PubMed, Scopus, Google Scholar and Web of Science. Retrospective or original case series with ≥3 histologically confirmed cases were selected for review, whereas review articles, single case reports and conference abstracts were excluded. Risk of bias was assessed using the Joanna Briggs Institute checklist. RESULTS: A total of 190 patients across 14 selected studies met the inclusion criteria. SCD34FT mainly affected middle-aged adults, with the most common site being the lower extremity. Diffuse CD34 expression was universal across all studies. A high number of cases displayed SynCAM/CADM3 positivity, while WT1 and Pan-CK (focal) were frequent. PRDM10 fusion was detected in 73% of cases, and the most common fusions were PRDM10::MED12 (66%) and PRDM10::CITED2 (28%). Only 9/169 (5.3%) had local recurrences, whereas 4/190 (2.1%) cases had lymph node metastasis, with no distant metastasis or disease-related death. CONCLUSION: SCD34FT is a novel entity with consistent CD34 expression and recurrent PRDM10 rearrangements and shows indolent behaviour. A high index of suspicion for SCD34FT should be warranted in a superficially located tumour with striking nuclear pleomorphism but an unexpectedly low mitotic index. Molecular studies demonstrating PRDM10 rearrangement may be helpful in atypical cases.
19 May 2026
Read appraisal →PloS one
A novel hybrid framework integrating GA-driven 3D ResUNetGAN for MRI brain tumor segmentation
Accurate brain tumor segmentation by multi-modal MRI is crucial for diagnosis, treatment planning, and prognostic assessment. This work proposes a novel hybrid architecture within a GA optimized by 3D residual U-Net (ResUNet) with generative adversarial network (GAN) for the accurate segmentation of tumor subregions, namely the tumor core (TC), whole tumor (WT), and enhancing tumor (ET), using a dataset collected from BraTS2023 (437 training and 188 validation). By using GAs, we enable the computer to automatically find the best model structure and training settings, which helps keep the model simple enough to avoid overfitting while being powerful enough to generalise well to new data. The GAN part of our method trains the model to draw more realistic and consistent boundaries around tumor regions in MRI scans, thereby improving the overall quality of the segmentation. In our experiments, this GA-ResUNetGAN approach performed better than several leading methods, such as 3D UNet, DynUNet, UNETR, SwinUNETR, and the standard ResUNet, especially in accurately identifying TC, WT, and ET parts-achieving Dice scores of 0.94, 0.96, and 0.91 on the validation set. In addition, K-Fold cross-validation was performed to ensure consistent performance across different data splits. While the method involves more processing work than some baseline techniques, it provides improved segmentation quality and boundary delineation on the evaluated dataset. These results demonstrate the advantages of combining evolutionary optimization with adversarial deep learning for challenging medical imaging tasks.
19 May 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Effectiveness of survivorship programmes to enhance health-related quality of life of colorectal cancer survivors: a systematic review and meta-analysis of randomised controlled trials
PURPOSE: This study aimed to assess the effectiveness of the survivorship programmes by synthesising and analysing the available evidence to enhance health-related quality of life (HRQoL) amongst colorectal cancer (CRC) survivors. METHODS: Ten English-language databases were searched from inception to June 2025 in this systematic review and meta-analysis. All randomised controlled trials (RCTs) were included. Available data were pooled in a meta-analysis using RevMan (version 5.4.0). Two independent reviewers performed the database searches, extracted the data, assessed the methodological quality by using the Cochrane Risk-of-Bias tool (version 2) and evaluated the overall quality of findings by using Cochrane GRADE. RESULTS: A total of 22 RCTs involving 2949 CRC survivors were identified. The meta-analysis results (thirteen studies) indicated a significant improvement in the physical (standardised mean difference [SMD] = 0.52, 95% confidence interval [CI, 0.18, 0.86], P = 0.002, I2 = 88%) and mental domains of HRQoL (SMD = 0.4, 95% CI [0.06, 0.74], P = 0.02, I2 = 86%). Sensitivity analysis involved reducing the heterogeneity after removing one study. No publication bias was found. The overall quality of findings was from 'low' to 'moderate'. CONCLUSIONS: The detailed components of the survivorship programme can enhance HRQoL in physical and mental domains, indicating its potential as valuable evidence for health providers to support CRC survivors post-treatment. Future research should focus on expanding the delivery of such programme comprehensively by integrating mobile health into a nurse-led approach to optimise geographic diversity and improve social HRQoL.
19 May 2026
Read appraisal →Biomedical physics & engineering express
Oral squamous cell carcinoma diagnosis with time and frequency domain features from optical coherence tomography A-scan signals
Optical coherence tomography (OCT) is extremely useful in the screening and detection of oral cancers. But various challenges, such as its subjectivity, operator dependence in interpretation, and lack of quantitative outcomes, have been delaying its adoption in clinical decision-making. The objective of this research is to quantify various A-scan features embedded in the OCT signal through advanced signal processing techniques and machine learning algorithms.Ex vivoimaging of biopsied oral tissues (normal mucosa, carcinomain situ(CIS), well-differentiated, and poorly differentiated oral squamous cell carcinoma) was performed using a spectral domain OCT system. Our A-scan dataset consisted of representative 1D signals obtained from different regions of the tissue bed. A set of 12 time- and 8 frequency-domain features were computed on each A-scan, and ten machine learning models were evaluated for binary and multi-label classification. LightGBM achieved the highest performance in both binary (accuracy: 0.8847,F1: 0.8878, AUC: 0.9539) and multi-label classification (accuracy: 0.8248,F1: 0.8201, AUC: 0.964). LightGBM was selected as the final model based on its superior and consistent performance across both classification paradigms. Our proof-of-concept feasibility study demonstrates good accuracy for differentiating oral mucosal tissues, highlighting the biomarker signature of A-scans.
19 May 2026
Read appraisal →Science advances
Magnetic medical microrobots with memory-capable genetic circuits
Autonomous microrobots can reach hard-to-access regions in the human body for minimally invasive therapy. However, their microscale size limits the integration of on-board memory, making their operation dependent on external controls. Here, we develop a magnetic probiotic microrobot integrated with memory-capable genetic circuit to execute autonomous antitumor treatment. Through a one-time magnetic hyperthermia trigger, the biological thermal sensor in the microrobot perceives temperature change and activates the memory module Bxb1-ssrA-attB-P7-attP, transferring the microrobots into a therapeutic state to continuously degrade fibrin and soften the tumor microenvironment. The genetic memory remains active for at least 12 days. A synergy toward deep tumor penetration is subsequently established between the memory-encoded softening and the physical penetration through magnetically controlled wave-like locomotion of microrobots. Compared with memory-absent microrobots, the proposed microrobots achieve a 6.70-fold tumor matrix stiffness reduction and boost in vivo anticancer efficacy from 21.86 to 87.52%. Beyond oncology, the proposed system establishes a generalizable framework of memory-encoded medical microrobots.
18 May 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Effects of yoga on depressive symptoms in breast cancer patients: a meta-analysis of randomized controlled trials
BACKGROUND: While yoga is known to benefit depression in breast cancer patients, its efficacy as an adjunct to diverse treatment regimens remains unclear. This study evaluated yoga's efficacy in alleviating depression during varied breast cancer therapies and supports its clinical integration as a complementary intervention. METHODS: Following PRISMA 2020 guidelines, we systematically searched for RCTs in English and Chinese. Literature quality was assessed using the Cochrane Risk of Bias tool. Data were analyzed with Review Manager 5.4.1, including subgroup analyses by treatment type and assessment scale. RESULTS: Analysis of 21 studies showed yoga significantly reduced depression levels [SMD = -0.58, 95% CI (-0.83, -0.32), P < 0.00001]. Subgroup analyses revealed a significant combined mean difference among the surgery group [SMD: -1.04], the postoperative radiotherapy group [SMD: -2.47], and the postoperative chemotherapy group [SMD: -0.46]. The group receiving postoperative radiotherapy demonstrated a significantly greater improvement in depression. Subgroup analyses of scale-assessed effects confirmed the validity of the Beck Depression Inventory (BDI), the Center for Epidemiologic Studies Depression Scale (CESD), the Hospital Anxiety and Depression Scale (HADS), and the Zung Self-Rating Depression Scale (SDS). The HADS demonstrated a greater effect size [SMD = -1.19, 95% CI (-2.13, -0.24), P = 0.01]. CONCLUSIONS: The impact varied among those undergoing different treatment regimens. Notably, the degree of improvement in depression appeared to be greater in the postoperative radiotherapy group. HADS is a practical self-assessment scale that has demonstrated considerable improvement and is recommended for use in breast cancer patients with depression.
18 May 2026
Read appraisal →Journal of gastrointestinal cancer
Long-term Outcomes of Protocol-based Neoadjuvant Selection and Liver Transplantation Versus Liver Resection for Perihilar Cholangiocarcinoma: A Systematic Review and Meta-analysis
BACKGROUND: Perihilar cholangiocarcinoma (CCA) is an aggressive malignancy that presents significant challenges in terms of diagnosis and treatment owing to its anatomical location. Liver resection and transplantation are the primary curative options; however, their long-term outcomes remain controversial. This study aimed to compare the long-term outcomes of protocol-based neoadjuvant selection and liver transplantation with liver resection for the management of perihilar cholangiocarcinoma. METHODS: A systematic review and meta-analysis were conducted according to the PRISMA guidelines. Electronic databases, including the Cochrane Library, PubMed/MEDLINE, and Embase, were searched for relevant clinical trials comparing liver transplantation with liver resection. RESULTS: Six studies comprising 768 patients (265 in the liver transplantation group and 503 in the liver resection group) were included. Liver transplantation was associated with significantly higher 5-year overall survival and 5-year disease-free survival. They also showed a higher 3-year disease-free survival rate. Patients in the liver transplantation group had a higher R0 resection rate. There were no significant differences in the 3- or 30-day postoperative mortality between the two groups. CONCLUSION: Liver transplantation was associated with improved long-term survival in highly selected patients; however, these findings are limited by significant selection bias and the retrospective study design. The apparent long-term benefit observed with LT must be interpreted cautiously because transplant series frequently report post-transplant rather than intention-to-treat outcomes.
18 May 2026
Read appraisal →Computers in biology and medicine
Generative deep learning-driven de novo design of targeted MAP4K6 inhibitors
The discovery of selective small-molecule inhibitors for pharmacologically underexplored kinases remains a critical barrier to precision drug development, particularly under data-sparse conditions, where ligand annotations are scarce. Here, we present MolEvoRNN, a generative deep learning (GDL) model that enables the de novo design of chemically valid, structurally diverse, and target-adapted compounds in low-data regimes. Through transfer learning and temperature-controlled sampling, MolEvoRNN efficiently navigates the kinase-relevant chemical space while preserving scaffold novelty, drug-likeness, and synthetic tractability. Applied to MAP4K6, a poorly characterized serine/threonine kinase implicated in hepatocellular carcinoma (HCC), MolEvoRNN generated >49,000 chemically valid molecules with >80% novel Bemis-Murcko scaffolds. MolEvoRNN outperformed the baseline GDL models across key generative metrics, including diversity (1.00), uniqueness (1.00), novelty (1.00), and internal diversity (0.869), demonstrating broad chemical exploration with minimal redundancy. A multilayered virtual screening cascade combining QSAR modeling, pharmacophore mapping, molecular docking, MM-GBSA binding energy estimation, and 1000 ns molecular dynamics simulations identified three lead candidates MINK-5730, MINK-13510, and MINK-16516 with high predicted affinity, favorable ADMET properties, and robust dynamic stability. Unlike end-to-end optimization pipelines, MolEvoRNN decouples generation from evaluation, promoting methodological transparency and rigorous post-hoc triaging. This study establishes a scalable, generalizable paradigm for rational small-molecule design in underexplored target spaces, bridging generative deep learning with structural pharmacology to accelerate early-phase therapeutic discovery.
17 May 2026
Read appraisal →Computers in biology and medicine
Mapping intratumoral heterogeneity through PET-derived washout and deep learning after proton therapy
The distribution of produced isotopes during proton therapy can be imaged with Positron Emission Tomography (PET) to verify dose delivery. However, biological washout, driven by tissue-dependent processes such as perfusion and cellular metabolism, reduces PET signal-to-noise ratio (SNR) and limits quantitative analysis. In this work, we propose an uncertainty-aware deep learning framework to improve the estimation of washout parameters in post-proton therapy PET, not only enabling accurate correction for washout effects, but also mapping intratumoral heterogeneity as a surrogate marker of tumor status and treatment response. We trained the models on Monte Carlo-simulated data from eight head-and-neck cancer patients, and tested them on four additional head-and-neck and one liver patient. Each patient was represented by 75 digital twins with distinct tumoral washout dynamics and imaged 15 min after treatment, when slow washout components dominate. We also introduced washed-out maps, quantifying the contribution of medium and fast washout components to the loss in activity between the end of treatment and the start of PET imaging. Trained models significantly improved resolution and accuracy, reducing average absolute errors by 60% and 28% for washout rate and washed-out maps, respectively. For intratumoral regions as small as 5.1 mL, errors predominantly fell below thresholds for differentiating vascular status, and the models generalized across anatomical areas and acquisition delays. This study shows the potential of deep learning in post-proton therapy PET to non-invasively map washout kinetics and reveal intratumoral heterogeneity, supporting dose verification, tumor characterization, and treatment personalization. The framework is available at https://github.com/pcabrales/ppw.git.
17 May 2026
Read appraisal →International journal of cancer
Testicular cancer in intersex individuals: A systematic review for clinical practice
The objective of this systematic review was to identify the evidence of testicular cancer risk for people with intersex conditions. This assessment is hoped to help refine risk stratification tools for assessing gonadal malignancy risk and guide the development of more robust evidence-based management strategies. The literature was searched in Ovid MEDLINE, Embase, and Cumulative Index of Nursing and Allied Health using a search string developed by a multidisciplinary team. The protocol was registered at Prospective Register of Systematic Reviews as CRD42021231313. A total of 3608 articles were found. After selection, 301 publications were included (1215 individuals). The results identified significant evidence that pre-pubertal gonadectomy may be linked to lower rates of malignant gonadal changes for patients with partial gonadal dysgenesis, Turner's syndrome with Y-chromosome material, complete androgen insensitivity, partial androgen insensitivity, and patients with ovotestis/es. The evidence was not significant for patients with complete gonadal dysgenesis, Klinefelter syndrome, nor WT1-related syndromes. Specific cancer outcomes were unable to be assessed due to small sample sizes and thus it is unknown if clinically significant cancer outcomes are meaningfully altered by pre-pubertal gonadectomy. Importantly, the quality of data on the topic of gonadal malignancy in intersex patients with testicular tissue was determined to be poor overall. The quality was relatively more robust regarding the conditions of Complete Androgen Insensitivity, Klinefelter syndrome, and patients with ovotestis/es. More high-quality research is needed to draw specific conclusions on the risks and benefits of performing pre-pubertal gonadectomy for intersex patients. When counseling these patients, clinicians should be transparent regarding the paucity of data supporting pre-pubertal gonadectomy.
17 May 2026
Read appraisal →The British journal of general practice : the journal of the Royal College of General Practitioners
Are non-injectable anticipatory medications a viable end-of-life intervention in the community? A systematic review and narrative synthesis.
BACKGROUND: The anticipatory prescribing of injectable medications for symptom management at the end-of-life is an established practice in the UK. Transmucosal and inhaled routes of administration offer alternatives to injections, when patients are no longer able to take medications orally. Their evidence base in the community setting has not been established. AIM: To systematically review and synthesise the published, peer-reviewed evidence regarding the clinical practice and effectiveness, including patients', family caregivers' and healthcare professionals' views, of transmucosal and inhaled anticipatory medications for symptom management at the end-of-life in the community. METHOD: Ten literature databases were searched from inception to February 2025, using terms for non-injectable anticipatory medications, or kits, combined with end-of-life terms. Two journals were hand-searched from 2015 to 2025. References and citations of included papers were searched. Gough's Weight of Evidence Framework was used to appraise the included studies and inform the narrative synthesis. RESULTS: Fifteen papers were included, reporting the use of transmucosal routes, of which nine used a standardised medication kit. The limited evidence of clinical effectiveness, and the views of patients, family caregivers, and healthcare professionals, suggested that transmucosal medications were effective at relieving patients' symptoms and enabling patients to die at home. CONCLUSION: The evidence regarding transmucosal anticipatory medications in the community suggests that they are acceptable, clinically effective, and contribute to reducing unplanned hospital attendances, but is limited and should be treated with caution. Further research is needed to assess whether they offer viable alternatives to injectable anticipatory medications in the community.
16 May 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Effectiveness of comprehensive interventions including exercise and exercise-only interventions on postoperative ileus and recovery of intestinal function in patients with colorectal cancer: a systematic review and meta-analysis
PURPOSE: This systematic review and meta-analysis aimed to evaluate the effectiveness of postoperative or preoperative comprehensive interventions including exercise and exercise-only interventions on postoperative ileus (POI) incidence and the recovery of intestinal function in patients with colorectal cancer (CRC). METHODS: Electronic searches were conducted in 6 databases. Cohort and randomized-controlled trial (RCT) studies investigating the effects of postoperative or preoperative comprehensive interventions including exercise or exercise-only interventions on POI and the recovery of intestinal function in patients with CRC were included. RESULTS: Thirty-eight studies involving 13,124 patients were included. Postoperative comprehensive interventions including exercise significantly shortened the time to first flatus (standardized mean difference [SMD] = -1.38, 95% confidence interval [CI]: -1.87 to -0.89) and time to first defecation (SMD = -1.56, 95% CI: -2.20 to -0.92), but did not reduce POI incidence (odds ratio [OR] = 1.38, 95% CI: 0.43 to 4.47). While, postoperative exercise-only interventions showed no significant effect on intestinal recovery (SMD = -0.31, 95% CI: -1.21 to 0.58). Preoperative comprehensive interventions including exercise shortened the time to first flatus (SMD = -0.41, 95% CI: -0.66 to -0.17). Also, preoperative exercise-only interventions significantly reduced the incidence of POI (OR = 0.56, 95% CI: 0.46 to 0.67). CONCLUSIONS: Postoperative comprehensive interventions effectively promote intestinal recovery; however, postoperative exercise-only interventions may not promote the recovery of intestinal function. On the other hand, preoperative comprehensive interventions or exercise-only interventions may promote the recovery of intestinal function and prevent POI onset; however, the limited number of included studies prevented definitive conclusions.
15 May 2026
Read appraisal →Scientific reports
The impact of tissue detection on diagnostic artificial intelligence algorithms in prostate digital pathology
Tissue detection is a crucial first step in most digital pathology applications. By applying image segmentation algorithms, all tissue is delineated and background discarded from further analyses, improving both computational efficiency and analytical results. Details of the segmentation algorithm are rarely reported, and there is a lack of studies investigating the downstream effects of a poor segmentation algorithm. Disregarding tissue detection quality could jeopardize patient safety if diagnostically relevant parts of the specimen are excluded from analysis in clinical applications. This study aims to determine whether performance of downstream tasks is sensitive to the tissue detection method, and to compare the performance of a classical and an AI-based tissue detection approach. To this end, we trained an AI model for Gleason grading of prostate cancer in whole slide images (WSIs) using two different tissue detection algorithms: thresholding (classical) and UNet++ (AI). A total of 33,823 WSIs scanned on seven digital pathology scanners were used to train the segmentation AI model. The downstream Gleason grading algorithm was trained and tested using 70,524 WSIs from 13 clinical sites scanned on 13 different scanners. On the slides where tissue could be detected by both algorithms, no significant difference in overall Gleason grading performance was observed. There was a decrease from 118 (0.43%) to 24 (0.09%) fully undetected tissue samples when switching from thresholding-based tissue detection to AI-based, suggesting this AI model may be more reliable than the classical model for avoiding total failures on slides with unusual appearance. Moreover, tissue detection dependent clinically significant variations in AI grading were observed in 3.5% of malignant slides, highlighting the role of tissue detection for optimal clinical performance of diagnostic AI.
15 May 2026
Read appraisal →BMJ open
AI chatbots for health information seeking among Chinese patients with precancerous ENT lesions: a descriptive qualitative study
OBJECTIVE: To explore how Chinese patients with precancerous ear, nose and throat (ENT) lesions experience using artificial intelligence (AI)-driven chatbots for health-related information seeking, with particular attention to perceived benefits, challenges and influences on information-seeking practices. DESIGN: Descriptive qualitative study. SETTING: Department of Otolaryngology, West China Hospital, Sichuan University, China. PARTICIPANTS: 12 adult patients with clinically diagnosed precancerous ENT lesions who had used AI-driven chatbots at least three times in the previous 30 days to seek health-related information about their condition were purposively recruited. Face-to-face, semistructured interviews were conducted between 11 October 2024 and 10 November 2024. RESULTS: Interviews were analysed using Colaizzi's method. Four themes were identified. First, participants described AI chatbots as an immediate and accessible source of health information, particularly when questions arose outside clinical encounters or during periods of uncertainty. Second, many reported moving away from conventional search engines towards conversational information seeking, valuing direct and synthesised responses over link-based retrieval. Third, participants emphasised that obtaining useful answers depended on learning to ask clear and specific questions, suggesting that effective prompting was an important user skill. Fourth, some participants perceived chatbot interaction as emotionally safer than asking healthcare professionals certain questions, particularly when they felt embarrassed, worried about asking repetitive questions or feared being judged. CONCLUSIONS: Among this sample of Chinese patients with precancerous ENT lesions, AI-driven chatbots were perceived as a convenient and conversational supplementary source of health information. Participants valued their accessibility and interactional ease, but also indicated that their usefulness depended partly on users' ability to formulate effective prompts. Some participants additionally perceived chatbots as a more comfortable channel for asking sensitive or basic questions. The findings suggest that AI chatbots may have a complementary role in patient information support, but further research is needed to evaluate response accuracy, safety and appropriate integration into clinician-led care.
15 May 2026
Read appraisal →Journal of robotic surgery
Longitudinal health-related quality of life after remote-access versus open thyroidectomy for thyroid cancer: a systematic review and meta-analysis
As remote-access thyroidectomy (RAT) becomes more widely used, evidence on patient- reported outcomes, particularly health-related quality of life (HRQoL), remains scarce and inconsistent. This meta-analysis compared postoperative HRQoL between thyroid cancer (TC) patients undergoing RAT and open thyroidectomy (OT) and assessed changes over time. A comprehensive search of five major databases was conducted from inception to August 2025. Studies reporting HRQoL after RAT or OT were included. Outcomes were grouped by postoperative timepoints. Outcomes were stratified across distinct postoperative timepoints to calculate pooled standardized mean difference (SMD) or mean difference (MD). Heterogeneity was explored through rigorous subgroup analyses encompassing surgical modalities, countries, and assessment instruments. Forty-one studies met the inclusion criteria, and 29 records were included in the quantitative synthesis. RAT demonstrated early advantages in comprehensive quality of life at 1 month and 3 months, and these advantages dissipated long term. Pain trajectories exhibited a biphasic pattern: RAT was associated with lower pain scores on postoperative day 1 but paradoxically higher scores during the 1-2 week period, with subsequent convergence. Cosmetic satisfaction and swallowing function consistently favored RAT from 1-2 weeks through 6 months, while voice outcomes showed no discernible differences. RAT appears to confer selected short- to medium-term patient-reported advantages over OT, particularly in cosmetic satisfaction and swallowing function. But these benefits are heterogeneous and not consistently maintained across all domains or timepoints. Future studies should standardize cross-culturally validated PRO instruments and adopt harmonized follow-up intervals and reporting guidelines to clarify the patient-centered value of RAT.
15 May 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Acupuncture for chemotherapy-induced dysgeusia (AcuDysg): a randomized controlled trial
PURPOSE: The study investigated the effectiveness of dysgeusia-specific acupuncture combined with self-acupressure compared to supportive acupuncture and self-acupressure for chemotherapy-induced dysgeusia in cancer patients with cancer-related fatigue (CRF) and dysgeusia. METHODS: This multicenter, randomized, controlled, two-armed parallel-group, single-blind trial included cancer patients with CRF and dysgeusia during chemotherapy. All participants received CRF treatment consisting of eight acupuncture sessions over 8 weeks and had access to an eLearning course for self-acupressure. Patients in the intervention group received additional dysgeusia-specific acupuncture treatment and an eLearning with instructions to acupressure points, whereas the control group received an intervention focused solely on addressing CRF. The main outcome was perceived dysgeusia over 8 weeks, measured weekly after each acupuncture session on a numeric rating scale from 1 to 10. Other outcomes included taste and smell tests, and assessments of perceived dysgeusia, fatigue, distress, and quality of life. Due to a small sample size, results can be only judged exploratory. The study was approved by the Cantonal Ethics Committee (CEC) (Kanton Zürich Kantonale Ethikkommission) (approval no. KEK-ZH-Nr. 2020-01900). RESULTS: Since we included only twenty-one patients (11 intervention, 10 control, 90% female) instead of the target sample size of 130, no confirmatory effectiveness analyses were conducted. All participants showed a substantial decrease in dysgeusia over time, regardless of treatment type (estimated mean reduction per session - 0.30; 95% CI - 0.43 to - 0.18), reflecting a 0.30-point reduction per session. Quality of life and cancer-related fatigue improved over time. The exploratory analyses did not show substantial differences between groups in any outcomes. No serious adverse events occurred. CONCLUSIONS: Exploratory findings showed no substantial differences between the study groups in perceived dysgeusia, but both study groups improved over time. TRIAL REGISTRATION NUMBER: DRKS00023348 at 03.11.2020, SNCTP000004128.
14 May 2026
Read appraisal →Journal of robotic surgery
NeuroSAFE-guided robot-assisted radical prostatectomy versus standard RARP: systematic review and meta-analysis of comparative studies
Prostate cancer is the most common male malignancy, and although robotic-assisted radical prostatectomy (RARP) is widely used, urinary incontinence and erectile dysfunction remain significant issues. NeuroSAFE, an intraoperative frozen section technique aimed at optimizing nerve preservation without compromising cancer control, is increasingly used, but its overall impact on outcomes remains unclear.To systematically evaluate the effectiveness and safety of the NeuroSAFE technique during RARP, comparing functional recovery and oncologic outcomes with non-NeuroSAFE approaches.This systematic review and meta-analysis followed PRISMA 2020 guidelines and was registered in PROSPERO (CRD420251032774). A comprehensive search of Embase, MEDLINE, and ClinicalTrials.gov (March, 2026) identified studies reporting oncologic and/or functional outcomes in men undergoing RARP with NeuroSAFE. Two reviewer teams independently conducted study selection, data extraction, and risk-of-bias assessment using ROBINS-I tool. Random-effects meta-analyses estimated pooled odds ratios (ORs) with 95% confidence intervals (CIs). Heterogeneity was quantified with the I2 statistic, and publication bias was evaluated using Egger's test.Thirteen studies (2 randomized controlled trials and 11 observational cohorts) comprising 22,183 patients met the inclusion criteria. NeuroSAFE was associated with significantly improved postoperative erectile function (OR 2.00; 95% CI 1.46-2.74) and urinary continence recovery (OR 1.36; 95% CI 1.05-1.76). Positive surgical margins were significantly reduced in the NeuroSAFE group (OR 0.73; 95% CI 0.59-0.89). No differences in biochemical recurrence were observed (OR 0.81; 95% CI 0.43-1.56). Heterogeneity was substantial for some outcomes, and most nonrandomized studies carried a serious risk of bias, which limits causal inference.This systematic review and meta-analysis, including randomized evidence, demonstrates that NeuroSAFE is a safe and effective intraoperative strategy that is associated with improved erectile function, enhanced continence recovery, and negative surgical margins without compromising oncologic outcomes. These findings support integrating NeuroSAFE into surgical decision-making for patients undergoing RARP, particularly when nerve preservation can be achieved without compromising oncologic outcomes.In this study, we compared a surgical approach that uses real-time analysis during prostate cancer surgery with the standard technique. This method helps surgeons decide how much tissue to preserve while the operation is still ongoing. We found that this approach is safe and may improve recovery of urinary continence and sexual function, while also reducing the risk of leaving cancer behind, without compromising cancer control.
14 May 2026
Read appraisal →JMIR research protocols
Technology-Based Prehabilitation for Patients With Cancer Before Elective Treatment: Protocol for a Scoping Review
BACKGROUND: Advances in cancer treatment have improved survival rates; however, patients continue to experience significant treatment-related side effects, leading to reduced quality of life. Prehabilitation is an intervention that occurs before treatment and can improve patients' functional capacity, recovery, and well-being through exercise, nutrition, and psychological support. Typical hospital-based prehabilitation is not accessible to all patients due to geographical, socioeconomic, and time-related barriers. Technology-based approaches, including eHealth and mobile health (mHealth) interventions, may overcome these barriers by enabling remote, patient-centered delivery. However, the current evidence base is heterogeneous and lacks synthesis regarding feasibility, acceptability, and outcomes. OBJECTIVE: This protocol for a scoping review aims to outline how we will systematically map and synthesize the evidence on technology-based prehabilitation interventions for people with cancer to identify intervention designs, assess feasibility and accessibility, and highlight knowledge gaps to guide future research and practice. METHODS: The review will follow the Joanna Briggs Institute (JBI) methodology and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. A 3-step search strategy will be applied across multiple databases and gray literature sources. Eligible studies will include adults (aged ≥18 years) with a cancer diagnosis who are scheduled for elective treatment (surgery, radiotherapy, chemotherapy, immunotherapy, or hormone therapy). Interventions must involve eHealth or mHealth approaches supporting unimodal or multimodal prehabilitation activities such as exercise, nutrition, psychological support, or lifestyle modification. Outcomes of interest include functional fitness, quality of life, psychological well-being, treatment preparedness, recovery, adherence, and feasibility. Two independent reviewers will conduct title, abstract, and full-text screening, with disagreements resolved through discussion or consultation with a third reviewer. Data will be charted and presented in tables and figures and as a narrative synthesis. Critical appraisal using JBI tools will contextualize methodological quality but not exclude studies. Risk of bias will be assessed using the Cochrane Risk of Bias Tool for Randomized Trials version 2 (RoB 2) and Risk of Bias in Non-Randomized Studies of Interventions version 2 (ROBINS-I V2) tool. This will not be used to exclude studies, but to determine the quality of articles included. RESULTS: The search strategy has been pilot tested and finalized. Database searches are scheduled to commence in March 2026, with study selection and screening anticipated to be completed by April 2026. Data analysis and synthesis are expected to begin in May 2026, and final results will be available by October 2026. CONCLUSIONS: This protocol outlines a rigorous and transparent approach to mapping the current evidence on technology-based prehabilitation in cancer care. By systematically characterizing intervention features, outcome domains, and evidence gaps, the review will provide an up-to-date evidence map to guide future research priorities, inform clinical implementation, and support the development of more standardized and inclusive prehabilitation pathways.
14 May 2026
Read appraisal →Endocrine pathology
Impact of Molecular Testing on Surgical Decision-Making in Indeterminate Thyroid Nodules: A Global Meta-Analysis Across Test Generations
Thyroid nodules with indeterminate cytology represent a clinical challenge owing to uncertain malignancy risk, often leading to diagnostic surgery. Molecular testing has emerged as a promising adjunct to improve risk stratification and guide surgical decision-making. However, the clinical utility and impact of different molecular platforms remain debated. The present study evaluates the impact of molecular testing on surgical decision-making in indeterminate thyroid nodules and compares outcomes between first- and second-generation molecular platforms. A PRISMA-guided systematic review and meta-analysis identified 132 studies including 66,448 thyroid nodules, of which 30,292 (45.6%) were indeterminate (Bethesda III/IV). Pooled analyses included studies directly comparing conventional management with molecular testing or different molecular platforms. Primary outcomes were surgical resection rates and risk of malignancy (ROM), stratified by diagnostic approach and test generation. Conventional management demonstrated significantly higher surgical rates than molecular testing group (OR = 2.258, 95% CI: 1.548-3.293, p < 0.001). ROM among resected nodules was lower in the conventional management compared with the molecular testing group (OR = 0.594, 95% CI: 0.388-0.911, p = 0.017). First-generation molecular tests showed higher surgical rates and lower ROM than second-generation assays (OR = 1.709, 95% CI: 1.122-2.601, p = 0.013; OR = 0.665, 95% CI: 0.515-0.858, p = 0.002, respectively). Exclusion of non-invasive follicular thyroid neoplasm with papillary-like nuclear features attenuated differences in malignancy rates. Molecular testing significantly reduces surgical interventions in indeterminate thyroid nodules without evidence of reduced detection of clinically significant malignancy, particularly with second-generation platforms supporting more individualized and evidence-based management strategies.
14 May 2026
Read appraisal →Scientific reports
Federated learning with swarm intelligence for efficient and secure medical image analysis
Collaborative learning in healthcare faces challenges, including strict regulations and fragmented data. This research introduces a federated learning framework that employs swarm intelligence to augment communication and enhance the analysis of medical images. The method optimizes hyperparameters, selects features, and assigns aggregation weights to federated clients simultaneously by combining Particle Swarm Optimization (PSO) and the Firefly Algorithm (FA) with deep Convolutional Neural Networks (CNNs). The framework was tested on three medical datasets: COVID-19 chest X-rays (5,856 images), monkeypox skin images (569 images), and breast cancer mammograms (320 images). These datasets were shared among four fake healthcare institutions. It strives to strike a balance between privacy, communication costs, and classification accuracy. The results showed that the test was 96.71% accurate in detecting COVID-19, 96.06% accurate in classifying monkeypox, and 97.0% accurate in diagnosing breast cancer. The framework was able to handle noise and attacks from individuals who sought to disrupt it, which reduced communication rounds by 25-30%. A privacy-utility analysis revealed that there were acceptable trade-offs, with accuracy remaining above 94%. This study employs robust privacy measures and statistical validation. It also shows how to use medical AI in smaller healthcare settings without putting patients' privacy at risk.
14 May 2026
Read appraisal →Cancer metastasis reviews
Risk of cancer incidence and mortality in patients with chronic pain: A systematic review and meta-analysis
Evidence from experimental studies suggests that pain may influence cancer development. We conducted a systematic review (registration osf.io/ms437) of the epidemiological studies assessing whether chronic pain, with minimal or non-inflammatory features, increases the risk of cancer incidence or mortality. The search used Scopus and Web of Science up to 11/03/2025. Quality assessment used the Newcastle-Ottawa Scale. Study characteristics and outcomes were analysed using narrative synthesis, with random-effects meta-analysis and meta-regression conducted where appropriate. A total of 23 studies (12 prospective, 11 retrospective) met the inclusion criteria, of which six were rated as high quality. The outcome data synthesis indicates a small increase in cancer mortality rate ratios, and a potential increase in a subset of organs. A total of three body site-specific analyses suggest that chronic pain may be associated with higher incidence for breast (SIR 2.12-4.8) and prostate (SIR 1.49-5.59) cancers, and with increased mortality for lung cancer (MRR 3.09, 95% CI 1.45-6.62). Ten studies were included for meta-analysis, including four studies rated as high quality. Meta-analysis of hazard ratios showed no association between cancer-related mortality and pain (HR 1.00, 95% CI 0.97-1.04), with consistent estimates across pain subtypes in meta-regression. Our review and meta-analysis do not support an overall association between chronic pain and increased risk of cancer mortality or incidence and highlight multiple methodological and conceptual obstacles to exploring this relationship, including reverse causation and confounding factors such as pain and opioid use association. High-quality studies that rigorously address reverse causation and confounding are warranted.
14 May 2026
Read appraisal →Endocrine-related cancer
Systematic review of the molecular basis for cavernous sinus invasion in somatotropinomas
Somatotropinomas are a subtype of pituitary adenomas that have a particular predilection to invade the cavernous sinus. The objective of this systematic review was to examine the evidence regarding the molecular basis for cavernous sinus invasion in somatotropinomas. This review was conducted in accordance with the 2020 PRISMA guidelines on 13 April 2025. Inclusion criteria were reports of associations between somatotropinoma molecular changes and cavernous sinus invasion in adult patients. Title/abstract screening and full-text screening were performed, with studies assessed for risk of bias using the Newcastle-Ottawa scale. A total of 43 studies were identified, encompassing 1,824 patients (724 invasive tumours). Overall, 33 studies identified molecules that were upregulated in invasive tumours and 20 studies identified molecules that were downregulated. Few studies incorporated modern proteomic or transcriptomic techniques. Risk of bias was low with a mean Newcastle-Ottawa scale score of 7.0 (±0.6). Molecules associated with invasion were related to epithelial-mesenchymal transition (E-cadherin, ESRP1, fascin-1, and MMP-9), cellular proliferation (PTTG, AIP, TCERG1, EIF2β, E2F1, Notch 2, STAT3, ARRB1, TGFB1, SMAD3, SOX9, SGK1, MAGEA6 DLL3, EGFL7, and pEGFR), hormonal signalling (GNAS, DRD5, DRD1, sst5TMD4, SSTR2, and SSTR5), and tumour angiogenesis (VEGF and Drp1). These molecular variations present a possible explanation for the proclivity of somatotropinomas for the cavernous sinus. Identified molecules represent options for novel targeted therapies or biomarkers that could inform prognostication. Modern proteomic and transcriptomic techniques and larger somatotropinoma datasets are required to further elucidate the molecular pathways responsible for cavernous sinus invasion in somatotropinomas.
13 May 2026
Read appraisal →Proceedings of the National Academy of Sciences of the United States of America
Deep-learning endomicroscope with large field-of-view and depth-of-field for real-time in vivo imaging of epithelial cancer hallmarks
In vivo microscopy (IVM) has shown great promise to improve early detection of epithelial precancer, but it suffers from fundamental trade-offs that limit the resolution, field-of-view (FOV) and depth-of-field (DOF). Here, we present PrecisionView, a compact, deep learning-enabled endomicroscope that breaks these constraints and achieves 20 mm2 FOV and 500 µm DOF with 4 µm resolution, representing approximately 5× increase in FOV and 8× larger DOF compared to conventional IVM with similar resolution. PrecisionView integrates a deep learning-optimized phase mask and real-time reconstruction, enabling rapid in vivo assessment of two key hallmarks of cancer: epithelial cell nuclear morphology and subsurface microvasculature through fluorescence and reflectance imaging. By imaging the oral cavity of healthy volunteers and cervical specimens with precancerous lesions, PrecisionView generates large-scale (1 to 3 cm2) coregistered maps of cellular and vascular structures, revealing distinct microscopic patterns associated with anatomic structures and precancerous lesions. Our results suggest the potential of this computational endomicroscope to address the unmet need for early cancer detection at the point of care.
13 May 2026
Read appraisal →Journal of chemical information and modeling
HLABrew for Human Leukocyte Antigen Class I-Presented Epitope Recognition and Mimotope Discovery
Accurate identification of human leukocyte antigen (HLA) class I-presented epitopes is essential for developing personalized vaccines and immunotherapies. Here, we present HLABrew, a unified deep learning framework for HLA class I epitope prediction and allele-specific mimotope design. By integrating the Transformer, variational autoencoder, and dual cross-attention, HLABrew achieves state-of-the-art performance with hierarchical and allele-aware representations. HLABrew accurately deconvolves multiallelic immunopeptidomics data to recover allele-specific binding motifs and expand epitope coverage. Leveraging its generative capacity, HLABrew further designs mimotopes guided by learned peptide-HLA binding preferences, with the potential to enhance antigen presentation and provide candidates for downstream immunogenicity evaluation.
13 May 2026
Read appraisal →Lancet (London, England)
Long-term effects of colonoscopy screening on colorectal cancer incidence and mortality: a multicountry, population-based randomised controlled trial
BACKGROUND: We previously reported the 10-year effects of colonoscopy screening on colorectal cancer incidence and mortality. Here, we report the effects after 13 years of follow-up. METHODS: In this multicountry, population-based randomised controlled trial, 84 583 men and women aged 55-64 years at enrolment from Norway, Poland, and Sweden were randomly allocated (1:2) to colonoscopy screening or no screening and analysed. The primary outcomes were colorectal cancer incidence and mortality after 10-15 years of follow-up in intention-to-screen analyses, with first analysis after 10 years, and repeated every other year or at longer intervals. This trial is registered with ClinicalTrials.gov, NCT00883792, and is ongoing. FINDINGS: At 13 years of follow-up, colorectal cancer incidence was 375 colorectal cancers (1·46%) of 28 217 individuals in the screening group and 912 colorectal cancers (1·80%) of 56 366 individuals in the no-screening group. The risk ratio (RR) was 0·81 (95% CI 0·71-0·90) in intention-to-screen analyses and 0·55 (0·33-0·81) in per-protocol analyses. The risk for proximal colorectal cancer was 129 (0·51%) in the screening group versus 283 (0·56%) in the no-screening group (RR 0·91 [0·71-1·09]), and the risk for distal colorectal cancer was 224 (0·87%) in the screening group versus 563 (1·11%) in the no-screening group (RR 0·79 [0·65-0·89]; interaction p<0·0001). In men, the colorectal cancer risk was 214 (1·69%) of 14 154 in the screening group and 541 (2·19%) of 28 247 in the no-screening group (RR 0·77 [0·64 to -0·88]); in women, the risk was 161 (1·24%) of 14 063 in the screening group versus 371 (1·43%) of 28 119 in the no-screening group (RR 0·87 [0·70 to 1·02]; interaction p<0·0001). Colorectal cancer mortality was 106 (0·41%) of 28 217 in the screening group and 236 (0·47%) of 56 366 in the no-screening group (intention-to-screen RR 0·88 [0·68-1·08], per-protocol RR 0·70 [0·26-1·25]). The observed colorectal cancer mortality in the non-screening group (0·47%) was substantially lower than expected at the time of designing the trial (0·82%). INTERPRETATION: One colonoscopy significantly reduced colorectal cancer incidence but not mortality over 13 years. Colorectal cancer mortality was lower in both study groups than when the trial was designed. FUNDING: The Norwegian Research Council, the Nordic Cancer Union, the Norwegian Cancer Society, and the Health Fund of South-East Norway.
13 May 2026
Read appraisal →Expert review of vaccines
Population-level impact of HPV vaccination: a global systematic review of ecological, cross-sectional, and cohort studies
BACKGROUND: Human papillomavirus (HPV) causes approximately 4.5% of cancers globally, with the highest burden in low- and middle-income countries (LMICs). Since their introduction in 2006, HPV vaccination programs have led to substantial declines in HPV-related outcomes, although impact varies across settings. RESEARCH DESIGN AND METHODS: We conducted a systematic review to evaluate the population-level impact of HPV vaccination on HPV infection, cervical intraepithelial neoplasia grade 2 or higher (CIN2+), genital warts, invasive cervical cancer (ICC), and oropharyngeal cancer (OPC), and examined the influence of coverage, age at initiation, and vaccine type. The review followed PRISMA 2020. RESULTS: Of 13,549 records screened, 63 were included: 9 assessed HPV infection, 24 on CIN2+, 25 on genital warts, and 7 on ICC. Greatest reductions were observed in settings with at least 70% coverage and early vaccination prior to sexual debut, typically achieved through school-based programs. Reported declines ranged from 58-100% for HPV infection, 30-88% for CIN2+, 60-90% for genital warts, and 70-88% for ICC. CONCLUSIONS: HPV vaccination offers strong protection, especially when delivered early and at high coverage within schools. Expanding access and prioritizing underserved populations are essential to achieving global cancer prevention goals. Limitations include heterogeneity across designs, outcome definitions, and follow-up.
13 May 2026
Read appraisal →Journal of clinical oncology : official journal of the American Society of Clinical Oncology
First-in-Human Study of IL15-Activated Cytokine-Induced Killer Cells After Allogeneic HCT Shows Durable Remission and Serotherapy-Associated Immune Reconstitution in Leukemia
PURPOSE: Patients with high-risk (HR) leukemia remain at substantial risk of early relapse, treatment-related toxicity, and poor survival, underscoring the need for effective relapse prevention therapies. To our knowledge, this first-in-human, disease burden-guided study evaluated the feasibility, safety, and efficacy of donor-derived allogeneic interleukin-15-activated cytokine-induced killer cells (IL15-CIK) combining T-cell and natural killer cell properties for post-transplant disease control. METHODS: In a prospective, multicenter phase I/II trial (EudraCT 2013-005446-11) and an identically designed pilot study, 53 adult and pediatric patients with HR leukemia received 56 courses of IL15-CIK monotherapy after human leukocyte antigen (HLA)-matched or HLA-mismatched transplantation. Treatment intent was categorized as consolidation (13%), preemptive (61%), or salvage (27%) with 169 infusions administered as a single dose (29%) or according to adaptable dose-escalation regimens (71%). RESULTS: Acute graft-versus-host disease (GVHD) grades 1-2 and grade 3 occurred in 27% and 4% of cases, respectively; no extensive chronic GVHD or treatment-related mortality was observed. IL15-CIK-associated adverse events were predominantly mild. Disease clearance, assessed by the cumulative incidence of complete molecular remission, peaked at day 700, reaching 74% in the preemptive and 13% in the salvage setting. The five-year progression-free survival was 50% overall and highest (69%) in pediatric acute myeloid leukemia. The five-year overall survival (OS) was 71% in the consolidation, 61% in the preemptive, and 20% in the salvage setting. Multivariable analysis demonstrated significantly lower relapse rates with Campath compared with ATG, superior OS in myeloid malignancies, and reduced IL15-CIK efficacy in advanced disease. The median follow-up was 7.3 years. CONCLUSION: IL15-CIK monotherapy is feasible and safe and demonstrates promising relapse-preventive activity after hematopoietic stem-cell transplantation. Clinical outcomes are strongly influenced by disease burden at treatment initiation and previous serotherapy, supporting optimized patient selection and timing in future post-transplant immunotherapeutic strategies.
13 May 2026
Read appraisal →European journal of clinical pharmacology
The impact of gene polymorphisms on the response of methotrexate-based treatments
PURPOSE: Methotrexate (MTX) is a long-standing drug used to treat leukemia (at high doses to inhibit DNA/RNA synthesis) and rheumatoid arthritis (at low doses for anti-inflammatory effects). Its characteristic narrow therapeutic range, in terms of efficacy and safety associated with MTX, are important factors to consider when deciding whether to prescribe it. A major challenge in its use is interindividual variability which is largely attributed to germline genetic polymorphisms in genes encoding proteins that control the pharmacokinetics or pharmacodynamics of MTX. METHODS: This study updates a previous 2018 review using a systematic literature search in PubMed/MEDLINE, Scopus, and SciELO. The search, conducted according to the PRISMA protocol, employed predefined keywords to identify studies published up to June 2025. RESULTS: This systematic review identified that, to date, only a few genetic polymorphisms influence clinical decision-making. Specifically, the MTHFR 677T allele and the MTHFR 677T-1298 A haplotype (rs1801133- rs1801131) have been associated with toxicity in both adults and children. Furthermore, the 677T-1298 A haplotype has been linked with reduced event-free survival in Caucasian patients with either adult RA or pediatric ALL. Regarding other markers, the TYMS rs34743033 3R allele has been implicated with reduced efficacy in adult patients receiving low-dose MTX, while the FPGS rs1544105 T allele has shown an association with diverse toxicities in ALL pediatric patients. CONCLUSION: Although considerable research has been conducted and numerous results have been obtained, the available evidence remains predominantly of moderate quality. Consequently, current guidelines from CPIC and the DPWG do not recommend routine MTX dose adjustments based solely on single gene variants. It is, therefore, imperative to develop and validate multifactorial risk prediction tools that integrate a range of other clinical factors. Accordingly, the establishment of a comprehensive pharmacogenetics-guided dosing guideline for MTX remains an elusive goal.
13 May 2026
Read appraisal →European journal of nutrition
Association of daily diet and incidence of head and neck cancer: a prospective cohort study of UK biobank participants
PURPOSE: The association between daily diet and head and neck cancer (HNC) remains unclear. Therefore, this study aimed to systematically investigate the relationship between different types of diet and incident HNC. METHODS: This cohort study analyzed UK Biobank (UKB) data (2006-2021, n = 480,577) using Cox proportional hazards models to evaluate the associations between daily diet and incident HNC and its subtypes: laryngeal cancer (LC), oral cancer (OC), and oropharyngeal cancer (OPC). Subgroup analyses were further performed for sex, age, body mass index (BMI), and smoking status. RESULTS: This large prospective study identified significant dietary associations with head and neck cancer (HNC) risk during 11.8 years of follow-up. Protective associations emerged for moderate dried fruit consumption (Hazard Ratio (HR) = 0.77-0.81), daily milk intake (> 299 mL/day, HR = 0.71), and regular coffee drinking (HR = 0.77-0.84). Conversely, elevated risks were observed with frequent red meat (≥ 4 times/week, HR = 1.20), non-oily fish (≥ twice/week, HR = 1.45), refined grains (≥ 3 bowls or slices/week, HR = 1.14), and habitual salt addition (HR = 1.21). Subtype-specific patterns include the protective effects of whole grains and vegetables against laryngeal cancer and reduced oral cancer risk with limited poultry consumption. Notable variations were observed in sex (sex-specific milk/oily fish effects), age (differential fruit/vitamin impacts), and BMI (heightened red meat risk in normal-weight individuals). CONCLUSION: Our findings demonstrated significant associations between dietary patterns and HNC risk, identifying both protective and risk-enhancing food components. These results highlight the potential of dietary interventions in HNC prevention, with varying effects across cancer subtypes and demographic groups.
12 May 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
A Noddings' caring theory-based intervention to enhance coping with death competence in advanced lung cancer patients: a randomized controlled trial
PURPOSE: This randomized controlled trial aimed to evaluate the effects of a Noddings' caring theory-based intervention on coping with death competence (CDC), death attitudes, and psychological distress in patients with advanced lung cancer. METHODS: Seventy inpatients were randomly allocated to the intervention group (n = 35) or the control group (n = 35). The 3-week intervention comprised modeling, dialogue, practice, and recognition phases. Outcomes, including CDC, attitude toward death, anxiety, and depression, were assessed at baseline (T0), post-intervention (T1), and 1-month follow-up (T2). RESULTS: No significant between-group differences were observed at baseline. Following the intervention, patients in the intervention group demonstrated significantly higher CDC total and subscale scores, more positive death attitudes, and lower anxiety and depression scores than those in the control group at both post-intervention and 1-month follow-up. CONCLUSION: The Noddings-based program effectively enhanced death-coping competence, improved death attitudes, and reduced psychological distress in patients with advanced lung cancer. TRIAL REGISTRATION: ChiCTR2600118530, 2/6/2026 retrospectively registered.
12 May 2026
Read appraisal →Physics in medicine and biology
Multi-model study of fast VMAT segment dose calculation with deep learning.
Objective.Deep learning (DL) methods enable photon dose calculation under two main coordinate representations: Beam's Eye View (BEV) and patient coordinates. We evaluate dose calculation accuracy and speed under these coordinate paradigms and with representative DL models within a unified dataset and pipeline, and introduce two lightweight models for fast photon dose calculation.Approach.Planning computed tomography (CT) scans and volumetric modulated arc therapy plans from 24 prostate cancer patients were used. Monte Carlo simulation generated 5940, 540, and 3053 segment doses for training (11 patients), validation (3), and testing (10), respectively. For BEV, we used a combination of convolutional neural network (CNN) and convolutional long short-term memory network (ConvLSTM) called CNN-ConvLSTM, a CNN-Mamba combination (CNN-Mamba), a transformer-based architecture (DoTA), and a cascaded 3D UNet (C3D). These were trained on CT and segment-projection BEV cuboids. For patient coordinates, the DeepDose individual segment dose prediction framework implemented with C3D (DeepDose-C3D) was trained on cropped CT volumes with four physical inputs. Segment and plan dose accuracy were assessed using local gamma passing ratesγPR(2%/3 mm and 1%/3 mm) and dose-volume histogram metrics. Dose calculation times (inference plus pre/post-processing) were measured on three different graphics processing unit (GPUs).Results.All five models achieved mean localγPRvalues⩾91.0% (2%/3 mm) for segment doses and⩾99.0% (1%/3 mm) for plan doses. Mean per-segment dose calculation times were 79, 67, 298, 490, 356 ms for CNN-ConvLSTM, CNN-Mamba, DoTA, C3D, and DeepDose-C3D, respectively. On the latest-generation GPU available, the corresponding per-plan (average 305 segments) dose calculation times were 5.5, 6.2, 33.6, 38.7, 35.4 s.Significance.Both BEV- and patient-coordinate DL methods achieved accurate photon plan dose calculation, with BEV-based approaches showing more robust segment performance. CNN-ConvLSTM and CNN-Mamba retain comparable accuracy at lower computational cost, enabling fast photon dose calculation.
12 May 2026
Read appraisal →Palliative & supportive care
Manic and hypomanic states in cancer patients: A systematic review
OBJECTIVES: While there have been reports on the relationship between cancer and depression, reports on the association between cancer and manic states, a reciprocal state of depression, have been relatively few. Therefore, we conducted a systematic review on the relationships between cancer and manic states, focusing on their etiology, clinical course, and impact on cancer treatments. METHODS: A systematic review was conducted using four electronic databases, following the PRISMA guidelines. The scope of the study included research on manic or hypomanic states associated with cancer in patients with no prior history of mental illness, published from 1950 to August, 2021. The study protocol was registered with PROSPERO (CRD42020182372). RESULTS: Fifty-six studies, including 67 cases, were identified. The etiology of manic states in cancer patients was classified into organic, drug-induced, and psychogenic, with steroids being the most predominant causative agent. Approximately half of the patients discontinued cancer treatment following the onset of manic states. This was associated with a low rate of pharmacological treatment during the acute and maintenance phase of mania. The onset of manic states was most frequent during cancer treatment; however, about 15% of the cases exhibit manic symptoms before cancer diagnosis. SIGNIFICANCE OF RESULTS: This systematic review illustrated the clinical characteristics of manic state regarding differences in the etiology, timing of onset, pharmacological treatments, duration to remission, recurrence, and impact on cancer treatment. Manic states, which are comorbid with cancer, have significant clinical impacts on cancer prognosis. Therefore, appropriate pharmacological treatment for manic states is critical to consolidate appropriate cancer treatment. A substantial proportion of patients exhibit manic symptoms prior to the diagnosis of cancer, warranting further investigation into the possibility of the concept of "premonitory mania."
12 May 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Impact of novel systemic cancer therapies on osseointegration and dental implant outcomes: a systematic review
PURPOSE: Advances in systemic cancer therapies such as immune checkpoint inhibitors (ICIs), tyrosine kinase inhibitors (TKIs), and anti-angiogenic agents have markedly improved survival in patients with solid and hematologic malignancies. As survivorship increases, oral rehabilitation with dental implants is increasingly sought to restore quality of life. However, these therapies may adversely affect bone healing, angiogenesis, and immune regulation, raising concerns regarding implant-related complications. This systematic review aimed to evaluate reported dental implant outcomes and complications in patients receiving novel systemic cancer therapies, emphasizing the implications for supportive oncology. METHODS: This review was conducted according to PRISMA 2020 guidelines and registered in PROSPERO (CRD420251142681). PubMed/MEDLINE, Scopus, ScienceDirect, and the Cochrane Library were searched for English-language clinical reports describing dental implant outcomes in patients treated with ICIs, TKIs, or anti-angiogenic agents. Study selection, data extraction, and quality appraisal using the Joanna Briggs Institute checklist for case reports were performed independently by two reviewers. RESULTS: Seven case reports (2017-2024) were included, representing Level IV evidence. Two cases involving TKIs reported successful osseointegration and long-term survival. In contrast, five cases described adverse outcomes including early failure and MRONJ predominantly associated with anti-angiogenic agents, either alone or in combination with ICIs and antiresorptive agents. These complications often resulted in significant functional morbidity and the need for invasive surgical intervention. CONCLUSION: Based on limited case report evidence, anti-angiogenic therapies were more frequently observed in cases with adverse outcomes, including implant-related MRONJ and failure, while TKIs were associated with favorable outcomes in limited cases. The role of ICIs remains unclear due to sparse data, particularly regarding monotherapy effects. Given the preliminary nature of current evidence derived from case reports, prospective studies with appropriate control groups are urgently needed to establish evidence-based guidelines. PROSPERO NUMBER AND DATE OF REGISTRATION: CRD420251142681; 08/09/2025.
12 May 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Night-time compression with a Mobiderm® auto-adjustable arm-sleeve in addition to daytime compression was superior to daytime compression alone for maintenance therapy of upper limb lymphedema in breast cancer patients in a randomized controlled trial: LYMphoNIGHT study.
PURPOSE: To assess the benefit of night-time compression in addition to daytime compression on arm excess volume after 3 months of maintenance treatment in patients with secondary upper limb breast cancer-related lymphedema (BCRL). METHODS: This multicenter, controlled, randomized study was carried out in six centers in France and Turkey. Women with upper limb BCRL who had undergone the intensive phase of decongestive lymphedema therapy (DLT) were randomized into two groups: the Control group wore a daytime compression sleeve without any night compression for 3 months and the MOBIDERM Autofit (MobA) group used a night-time compression sleeve in addition to the daytime compression. The primary outcome was the change in arm excess volume between day 0 (D0) and D90. The main secondary endpoints were: quality of life (QoL), sleep quality, skin thickness and suppleness, satisfaction, compliance, and safety. RESULTS: Fifty-six patients were recruited (mean (± SD) age: 61.4 ± 12.3 years). Between D0 and D90, the mean excess volume decreased by 29.2% in the MobA group and increased by 10.7% in the Control group (p = 0.001). Only 3.6% of patients in the MobA group presented with treatment failure vs. 23.1% in the Control group. At D90, patients in the MobA group had a significantly greater improvement in QoL (p < 0.05), sleep quality (p = 0.018), skin suppleness (p = 0.01) reduced skin thickness (p = 0.025) compared with the Control group. CONCLUSION: MOBIDERM Autofit used in addition to daytime compression for 3 months during the maintenance phase after DLT was superior to daytime compression alone for the reduction of arm excess volume. Registered on ClinicalTrials.gov (NCT04203069; 17/12/2019).
11 May 2026
Read appraisal →Lasers in medical science
Machine learning enhanced optical spectroscopy for breast cancer diagnosis: A review
This literature review examines the transformative role of machine learning (ML) and deep learning (DL) in enhancing optical spectroscopy for breast cancer diagnosis. By synthesizing advancements from peer-reviewed studies (2015-2025), we evaluate how ML/DL integration improves the detection of malignancy-associated biochemical changes, enabling noninvasive, rapid, and accurate differentiation between healthy and cancerous tissues. This review highlights key spectroscopic modalities, such as Raman, fluorescence, diffusive optical spectroscopy (DOS), and photoacoustic spectroscopy (PAS), and their integration with AI-driven models, such as convolutional neural networks (CNNs), support vector machines (SVMs), and logistic regression. These techniques achieve diagnostic accuracies of up to 94% in subtype classification (e.g., luminal A, HER2-positive) by analyzing spectral biomarkers such as hemoglobin, lipids, and collagen. Challenges such as data variability, model interpretability, and clinical integration barriers are critically assessed. These findings underscore the potential of ML/DL-enhanced spectroscopy to standardize diagnostics, reduce unnecessary biopsies, and personalize treatment monitoring. Future directions emphasize the need for explainable AI (XAI), multimodal data fusion, and large-scale, diverse datasets to bridge translational gaps. By addressing technical, ethical, and regulatory hurdles, this integration promises to advance early detection, improve clinical outcomes, and reshape precision oncology.
11 May 2026
Read appraisal →Analytical chemistry
Quantitative Analysis of Multiple Serum Tumor Biomarkers by an Interpretable Stacked Ensemble Model
Surface-enhanced Raman spectroscopy (SERS) offers exceptional sensitivity and specificity for biomolecular detection, particularly in analyzing serum tumor biomarkers. However, its application is hindered by spectral complexity and matrix interference. Although machine learning (ML) has shown promise in SERS data analysis, existing quantitative models for biomarker detection lack generalizability and interpretability. To address these challenges, we developed an interpretable stacked ensemble model (ISEM) that integrates feature selection, ensemble learning, and explainable artificial intelligence (XAI) for quantifying 12 serum tumor biomarkers. To ensure reliable quantitative analysis, we validated the acquired SERS data using uniform and reproducible Au nanoparticle substrates. Preprocessing, least absolute shrinkage, and selection operator (LASSO) feature selection were employed to establish a foundation for accurate quantification. Subsequently, 12 serum tumor biomarkers were quantified, demonstrating the superior performance of ISEM, which achieved a high predictive accuracy, with R2 values exceeding 0.9 for all biomarkers. Crucially, based on the quantitative results, we provided molecular-level interpretability for structure-spectrum correlations using Shapley additive explanations (SHAP), revealing how glycosylation reactions, matrix interference, and spectral overlap influence prediction accuracy. Furthermore, the capability of ISEM to quantify multiple biomarkers in unseen samples was confirmed through validation of its generalizability. Our study establishes an ensemble-driven, interpretable framework for quantitative biomarker analysis in a complex biological matrix, demonstrating significant potential for early cancer diagnosis and screening.
11 May 2026
Read appraisal →Journal of medical Internet research
AI in Clinical Decision Support Systems: Promising Applications and Strategies for Managing Data Challenges
The translation of big data analytics and artificial intelligence (AI) into clinical decision support systems (CDSSs) has advanced from proof of concept to real-world clinical practice. AI-informed CDSSs show measurable improvements in diagnostic accuracy, risk stratification, resource use, and patient outcomes compared to traditional models, offering the potential to assist clinicians in managing symptom complexity and uncertainty in health care delivery. Despite this potential, access to large amounts of high-quality and granular data remains one of the most significant bottlenecks to AI-enabled CDSSs. We argue that as health care systems increasingly adopt data-driven decision support, addressing the challenges of data accessibility and protection is essential to realizing the full potential of AI in clinical medicine. We use selected case examples of AI-informed CDSSs in oncology, organ transplantation, diabetic retinopathy, epilepsy, spinal cord injury, rare disease diagnosis, and emergency medicine to illustrate opportunities and challenges related to AI's potential to improve patient outcomes. We discuss public and semipublic, medical institutional and commercial, and government and national data sources that are currently available for the development of CDSSs and highlight the practical and ethical constraints associated with these data. We consider alternative data resources and ways in which health care systems can strengthen data ecosystems to increase AI-driven CDSS efficacy and implementation to improve patient outcomes.
11 May 2026
Read appraisal →Biomedical physics & engineering express
Enhanced ResU-Net for brain tumor segmentation using EfficientNetB0, channel attention, and ASPP
Accurate and efficient segmentation of brain tumors is critical for diagnosis, treatment planning, and monitoring in clinical practice. In this study, we present an enhanced ResU-Net architecture for automatic brain tumor segmentation, integrating an EfficientNetB0 encoder, a channel attention mechanism, and an atrous spatial pyramid pooling (ASPP) module. The EfficientNetB0 encoder leverages pre-trained features to improve feature extraction efficiency, while the channel attention mechanism enhances the model's focus on tumor-relevant features. ASPP enables multi-scale contextual learning, which is crucial for handling tumors of varying sizes and shapes. The proposed model was evaluated on two benchmark datasets: The Cancer Genome Atlas Low Grade Glioma and brain tumor segmentation (BraTS-2020). Experimental results demonstrate that our method consistently outperforms the baseline ResU-Net and its EfficientNet variant, achieving dice similarity coefficient of 0.903 and 0.851, and HD95 scores of 9.43 and 3.54 for whole tumor and tumor core (TC) regions on the BraTS 2020 dataset, respectively. Compared to state-of-the-art methods, our approach shows competitive performance, particularly in whole tumor and TC segmentation. These results indicate that combining a powerful encoder with attention mechanisms and ASPP can significantly enhance BraTS performance. The proposed approach holds promise for further optimization and application in other medical image segmentation tasks.
11 May 2026
Read appraisal →Analytical chemistry
Precise Artificial Intelligence Nanomotor Tracking-Powered Amplification-Free Digital MicroRNA Sensing
Digital assays have become a state-of-the-art technology for the sensing of nucleic acid molecules. Nevertheless, most of the prevalent digital nucleic acid assays heavily rely on sealed microchambers and sophisticated nucleic acid amplification strategies to achieve an end-point digital signal readout. Herein, we proposed an artificial intelligence (AI)-facilitated, digital fluorescence nanomotor tracking-powered assay (AI-dFNA) for one-step, amplification-free, and highly sensitive detection of microRNAs (miRNAs). In this design, even a single target miRNA-templated amplification-free ligation can introduce a sufficient driving force on the surface of a fully open fluorescence nanoparticle to cause remarkable motion behaviors. With the assistance of a self-developed AI algorithm, clear discrimination of positive and negative fluorescent nanomotors can be achieved, enabling real-time digital miRNA sensing. The AI-dFNA avoids the use of enclosed microchambers and intricate signal amplification techniques, which could even identify varying fluorescence colors of nanomotors to allow for the analysis of different kinds of miRNA molecules, pioneering a new direction toward next-generation digital nucleic acid sensing.
11 May 2026
Read appraisal →Analytical chemistry
TPP-Fabricated All-Fiber Nanoforce Sensor with Deep Learning Analysis Enables Ultrasensitive Cancer Cell Mechanophenotyping
Cellular mechanics provide sensitive, quantitative markers of cancer progression, yet routine nanoforce measurements remain constrained by the cost, footprint, and electromagnetic limitations of conventional platforms. Here we report an all-fiber nanoforce sensor (AFNS) that integrates a two-photon-polymerized (TPP) microcantilever on the end facet of a multimode fiber, establishing a compact and robust analytical strategy for ultrasensitive force measurements. The printed structure comprises a rigid base, a ∼2 μm-thick cantilever disk, and a probe tip. Finite-element analysis is employed to estimate the effective spring constant and to optimize mechanical compliance. Force-dependent multimode specklegrams are quantitatively decoded using an EfficientNet-based regression model, enabling highly accurate force readout (R2 = 0.9999; mean absolute error <0.2%). The sensor achieves a force resolution of 0.41 nN and maintains stable analytical performance over 100 repeated loading cycles, representing a rigorous repeatability assessment for this class of fiber-tip force sensors. The analytical capability of the AFNS is validated by quantifying the Young's modulus of polydimethylsiloxane and by mechanically phenotyping A549 and HepG2 cells at different invasion stages, showing strong agreement with atomic force microscopy. With low cost, replaceable probes, and a compact all-fiber configuration, this work provides a scalable analytical methodology for high-precision micro/nano-force characterization and quantitative cellular mechanics assays.
11 May 2026
Read appraisal →Biomedical physics & engineering express
SAM2HIPT: a hybrid deep learning framework integrating SAM2 and HIPT with joint loss optimization for immunohistochemical cell nucleus segmentation
Nucleus segmentation in immunohistochemistry images plays a critical role in cancer diagnosis and treatment assessment. However, existing methods remain limited in segmentation accuracy and boundary delineation due to staining heterogeneity, densely packed cell distributions, and complex background interference. To address these challenges, this paper proposes a two-stage nucleus segmentation framework, termed SAM2HIPT. In the first stage, the pre-trained Segment Anything Model 2 (SAM2) is employed to generate initial segmentation predictions for input images, wherein the image encoder is kept frozen to preserve the pre-trained visual representation capacity while the mask decoder is fine-tuned to adapt to the characteristics of the pathological image domain; local texture, morphological, and boundary information are extracted through visual feature encoding to produce initial nucleus segmentation masks and spatial prior representations. In the second stage, the Hierarchical Image Pyramid Transformer is introduced to refine the initial segmentation results, performing multi-scale, multi-level feature representation and fusion of morphological, textural, and spatial structural information through a hierarchical vision Transformer architecture, thereby enhancing nuclear structural representation and boundary consistency. To enable collaborative optimization across both stages, a joint loss function is designed to impose unified constraints on segmentation accuracy and feature representation. Evaluated on two public histopathological benchmark datasets, BCData and DeepLIIF, the proposed method achieves Dice coefficients of 0.92 and 0.91, respectively, and HD95 boundary error values of 1.05 pixels and 1.10 pixels, demonstrating superior segmentation performance and robustness over multiple state-of-the-art baseline methods.
11 May 2026
Read appraisal →Cancer immunology, immunotherapy : CII
Artificial intelligence-powered H&E-based quantification of spatial tumor-infiltrating lymphocyte distribution identifies prognostic immune niches in colorectal cancer
PURPOSE: The prognostic significance of tumor-infiltrating lymphocytes (TILs) in colorectal cancer (CRC) is well established; however, existing approaches inadequately capture their spatial distribution. We investigated the prognostic implications of TIL spatial distribution in CRC using an artificial intelligence (AI)-based method. METHODS: A total of 202 patients with stage II-III CRC were included. TIL densities in intratumoral (iTIL) and stromal (sTIL) regions were quantified using AI-based analysis of hematoxylin and eosin (H&E)-stained images. Based on proximity to the tumor-stromal border (TSB), TILs were subclassified into core iTIL, bounding iTIL, bounding sTIL, and outermost sTIL. Immunoscore was calculated from CD3+ and CD8+ T-cell densities in the tumor center and invasive margin. RESULTS: Correlations between AI-based and pathologist assessments (iTIL: r = 0.57; sTIL: r = 0.70) were comparable to inter-pathologist correlations (iTIL: r = 0.47; sTIL: r = 0.70). In univariate Cox regression analysis, bounding iTIL, bounding sTIL, and outermost sTIL were significantly associated with recurrence-free survival (RFS), whereas core iTIL was not. Composite TIL and TSB scores were developed by incorporating the prognostically significant regions. In multivariable analysis, the TIL score (p = 0.001), TSB score (p < 0.001), and Immunoscore (p < 0.001) independently predicted RFS. In microsatellite instability-high tumors, only the TSB score remained prognostically significant. CONCLUSION: AI-powered spatial analysis of TILs, particularly the TSB score, demonstrated prognostic performance comparable to conventional Immunoscore, thereby supporting the value of spatial immune profiling and AI-driven analysis of H&E-stained slides for improved risk stratification in CRC.
11 May 2026
Read appraisal →Palliative & supportive care
Relational-temporal dignity at the end of life: Ethical foundations for the DiRePal model in the algorithmic age
OBJECTIVES: The increasing presence of artificial intelligence (AI), electronic patient-reported outcomes (ePROMs), and digital infrastructures in palliative care is transforming how clinical encounters are organized and how suffering is interpreted. These technological shifts heighten the risk of relational compression and a reduction of dignity to measurable outputs. This paper proposes the DiRePal model (Relational-Temporal Dignity in Palliative Care) as a philosophical framework to re-examine dignity beyond coherent narrative identity or autonomy-centered ethics, emphasizing relational presence, temporal sensitivity, and structural conditions of care. METHODS: A philosophical-ethical analysis informed by narrative identity (P. Ricoeur), ethics of alterity (E. Levinas), capabilities theory (M. Nussbaum), and care ethics (J. Tronto). Critical readings of dignity frameworks, AI ethics, and digital health literature were synthesized to develop a relational-temporal account of dignity and 2 operational concepts: the temporal dignity indicator and the architecture of prudence. RESULTS: While digital tools can enhance communication and support anticipatory care, they also risk reducing patients to data profiles, narrowing listening practices, and eroding opportunities for narrative, silence, and relational presence. The DiRePal model reframes dignity as a fluctuating, co-constructed achievement that depends on temporal attentiveness, ethical listening, institutional conditions, and prudent integration of AI and ePROMs. It further expands dignity to include post-biographical dimensions such as memory, grievability, and digital legacy. SIGNIFICANCE OF RESULTS: End-of-life care in the algorithmic age requires an ethics that recognizes dignity as relational, temporal, and structurally mediated. The DiRePal model offers clinicians and institutions a conceptual grammar to resist technological reductionism, protect time for presence, and safeguard the narrative and post-biographical continuity of persons whose voices may be fragmented, vulnerable, or digitally extended.
11 May 2026
Read appraisal →Palliative & supportive care
Definitional confusion in nurses' and physicians' attitudes toward euthanasia in India: A systematic review and meta-analysis
INTRODUCTION: In India, the term euthanasia is frequently used to describe a wide range of end-of-life practices, including withdrawal of life-sustaining treatment and administration of lethal drugs. Such usage diverges from more narrow definitions that restrict euthanasia to the intentional administration of lethal drugs. OBJECTIVES AND SIGNIFICANCE OF THE RESULTS: This systematic review and meta-analysis examines how euthanasia has been defined and operationalized in quantitative studies of Indian physicians' and nurses' attitudes, and estimates the prevalence of approval when euthanasia is defined narrowly. METHODS: Following PRISMA 2020 guidelines, searches were conducted in PubMed, EMBASE, PsycINFO, and CINAHL for studies published from 2010 onward. Two investigators independently screened studies, extracted data, and assessed risk of bias using the Mixed Methods Appraisal Tool (MMAT). Studies were included in the meta-analysis only if they reported attitudes toward euthanasia as narrowly defined. RESULTS: Nine studies met inclusion criteria for the systematic review. Definitions of euthanasia varied considerably, and several studies combined attitudes toward treatment withdrawal with attitudes toward the administration of lethal drugs. Four studies reporting on 519 nurses and physicians provided data suitable for meta-analysis. Approval of euthanasia, defined as the intentional administration of lethal drugs, ranged from 12% to 20%, with a pooled prevalence of 16% (95% confidence interval: 0.13-0.19). This prevalence is notably lower than in earlier reports on Indian healthcare professionals' attitudes. CONCLUSION: Definitional inconsistency substantially affects reported attitudes toward euthanasia in Indian research. When euthanasia is defined narrowly, approval among healthcare professionals is low and consistent across studies. These findings highlight the need for conceptual clarity in future research to support accurate interpretation of empirical data and to strengthen the contribution of studies on ethical attitudes to ethical and palliative care scholarship.
10 May 2026
Read appraisal →Briefings in bioinformatics
Comprehensive evaluation of artificial intelligence-empowered approaches for protein-aptamer complex prediction
Drug discovery is a time-consuming, expensive, and high-risk process. Recent advances in artificial intelligence (AI) have enabled major breakthroughs in small-molecule and protein therapeutics. However, AI-driven design of aptamer drugs remains largely unexplored. Aptamers are short (15-100 nt) single-stranded DNAs or RNAs that exhibit high binding affinity, high specificity, and low immunogenicity, making them promising candidates for disease (such as cancer) therapeutics. Compared with protein-ligand or protein-protein systems, protein-aptamer complexes are under-represented in public structural databases, and aptamers themselves are highly flexible and relatively large molecules. These characteristics present distinct challenges for AI-based structural modeling. Here, we systematically evaluate recent AI frameworks, including AlphaFold3, Chai-1, Boltz-2, and RoseTTAFold2NA, along with a template-based approach, in predicting protein-aptamer complex structures and estimating binding free energies. We establish an independent benchmark to assess their performance in structural accuracy, stability, and energetic consistency. This study provides a foundation for the application of AI in aptamer drug design and offers a reference framework for future research in nucleic-acid therapeutics and biomolecular modeling.
10 May 2026
Read appraisal →Briefings in bioinformatics
MLN2SVG: domain-aware spatially variable gene detection using contrastive variational autoencoder and multi-level neighbor search
Spatial transcriptomics (ST) technologies have transformed our ability to examine gene expression within intact tissues, yet accurately identifying spatially variable genes (SVGs) remains challenging due to spatial heterogeneity, data sparsity, and incomplete modeling of domain-level dependencies. To address these limitations, we propose MLN2SVG, a domain-aware framework that integrates contrastive variational autoencoding with a multi-level neighbor (MLN) search algorithm to jointly learn tissue domains and SVGs. MLN2SVG constructs a weighted spatial graph to capture both local and long-range spatial relationships, employing a deep contrastive variational autoencoder to align augmented and original data representations while preserving biological diversity. The MLN algorithm dynamically expands neighborhood connectivity to mitigate sparsity and enhance domain coherence. Across multiple human and mouse ST datasets, including dorsolateral prefrontal cortex, breast cancer, and brain tissues, MLN2SVG consistently outperformed existing methods in clustering accuracy, robustness, and biological interpretability. Notably, in breast cancer tissues, MLN2SVG uncovers fine-grained spatial organization of tertiary lymphoid structures, delineating region-specific immune architectures spanning intratumoral, tumor-edge, and extratumoral compartments. Through the integration of spatial domain discovery and SVG detection, MLN2SVG delivers a robust and biologically interpretable framework for uncovering the molecular and structural complexity of tissue organization.
10 May 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Modified Frailty Index-11 (mFI-11) measured frailty as a predictor of post-transplant outcomes in non-Hodgkin's lymphoma patients undergoing hematopoietic stem cell transplantation: a retrospective analysis of National Inpatient Sample database
BACKGROUND: Hematopoietic stem cell transplantation (HSCT) is a critical treatment for non-Hodgkin lymphoma (NHL) but involves significant post-transplant risks. Frailty, measured by the Modified Frailty Index-11 (mFI-11), may influence outcomes. METHODS: We analyzed NHL patients undergoing autologous or allogeneic HSCT from the Nationwide Inpatient Sample (2016-2020). Frailty was defined by mFI-11, and outcomes included in-hospital mortality, unfavorable discharge, prolonged length of stay (LOS), complications, and costs. Multivariable regression models with stratification by age and Charlson Comorbidity Index were applied. RESULTS: Of 2807 patients (ages 18-85), frail patients were older and had more comorbidities. In autologous HSCT, frailty was linked to higher mortality (2.02% vs. 0.27%, p < 0.001), increased unfavorable discharges (19.75% vs. 10.80%, p < 0.0001), and prolonged LOS (15.92% vs. 7.75%, p < 0.0001). Similar trends were observed in the allogeneic cohort. CONCLUSIONS: Frailty predicts worse post-transplant outcomes in NHL patients undergoing HSCT, emphasizing the need for pre-transplant frailty screening.
10 May 2026
Read appraisal →Biomedical physics & engineering express
Dosimetric evaluation of simultaneous multi-energy (2-18 MV) and intensity optimization for IMRT and VMAT.
Objective.This study aimed to develop and evaluate a framework for the simultaneous optimization of beam fluence and multiple photon energies beyond dual-energy configurations for intensity modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT), with the goal of improving treatment plan quality and normal tissue sparing.Approach.An Elekta Versa HD linear accelerator (linac) was modeled using the BEAMnrc/EGSnrc Monte Carlo platform to simulate 2 MV, 4 MV, and 6 MV photon beams, complemented with measured beam data for 10 MV and 18 MV. A custom MATLAB-based framework was developed to perform simultaneous optimization of energy and fluence, allowing energy weighting of individual beamlets. A total of 12 various energy configurations including single (SE), dual (DE), triple (TE), and quadruple (QE)-energy combinations for IMRT and VMAT were generated for thirteen prostate cancer patients. Plans were evaluated using dose-volume histograms, homogeneity index, conformity index, and organ-at-risk (OAR) dose metrics.Main results.Gamma analysis confirmed the high degree of agreement between measured and simulated beam data, which validated our Monte Carlo linac model. Target coverage remained comparable across all energy configurations. Compared with 6 SE, multi-energy (ME) IMRT and VMAT achieved superior OAR sparing, with the greatest benefits for bladder and rectum in the 5-40 Gy range. TE-IMRT reduced bladder and rectal mean doses by up to 1.6 Gy, with V10 reductions of ∼8%; on average, mean doses were reduced by 0.9 Gy (p= 0.001) and 1.0 Gy (p= 0.001). For VMAT, the 6&18 DE-VMAT plan yielded the largest sparing, lowering bladder and rectal mean doses by ∼1.0 Gy. Relative to 10 SE, all ME plans achieved equal or lower OAR doses, with TE-IMRT and 6&18 DE-VMAT showing the most consistent benefit, reducing mean doses to the bladder, rectum, right femoral head, and left femoral head by up to 1.26, 0.77, 0.6, and 0.55 Gy, respectively.Significance.This study presents the first assessment of the feasibility and potential clinical value of simultaneous optimization of beam intensity and more than two photon energies for IMRT and VMAT plans, producing optimized ME fluence maps that require subsequent conversion into clinically deliverable treatment plans. The findings demonstrate that ME optimization can enhance OAR sparing without compromising target coverage, supporting its promise for future clinical application.
10 May 2026
Read appraisal →Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation
Responsiveness and clinical utility of patient-reported outcome instruments in head and neck cancer: a systematic review
PURPOSE: To synthesize and evaluate current evidence on the responsiveness, interpretability (MID/MIC), and clinical utility of validated health-related quality of life (HRQoL) patient-reported outcome (PRO) instruments used in adults with head and neck cancer (HNC). METHODS: A systematic search of PubMed, Scopus, Web of Science, Embase, and Cochrane Library (Central) identified studies published from January 2015 to July 2025. The review was registered in PROSPERO (CRD420251128978) and followed PRISMA 2020 guidelines. Observational and interventional studies assessing HRQoL using validated HNC instruments (EORTC QLQ-C30, QLQ-H&N35/H&N43, UW-QOL, FACT-H&N or MDADI) were included. Eligible studies reported responsiveness (ability to detect meaningful change) or clinical applicability. Methodological quality was evaluated using the COSMIN Risk of Bias checklist, and results were narratively synthesized due to methodological heterogeneity. RESULTS: Thirteen studies involving 8,075 patients met the inclusion criteria. The EORTC QLQ-C30 and H&N35/43, UW-QOL, FACT-H&N, and MDADI exhibited consistent responsiveness, capturing expected HRQoL deterioration during treatment and partial recovery within 6-12 months. Reported minimal important difference (MID) and minimal important change (MIC) values typically ranged from 4 to 15 points for improvement and 10-20 points for deterioration, supporting the interpretability of change scores. Several patient-reported outcome measures (PROMs) also demonstrated prognostic relevance, with early HRQoL changes associated with survival or recurrence. CONCLUSION: Validated HRQoL questionnaires in HNC demonstrate robust responsiveness and meaningful clinical applicability, supporting their integration into routine oncology practice. Their use may enhance early detection of complications, guide rehabilitative interventions, and facilitate more individualized, patient-centered care.
10 May 2026
Read appraisal →Annals of hematology
Hematologic and molecular response to ropeginterferon alfa-2b in patients with polycythemia vera: a systematic review and meta-analysis
Ropeginterferon alfa-2b is an interferon used in the treatment of myeloproliferative neoplasms, particularly polycythemia vera. Its efficacy in achieving hematologic and molecular responses has been demonstrated in clinical trials, but pooled data on long-term outcomes and sustained response remain limited. This systematic review and meta-analysis aimed to evaluate the hematologic and molecular response over 36 months. PubMed, Scopus, Science Direct, and Google Scholar databases were searched to identify studies reporting hematologic and molecular responses to ropeginterferon alfa-2b. Studies were included if they provided data on complete hematologic response (CHR) and JAK2V617F variant allele frequency (VAF) reduction. Pooled proportions and mean reductions were calculated using random-effects models. The pooled proportion of CHR increased progressively from 0.19 (95% CI: 0.04-0.57) at 3 months to 0.73 (95% CI: 0.17-0.97) at 36 months. Molecular response, measured by VAF reduction, deepened over time from - 7.33 (95% CI: -9.85 to -4.81) at 3 months to -54.90 (95% CI: -65.61 to -43.99) at 36 months. Subgroup analyses revealed significant variability in response rates, particularly in early follow-up periods. Ropeginterferon alfa-2b achieves significant and sustained hematologic and molecular responses over 36 months. This makes it a promising treatment for polycythemia vera. While variability in early responses needs further investigation, the sustained long-term efficacy compared to hydroxyurea supports its use in clinical practice. Future studies should focus on identifying predictors of response and optimizing treatment protocols to maximize patient outcomes.
10 May 2026
Read appraisal →Medical image analysis
Context-enriched contrastive auto-encoder with topology learning for medical hyperspectral image classification to diagnose tumors
Deep learning has emerged as a highly effective approach for the automatic classification of medical hyperspectral images (MedHSIs), facilitating the accurate diagnosis of diverse tumors. Most of current methods suffer from the challenges in the separability of latent representation and the preservation of topological structure. To remedy such deficiencies, we propose a novel context-enriched contrastive auto-encoder with topology learning (CCAET) based MedHSI classification for tumor diagnosis. Concretely, we initially employ an auto-encoder (AE) network as the foundational framework to achieve latent representation and data reconstruction. Subsequently, to enhance the separability of the latent representation, we integrate a context-enriched contrastive loss into the AE framework, where both individual pixels and their contextual information are used to redefine contrastive learning, so that small distances between intra-class features and large distances between inter-class features are achieved. Further, we construct a topology-preserved loss by incorporating a graph based approach into the foundational framework, which aims to maintain the topological structure during latent representation learning. Furthermore, we devise a gradient descent-based optimization scheme to optimize the overall loss of the proposed CCAET method, so as to obtain the final latent representation of MedHSI. Finally, we implement soft-max-based label prediction to obtain the classification results. Different from most existing methods, the CCAET approach considers both the separability of latent representations and the preservation of topological structure to enhance MedHSI classification performance. Extensive experimental evidence has been presented to substantiate that the proposed CCAET method outperforms several state-of-the-art techniques in terms of MedHSI classification based tumor diagnosis.
9 May 2026
Read appraisal →Journal of clinical ultrasound : JCU
Integrative Machine Learning Model Leveraging DCE-MRI and PSA Values for Advanced Risk Stratification in Prostate Cancer
PURPOSE: Accurate grading of prostate cancer is critical for treatment strategies and risk stratification. This study aims to develop a machine learning (ML) model integrating Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) pharmacokinetic parameters with Prostate-Specific Antigen (PSA) values to predict ISUP grade metastatic risk groups. METHODS: This retrospective study included 102 patients with histologically confirmed prostate cancer. DCE-MRI pharmacokinetic parameters (Ktrans, Kep, Ve, CER, MaxSlope, IAUGC) were standardized. The dataset was balanced using the Synthetic Minority Oversampling Technique and split into training, validation, and test sets. ML models, including Random Forest, were evaluated using Area Under the Curve (AUC) values. RESULTS: The Random Forest classifier achieved the highest performance, with an AUC of 0.92. Precision-recall analysis identified an optimal threshold of 0.3, balancing sensitivity and specificity for high-risk group detection. SHAP analysis highlighted PSA, MaxSlope, and Kep as key predictors contributing to model accuracy. CONCLUSION: Integrating DCE-MRI parameters with PSA values using ML algorithms enhances the prediction of ISUP grade metastatic risk groups. This method provides a robust tool for metastasis screening and personalized treatment in prostate cancer.
9 May 2026
Read appraisal →Scientific reports
From intelligent models to clinical tools: the evolving landscape of AI in medical imaging
Integration of Artificial Intelligence (AI), particularly deep learning, into medical imaging represents a profound shift in diagnostic medicine, moving from purely descriptive analysis to advanced predictive and prescriptive analytics. This Collection explores the rapid advancement of AI-driven tools in their specific fields such as oncology, cardiology, ophthalmology and so on, highlighting their potential to improve diagnostic accuracy, workflow efficiency, and personalized treatment planning. However, significant challenges remain, including the heterogeneity of medical image data, the "black box" nature of some intelligent models, and the critical hurdles of clinical integration and validation. The research presented here addresses these frontiers, showcasing innovations in algorithm development, explainable AI, and translational application. This Editorial synthesizes the contributions and outlines the essential collaborative pathway-uniting computer scientists, clinicians, and regulatory bodies-required to translate algorithmic promise into robust, trustworthy, and equitable clinical tools that genuinely improve patient care.
9 May 2026
Read appraisal →Medical image analysis
Diversity-driven MG-MAE: Multi-granularity representation learning for non-salient object segmentation
Masked Autoencoders (MAEs) have grown increasingly prominent as a powerful self-supervised learning paradigm. They are capable of effectively leveraging inherent image prior information and are gaining traction in the field of medical image analysis. However, their application to feature representations of the non-salient objects, such as microvasculature, accessory organs, and early-stage tumors-is fundamentally limited by dimensional collapse problem, which diminishes feature diversity critical for non-salient structure discrimination. To address this, we propose a Multi-Granularity Masked Autoencoder (MG-MAE) framework for feature diversity learning: (1) We extend the conventional MAE into a multi-granularity framework, a global branch reconstructs global pixels, with a local branch recovering Histogram of Oriented Gradients (HOG) features, enabling hierarchical representation of both coarse-grained and fine-grained patterns; (2) Critically, in the local branch, a diversity-enhanced loss function incorporating Nuclear Norm Maximization (NNM) constraint to explicitly mitigate feature space collapse through orthogonal embedding regularization; and (3) A Dynamic Weight Adjustment (DWA) strategy that dynamically prioritizes hard-to-reconstruct regions via entropy-driven gradient modulation. Comprehensive evaluations across five clinical benchmarks-CCTA139, BTCV, LiTS, ACDC, and MSD Pancreas Tumour datasets-demonstrate that MG-MAE achieves statistically significant improvements in Dice Similarity Coefficient (DSC) scores for non-salient object segmentation, outperforming state-of-the-art methods. The code is available at https://github.com/zhangbbin/mgmae.
9 May 2026
Read appraisal →Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
Training With Local Data Remains Important for Deep Learning MRI Prostate Cancer Detection
OBJECTIVES: Domain shift has been shown to have a major detrimental effect on AI model performance however prior studies on domain shift for MRI prostate cancer segmentation have been limited to small, or heterogenous cohorts. Our objective was to assess whether prostate cancer segmentation models trained on local MRI data continue to outperform those trained on external data with cohorts exceeding 1000. METHODS: We simulated a multi-institutional consortium using the public PICAI dataset (PICAI-TRAIN: 1241 exams, PICAI-TEST: 259) and a local dataset (LOCAL-TRAIN: 1400 exams, LOCAL-TEST: 308). IRB approval was obtained and consent waived. We compared nnUNet-v2 models trained on the combined data (CENTRAL-TRAIN) and separately on PICAI-TRAIN and LOCAL-TRAIN. Accuracy was evaluated using the open source PICAI Score on LOCAL-TEST. Significance was tested using bootstrapping. RESULTS: Just 22% (309/1400) of LOCAL-TRAIN exams would be sufficient to match the performance of a model trained on PICAI-TRAIN. The CENTRAL-TRAIN performance was similar to LOCAL-TRAIN performance, with PICAI Scores [95% CI] of 65 [58-71] and 66 [60-72], respectively. Both of these models exceeded the model trained on PICAI-TRAIN alone which had a score of 58 [51-64] (P < .002). Reducing training set size did not alter these relative trends. CONCLUSION: Domain shift limits MRI prostate cancer segmentation performance even when training with over 1000 exams from 3 external institutions. Use of local data is paramount at these scales.
9 May 2026
Read appraisal →Nuclear medicine communications
Refining deep learning segmentation in gallium-68-prostate-specific membrane antigen-11 positron emission tomography: evaluation of small lesion filtering and intersection-over-union thresholds
PURPOSE: Evaluate how small lesion filtering and different intersection-over-union (IoU) thresholds influence deep learning segmentation of Ga-68-PSMA-11 PET images in prostate cancer. METHODS: A 3D U-Net was trained on 115 patient scans with manual contours as ground truth. Performance was assessed at voxel, lesion and patient levels. Lesions less than 8 voxels (195 mm 3 ) or less than 27 voxels (658 mm 3 ) were optionally discarded, and lesion-level metrics were computed across 10-50% IoU thresholds. RESULTS: Excluding lesions less than 27 voxels increased voxel-level Dice from 0.7975 to 0.8173 and improved precision. Lesion-level metrics were stable across IoU thresholds of 20-40% after 27-voxel filtering, while sensitivity declined at higher overlap thresholds. Patient-level sensitivity and positive predictive value reached 96.6 and 94.5%, respectively. CONCLUSION: Implementing small lesion filtering criteria improves the reliability of automated segmentation outputs, particularly for quantitative evaluation. For lesion-level metrics, defining true positives within the range of 20-40% IoU thresholds is optimal. Further validation through multicenter studies and larger datasets is essential to ensure the generalizability of these findings.
9 May 2026
Read appraisal →Computers in biology and medicine
A decoction-inspired genetic algorithm and PPO reinforcement learning for intelligent molecular discovery: Anti-colorectal cancer candidates from the tiao-pi AnChang formula
BACKGROUND: Colorectal cancer (CRC) remains among the leading causes of cancer-related mortality worldwide. The TiaoPi AnChang Decoction (TPACD), a traditional Chinese herbal formula, has shown potential anti-CRC activity in preclinical studies (in vitro and in vivo); however, its active chemical basis and key target interactions remain unclear. The complex, multicomponent nature of TPACD poses significant challenges for systematically identifying and optimizing compounds. PURPOSE: This study aimed to establish an intelligent molecular discovery framework that integrates a decoction-inspired genetic algorithm (EGA) with proximal policy optimization (PPO)-based reinforcement learning to simulate the dynamic transformation and recombination processes underlying traditional herbal decoctions and to generate and prioritize in silico candidate molecules for further anti-CRC evaluation. STUDY DESIGN: A parallel molecular generation strategy was developed, comprising three algorithmic pathways, namely, the basic molecular generation algorithm (BMGA), enhanced molecular generation algorithm (EMGA), and intelligent molecular generation algorithm (IMGA), which represent progressive levels of structural diversity, biological relevance, and optimization capacity. METHODS: The EGA simulated the selection-transformation-recombination principles of decoction through adaptive mutation, fragment recombination, and a diversity-preserving selection procedure. PPO-based reinforcement learning further refined the molecular properties via a reward-guided exploration of the chemical space. The BMGA enabled baseline molecular generation; the EMGA incorporated affinity-based selection; and the IMGA achieved multiobjective optimization, integrating drug likeness, novelty, and toxicity filtering. The candidate molecules were validated by affinity prediction, QED scoring, and molecular docking. RESULTS: All three methods generated chemically valid and pharmacologically enhanced molecules relative to the original TPACD components. The EMGA provided improved biological affinity and exploration efficiency, whereas the IMGA achieved the highest overall performance level, with superior QED and docking stability, and predicted the CRC inhibition potential of the molecules. CONCLUSION: The proposed EGA-PPO framework effectively connects traditional decoction principles with modern AI-based drug design. The tri-algorithmic system (BMGA-EMGA-IMGA) provides a scalable and interpretable strategy for de novo molecular discovery, offering promising leads for acquiring natural product-derived CRC therapeutics.
9 May 2026
Read appraisal →Annals of surgical oncology
CT-based Prediction of Visceral Pleural Invasion in Lung Adenocarcinoma ≤ 3 cm: Enhancing Deep Learning Specificity by Waiving Chest Wall Information
BACKGROUND: Accurate preoperative prediction of visceral pleural invasion (VPI) in lung adenocarcinoma is essential for guiding surgical decision-making. However, existing prediction models often sacrifice specificity when optimized for high sensitivity, increasing the risk of overtreatment. This study aimed to develop a computed tomography (CT)-based deep learning (DL) model that improves specificity by excluding chest wall information. METHODS: We retrospectively enrolled 835 patients with pathologically confirmed lung adenocarcinoma who underwent complete resection at two medical centers. A VPI-DL model was developed using a four-layer convolutional neural network incorporating a novel attention mechanism trained on chest-wall-masked CT inputs. The model was trained on 692 cases and externally validated on an independent cohort of 143 patients. Performance was benchmarked against the consolidation-to-tumor ratio (CTR), a published DL model, and assessments by three thoracic surgeons. Evaluation metrics included area under the curve (AUC), sensitivity, specificity, and accuracy under a high-sensitivity operating threshold. RESULTS: In external validation, VPI-DL achieved an AUC of 0.91, sensitivity of 91%, specificity of 82%, and accuracy of 83%, outperforming the CTR (specificity 63%), the previously published model (specificity 54%), and thoracic surgeons (specificity 57%) at comparable sensitivity levels. The attention-guided architecture effectively reduced spurious correlations and improved interpretability. CONCLUSION: The proposed VPI-DL model improves the specificity of VPI prediction while preserving high sensitivity. By intentionally excluding chest wall information, the model offers a robust and interpretable tool to aid preoperative planning and minimize the risk of both under- and overtreatment in early-stage lung adenocarcinoma.
6 May 2026
Read appraisal →Radiology. Imaging cancer
ONCO-RADS-guided Large Language Models for Extraction and Classification of Incidental Findings on Whole-Body Imaging Reports
Purpose To evaluate large language model (LLM)-based strategy performance for extraction and classification of incidental findings from whole-body (WB) imaging reports, particularly strategies incorporating Oncologically Relevant Findings Reporting and Data System (ONCO-RADS). Materials and Methods In this retrospective bicenter study, authors included all WB MRI reports from January 2016 to December 2023 at a referral center (internal dataset). Two observers extracted all incidental findings, and patient records were used to confirm final diagnoses. First, authors evaluated ONCO-RADS performance and the reproducibility of its incidental finding classifications by six radiologists. Then, authors evaluated the accuracy of three LLM-based strategies: (a) a fine-tuned DeBERTa/medical named entity recognition (NER) model; (b) zero-shot LLMs (ChatGPT-o1 [OpenAI], Gemini-2.5-Pro [Google]); and (c) reference-guided prompting of these LLMs using ONCO-RADS. Authors then expanded these strategies to an external dataset of 605 reports with multiple imaging techniques (405 WB MRI; 100 fluorodeoxyglucose PET/CT; and 100 chest-abdomen-pelvis CT acquisitions) from January 2022 to January 2025. Results The internal dataset included 823 patients (mean age, 63.7 years ± 11.7 [SD]; 457 male patients) with 1488 WB MRI reports. The average interobserver reproducibility of ONCO-RADS incidental finding classifications was excellent (Cohen κ, 0.87). The per-report accuracies of ONCO-RADS-guided LLMs (95.6% [151 of 158] and 86.7% [137 of 158] for ChatGPT-o1 and Gemini-2.5-Pro, respectively) were higher than those of the medical NER (69.0% [109 of 158]) and zero-shot LLMs (57.0% [90 of 158] and 70.9% [112 of 158] for ChatGPT-o1 and Gemini-2.5-Pro, respectively) (P < .001). In the external test set (mean age, 60.6 years ± 12.9; 330 male patients), the per-report accuracies of ONCO-RADS-guided ChatGPT-o1 (83.5% [505 of 605]) and Gemini-2.5-Pro (82.0% [496 of 605]) were higher than those of the models without ONCO-RADS prompting (63.1% [382 of 605] and 61.2% [370 of 605], respectively) and the medical NER (55.7% [337 of 605]) (P < .001). Conclusion Reference-guided prompting of the LLMs ChatGPT-o1 and Gemini-2.5-Pro with ONCO-RADS improved their performance in extracting and classifying incidental findings on WB imaging reports compared with zero-shot prompting and medical NER. Keywords: Large Language Models, Incidental Findings, Whole-Body MRI Supplemental material is available for this article. © RSNA, 2026.
6 May 2026
Read appraisal →Cytopathology : official journal of the British Society for Clinical Cytology
Comparative Evaluation of Transfer Learning Models for Detecting Malignant Cells in Urinary Cytology
AIMS AND OBJECTIVES: In the present paper, we compared the efficiency of six transfer learning models to detect malignant cells in urine cytology. We also applied an ensemble learning with weighted soft voting to assess its importance in the diagnostic accuracy in urine cytology. MATERIALS AND METHODS: The voided urine samples of total 104 cases of urothelial cell carcinoma (UCC) and 86 cases with no malignancy (benign) were selected. All the 104 cases of UCC were histopathology proven high grade urothelial cell carcinoma (UCC). Urine with negative cytology reports were followed up clinically. There were 446 images of benign samples and 1369 images from malignant samples on 100 x objective. We applied six transfer learning models (DenseNet121, inception_v3, ResNet50, MobileNetV2, VGG16 and Xception) to detect malignant cells in urine. To compare the performance of different models, dynamic training optimization was done and each model was auto stopped after their maximum performance. In addition, an ensemble learning with soft voting was used including the top three models to enhance the diagnostic accuracy. RESULT: Xception transfer model showed the highest sensitivity (88.57%), accuracy (86.55%), precision (80.52%) and FI score (84.35%). It was the best performing model. The other two top performing models were InceptionV3 and ResNet50. The area under curve of receiver operating characteristic (AUCROC) was ≤ 90 in all the transfer learning models. The accuracy, sensitivity, specificity, precision, F1-Score and AUCROC of the ensemble transfer learning model were as follows: 92.10%, 95.41%, 85.51%, 91.23%, 93.24% and 0.977 respectively. CONCLUSIONS: First time, we evaluated a large number of transfer learning models in urine cytology to detect malignant cells. All the models showed high sensitivity, specificity and accuracy. In addition, the ensemble learning technique with soft voting showed remarkable superior performance than individual top three transfer learning models. The techniques transfer learning and ensemble models have high potential to use in routine screening of urine.
6 May 2026
Read appraisal →Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Validation of a Deep-Learning Coregistration Framework for Long-Axial-Field-of-View PET/CT Using Low-Radiation-Exposure Protocols Across Various Tracers
The advent of long-axial-field-of-view (LAFOV) PET/CT systems has significantly improved whole-body imaging by providing higher sensitivity and extended torso coverage. However, PET/CT potential misalignment remains a challenge, potentially introducing artifacts and quantification errors. Moreover, PET protocols with reduced scanning duration and dose, as well as the use of ultra-low-dose CT (ULD-CT), are increasingly relevant in clinical practice and research. This study aimed to evaluate the robustness and generalizability of a deep-learning coregistration framework for PET/CT alignment using low-dose PET and ULD-CT across various tracers with a LAFOV system. Methods: In total, 63 scans with 4 different tracers (89Zr-trastuzumab, 15O-H2O, 18F-MC225, and 18F-FDG) were included to assess PET and CT alignment improvements. Further evaluation was performed by comparing CT scans coregistered to the original PET images (rCT) with those coregistered to low-count (50%, 25%, and 12.5% of original counts) PET images (LC-rCT). Dice similarity coefficient, Jaccard similarity coefficient, Hausdorff distance, and average surface distance were used as evaluation metrics. Furthermore, ULD-CT coregistered to PET (rULD-CT) was compared with low-dose CT coregistered to the same PET (rLD-CT) using the same metrics. PET accuracy was evaluated by calculating SUVs. Results: The robustness of the deep-learning coregistration framework was demonstrated in reduced PET counts and ULD-CT scenarios across tracers. All metrics indicated robust performance when comparing LC-rCT with rCT and rULD-CT with rLD-CT. Consistent SUVs across varying PET counts and ULD-CT conditions further validated the quantitative accuracy of the approach. Conclusion: This work highlights that neither the use of low-dose PET protocols nor ULD-CT compromise the performance of this coregistration framework across 4 tracers. For LAFOV PET, these findings support the feasibility of such a framework in imaging protocols with reduced radiation exposure.
5 May 2026
Read appraisal →The British journal of radiology
Automatic segmentation of clinical target volume for radiation therapy in breast-conserving patients and exploration of clinical factors influential to its performance
OBJECTIVES: To develop and validate a deep learning model for whole breast clinical target volume (CTV) contouring and evaluate clinical features affecting its performance. METHODS: Five datasets with 857 patients from a single center were used. Dataset 1 (n = 300) trained and tested the model. Dataset 2 (n = 10) evaluated contouring time and dosimetric parameters. Datasets 3 (n = 20) and 4 (n = 10) were for clinical evaluation. Dataset 5 (n = 517) identified clinical factors influencing auto-contouring accuracy. Model performance was assessed using Dice Similarity Coefficient (DSC) and 95th percentile Hausdorff Distance (HD95). RESULTS: The median DSC and HD95 for left- and right-sided models in Dataset 1 were 0.941, 1.75 mm and 0.937, 2.47 mm, respectively. In Dataset 2, both auto-contouring and auto-contouring with manual corrections were significantly faster than manual contouring (P = .005 for both), while still achieving clinically acceptable dosimetric results. In Dataset 3, two physicians rated automatic and manual contours as equivalent (P = .214, P = .075), while the other rated auto-contouring higher (P < .001). In Dataset 4, the auto-contouring model outperformed 1/5 physicians by DSC (P = .009) and 3/5 by HD95 (P = .015, P = .007, P = .017). In Dataset 5, peripheral tumor-bed and low-density breast tissue were associated with lower DSC (P < .001 for both) and higher HD95 (P < .001 for both). Cases without unfavorable factors performed better than those with (P < .001 for both). CONCLUSIONS: The proposed model demonstrated acceptable accuracy, consistency, and efficiency in breast CTV contouring. Peripheral tumor-bed and low-density breast tissue reduced auto-contouring performance. ADVANCES IN KNOWLEDGE: The characteristics of challenging cases in whole breast CTV auto-contouring should be identified.
5 May 2026
Read appraisal →Journal of medical virology
A Triage Model for HR-HPV Positive Women Based on Methylation of Human and HPV Genes
The aim of this study is to develop a machine learning triage model for HR-HPV positive women based on methylation patterns of human and HPV genes to predict the risk of CIN2 +, and to compare its performance with traditional detection techniques. A total of 512 eligible women participated in the study, with exfoliated cervical cell specimens collected for methylation analysis of both human and HPV genes. Highly methylated gene fragments related to cervical cancer were identified through high density, high association, high MHL (Methylation Haplotype Load) screening and Logistic regression. The samples were divided into a training set and a validation set at a ratio of 7:3, and a machine learning triage model was constructed using CIN2+ as the outcome. Among 512 HR-HPV positive women, 174 (34.0%) had CIN2 +. To construct the random forest model, 10 high methylation human gene fragments were identified from a pool of 45 genes, along with HPV 16, 18, and 52 L2 genes. In the validation set, the random forest model identified 45 samples as positive (38 were true positives) and 112 samples as negative (96 were true negatives). The model achieved an AUC of 0.9, and DeLong test confirmed its performance was significantly superior (p < 0.001) to HPV 16/18 genotyping and cytology testing. The random forest model constructed based on methylation patterns of human and HPV genes demonstrated strong predictive efficacy for CIN2+ in HR-HPV positive women, and may optimize the triage method in cervical cancer screening.
5 May 2026
Read appraisal →Journal of the European Academy of Dermatology and Venereology : JEADV
A deep learning-clinical nomogram hybrid for predicting sentinel lymph node metastasis in melanoma
BACKGROUND: Melanoma is a life-threatening skin malignancy, with sentinel lymph node metastasis (SLNM) serving as a critical prognostic factor. While machine and deep learning models using histopathology have focused on melanoma diagnosis, limited efforts have aimed to predict SLNM. OBJECTIVES: This study aimed to develop a collaborative machine and deep learning model that integrates histopathological patches and clinical data to predict SLNM in patients with invasive melanoma, thereby aiding decision-making regarding sentinel lymph node biopsy. METHODS: A total of 78 invasive cutaneous melanoma cases (43 SLNM-positive and 35 SLNM-negative) from the University of Yamanashi Hospital (2007-2022) were divided into training (n = 60) and test (n = 18) datasets. A clustering-constrained attention multiple-instance learning (CLAM) model was developed using haematoxylin and eosin-stained whole-slide images. Among the four CLAM variants, the model using a ResNet50trunc encoder trained on whole-tissue regions (CLAM-R50) demonstrated the best performance and was further integrated with a clinical nomogram. Hybrid modelling was performed using logistic regression, a support vector machine and gradient boosting. RESULTS: CLAM-R50 achieved an area under the receiver operating characteristic curve (AUROC) of 0.875, while the clinical nomogram achieved an AUROC of 0.826 on the test set. The gradient boosting hybrid model, termed the Melanoma Indicative Scorer for Sentinel Lymph node Evaluation (MISSLE), demonstrated the highest AUROC of 0.950 (95% bootstrapped confidence interval (CI): 0.933-1.00) on the test set. CLAM-R50 attention maps identified pale cytoplasm and plasma cell-like tumour cells as predictors of positive SLNM, whereas dense melanin deposition and lymphocyte-like tumour cells predicted negative SLNM. CONCLUSIONS: MISSLE integrates histopathological and clinical data to predict SLNM with high AUROC, surpassing the previous reports and offering a tool for clinical decision-making. Limitations include the single-centre design and predominantly Japanese cohort, limiting generalizability. The code and models are publicly available for external validation.
5 May 2026
Read appraisal →Abdominal radiology (New York)
A CT-based 2.5D deep learning model for preoperative T-staging in gastric cancer: a retrospective multicenter study
PURPOSE: To develop and validate a 2.5D multi-angle deep learning (MADL) model for preoperative T-staging in patients with gastric cancer (GC) and to explore the predictive potential for survival outcome. MATERIALS AND METHODS: In this retrospective study, GC datasets from four centers were used to develop a quaternary for preoperative T-staging model by integrating CT images with nine 2D slicers and three angles (0°, + 45°, -45°) from transverse, sagittal, and coronal views. Diagnostic performance (accuracy, sensitivity, specificity, F1-score) were compared between the 2.5D MADL and radiologists. The prognostic potential of 2.5D MADL-derived features was evaluated using Kaplan-Meier analysis and multivariate Cox regression. RESULTS: A total of 433 patients were divided into the training (n = 346, mean age, 64.8 years ± 10.5 [SD]; 95 female) and the internal validation (n = 87, mean age, 63.0 years ± 11.9; 23 female) sets from Centers 1-3, and 41 patients (mean age, 64.6 years ± 8.9; 10 female) formed the external testing set from Center 4. The 2.5D MADL model exhibited excellent diagnostic performance for T-staging in external testing set (AUCs of T1-T4: 0.962, 0.722, 0.913, 0.962). For T1 staging, it outperformed radiologists in accuracy (95% vs. 83%), sensitivity (75% vs. 12%), and F1 score (86% vs. 22%) in external testing set. Survival analysis showed significant overall survival differences between high- and low-risk groups stratified (P < 0.001), with high-risk patients having shorter survival (HR = 1.632, 95% CI:1.239-2.150). CONCLUSION: The 2.5D MADL model achieves accurate and reliable preoperative T staging of gastric cancer, with performance comparable to radiologists and superior accuracy for early-stage disease.
5 May 2026
Read appraisal →Abdominal radiology (New York)
Deep learning-based combined noise reduction and contrast enhancement for post-neoadjuvant pancreatic cancer CT: does improved image quality translate to better resectability assessment?
PURPOSE: To evaluate whether deep learning-based combined noise reduction and contrast enhancement reconstruction (DLR) improves image quality and resectability prediction accuracy compared to conventional iterative reconstruction (IR) in post-neoadjuvant pancreatic cancer CT assessment. METHODS: This retrospective study included 114 patients with pancreatic cancer following neoadjuvant therapy. Contrast-enhanced CT images were reconstructed using conventional IR and vendor-neutral ClariACE. Three abdominal radiologists independently assessed image quality (based on 8 parameters: tumor conspicuity, tumor margin, image noise, sharpness of the main pancreatic duct, arterial depiction, venous depiction, plasticity and overall image quality) and determined tumor resectability with confidence levels. Quantitative analysis included aortic and portal venous attenuation measurements and pancreas-to-tumor contrast-to-noise ratio (CNR). Diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC) with DeLong's test. Sensitivity, specificity, accuracy, and reader confidence were compared using McNemar's test. RESULTS: DLR demonstrated significantly superior vessel enhancement (p < 0.001) and improved CNR (p < 0.001) versus conventional IR. Two readers consistently rated DLR images higher across all qualitative categories (p < 0.001) except plasticity, while the third reader favored DLR in five of eight parameters (p < 0.001 to 0.020). However, all readers noted increased artificial appearance in DLR images (p < 0.001). Despite image quality improvements, no significant differences were observed in resectability assessment accuracy (62.3%-65.8%), AUC values (0.485-0.520), or high-confidence diagnosis rates between reconstruction methods. CONCLUSION: Although deep learning-based combined noise reduction and contrast enhancement reconstruction significantly improved quantitative and subjective image quality metrics, it did not enhance diagnostic accuracy for predicting R0 resectability in post-neoadjuvant pancreatic cancer patients.
5 May 2026
Read appraisal →Biosensors & bioelectronics
Peptide-responsive photonic hydrogels integrated with deep learning assistance for early MMP-9 detection
Matrix metalloproteinase-9 (MMP-9) is crucial for extracellular matrix remodeling, and its dysregulation is associated with inflammatory diseases and different forms of cancer. Conventional MMP-9 detection methods such as enzyme-linked immunosorbent assay (ELISA) are limited by complexity, expensive equipment, and lengthy antibody incubation times. These limitations prevent their use in point-of-care testing. An MMP-9 responsive photonic crystal (PC/PEG-M9SP) hydrogel has been developed to address these challenges. The hydrogel is synthesized from 4-arm polyethylene glycol-acrylate and an MMP-9 sensitive peptide via Michael-type addition reaction. Upon MMP-9-specific enzymatic cleavage, the hydrogel undergoes a structural reconfiguration, resulting in a distinct color shift. Integrated with a deep learning-based smartphone app, this platform enables both visual and quantitative detection within 10 min, achieving high sensitivity (10.60 nm mL/ng) and a detection limit of 0.62 ng/mL. Validation in complex biological fluids demonstrated strong concordance with ELISA, confirming the analytical reliability of the hydrogel. This system provides a rapid, portable, and cost-effective solution for accurate MMP-9 detection, with strong potential for clinical and point-of-care applications.
5 May 2026
Read appraisal →Journal of proteome research
NetMHCIIphosPan: A Machine Learning Tool for Predicting HLA Class II Antigen Presentation of Phosphorylated Peptides
Phosphorylated peptides presented by human leukocyte antigen (HLA) class II molecules play pivotal roles in immune regulation, yet their characterization and prediction remain challenging due to data noise and limited HLA coverage. Here, we introduce NetMHCIIphosPan, a prediction method for HLA-II antigen presentation of phosphorylated peptides, developed using mass spectrometry (MS)-based immunopeptidomics data sets. Employing a refined peptide identification workflow, we reanalyzed earlier HLA-II phospholigand data sets and trained predictive models, achieving superior performance compared to models trained on the original data. Binding motif analysis revealed that HLA-specific preferences for phospholigands closely aligned with those of unmodified ligands. Incorporating unmodified ligands into training further enhanced predictive accuracy, particularly for HLA-DP and HLA-DQ molecules. NetMHCIIphosPan outperformed existing tools, such as NetMHCIIpan-4.3 and MixMHC2pred-1.3, for prediction of HLA antigen presentation of phosphorylated peptides, demonstrating robustness and utility. This work establishes NetMHCIIphosPan as a state-of-the-art tool for understanding the HLA-II phospholigandome, with potential applications in immunotherapy and vaccine design.
4 May 2026
Read appraisal →Computer methods in biomechanics and biomedical engineering
Multiple instance learning method based on convolutional neural network and self-attention for early cancer detection.
Early cancer detection using T-cell receptor sequencing (TCR-seq) and multiple instances learning methods has shown significant effectiveness. We introduce a multiple instance learning method based on convolutional neural networks and self-attention (MICA). First, MICA preprocesses TCR-seq using word vectors and then extracts features using convolutional neural networks. Second, MICA uses an enhanced self-attention mechanism to extract relational features of instances. Finally, MICA can extract the crucial TCR-seq. After cross-validation, MICA achieves an area under the curve (AUC) of 0.911 and 0.946 on the lung and thyroid cancer datasets, which are 7.1% and 2.1% higher than other methods, respectively.
4 May 2026
Read appraisal →Nano letters
Seeing and Feeling DNA Methylation: Single-Molecule Biophysics Meets Machine Learning
DNA methylation at 5-methylcytosine (5mC) is crucial for embryonic development and cellular function, while aberrant patterns strongly drive disease onset and progression. Its reversible nature offers substantial therapeutic potential, emphasizing the need for precise, context-specific genome wide 5mC mapping. Conventional techniques such as bisulfite sequencing and ensemble biosensor assays are hindered by DNA degradation, amplification bias, high cost, and inability to resolve single-molecule structural and mechanical effects of methylation. This review examines advances in single-molecule biophysical methods (nanopore sensing, smFRET, optical/magnetic tweezers, and AFM) that provide direct, label-free/minimally invasive 5mC detection, along with quantitative insights into DNA conformation, mechanics, and protein-DNA interactions. These techniques complement traditional methylome mapping by linking genomic localization to molecular mechanisms. Emerging machine-learning approaches are revolutionizing analysis, particularly in nanopore sensing, while promising applications in smFRET, tweezers, and AFM address throughput and reproducibility challenges. Their convergence promises scalable, high-resolution epigenetic profiling, advancing precision epigenomics toward clinical application.
4 May 2026
Read appraisal →PloS one
Deep feature optimization using fusion of multiple self-supervised learning approaches and filter-based feature selection for lung cancer histopathology classification
One of the most deadly illnesses in the world is lung cancer, and increasing survival rates require early detection. Lung cancer diagnostics from the imaging modalities is always subjective, and this paves the way for deep learning assisted computer aided techniques. Still, the accuracy of such a technique is the major concern. This research work attempts to enhance lung cancer diagnostics from histopathological images using a previously unaddressed combination of multiple self-supervised learning techniques, filter-based feature selection, and Vision Graph Convolutional Networks. The main contribution lies in optimized feature fusion, and it brings complementary strengths of three different self-supervised learning approaches- contrastive alignment, redundancy reduction, and semantic grouping. The first step involves extracting the key features from histopathological images using a custom Convolutional Neural Network. Three complementary self-supervised learning methods - Deep Cluster, Bootstrap Your Own Latent, and Simple Framework for Contrastive Learning of Visual Representations - are then used to refine each of these features separately. Following optimization, the improved features are combined, and the most significant features from the combined set are found and preserved using a filter-based feature selection technique called Minimum Redundancy Maximum Relevance. Vision Graph Convolutional Neural Network is used as a classifier. Initial experiments are carried out on the 691 histopathological images extracted from the publicly available LungHist700 dataset. This dataset contains histopathological images under three categories: 151 normal subjects, 280 lung adenocarcinoma subjects, and 260 lung squamous cell carcinoma subjects. The proposed approach provided a balanced accuracy of 97% while the plain Vision Graph Convolution Neural Network offers only an 86% balanced accuracy score. Further validation is performed using two more publicly available datasets, namely LC25000 and TCGA UT datasets. The experimental results demonstrate the enhanced lung cancer prediction performance of the proposed approach in all three datasets.
2 May 2026
Read appraisal →Journal of investigative surgery : the official journal of the Academy of Surgical Research
Comparison of Functional Outcomes Between Robotic and Laparoscopic Surgery in Rectal Cancer Patients: Systematic Review and Meta-Analysis
BACKGROUND: Robotic and laparoscopic approaches are widely used for rectal cancer surgery. Although robotic systems provide enhanced visualization and dexterity, it remains unclear whether these advantages improve postoperative urinary and sexual function. METHODS: MEDLINE, Embase, Web of Science, and CENTRAL (2000-2025) were searched for studies comparing robotic and laparoscopic rectal cancer surgery. Outcomes included postoperative ileus, urinary retention, and urinary and sexual function. Random-effects meta-analysis was performed using RevMan. Study quality was assessed with MINORS, and certainty of evidence with GRADE. Publication bias was evaluated using funnel plots and Egger's test. RESULTS: Forty-four observational studies (6,121 patients) were included. Robotic surgery was associated with lower urinary retention, though this may reflect publication bias. No significant difference was found in postoperative ileus. Subgroup analyses at 3, 6, and 12 months showed no significant differences in urinary or sexual function. Although pooled results slightly favored robotic surgery, these effects were inconsistent, not sustained, and clinically modest. CONCLUSION: Robotic surgery may reduce urinary retention but shows no consistent functional superiority over laparoscopy. Outcomes at key follow-up points are comparable. As all studies were observational, evidence certainty is low, and findings should be interpreted cautiously. This study provides an updated and comprehensive synthesis of functional outcomes following robotic versus laparoscopic rectal cancer surgery, with a specific focus on clinically relevant time-point analyses (3, 6, and 12 months). Unlike prior meta-analyses, which often emphasize pooled outcomes across heterogeneous follow-up periods, this study demonstrates that no consistent or durable improvements in urinary or sexual function are observed at predefined postoperative intervals. Furthermore, by integrating GRADE-based certainty assessment, this work highlights that the current evidence is predominantly observational and of low to very low certainty. Importantly, the study incorporates contemporary evidence up to 2025, including recent systematic reviews and randomized data, and provides a clinically grounded interpretation by contextualizing effect sizes against minimal clinically important difference (MCID) thresholds. Collectively, this analysis offers a more cautious and clinically meaningful appraisal of functional outcomes, challenging assumptions of functional superiority associated with robotic rectal surgery.
2 May 2026
Read appraisal →Science advances
Repertoire-scale antibody structural prediction informs therapeutic design
We present AF3-TurboAb, a scalable framework that makes a repertoire-scale antibody-antigen complex structural decoding routine for antibody engineering. By eliminating preprocessing bottlenecks, AF3-TurboAb enables end-to-end complex modeling in 0.5 minutes per seed on a single GPU while preserving near-experimental interface fidelity, as validated on ~1000 posttraining Protein Data Bank (PDB) benchmarks and 12 experimentally determined cryo-electron microscopy nanobody-antigen structures. Applying this capability to 275,371 immunization-derived antigen-specific nanobodies produced 28,013 high-confidence complex predictions, substantially expanding the structural landscape of antibody recognition. The resulting atlas reveals hundreds of previously unmapped epitopes, extensive coverage of solvent-exposed surfaces, and recurrent affinity hotspots enriched in aromatic and charged residues. Despite wide sequence diversity, we observe structural convergence at shared epitopes and consistent physicochemical and geometric features that complement and extend existing PDB entries. We demonstrate translational utility by (i) designing durable (escape-proof), multiepitope neutralizers against highly evolved viruses, (ii) identifying cross-species and glycoform-specific binders to a cancer checkpoint, and (iii) enabling near-real-time in silico binder triage. The models and metadata will be shared for community use, establishing repertoire-scale structural decoding as a practical design modality that transforms the scale and speed of structure-guided antibody engineering.
27 Apr 2026
Read appraisal →Physics in medicine and biology
MRDT-GAN: generative adversarial network with multi-scale residual dense transformer generator for low-dose CT denoising
Objective.Low-dose computed tomography (LDCT) reduces radiation exposure but introduces noise and artifacts that degrade diagnostic quality. Existing deep learning-based denoising methods still face challenges such as over-smoothing, loss of fine structures, and uneven contrast. This study aims to develop an LDCT denoising framework that enhances noise suppression while preserving anatomical details and structural fidelity.Approach.We propose a multi-scale residual dense transformer generative adversarial network (MRDT-GAN). In the generator, we adopt the MRDT Block as the core unit, which introduces multi-scale strategy into residual dense network to reduce over-smoothing and preserve fine details, and also patching transformer block to capture long-range dependencies, mitigating distortions caused by localized receptive fields in convolutional neural network-based approaches. A hybrid attention module is also introduced in the generator to process spatial, frequency, and contrast information, enabling the network to focus on critical regions for noise suppression, improve contrast uniformity, and maintain texture consistency. In the discriminator, we adversarially explore differences on global, pixel, and also sub-scale between denoised LDCT and normal dose CT to better capture structural variations, reduce local noise and distortions, and ensure more realistic texture reconstruction while minimizing artifacts.Main results.We validate MRDT-GAN on both the NIH-AAPM-Mayo Clinic LDCT dataset and a real-world dataset. Experimental results indicate that MRDT-GAN achieves superior denoising performance compared with existing methods, effectively preserves details, enhances visual quality, and achieves a better balance between noise suppression and structural integrity.Significance.MRDT-GAN provides an effective and generalizable LDCT denoising solution that balances noise reduction with fine-detail preservation. By integrating MRDT modeling, hybrid attention mechanisms, and multi-difference adversarial learning, the framework offers improved clinical applicability and supports high-quality image reconstruction for downstream diagnostic tasks.
27 Apr 2026
Read appraisal →Annals of medicine
Construction of an XGBoost-SHAP-based malignant transformation risk prediction model for gallbladder polyps
BACKGROUND: To develop and validate a risk prediction model for malignant transformation in patients with gallbladder polyps (GBPs) using an interpretable machine learning framework and evaluate its predictive accuracy. METHODS: A retrospective cohort of 1,027 surgical patients was enrolled from Yantai Yuhuangding Hospital (training set: n = 933) and Shanghai Eastern Hepatobiliary Surgery Hospital (validation set: n = 94). Feature selection for the training set was performed using the least absolute shrinkage and selection operator (LASSO) regression method. A predictive model was constructed with the XGBoost machine learning algorithm and evaluated using Shapley Additive exPlanation (SHAP). RESULTS: LASSO regression identified five significant risk factors for malignant transformation in GBPs: presence of concomitant cholecystitis, polyp count, polyp base width, age, and maximum polyp diameter. The area under the receiver operating characteristic curve (AUC) was 0.862 (95% confidence interval [CI]: 0.8342-0.8893) in the training set and 0.777 (95% CI: 0.6804-0.8737) in the validation set. SHAP analysis illustrated the contribution of each factor. CONCLUSION: This study developed and validated a risk prediction model for malignant transformation in patients with GBPs. The model demonstrated favorable discrimination, calibration, accuracy, and clinical applicability. Integration with SHAP technology may assist clinicians in optimizing treatment and management strategies.
27 Apr 2026
Read appraisal →ACS sensors
Nanobiosensors and Artificial Intelligence Strategies for Glycan Profiling in Cancer Progression: A Critical Review
Glycans are complex carbohydrates that are integral to cancer progression and tumor biology. Conventional methods face challenges due to the structural diversity and low abundance of glycans, limiting the detection of cancer-associated glycan signatures. This bottleneck hampers the accurate monitoring of cancer progression and the development of effective diagnostic tools. Nanobiosensor-enabled glycan profiling overcomes conventional limitations by providing highly sensitive detection of low-abundance glycans and precise discrimination of similar forms through nanoscale electrochemical or optical signal amplification and selective biorecognition. This review evaluates emerging nanobiosensors, including optical modalities such as surface plasmon resonance, localized surface plasmon resonance, surface-enhanced Raman scattering, fluorescence, and colorimetric nanobiosensors. It also covers electrochemical systems, including impedimetric, voltammetric, and amperometric sensors that use nanomaterials to enhance the sensitivity, specificity, and reliability of glycan detection. After a critical evaluation of nanobiosensors, this review highlights their applications for glycan analysis in cancer progression and potential of miniaturized point-of-care platforms. In parallel, this review highlights emerging artificial intelligence (AI) and machine learning (ML) approaches that are being explored to analyse complex biosensor outputs. By enabling multivariate pattern recognition, dimensionality reduction, and nonlinear modeling of biosensor responses, these computational frameworks may facilitate the extraction of diagnostically relevant signatures from high-dimensional optical and electrochemical datasets. The review identifies challenges in standardization and clinical scalability of nanobiosensors and explores future directions, including AI integration to advance early cancer detection and personalized patient management.
27 Apr 2026
Read appraisal →Lab on a chip
MaGIC-OT: an AI-guided optical tweezers platform for autonomous single-cell isolation in microfluidic devices
Automating the isolation of rare cells such as circulating tumour cells (CTCs) within crowded microfluidic environments remains a bottleneck in liquid biopsy workflows. Optical tweezers offer contact-free, selective manipulation but traditionally rely on expert operators. We present MaGIC-OT (machine-guided isolation of cells using optical tweezers), a platform that integrates classical path planning and deep reinforcement learning (DRL) to automate single-cell manipulation inside a microfluidic chip. We built a high-fidelity simulation to train and benchmark control policies and show that cooperative, human-in-the-loop training improves DRL performance. Trained agents outperform expert users in speed and isolation success in silico, and we demonstrate proof-of-concept isolation of a cancer cell from a spiked blood sample on-chip. MaGIC-OT provides a flexible framework for intelligent optical manipulation, aligning microfluidic device design with autonomous control strategies and offering a pathway toward high-purity, label-free single-cell workflows.
26 Apr 2026
Read appraisal →BMJ open
Effects of nurse-led shared decision-making on low-dose CT uptake and screening outcomes in high-risk populations: a systematic review and meta-analysis
OBJECTIVES: To evaluate the effects of nurse-led shared decision-making (SDM) on lung cancer screening outcomes, including low-dose CT (LDCT) uptake, benign findings, early cancer detection and willingness to participate among high-risk populations. DESIGN: Systematic review and meta-analysis. DATA SOURCES: PubMed, Medline via OvidSP, Cochrane Central Register of Controlled Trials, EMBASE via OvidSP, Web of Science, Scopus, grey literature databases and clinical trial registries were searched from inception to March 2025. ELIGIBILITY CRITERIA: Studies evaluating nurse-led SDM interventions in high-risk lung cancer populations, reporting outcomes including LDCT uptake rates, screening results (Lung-RADS (Lung Imaging Reporting and Data System) classifications), early-stage cancer detection or willingness to participate. Randomised controlled trials, quasi-experimental studies and observational studies were included. DATA EXTRACTION AND SYNTHESIS: Two reviewers independently extracted data and assessed risk of bias using the Risk of Bias in Non-randomised Studies of Interventions (for non-randomised studies) and Cochrane Risk of Bias 2.0 (for randomised controlled trials). Meta-analyses were conducted using random-effects models. Meta-regression explored sources of heterogeneity. RESULTS: 13 studies (n=13 608 participants) were included, comprising 10 single-arm studies and three comparative studies. In single-arm studies without control groups, nurse-led SDM programmes achieved a pooled LDCT uptake rate of 98% (95% CI 28% to 100%; I²=99%), and willingness to participate was 68% (95% CI 24% to 93%; I²=98%). In comparative studies, nurse-led SDM showed no significant difference in LDCT uptake compared with usual care (RR 1.00, 95% CI 0.99 to 1.02; I²=0%), suggesting non-inferiority rather than superiority. Among individuals who completed screening, 81% (95% CI 77% to 85%) had benign or low-risk findings (Lung-RADS [Lung Imaging Reporting and Data System] I/II), and 2% (95% CI 1% to 3%) were diagnosed with early-stage lung cancer, rates consistent with benchmark screening trials. Meta-regression identified female sex as positively associated with uptake (β=0.54, p<0.001), while current tobacco use was negatively associated (β=-0.37, p=0.033). The risk of bias was moderate to serious across studies. CONCLUSIONS: Comparative evidence suggests that nurse-led SDM achieves equivalent LDCT uptake to standard care approaches, indicating feasibility as an alternative service delivery model. However, the predominance of single-arm studies, high heterogeneity and moderate-to-serious risk of bias limit causal inference. High uptake rates in single-arm studies likely reflect selection bias rather than intervention effectiveness. Current evidence supports the feasibility but not the superiority of nurse-led SDM. Well-designed randomised controlled trials are needed to establish comparative effectiveness and cost-effectiveness before recommending widespread integration of nurse-led SDM into lung cancer screening programmes. TRIAL REGISTRATION NUMBER: PROSPERO CRD420251033595. https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=1033595.
26 Apr 2026
Read appraisal →Biomedizinische Technik. Biomedical engineering
Advanced deep learning framework for breast cancer detection using digital breast tomosynthesis images
OBJECTIVES: Early and accurate detection of breast cancer is critical for improving patient survival outcomes. This study proposes a robust deep learning-based framework for breast cancer detection using digital breast tomosynthesis (DBT) images, leveraging both single-slice and multi-slice inputs. METHODS: The proposed work includes image normalization, resizing and Laplacian Pyramid Enhancement (LPE). Various features were extracted and fused in different combinations. To retain the most discriminative features Exhaustive Feature Selection (EFS) is used. A hybrid model integrated using ResNet V2, MobileNet V3 and Inception V3+ for classification. Finally ensemble learning with XGBoost was applied and Hyperparameters were optimized using a grid search strategy. RESULTS: The hybrid model with multi-slice DBT inputs achieved better improvements with accuracy, sensitivity, specificity and area under the curve (AUC). While applying LPE, feature fusion and EFS substantially enhanced the hybrid model's diagnostic performance. CONCLUSIONS: The findings demonstrate the strong potential of advanced methods in enhancing performance. Future research work will focus on integrating this pipeline with clinical decision support systems, multi-center datasets and extending to other breast imaging modalities.
26 Apr 2026
Read appraisal →Blood
A phase 1/2 study of donor-derived anti-CD33 CAR T-cell therapy (VCAR33) for relapsed/refractory AML after allogeneic HCT
VCAR33, a donor-derived CD33-directed chimeric antigen receptor T-cell (CAR T) product, was developed to decrease relapse of high-risk acute myeloid leukemia (AML) or myelodysplastic syndrome (MDS) after allogeneic hematopoietic cell transplantation (alloHCT). We describe preclinical characterization of the VCAR33 construct, which was optimized for long-term antitumor surveillance based on killing and persistence assays. Prior to its use in post-alloHCT maintenance, we evaluated safety and efficacy of VCAR33 in a phase 1/2 clinical study for adults with relapsed or measurable residual disease (MRD)-positive CD33+ AML/MDS after alloHCT. Fifteen patients received VCAR33 across 2 arms stratified by disease burden: 7 patients in arm A (bone marrow blasts ≥5%) at dose level 1 (DL1; 1 × 106 CAR+ Ts per kg) and 8 patients in arm B (bone marrow blasts <5%) at DL1 (n = 5) and DL2 (3 × 106 CAR+ Ts per kg; n = 3). The study ended for nonsafety reasons before escalation to DL3 (1 × 107 CAR+ Ts per kg) and maximum tolerated dose was not determined. The most common treatment-related adverse event was cytokine release syndrome (93.3%; all <grade 3). Four patients (26.7%) experienced immune cell-associated neurotoxicity syndrome (1 ≥grade 3) and 1 patient (6.7%) had grade 3 acute graft-versus-host disease within 28 days of VCAR33 infusion. Fourteen patients (93.3%) had transient VCAR33 expansion. Overall response rate was 20%: 2 patients had complete remission with incomplete count recovery in arm A and 1 arm B patient achieved MRD clearance. This allogeneic CAR T product demonstrated acceptable safety and preliminary antileukemic activity. This trial was registered at www.clinicaltrials.gov as #NCT05984199.
25 Apr 2026
Read appraisal →Journal of medicinal chemistry
Automated Molecular Design in BRADSHAW, Applied to the Optimization of ERAP1 Inhibitors
Generative design and machine learning are increasingly prevalent in medicinal chemistry. To pilot the comprehensive use of automated molecular design on a project, the BRADSHAW platform was used to optimize a series of inhibitors of Endoplasmic Reticulum Aminopeptidase 1 (ERAP1), an emerging target in cancer immunotherapy and autoimmune diseases. Through four consecutive iterations applying in silico molecular generation, property prediction and filtering, we conducted a multiparameter optimization of potency, physicochemical properties and pharmacokinetics. Continuous refinement of Machine Learning (ML) models led to improved scoring accuracy and compound quality, culminating in identification of in vitro and in vivo tool molecules. We also discuss our reflections on the pilot and integration of automated design into medicinal chemistry projects, including observations of the human factors resulting from increased use of computational design, and recommendations for future projects.
25 Apr 2026
Read appraisal →Journal of plastic surgery and hand surgery
Characteristics and prognosis of skin cancer arising from burn scars: a systematic review
BACKGROUND: Burn scars are recognized risk factors for malignant skin transformation, most notably Marjolin's ulcer (MU). Despite extensive documentation in case reports and series, the epidemiological characteristics and prognosis of burn scar-related skin cancers have lacked large-scale systematic synthesis. METHODS: We conducted a systematic review in line with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, registered with PROSPERO (CRD42024545404), covering literature from PubMed, Scopus, and Web of Science up to 18 January 2024. Eligible studies included case reports, case series, and observational studies reporting any type of skin cancer in burn scars. Data were extracted on demographics, burn and tumor characteristics, treatment, latency, outcomes, and risk of bias using Joanna Briggs Institute tools. RESULTS: A total of 211 studies reporting 830 cases were included. The mean latency period from burn injury to cancer diagnosis was 21.7 years (SD = 19.6). Males constituted 53% of patients, with third-degree burns predominating (89.37%) and lower limbs being the most affected site (33.59%). Squamous cell carcinoma (SCC) was the most frequent malignancy (67.19%), followed by basal cell carcinoma (BCC, 3.83%) and other cancers (15.33%). Recurrence occurred in 13.2% of cases; mortality was 6.96%. SCC accounted for most deaths (63.8%), while melanoma and sarcoma exhibited high rates of recurrence and mortality. Lymph node metastasis and distant metastasis were found in 7.56 and 4.74% of cases, respectively. CONCLUSION: Skin cancers arising from burn scars, especially SCC, demonstrate aggressive clinical behavior with significant morbidity and mortality. Reduced latency periods and high metastatic potential highlight the importance of vigilant, long-term surveillance and radical initial treatment. This review provides a contemporary benchmark for epidemiological understanding and supports calls for international registries, molecular diagnostics, and standardized management protocols for burn scar malignancies.
25 Apr 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Online self-compassion-based interventions on patient outcomes in patients with cancer: a systematic review
PURPOSE: This systematic review investigates the effectiveness of online self-compassion-based interventions and their impact on the outcomes of cancer patients. METHODS: A comprehensive search was carried out in the Web of Science Core Collection, Cochrane Library, CINAHL, PubMed, ScienceDirect, Scopus, Ovid, and SpringerLink databases without any year limitation until January 01, 2026. Cochrane and PRISMA guidelines were used for systematic review and reporting. RoB2 and Joanna Briggs Institute guidelines were utilized to assess the risk of bias. RESULTS: Eight studies included in the systematic review were conducted between 2017 and 2025 in Australia, the USA, the Netherlands, China, and Türkiye. The self-compassion interventions applied in the studies included mindful self-compassion, self-compassion-focused writing, compassion mind training, and kindness interventions. Online self-compassion-based interventions have been found to increase patients' levels of self-compassion, mindfulness, body image appreciation, well-being, self-acceptance, posttraumatic growth, and positive affect while reduce their levels of self-criticism, social isolation, anxiety, stress, and depression, body image distress. CONCLUSIONS: Studies have shown the positive effects of self-compassion-based interventions on patient outcomes. A sensitivity analysis that included only randomized controlled trials demonstrated robustness in all outcomes except anxiety; however, some research results were obtained from a single randomized controlled trial. Since some study results were derived solely from quasi-experimental studies, they were not included in the analysis. Therefore, more randomized controlled research is required to integrate these interventions into clinical patient care. Online self-compassion interventions can contribute to the planning of holistic nursing interventions for patient outcomes.
25 Apr 2026
Read appraisal →The New England journal of medicine
Transdermal Estradiol Patches in Locally Advanced Prostate Cancer
BACKGROUND: Transdermal estradiol (tE2) is an alternative to luteinizing hormone-releasing hormone (LHRH) agonists as androgen-deprivation therapy in patients with prostate cancer. With tE2, testosterone is suppressed, and the side effects of estrogen depletion due to LHRH agonists and the thromboembolic side effects of oral estrogen are mitigated. METHODS: In this phase 3, noninferiority, randomized trial, we assigned men with locally advanced (M0 and N0 or N+) prostate cancer to receive tE2 patches (100 μg of estradiol every 24 hours) or LHRH agonists. The primary outcome was 3-year metastasis-free survival. The noninferiority margin was 4 percentage points; this corresponded to a target hazard ratio of 1.31, as derived from the observed 3-year metastasis-free survival in the LHRH agonist group. Secondary outcomes included castrate levels of testosterone (<1.7 nmol per liter), overall survival, and safety. RESULTS: Between 2007 and 2022, we recruited 1360 patients at 75 U.K. centers. The median age of the patients was 72 years (interquartile range, 68 to 77); 85% had a T3 tumor stage and 65% an N0 nodal stage. Observed 3-year metastasis-free survival was 87.1% with tE2 and 85.9% with LHRH agonists (hazard ratio for confirmed metastasis or death, 0.96; upper limit of the one-sided 95% confidence interval [CI], 1.11, which met the criterion for noninferiority). Among patients continuing the assigned treatment, castrate levels of testosterone were sustained during the first year after randomization in 85% in each group. Observed 5-year overall survival was 81.1% with tE2 and 79.2% with LHRH agonists (hazard ratio for death, 0.90; 95% CI, 0.75 to 1.07). During treatment, hot flashes occurred in 44% of the patients who received tE2 and 89% of those who received LHRH agonists (grade ≥2 events, 8% and 37%, respectively) and gynecomastia in 85% and 42% (grade ≥2 events, 37% and 9%). CONCLUSIONS: In patients with locally advanced prostate cancer, tE2 was noninferior to LHRH agonists for 3-year metastasis-free survival, with a lower incidence of hot flashes but a higher incidence of gynecomastia. (Funded by Cancer Research U.K. and the U.K. Research Institute Medical Research Council; PATCH ClinicalTrials.gov number, NCT00303784; STAMPEDE-1 ClinicalTrials.gov number, NCT00268476.).
24 Apr 2026
Read appraisal →Cell reports methods
A high-speed attention network for MHC-bound peptide identification and 3D modeling
We developed SwiftMHC, an ultra-fast and accurate structure-based framework for peptide-MHC (pMHC) modeling and binding affinity prediction. Using task-specific deep learning trained on physics-derived synthetic data, SwiftMHC predicts pMHC binding affinities in 0.009 s per case on a single A100 GPU when running in batch mode, offering improved speed compared with leading sequence-based tools such as netMHCpan and MHCflurry while maintaining competitive accuracy. In addition, SwiftMHC generates all-atom 3D pMHC structures with a median Cα-RMSD of 1.32 Å against crystallographic benchmarks, matching or exceeding state-of-the-art methods such as AlphaFold2-finetune but at a lower computational cost. Optimized for HLA-A∗02:01 9-mer peptides but readily extensible to other alleles, SwiftMHC unites structural insight with high-throughput scalability to accelerate safe and effective epitope discovery in cancer immunotherapy.
24 Apr 2026
Read appraisal →European heart journal
Predictors of recurrent venous thromboembolism and bleeding in patients with cancer: a meta-analysis
BACKGROUND AND AIMS: Patients with cancer and venous thromboembolism (VTE) have a high risk of recurrent VTE and anticoagulant-related bleeding. This study aimed to identify prognostic factors for these complications. METHODS: A systematic review was performed for randomized trials and cohort studies evaluating prognostic factors for recurrent VTE or anticoagulant-related bleeding in adult patients with cancer and VTE. Adjusted hazard ratios (aHRs) for factors were pooled using random-effects meta-analysis. The certainty of evidence was assessed using the Grading of Recommendations, Assessment, Development and Evaluation approach. RESULTS: Thirty-three studies (n = 96 753) were included in the meta-analyses. Factors with high certainty of association with increased risk of recurrent VTE included a previous history of VTE [aHR 1.50 (95% CI 1.08-2.09)], Eastern Cooperative Oncology Group (ECOG) performance status >0 [1.81 (1.34-2.46)] or >1 [2.44 (1.55-3.84)], advanced cancer [1.38 (1.15-1.65)], and specific cancer sites including lung [1.78 (1.29-2.46)], hepatobiliary [2.37 (1.70-3.30)], pancreas [3.20 (2.06-4.96)], and genitourinary [1.38 (1.14-1.67)]. Conversely, recent surgery [aHR 0.56 (95% CI 0.40-0.76)] and breast cancer [0.43 (0.23-0.81)] had a high certainty of association with a decreased risk. Factors with a high certainty of association with an increased risk of anticoagulant-related bleeding included a history of bleeding [aHR 2.41 (95% CI 1.50-3.88)], ECOG performance status ≥2 [2.10 (1.48-2.99)], advanced cancer [1.60 (1.29-1.97)], and cancers of the brain [2.25 (1.64-3.09)], gastrointestinal system [1.74 (1.44-2.11)], genitourinary system [1.90 (1.48-2.45)], and prostate [1.72 (1.26-2.34)]. CONCLUSIONS: The prognostic factors identified in this meta-analysis should be considered as part of risk stratification frameworks for anticoagulation management in patients with cancer and VTE.
24 Apr 2026
Read appraisal →Biomedical physics & engineering express
Evaluating the impact of reconstruction algorithms on digital breast tomosynthesis system optimization for microcalcification detection
Early detection of microcalcifications in digital breast tomosynthesis (DBT) depends on optimizing both image reconstruction algorithms and system design parameters. Thisin-silicostudy evaluates the combined effects of x-ray tube motion, angular dose distribution, and detector characteristics on reconstruction algorithms for microcalcification detectability. A synthetic breast phantom containing 127 microcalcification clusters was generated and imaged under four acquisition conditions varying in tube motion (continuous vs. step-and-shot), angular dose distribution (uniform vs. non-uniform), and detector performance (noise level and pixel size). Projection data were reconstructed using filtered back-projection (FBP) and the simultaneous algebraic reconstruction technique (SART). Detection performance was assessed using a 2D Filtered Channel Observer in a multi-reader, multi-case study. Evaluation metrics included signal-to-noise ratio and area under the receiver operating characteristic (ROC) curve. Results show that SART performs best under step-and-shot acquisition by minimizing motion blur and enhancing fine detail, while FBP benefits more from non-uniform angular dose distributions. Detector improvements-lower noise and finer resolution-enhance detectability across all configurations, with FBP showing the largest gains. These findings highlight the interdependence of reconstruction algorithm, tube motion, dose strategy, and detector design in DBT system optimization. This study provides a detailed investigation into classical reconstruction strategies and establishes a methodological baseline for future DBT optimization research, offering actionable guidance for acquisition protocol design and hardware development.
24 Apr 2026
Read appraisal →PloS one
LHW-Net: An ensemble-based machine learning framework for brain tumor classification
The classification of brain tumors is an unsolved problem associated with heterogeneity of tumors and fluctuations in imaging conditions. In this work, the investigation introduces a powerful novel framework, named LHW-Net that combines handcrafted features called local binary patterns (LBP), histogram of oriented gradients (HOG), and wavelet transform (WT). Within the LHW-Net framework, the extracted features are utilized in different machine learning classifiers, such as K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Classifier (SVC). The results of the individual classifiers are further combined using probabilistic score fusion approach to improve classification performance. The effectiveness and robustness of the proposed work are validated by the achieved experimental results on commonly accepted benchmark datasets.
24 Apr 2026
Read appraisal →European heart journal
Venous thromboembolism and bleeding in cancer patients: role of inflammatory and cardiac biomarkers.
BACKGROUND AND AIMS: Patients with cancer have increased risk of venous thromboembolism (VTE) and bleeding. Inflammatory and cardiac biomarkers may predict these complications, but their role remains unclear. This study examined associations between two inflammatory-related markers (C-reactive protein and growth differentiation factor-15) and two cardiac markers [N-terminal pro-B-type natriuretic peptide and high-sensitivity troponin T (hs-TnT)] with VTE and clinically relevant bleeding in cancer patients. METHODS: A post hoc analysis of the AVERT trial, which evaluated apixaban for VTE prevention in ambulatory cancer patients with a Khorana score of ≥2, was performed. Biomarkers were measured at baseline and 1 month, with C-reactive protein also at 3 months. Fine and Gray regression, accounting for competing risk of death and adjusted for age and advanced cancer, estimated subdistribution hazard ratios (SHRs) for VTE and clinically relevant bleeding. RESULTS: Of 574 patients, 514 provided baseline samples. One- and 3-month samples were available from 454 and 447, and 378 and 364, patients without prior VTE and bleeding events, respectively. Elevated baseline growth differentiation factor-15 was associated with increased VTE risk [SHR 1.36, 95% confidence interval (CI) 1.01-1.84]. N-terminal pro-B-type natriuretic peptide (SHR 1.44, 95% CI 1.08-1.92) and C-reactive protein (SHR 1.38, 95% CI 1.07-1.76) were linked to bleeding risk. Increasing high-sensitivity troponin T from baseline to 1 month was associated with higher VTE risk (SHR 1.89, 95% CI 1.14-3.16). Nomograms were developed to estimate VTE and clinically relevant bleeding risks. CONCLUSIONS: Select inflammatory-related and cardiac markers were associated with VTE and bleeding risks in cancer patients, which can be determined using developed nomograms. Prospective research is needed to confirm these findings.
23 Apr 2026
Read appraisal →Human mutation
HSPB6: A Potential Prognostic Biomarker, Inhibiting the Epithelial-Mesenchymal Transition (EMT) Process Through the PI3K/Akt Signaling Pathway Based on the Machine Learning and Experimental Validation
Bladder cancer (BC) is a prevalent malignant tumor worldwide, posing a significant public health burden and challenge to human society. Current therapeutic modalities for BC include surgical treatment, radiotherapy, chemotherapy, targeted therapy, and immunosuppressive therapy. However, almost all patients experience disease progression and ultimately succumb to BC. Our study demonstrated that elevated expression of Heat Shock Protein Beta-6 (HSPB6) correlated with higher clinical grades and stages, establishing it as an independent prognostic risk factor for BC. Enrichment analysis indicated that HSPB6 is associated with the extracellular matrix in BC. Experimental validation revealed that HSPB6 overexpression inhibits the proliferation of BC cell line T24. This effect may be achieved by inhibiting the PI3K/Akt signaling pathway, which in turn leads to inhibition of epithelial-mesenchymal transition (EMT). Furthermore, we developed a prognostic risk model that incorporated DDR2, DPYSL3, MFAP5, PDGFRB, and SPOCD1, allowing accurate prediction of patient outcomes based on immunological status. In conclusion, this study highlights that HSPB6 overexpression can restrain the proliferation of BC cells and inhibit EMT, underscoring its potential as a diagnostic marker and therapeutic target in BC.
23 Apr 2026
Read appraisal →The Journal of clinical endocrinology and metabolism
An Interpretable Machine-Learning Model for Predicting Occult Central Lymph Node Metastasis in Papillary Thyroid Cancer
CONTEXT: Accurate preoperative prediction of occult lymph node metastasis (OLNM) in clinically lymph node negative (cN0) papillary thyroid carcinoma (PTC) is critical for optimizing therapeutic strategy, particularly for thermal ablation and active surveillance. OBJECTIVE: The aim of this study was to develop an interpretable machine-learning (ML) model to predict the risk of OLNM in cN0 PTC patients. METHODS: This retrospective study analyzed data of 961 cN0 PTC patients (August 2018-August 2023). Multivariable logistic regression identified independent risk factors for OLNM in cN0 PTC. The cohort was randomly divided into the training and test sets, and a subset of patients with tumors sized 1 cm or less was further extracted from the test set for internal validation. Eight ML models incorporating clinical, ultrasonographic, and molecular features were developed and evaluated. Shapley Additive exPlanations (SHAP) enhanced interpretability. RESULTS: RET fusion positivity and BRAF mutation positivity were identified as independent molecular risk factors for OLNM in cN0 PTC, alongside 6 clinical and ultrasonographic variables. Nine predictors were incorporated into the predictive model. The random forest (RF) model achieved optimal performance with an area under the curve (AUC) of 0.906 in the training set and 0.733 in the test set, along with the lowest Brier scores of 0.135 and 0.212, respectively. Analysis of tumors sized 1 cm or less internally validated the model's robustness with an AUC of 0.719. SHAP analysis identified size, age, and clustered punctate echogenic foci as the top predictors. CONCLUSION: This is the first study to identify RET fusion positivity as an independent OLNM risk factor in cN0 PTC. The developed RF model demonstrates moderate predictive performance for OLNM risk and provides a framework for integrating clinical, sonographic, and molecular data, and is deployed as a web calculator (https://predictingoccultlymphnodemetastasis.shinyapps.io/web3/).
23 Apr 2026
Read appraisal →Analytical chemistry
scAClc: A Multi-Objective Adaptive Clustering Framework for Single-Cell Transcriptomics via Contrastive and Resolution-Aware Representation Learning
Single-cell RNA sequencing (scRNA-seq) enables whole-transcriptomic profiling at single-cell resolution, facilitating the construction of virtual cell representations that capture the full spectrum of cellular identities. Realizing this goal hinges on accurate clustering, which remains challenging due to data sparsity, high dimensionality, noise, and the need to specify cluster numbers a priori. We propose scAClc, a novel clustering framework featuring multiobjective optimization and adaptive resolution discovery, designed to address these limitations through three key innovations. First, a Hierarchical Gene Relevance Module integrates global gene variability with local neighborhood-specific signals to eliminate redundancy while retaining biologically informative features. Second, an Anchor-Centered Contrastive Learning Module adaptively selects representative anchors to guide embedding learning, promoting compact intracluster structure and clear intercluster separation. Third, based on the robust low-dimensional embedding, we propose a Self-Adaptive Resolution Discovery Module to automatically infer the number of clusters by jointly modeling intra- and intercluster distances. Extensive experiments on 15 real scRNA-seq data sets demonstrate that scAClc consistently outperforms six state-of-the-art methods across multiple evaluation metrics. Ablation studies further confirmed the complementary contributions of each module. In addition, interpretability analysis effectively mitigates the "black box" nature of clustering models and sheds light on the biological mechanisms underlying cell clustering. The source code is publicly available at https://github.com/scAClc/scAClc.
23 Apr 2026
Read appraisal →mSystems
Short-chain fatty acid-producing microbes differentiate non-infectious and infectious neutropenic fever in leukemia.
UNLABELLED: Neutropenic fever (NF) is often the first sign of infection in patients with hematologic malignancies, but its cause is frequently unknown, leading to broad-spectrum antibiotic use without confirmed infections. Although research links gut microbiome disruptions to treatment-related infections, it typically examines NF as the outcome, leaving a gap in understanding how the microbiome and metabolic factors distinguish infectious from non-infectious cases. Stool samples from acute myeloid leukemia patients were analyzed to characterize gut microbiome composition and fecal metabolites at baseline and at fever onset. Machine learning models, network analyses, and functional profiling were used to differentiate infectious NF vs non-infectious NF at baseline and at fever onset. The baseline model (area under the receiver operating characteristic [AUROC] = 0.769) identified higher levels of Akkermansia, Enterobacter, Escherichia-Shigella, and Flavonifractor as predictors of infectious NF, while Collinsella, Lachnospiraceae, Coprococcus, and acetic acid were linked to non-infectious cases. At fever onset, Enterococcus was enriched in infectious NF, whereas Gemella, Butyrate, Lachnospiraceae, Ruminococcaceae, and Eisenbergiella abundances favored non-infectious NF outcomes (AUROC = 0.752). Network analyses also revealed greater functional diversity and microbiome-metabolome connectivity in non-infectious cases at fever onset. This study suggests that gut microbiota and metabolites may serve as biomarkers for distinguishing infectious from non-infectious neutropenic fever, warranting further validation in larger cohorts. IMPORTANCE: Our study tackles the challenge of managing neutropenic fever (NF) in immunocompromised patients whose numbers have increased due to various immunodeficiencies and treatments that suppress immune function. Fever is often the only sign of a serious infection in these patients, yet there are neither clear patterns linking risk factors to infection nor biomarkers reliable for ruling out non-infectious causes. As a result, febrile patients are typically empirically treated for major pathogens, even in the absence of confirmed infections, which propagates antimicrobial resistance and gut dysbiosis. Our research utilizes gut microbiome and targeted metabolomic profiling from two cohorts of patients with acute myeloid leukemia undergoing chemotherapy and employs a machine learning framework to distinguish between infectious and non-infectious NFs at baseline and upon fever onset.
23 Apr 2026
Read appraisal →Journal of hematology & oncology
Uncoupling toxicity from efficacy in donor selection for allogeneic hematopoietic cell transplantation: a retrospective cohort study using double machine learning
Haploidentical hematopoietic cell transplantation (HCT) offers potent graft-versus-tumor effects but is historically limited by higher non-relapse mortality (NRM) compared to matched unrelated (MUD) or related donors (MRD). To algorithmically decouple the competing risks of NRM and relapse, we analyzed 1,713 adult patients (MD Anderson discovery cohort) and 6,829 patients (registry replication cohorts) using a double machine-learning framework of Causal Survival Forests to estimate individualized treatment effects, while Random Survival Forests defined absolute prognostic risk. We found biological uncoupling of relapse and NRM: relapse was driven by disease biology, while NRM was governed by physiological reserve. Specifically, cardiovascular or cerebrovascular comorbidities acted as selective amplifiers of haploidentical toxicity-a "vascular penalty" that doubled mortality risk in the bootstrapped model (sub-distribution hazard ratio 2.09, 95% confidence interval 1.26-3.35). We developed a 3-factor nomogram (recipient age, vascular comorbidity, B-leader matching status) to identify an "Optimized Haploidentical" phenotype. Exploratory multivariable modeling suggested MUD consistently maximized predicted disease-free survival across all subgroups. Among haploidentical versus MRD comparisons, B-leader mismatch appeared to favor MRD across ages, while B-leader matched haploidentical donors in older patients (≥ 60 years) modeled near-equivalent DFS to MRD, representing a hypothesis-generating "zone of equivalence" requiring prospective validation.
22 Apr 2026
Read appraisal →Medicine
Drug-induced neutropenia: A comprehensive pharmacovigilance study of the FAERS database.
Drug-induced neutropenia (DIN) deserves both clinicians' and researchers' attention for the serious clinical consequences. This study aimed to conduct an exploratory analysis of DIN reports in the United States Food and Drug Administration Adverse Event Reporting System database to identify drugs associated with neutropenia. A disproportionality analysis of DIN was conducted using reports submitted to the United States Food and Drug Administration Adverse Event Reporting System from the first quarter of 2004 to the fourth quarter of 2024. Both the reporting odds ratio algorithms and the information component were applied to identify an association between target drugs and DIN events. A total of 223,736 DIN cases were found, and 254 drugs were identified as potentially associated with neutropenia. The top-ranked drug categories were "monoclonal antibodies and antibody-drug conjugates," "other antineoplastic agents," "protein kinase inhibitors," "direct-acting antivirals," "antimetabolites," "alkylating agents," and "immunosuppressants." The top 5 drugs ranked by number of cases were clozapine, lenalidomide, rituximab, palbociclib, and carboplatin. The top 5 drugs ranked by reporting odds ratio signal strength were calcium levofolinate, vinorelbine, gemtuzumab ozogamicin, fludarabine, and daunorubicin. This comprehensive pharmacovigilance study identified drugs potentially associated with neutropenia, which can help clinicians and researchers further understand DIN.
20 Apr 2026
Read appraisal →Journal of medical Internet research
Long-Term Outcomes, Moderators, and Predictors in Online Mindfulness-Based Cognitive Therapy for People With Cancer: Secondary Analysis of a Randomized Controlled Trial
BACKGROUND: A 3-armed randomized controlled trial (RCT) demonstrated that 2 formats of online mindfulness-based cognitive therapy (eMBCT)-group-blended and individual-unguided-effectively reduced psychological distress and improved positive health outcomes in people with cancer and survivors, when compared to care as usual, up to 3 months posttreatment. However, the long-term effectiveness and possible predictors and moderators of treatment outcomes remain unknown. OBJECTIVE: This study examined the long-term effects (up to 9 months posttreatment) of group-blended and individual-unguided eMBCTs on psychological distress and other mental health outcomes in people with cancer. Additionally, it explored possible predictors and moderators of treatment effects across the 2 delivery formats. METHODS: The study population consisted of people with cancer who were initially randomized to group-blended or individual-unguided eMBCT within a 3-arm RCT, augmented by those who completed the waitlist condition and were subsequently randomly allocated to one of the eMBCT formats. Both groups were assessed over a 9-month follow-up period. Outcomes completed at baseline, posttreatment, and 3-, 6-, and 9-month follow-up assessments included psychological distress (primary), fear of cancer recurrence, rumination, fatigue, mindfulness skills, decentering, self-compassion, and well-being. Linear mixed-effects models examined changes over time, while linear mixed-effects models and binary logistic regression analyzed potential predictors and moderators of psychological distress and dropout. RESULTS: Of the 186 participants enrolled in the 3-arm RCT, 161 participants were randomly assigned to either group-blended or individual-unguided eMBCTs after adding those initially assigned to the waiting condition (group-blended: n=71; individual-unguided: n=90). The majority of participants were female (n=129, 80%), diagnosed with breast cancer (n=78, 48%), and were undergoing or had completed treatment with curative intent (n=124, 77%). The mean age was 52.8 (SD 11.4) years. Both eMBCT formats resulted in significant reductions in psychological distress, fear of cancer recurrence, rumination, and fatigue, alongside improvements in mindfulness skills, decentering, and self-compassion, up to 9 months posttreatment. Higher baseline rumination, as well as lower mindfulness skills and self-compassion at baseline, predicted larger reductions in psychological distress from baseline to the 9-month follow-up period. Additionally, highly distressed participants in the group-blended eMBCT arm were more prone to dropout than those with lower distress scores, whereas psychological distress was not associated with dropout in the individual-unguided format. No other significant moderators were identified. CONCLUSIONS: Group-blended and individual-unguided eMBCTs effectively reduced psychological distress and improved well-being among people with cancer and survivors, with greater benefits for those with fewer psychological resources. However, individuals experiencing higher levels of distress were more likely to discontinue group-blended eMBCT. These findings highlight the importance of considering individual preferences and pragmatic factors in treatment decisions. Larger, fully powered RCTs are needed to confirm these results and provide more definitive guidance on treatment format selection.
20 Apr 2026
Read appraisal →JMIR cancer
Digital and Paper-Based Hospital Workflows and 60-Day Mortality in Acute Leukemia: Retrospective Natural Experiment
BACKGROUND: Electronic medical record (EMR) systems have been associated with better clinical workflows and fewer documentation errors. However, evidence regarding their effect on time-sensitive leukemia care in public hospitals in Latin America remains limited. OBJECTIVE: This study aimed to compare 60-day mortality and urgent supportive-care processes between 2 tertiary public hospitals in Mexico with different documentation models (integrated EMR vs traditional physical records [TPR]), under the hypothesis that digital workflows may facilitate more timely treatment. METHODS: We conducted a retrospective natural experiment including 274 patients with newly diagnosed acute leukemia treated between February 2023 and April 2025. Clinical characteristics, treatment intensity, complications during induction, antibiotic administration times, and survival outcomes were abstracted from finalized institutional records that are routinely reviewed at discharge by the institutional medical record committee as part of standard quality-assurance procedures. The primary outcome was 60-day mortality. Secondary outcomes included time to first antibiotic dose after recognition of febrile neutropenia, treatment-related complications, and the number of operational steps required for urgent care processes. A subgroup analysis of 70 patients with complete timing documentation was performed for antibiotic administration. Comparisons between hospitals were performed using univariate tests, Kaplan-Meier 60-day survival curves, and multivariable logistic regression. RESULTS: Of the 274 included patients, 104 (38%) were treated at the hospital using an integrated EMR, and 170 (62%) at the hospital using TPR. Sixty-day mortality was lower in the EMR hospital (6/104, 5.8%) than in the TPR hospital (61/170, 35.9%; P<.001). In the subgroup with complete timing data, the mean time from febrile episode recognition to first antibiotic administration was shorter in the hospital using EMR than in the hospital using TPR (54, SD 18.4 minutes; P<.001). Although the EMR hospital used a higher proportion of high-intensity regimens, patients in that hospital had better 60-day outcomes. In multivariable analysis, hospital type remained independently associated with 60-day mortality (odds ratio 0.11, 95% CI 0.05-0.26; P<.001), whereas kidney injury and hepatotoxicity were associated with worse outcomes. CONCLUSIONS: In this natural experiment, the hospital using an integrated EMR had a lower 60-day mortality and shorter time to antibiotic administration than the hospital using TPR. These findings are hypothesis-generating and suggest that digital workflows may contribute to more timely urgent supportive care, but they should be interpreted with caution, given the retrospective 2-center design and the potential for residual confounding.
19 Apr 2026
Read appraisal →mAbs
Multi-modal feature learning to prioritize ADCs with favorable half-life in mice
Favorable pharmacokinetic (PK) properties of antibody-drug conjugates (ADCs) enable higher systemic exposure and are associated with improved clinical efficacy, yet ADC PK optimization remains largely empirical and resource-intensive. We developed an in silico framework to predict half-lives of monoclonal antibodies (mAbs) and ADCs in C57BL/6 mice using multi-modal feature learning. To address structural complexity of ADCs, each compound was deconvoluted into three components: (1) antibody features represented using sequence descriptors, AlphaFold-based structural properties, or protein language model (PLM) embeddings; (2) conjugation-related features, including drug-to-antibody ratio and conjugation sites; and (3) physicochemical properties of linker-payloads. XGBoost models were trained using experimental half-life data from 118 mAbs and ADCs. Structure-based and PLM approaches achieved strong cross-validation (Pearson's r = 0.76), outperforming sequence-only models. In temporal validation of 32 mAbs and ADCs, the structure-based models demonstrated robust generalization (r = 0.79). PLM embeddings achieved comparable rank ordering (Spearman's ρ up to 0.81 for ESM-2), but lower quantitative accuracy relative to structure-based models. Overall, our study presents a scalable machine learning framework for early PK screening of mAbs and ADCs prior to synthesis and in vivo testing.
19 Apr 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Artificial intelligence in cancer survivorship: evaluating ChatGPT's performance in rehabilitation-related queries
PURPOSE: This study aimed to evaluate the performance of ChatGPT in responding to patient-centered queries related to cancer rehabilitation. It examined the accuracy, completeness, and overall quality of ChatGPT's responses across major domains of cancer rehabilitation from a physical therapy perspective. METHODS: Fifteen representative patient queries covering eight domains of cancer rehabilitation-including general exercise, fatigue, bone metastasis, neuropathy, balance and falls, breast cancer, women's health, and pediatric cancer-were developed by two licensed physical therapists. Each query was input into ChatGPT-4, and responses were independently evaluated for accuracy, completeness, and global quality based on established clinical practice guidelines and expert consensus. RESULTS: ChatGPT demonstrated a mean accuracy score of 3.93 (SD = 1.10) on a 5-point scale, a mean completeness score of 2.13 (SD = 0.83) on a 3-point scale, and a mean global quality score of 3.60 (SD = 1.12) on a 5-point scale. Responses were generally accurate and clinically relevant but often lacked details regarding exercise dosage, safety precautions, and guideline-concordant recommendations. CONCLUSION: ChatGPT shows potential as an accessible tool for general cancer rehabilitation education but requires refinement to ensure accuracy, contextual relevance, and safety. IMPLICATIONS FOR CANCER SURVIVORS: AI-driven conversational models may improve patient access to rehabilitation information and self-management support when used under professional oversight and guided by evidence-based frameworks.
19 Apr 2026
Read appraisal →BMJ open
Patients' experiences of the path to sarcoma diagnosis: a qualitative systematic review and thematic synthesis
OBJECTIVE: To explore existing qualitative research on patients' experiences from the onset of symptoms to the diagnosis of sarcoma, with the purpose of identifying patient-perceived barriers at both patient and healthcare system levels and to highlight opportunities for improvement. DESIGN: Systematic review of qualitative studies using thematic synthesis. METHODS: We systematically searched Medline [Ovid], Embase [Ovid], PsycINFO [EBSCOhost] and CINAHL [EBSCOhost] from database inception to 11 April 2025 for qualitative studies reporting sarcoma patients' experiences during their diagnostic trajectory. The final search was conducted on 11 April 2025. All articles were screened against predefined inclusion and exclusion criteria and methodological quality was appraised using the Critical Appraisal Skills Programme qualitative checklist. Six studies conducted in Australia, the Netherlands and the UK were included. Data were analysed using a thematic synthesis approach guided by Thomas and Harden (2008). Confidence in the synthesised findings was assessed using the Grading of Recommendations Assessment, Development and Evaluation - Confidence in the Evidence from Reviews of Qualitative Research (GRADE-CERqual) approach. RESULTS: Four overarching themes emerged: patients' experience and interpretation of symptoms, diagnostic pathways, healthcare system factors, and reflections and recommendations. Key contributors to delayed diagnosis were symptom normalisation, misattribution by both patients and healthcare professionals, limited continuity of care and communication challenges. These findings were consistent across multiple countries, indicating relevance and validity in many settings. Patients emphasised the importance of disease awareness, persistence of patients and coordinated care. CONCLUSIONS: Diagnostic delays in sarcoma are influenced by both patient and healthcare systemic factors. Addressing these factors requires increased disease awareness among healthcare professionals and the public, improved coordination within the healthcare system, and targeted research to guide future interventions. This review provides cross-country insights into barriers to early sarcoma diagnosis, informing future priorities in clinical practice and research. PROSPERO REGISTRATION NUMBER: CRD420251030726.
18 Apr 2026
Read appraisal →Oncology reports
Incorporating artificial intelligence into morphological diagnosis of acute leukemias: Current landscape, challenges and prospects (Review)
Acute leukemias (ALs) are a diverse group of hematological malignancies characterized by the abnormal proliferation of immature cells. Microscopic observation of cell morphology based on the French‑American‑British classification remains a fundamental diagnostic method for ALs. However, manual screening from bone marrow smear images is often inefficient, laborious and prone to subjective bias, leading to potential misdiagnosis or missed diagnosis. Artificial intelligence (AI), particularly machine learning (ML), has expanded human capabilities in analyzing complex datasets, leading to breakthroughs in multiple fields, including medical research and clinical practice. Increasingly, ML applications are being developed to diagnose hematological diseases by extracting and aggregating morphological characteristics from peripheral blood and bone marrow smears. However, applying ML methods to recognize cell morphology in hematological diseases presents unique challenges compared with other pathology subspecialties. The present review provided an overview of AI and ML applications in ALs diagnosis, focusing on cell segmentation and data mining methods from microscopy images, and highlights their advantages over manual microscopy.
18 Apr 2026
Read appraisal →BMJ open
Prevalence of incidental findings requiring interventions in CT for trauma patients in the emergency department: a systematic review and meta-analysis
OBJECTIVE: To systematically review and meta-analyse the prevalence of incidental findings (IFs) requiring interventions identified in CT performed for patients with traumatic injuries in the emergency department, including pathologically confirmed cancers and emergent non-traumatic vascular pathologies. DESIGN: Systematic review and meta-analysis. DATA SOURCES: PubMed, EMBASE and Cochrane CENTRAL databases from database inception to 22 November 2024. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Prospective or retrospective studies involving traumatic injury patients presented at the emergency department reporting IFs of clinical significance detected through CT with any interventions proposed were included. Studies that exclusively included paediatric populations were excluded. The systematic review methods included double-screening, dual assessment of eligibility and study validity, dual data extraction, Bayesian multivariate random-effects meta-analysis of prevalence and employing the Grading of Recommendations, Assessment, Development and Evaluations' rating for the certainty of evidence. The primary outcomes were IFs requiring any interventions, and subset IFs requiring urgent interventions. Secondary outcomes were histologically confirmed cancers and emergent non-traumatic vascular pathologies. RESULTS: 22 studies (1 with a prospective and 21 with a retrospective design) mainly from high-income countries using limited-quality data based on clinical practice involving 18 538 patients were included. 9 studies evaluated the whole body, while 13 evaluated selective body regions. The grading criteria for IFs were non-uniform, and the image interpreters involved had diverse experience and expertise. The summary prevalence estimates for IFs requiring any interventions, urgent interventions, pathologically confirmed cancers and emergent non-traumatic vascular pathologies detected in the whole body were 29.8% (95% credible interval (CrI) 20.4% to 42.9%; very low certainty), 7.6% (95% CrI 4.5% to 14.8%; low certainty), 0.6% (95% CrI 0.3% to 1.6%; moderate certainty) and 0.3% (95% CrI 0.1% to 0.9%; moderate certainty), respectively. These findings were largely identified in the chest or abdomen and pelvis, with the overall detection frequency reduced with the scanned body regions narrowed (very low to moderate certainty). Sparse data on the head, neck and spine resulted in limited results. CONCLUSIONS: IFs identified in trauma whole-body CT requiring intervention are prevalent and can lead to substantial medical costs. The widely reported prevalence range suggests variations in radiologist recommendations and reporting in clinical practice and calls for standardisations. IFs requiring urgent intervention are not rare, which leads to a diagnosis of significant diseases including cancers and urgent vascular pathologies. Future studies should report long-term, patient-relevant results based on standardised classification and reporting systems. PROSPERO REGISTRATION NUMBER: CRD42020187852.
18 Apr 2026
Read appraisal →Thorax
Cardiopulmonary exercise testing before lung resection surgery: still indicated? Evaluating predictive utility using machine learning.
RATIONALE: Despite significant advances in patient care and outcomes, criteria for cardiopulmonary exercise testing (CPET) in risk stratification guidelines for lung resection have not been updated in over a decade. We hypothesised that CPET no longer holds additional predictive value for postoperative complications. METHODS: In this secondary analysis, we included lung resection candidates from two prospective, multicentre studies eligible for CPET and assessed with preoperative pulmonary function tests (PFTs) and arterial blood gas analysis. Postoperative pulmonary (PPCs) and cardiovascular complications (PCCs) were documented during hospitalisation. We trained five types of machine learning models applying nested cross-validation to predict complications and compared predictive performance based on four metrics, including area under the receiver operating characteristic curve (AUC-ROC). RESULTS: A total of 497 patients were included. PPCs developed in 71 (14%) patients. Adding CPET parameters to PFTs and baseline clinical data did not improve the ability of models to predict PPCs in unselected patients (AUC-ROC=0.72-0.78; p=0.47), nor in those meeting American College of Chest Physicians (ACCPs) (n=236; AUC-ROC=0.64-0.78; p=0.70) or European Respiratory Society/European Society of Thoracic Surgery (ERS/ESTS) criteria (n=168; AUC-ROC=0.59-0.76; p=0.92). PCCs developed in 90 (18%) patients. CPET parameters likewise did not improve model performance for the prediction of PCCs in unselected patients (AUC-ROC=0.65-0.73; p=0.96), nor in the ACCP (AUC-ROC=0.61-0.73; p=0.82) or ERS/ESTS subgroups (AUC-ROC=0.62-0.69; p=0.87). CONCLUSIONS: In contemporary surgical practice, CPET did not improve the predictive performance of machine learning models for PPCs or PCCs in patients with an indication based on established guidelines or in those without. The role of CPET in preoperative risk stratification for lung resection should be re-evaluated. TRIAL REGISTRATION NUMBER: NCT03498352, NCT04826575.
17 Apr 2026
Read appraisal →PloS one
LungNet: Leveraging state-space models with SE-enhanced skip connections for precise CT-based lung lesion segmentation
Lung cancer, which accounted for 2.48 million new cases and 1.82 million deaths worldwide in 2022, continues to be the most lethal cancer across the globe, underscoring the urgent demand for more advanced diagnostic tools. Although computed tomography (CT) imaging has long been central to lung cancer detection, the heterogeneous and complex characteristics of lung lesions make accurate segmentation particularly challenging. Current deep learning methods face a critical bottleneck: convolutional neural networks (CNNs) often struggle to capture long-range dependencies due to limited receptive fields, while Transformer-based architectures incur prohibitive computational costs when processing high-resolution CT volumes. Furthermore, standard skip connections in traditional UNet models frequently introduce redundant noise, leading to the dilution of subtle lesion features. To address these specific technical gaps, we introduce a novel deep learning framework that integrates Mamba state-space models with an improved UNet architecture. In this design, to mitigate feature redundancy, Squeeze-and-Excitation networks are embedded into skip connections, while auxiliary losses are introduced to address the degradation of shallow features and capture fine-grained lesion features across varied morphologies. Such a framework not only accommodates the intricate differences in lesion size, shape, and spatial distribution but also achieves a balance between global context modeling and linear computational efficiency. By uniting the local feature extraction strengths of convolutional layers with the long-range dependency modeling power of state-space models, our approach achieves more precise delineation of lung lesion boundaries. Extensive experiments conducted on multiple datasets provide compelling evidence of the method's effectiveness: it attains state-of-the-art segmentation accuracy and demonstrates significant promise for enhancing early detection, ongoing disease monitoring, and treatment planning in lung cancer patients. This advancement delivers a robust solution for the inherently complex task of lung lesion segmentation. Moreover, because lung cancer treatment is costly and insurance coverage plays a decisive role in distributing expenses, the study's outcomes also carry considerable implications for the insurance sector.
17 Apr 2026
Read appraisal →JMIR formative research
Artificial Intelligence Design for Race-Based Prostate Cancer Stage Classification With Multilayer Perceptron: Feature Selection Optimization Approach
BACKGROUND: Prostate cancer progression exhibits significant variability influenced by biological and racial factors. DNA methylation profiling has shown potential in early cancer detection, but its integration with machine learning across racially diverse populations remains limited. OBJECTIVE: This study aimed to develop a prostate cancer stage classifier for the majority White cohort using DNA methylation data and a multilayer perceptron (MLP) model in order to classify prostate cancer stages into early (stages I-II) and late (stages III-IV) stages and assess its performance when applied to other racial groups to highlight the need for race-specific models. METHODS: Methylation and phenotype data from the TCGA-PRAD (The Cancer Genome Atlas Prostate Adenocarcinoma) dataset were processed using differentially methylated position (DMP) analysis to identify CpG sites correlated with cancer stages. These features were further refined through recursive feature elimination (RFE) and used to train MLP models. Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) were used to interpret the model and identify key DNA methylation features contributing to model predictions. RESULTS: The best-performing model achieved 95% accuracy and up to 99% area under the curve on the majority race (White) training data using 90 selected features. However, performance declined sharply in racial minority groups, revealing the effects of sample imbalance and race-specific methylation patterns. Feature importance examination indicated strong patterns within certain CpG sites driving model predictions. CONCLUSIONS: We propose a race-aware MLP model for prostate cancer stage classification using DNA methylation data, which has been optimized through DMP and RFE-based feature selection. SHAP and LIME confirmed the predictive relevance of selected CpG sites, supporting model transparency. The results highlight high performance within the White cohort but reveal poor generalization to racial minority groups, emphasizing the importance of race-specific modeling strategies.
17 Apr 2026
Read appraisal →Physics in medicine and biology
A surface-conforming flexible collimator for improving electron radiotherapy.
Objective.We aim to simplify the traditional method of patient-specific skin collimation and to improve the precision of electron radiotherapy treatment with a new surface-conforming collimation system.Method.A surface-conforming collimation device for electron radiotherapy, consisting of parallel steel cables, was created. The cables move freely relative to each other and can be fixed and reshaped with a clamp system along the length of the device. The cables do not completely fill the space; thus, their packing fraction was determined to characterize dosimetric effects. The cable collimator was tested with a 10 × 10 cm cone with four electron energies (6, 9, 12, and 15 MeV) at three SSDs (100 cm, 105 cm, and 110 cm). Radiochromic film was placed at the surface and at the maximum dose depth (dmax) for each energy. Penumbral width, defined as the distance between 20% and 80% of the maximum dose, was measured for each energy and depth.Results.The largest improvement with respect to traditional collimation was seen for 6 MeV with 110 cm SSD at the surface. The penumbra at the surface for the conventional collimator in this case was 18.7 mm, while the cable collimator penumbra was 1.4 mm. The smallest improvement was seen for 15 MeV with 100 cm SSD atdmax(2.7 cm), where the penumbra for the traditional collimator was 11.1 mm, while the cable collimator penumbra was 9.8 mm. In all cases, the cable collimator reduced penumbral width. Surface dose inhomogeneities due to scatter were most pronounced at higher energies. The packing fraction was measured to be 0.551 ± 0.016.Significance.The prototype shows significant improvement in penumbra compared to conventional collimation. The system aims to serve as a replacement for current skin collimation techniques and could be used with Cerrobend applicators for additional surface blocking.
17 Apr 2026
Read appraisal →Physical chemistry chemical physics : PCCP
TransG4: an interpretable deep-learning approach for sequence-based G-quadruplex prediction
G-quadruplexes (G4) are non-canonical nucleic acid secondary structures formed in guanine-rich regions and have been shown to regulate diverse cellular processes such as gene expression, DNA replication, and telomere maintenance, with increasing evidence linking G4 to cancer and other human diseases. G4 predominantly emerge in guanine-rich regions and are implicated in a spectrum of molecular interactions and disease phenotypes, thus researchers are interested in the formation of G4. However, predicting the formation of G4 from nucleotide sequences is a persistent problem. Existing computational tools for G4 prediction are either rule-based on domain knowledge or rely on a single neural network model like a convolutional neural network (CNN), which lacks interpretability and struggles to capture long-range dependencies among bases. Here, we introduce TransG4, a novel neural network architecture that integrates a CNN, a transformer, and bidirectional gated recurrent units (BiGRUs) to identify potential G4 structures. TransG4 demonstrates strong predictive performance on both G4-seq and rG4-seq datasets, accurately predicting DNA mismatch scores and consistently outperforming existing methods in RNA RSR-ratio prediction. Attention-based interpretations further show that TransG4 captures biologically meaningful motifs consistent with canonical G4 structures, providing an interpretable and generalizable framework and representing a novel and impactful contribution to sequence-based G4 propensity prediction.
17 Apr 2026
Read appraisal →JMIR cancer
Exploring the Adoption of Mobile Health Apps Among Patients with Head and Neck Cancer After the COVID-19 Pandemic: Secondary Analysis of Cross-Sectional Survey
BACKGROUND: Patients with head and neck cancer (HNC) face significant challenges in accessing coordinated care due to the complexity and multimodality of their treatment and the impact on vital functions. The COVID-19 pandemic has disrupted cancer care while accelerating the adoption of digital health solutions. Mobile health (mHealth) apps offer potential solutions for remote symptom monitoring, communication between patients and providers, and continuity of care. Nevertheless, their acceptance among patients with HNC remains limited due to age-related digital divides and concerns about accessibility. OBJECTIVE: The aim of this study was to investigate the impact of the COVID-19 pandemic on patients with HNC and to explore their attitudes toward mHealth apps as a supplement to cancer treatment. METHODS: A secondary analysis of a cross-sectional survey was conducted between January 2023 and May 2024 at Julius Maximilian University of Würzburg in Germany. A total of 355 patients with HNC were recruited and completed the structured "Cancer and COVID-19" survey via the Corona Health app or in paper form. The 25-question survey assessed sociodemographic information, the impact of the pandemic on diagnosis/treatment/aftercare, and interest in mHealth apps. Descriptive statistics and bivariate correlations were used for the analysis. RESULTS: The cohort comprised 261 (74%) men and 94 (26%) women with an average age of 67 (SD 11.5) years. Most participants (n=264, 74%) stated that the pandemic had no impact on their cancer treatment, although 20% (n=71) experienced disruptions, particularly in follow-up appointments and treatment monitoring. Only 10% (n=36) currently used health apps, but 57% (n=203) expressed a willingness to use mHealth technologies. Younger patients, patients with higher education, and participants who were more affected by the pandemic showed greater openness to digital health solutions. The most significant barriers included age, digital literacy, and perceived usefulness, while preferred app features included interaction with physicians (n=160, 45%) and data sharing with researchers (n=153, 43%). CONCLUSIONS: Although the COVID-19 pandemic had only a limited direct impact on HNC care at the institution at hand, it revealed significant patient interest in mHealth apps. However, significant barriers remain, particularly among older adults with lower digital literacy. Future mHealth initiatives should focus on improving digital literacy, addressing privacy concerns, demonstrating clinical benefits, and developing personalized, accessible solutions to optimize cancer care for this vulnerable population.
17 Apr 2026
Read appraisal →JMIR medical informatics
Research on Risk Transfer Pathways for Lung Cancer Among Middle-Aged and Older Individuals Using Deep Reinforcement Learning: Retrospective Cohort Study
BACKGROUND: Nowadays, lung cancer has a significantly high incidence rate worldwide. The mortality rate of lung cancer continues to rise; it is more common in middle-aged and older individuals and poses a great threat to human health. OBJECTIVE: This study aimed to assess the lung cancer risk among middle-aged and older individuals in a timely manner and to establish an efficient pathway for the risk transfer. METHODS: We proposed a deep reinforcement learning model based on deep Q-network (DQN) to explore the risk transfer pathway for lung cancer among middle-aged and older individuals. Risk stratification of lung cancer occurrence was deduced through deep neural network. The DQN model was developed using the Health and Retirement Study cohort for model training and internal validation. We also used the China Health and Retirement Longitudinal Study cohort for model external validation. Transfer simulation of multiple pathways in different cycles was calculated in a stratified risk groups-leveraged DQN model. RESULTS: We developed and evaluated the DQN method for optimizing the risk transfer pathway among middle-aged and older individuals, with accuracy ranging from 0.917 (95% CI 0.896-0.928) to 0.949 (95% CI 0.909-0.961) and area under curve ranging from 0.906 (95% CI 0.887-0.933) to 0.927 (95% CI 0.893-0.938). External validation was conducted to assess the model's effectiveness and availability. A total of 8780 and 3763 samples from the Health and Retirement Study were used for model training and internal testing, respectively, and 16,442 samples from the China Health and Retirement Longitudinal Study were used for external validation. The results showed that DQN models illuminated the optimal risk transfer pathways for stratified risk groups. Lung cancer incidence in the high risk group had declined by 68.2% through risk transfer of model-based simulation, which had declined by 56.9% in the medium risk group. Through simulative intervention and risk transition deduced from the DQN model, lung cancer incidences of the high risk, medium risk, and low risk groups were obviously decreased. CONCLUSIONS: A DQN-based deep reinforcement learning model was proposed and validated to develop and simulate a risk transfer pathway of lung cancer among middle-aged and older individuals. Risk stratification supplied an effective foundation for lung cancer risk transition.
17 Apr 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Beyond breast cancer: mapping unmet needs and advancing an evidence-based framework of survivorship care
PURPOSE: The growing population of breast cancer survivors (BCS) underscores the need to address ongoing challenges that persist beyond active treatment. This scoping review aimed to systematically map the unmet needs of BCS and inform the development of an evidence-based survivorship care framework. METHODS: A systematic search of databases identified 13,401 studies, of which 170 met the inclusion criteria. Data were analyzed using qualitative content analysis and descriptive statistics to categorize unmet needs across studies. RESULTS: Six major categories of unmet needs were identified: physical symptom burden, psychosocial/emotional needs, informational needs, healthcare support needs, relational/sexual needs, and financial/occupational needs. Physical symptoms (33.5% of studies) and psychosocial/emotional concerns (32.4%) were most frequently reported. Based on these domains, a three-phase conceptual framework was developed: (1) universal needs screening, (2) targeted, multidisciplinary interventions tailored to individual survivors, and (3) ongoing evaluation through patient-reported outcomes to optimize care. CONCLUSIONS: Unmet needs among BCS are widespread and multifaceted, particularly regarding chronic physical symptoms and persistent psychosocial distress. Existing care frameworks are fragmented and insufficient. The proposed three-phase framework provides a structured approach to delivering proactive, patient-centred survivorship care. IMPLICATIONS FOR CANCER SURVIVORS: Implementing this framework can improve identification and management of unmet needs, enhance quality of life, support long-term physical and emotional recovery, and foster resilience throughout survivorship.
17 Apr 2026
Read appraisal →Computers in biology and medicine
Empirical evaluation of variability and multi-institutional generalizability of deep learning survival models: application to renal cancer CT scans
Deep learning survival (DLS) modeling has shown significant promise across multiple disease conditions for predicting patient outcomes via biomedical data, but remain relatively under-explored in conjunction with medical imaging. This poses the question of what methodological choices promote model generalization, especially when predicting continuous survival outcomes in external validation cohorts. In this work, we systematically examined the impact of data partitioning, data order, model initialization, and augmentation strategies on the generalization of CT-based DLS survival models in renal cancers. A multi-institutional CT cohort was assembled, leveraging public repositories such as TCGA-KIRC, TCGA-KICH, TCGA-KIRP, and KITS19; totaling 525 patients from across 9 institutions. A 3D ResNet-18 model was trained to predict overall patient survival with a Cox loss function and evaluated across three controlled experiments: (i) random versus covariate-balanced data partitioning, (ii) randomizing vs fixing data ordering and initial model weights, and (iii) varying intensity, spatial, and noise based augmentations; evaluated in terms of concordance index (c-index) and hazard ratio (HR). Intelligent covariate-balanced data partitioning demonstrated optimal performance on an external validation set (c-index 0.74, HR 5.08) compared to random partitioning (c-index 0.56, HR 0.29). Different model initializations significantly impacted the average performance and variance across replicate trainings, while data ordering had little to no effect. Combining multiple augmentations produced the best performance on external validation (c-index +4.76%, HR +44.39%) compared to no augmentations, while most impactful individual augmentation type was additive gaussian noise (c-index +3.17%, HR +23.47%). Our results demonstrate that careful selection of strategies for data partitioning and ordering, weight initialization, and image augmentation are critical for developing robust and generalizable DL models for continuous survival prediction using medical imaging data.
17 Apr 2026
Read appraisal →Physics in medicine and biology
A deep learning-based framework for patient-specific radiation dose prediction in beta-emitting radionuclide therapies
Objective.Conventional image-based models for radionuclide therapy dosimetry are typically radionuclide-specific and rely on nuclear medicine (NM) images for training. We developed a deep learning (DL) model that predicts doses for not only training radionuclides but also radionuclides not included in the training data without patient NM images.Approach.The DL model was trained to predict voxel dose kernels (VDKs) for beta-emitters using CT-derived density maps and energy spectra. The DL model-derived VDKs were convolved with time-integrated activity to generate patient-specific dose maps. Zero-shot prediction was tested on Ho-166 and Lu-177. The performance of DL model was validated at kernel, phantom, and patient levels. The proposed DL model, conventional DL model trained on Y-90 NM/CT images of 22 patients, voxelS-value (VSV), and organ-level dosimetry (OLINDA/EXM) were benchmarked against Monte Carlo simulation (MC).Main results.Training utilized a single CT image, circumventing large patient cohort requirements. The DL model-based VDKs showed strong correlation with MC-based VDKs (R2: 0.99 and 0.91 for test Ho-166 and Lu-177) and, at the phantom level, achieved overall lower errors than VSV in heterogeneous region. At the patient level, the proposed DL model predicted organ doses with the smallest differences from MC while reducing computation time from 4.6 h to 2.6 min. For the test Lu-177, the proposed model exhibited good agreement with MC (2.1 Gy, 11.6%), while the conventional model trained using Y-90 showed large discrepancies (129.4 Gy, 657.4%). For the mean tumor dose averaged across the patient cohort for test Ho-166, the proposed DL model yielded the most accurate results with a difference of only 1.1 Gy (2.9%), outperforming multiple VSV (3.8 Gy, 7.2%), single VSV (3.8 Gy, 7.3%), and OLINDA/EXM (1.4 Gy, 4.5%).Significance.The proposed DL model enables dose prediction for radionuclides not included in the training data without NM images.
16 Apr 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
The effectiveness of electroacupuncture in the treatment of cancer-related fatigue: a systematic review
INTRODUCTION: Cancer-related fatigue (CRF) is a frequent, subjective, and difficult-to-manage symptom, for which few interventions have proven effective. Recent evidence suggests that electroacupuncture (EA) can reduce fatigue, although specific studies on this technique remain scarce. OBJECTIVE: To evaluate the efficacy of EA in treating CRF in cancer patients. METHODS: A systematic review registered in PROSPERO was conducted, including clinical trials assessing the effects of EA in patients with CRF. Searches were performed in the PubMed, Embase, Virtual Health Library, Scopus, and CAPES databases, with no restrictions on date, language, sex, ethnicity, or type of cancer. We assessed the methodological quality using the PEDro scale and evaluated the risk of bias using the RoB-2 tool. RESULTS: A total of 2,110 studies were identified. After applying inclusion criteria, five trials were included: three randomized, one non-randomized, and one single-arm clinical trial. In total, 251 patients were analyzed, 132 of whom received EA treatment. Interventions varied from two to three weekly sessions, each lasting 20 to 30 minutes, over a period of four to eight weeks. Acupuncture points and electrical frequencies (0.2-25 Hz) differed between studies. Outcomes were measured by the Brief Fatigue Inventory, Functional Assessment of Cancer Therapy - Fatigue, and European Organization for Research and Treatment of Cancer Quality of Life Questionnaire. Despite the potential benefits observed in EA-treated groups, the findings remain inconsistent. CONCLUSION: EA shows promising therapeutic potential for managing CRF. However, the small number of studies, methodological heterogeneity, and small sample sizes limit generalizability. Multicenter clinical trials with larger samples and standardized protocols are needed to confirm efficacy.
16 Apr 2026
Read appraisal →The Journal of clinical investigation
Carriage of rare APOB variants predisposes to severe steatotic liver disease and hepatocellular carcinoma
BACKGROUNDMetabolic dysfunction-associated steatotic liver disease (MASLD) has a substantial inherited component. Rare variants in apolipoprotein B gene (APOB) have been implicated in susceptibility to liver steatosis, but their role in disease progression and outcomes is unclear.METHODSWe investigated APOB rare variants in a case-control cohort of people with advanced MASLD versus healthy controls (n = 510 and 261, respectively), a family-based study (n = 43 and literature meta-analysis), the Million Veteran Program (MVP) cohort (n = 94,885), and the UK Biobank (UKBB) (n = 417,657).RESULTSIn the clinical cohort, APOB variants were enriched in people with advanced MASLD (OR 13.8, 95% CI: 2.7-70.7, P = 0.002) and associated with lower circulating lipids, but higher MASLD activity and fibrosis (P < 0.05). In the family study, APOB variants segregated with hepatic steatosis and fibrosis (P < 0.05). Cross-ancestry meta-analysis of the study cohorts yielded pooled ORs for cirrhosis and hepatocellular carcinoma (HCC) of 1.82, 95% CI: 1.33-2.49 and 3.53, 95% CI: 2.09-5.98, respectively. Variants affecting specifically ApoB100 had a 3-fold greater effect on hepatic lipid metabolism compared with those impairing also ApoB48 and were specifically protective against coronary artery disease (P < 0.05). The variants affected cirrhosis risk similarly, but ApoB48/100 had a larger effect on HCC (P < 0.05).CONCLUSIONSRare APOB variants predispose individuals to advanced MASLD and HCC, with distinct contributions from disrupted VLDL and chylomicrons secretion. These findings highlight the interplay between hepatic and intestinal lipid handling, suggesting that APOB genotyping may enhance MASLD risk stratification and patient identification.FUNDINGEuropean Union, Italian Ministry of Health, Swedish Research Council, Veterans Health Administration, NIH.
16 Apr 2026
Read appraisal →Computers in biology and medicine
MIPHEI-ViT: Multiplex immunofluorescence prediction from H&E images using ViT foundation models
Histopathological analysis is a cornerstone of cancer diagnosis, with Hematoxylin and Eosin (H&E) staining routinely acquired for every patient to visualize cell morphology and tissue architecture. On the other hand, multiplex immunofluorescence (mIF) enables more precise cell type identification via proteomic markers, but has yet to achieve widespread clinical adoption due to cost and logistical constraints. To bridge this gap, we introduce MIPHEI (Multiplex Immunofluorescence Prediction from H&E Images), a U-Net-inspired architecture that leverages a ViT pathology foundation model as an encoder to predict mIF signals from H&E images using rich pretrained representations. MIPHEI targets a comprehensive panel of markers spanning nuclear content, immune lineages (T cells, B cells, myeloid), epithelium, stroma, vasculature, and proliferation. We train our model using the publicly available OrionCRC dataset of restained H&E and mIF images from colorectal cancer tissue, and validate it on five independent datasets: HEMIT, PathoCell, IMMUcan, Lizard and PanNuke. On OrionCRC test set, MIPHEI achieves accurate cell-type classification from H&E alone, with F1 scores of 0.93 for Pan-CK, 0.83 for α-SMA, 0.68 for CD3e, 0.36 for CD20, and 0.28 for CD68, substantially outperforming both a state-of-the-art baseline and a random classifier for most markers. Our results indicate that, for some molecular markers, our model captures the complex relationships between nuclear morphologies in their tissue context, as visible in H&E images and molecular markers defining specific cell types. MIPHEI offers a promising step toward enabling cell-type-aware analysis of large-scale H&E datasets, in view of uncovering relationships between spatial cellular organization and patient outcomes.
15 Apr 2026
Read appraisal →Blood advances
Targeting Aurora kinase A to prevent GVHD and relapse after myeloablative allogeneic hematopoietic cell transplantation
The success of posttransplant cyclophosphamide (PTCy) for graft-versus-host disease (GVHD) prophylaxis has driven efforts to optimize partner therapies that balance GVHD and relapse prevention. We report phase 1 dose-finding results of PTCy, sirolimus (SIR), and VIC-1911, a selective oral Aurora kinase A (AURKA) inhibitor, after myeloablative allogeneic hematopoietic cell transplantation (allo-HCT). Results from murine and in vitro studies informed the development of the novel drug combination. In the phase 1 trial, patients aged 18 to 60 years received myeloablative conditioning, followed by PTCy (50 mg/kg; days +3/+4), SIR (from day +5; target 8-12 ng/mL), and VIC-1911 (25, 50, or 75 mg twice daily; days +5 to +45). The primary end point was achieving <54% phosphorylated histone H3 serine 10 expression in CD4+ T cells by day +21. Preclinical models show that combining SIR with AURKA inhibition suppresses signaling downstream of CD28, enhancing GVHD prevention while leveraging the antileukemia activity of AURKA inhibition. In the phase 1 trial, the optimal VIC-1911 dose achieving target pathway inhibition without dose-limiting toxicities was 75 mg twice daily. Clinical outcomes included no grade 3 to 4 acute GVHD through day 180 (0%) and low rates of moderate/severe chronic GVHD (6%) and relapse (0%) through 1 year (5/16 received maintenance). The 1-year overall survival for this cohort was 94%. VIC-1911 at 75 mg twice daily, combined with PTCy and SIR, effectively suppressed AURKA activity, offering promising GVHD and relapse prevention with a favorable safety profile and promising early clinical outcomes. The trial was registered at www.clinicaltrials.gov as NCT05120570.
15 Apr 2026
Read appraisal →ACS nano
Deep Learning Enables Pixel-Level Nanoparticle Distribution Mapping in Routine Histological Sections by Integrating Cancer Associated Fibroblasts Features
The efficient accumulation and uniform distribution of nanomedicine within tumors are critical for achieving therapeutic outcomes. However, conventional medical imaging technologies struggle to efficiently and accurately detect nanoparticles (NPs) distribution, especially in resolving their cellular-level spatial heterogeneity. Existing deep-learning-based predictive models focus on tumor cell density and vascular distribution but do not address the complex spatial relationship between cancer-associated fibroblasts (CAFs) and drug distribution. This study presents NanoNet, a deep-learning framework that leverages fibroblast activation protein (FAP) immunostaining to spatially characterize CAFs and predict NPs distribution at high resolution. NanoNet achieved high predictive accuracy (ICC = 0.963, R2 = 0.9849) by transforming tumor section images into pixel-level NPs distribution maps. The FAP channel contributed substantially to predictive accuracy, indicating its important role in guiding NPs behavior. This study provides a spatially resolved predictive framework that enables pixel-level predictions of NPs distribution from conventional histological sections, with potential applications for optimizing nanomedicine design and personalized nanomedicine.
15 Apr 2026
Read appraisal →Blood advances
Pirtobrutinib, a highly selective, noncovalent (reversible) BTKi in R/R marginal zone lymphoma: phase 1/2 BRUIN study.
Marginal zone lymphoma (MZL) is a group of indolent B-cell malignancies with a remitting and relapsing course. For systemic disease, available first-line therapies include anti-CD20 antibody as monotherapy or in combination with chemotherapy (chemoimmunotherapy), with second-line options including covalent Bruton tyrosine kinase inhibitors (cBTKi). However, management of relapsed and refractory (R/R) MZL remains challenging. Pirtobrutinib, a highly selective, noncovalent BTKi has shown promising efficacy and tolerability in poor-prognosis B-cell malignancies following prior therapy, including cBTKi. Here, we report the safety and efficacy of pirtobrutinib in MZL from the phase 1/2 BRUIN study. Endpoints included investigator-assessed objective response rate (ORR) by Lugano 2014 criteria, duration of response (DOR), progression-free survival (PFS), overall survival (OS), and safety. Among 36 patients with R/R MZL (extranodal [n = 6]; nodal [n = 17]; splenic [n = 13]), median age was 68 years (range, 22-83) and median prior lines of therapy were 3 (range, 2-10). The ORR was 55.6% (95% confidence interval [CI], 38.1- 72.1), including 3 (8.3%) complete responses and 17 (47.2%) partial responses. Median DOR was 17.8 months (95% CI, 7.4 to nonestimable [NE]), and median PFS was 16.6 months (95% CI, 9.0-22.1). With median follow-up of 32.4 months (IQR, 28.0-41.3), median OS was NE (95% CI, 29.5-NE). The ORR for patients with prior cBTKi therapy was 53.8% (95% CI, 33.4-73.4). Pirtobrutinib was well tolerated with dose reductions in 4 patients (11.1%) and permanent discontinuation due to treatment emergent adverse events in 4 patients (11.1%). Pirtobrutinib showed promising efficacy and safety in patients with heavily pretreated R/R MZL, including prior cBTKi. This trial was registered at www.clinicaltrials.gov as #NCT03740529.
15 Apr 2026
Read appraisal →Blood advances
HCT Pre-App program: a telemedicine-based prehabilitation for allogeneic hematopoietic cell transplant candidates
Frailty has emerged as a key component of allo-HCT candidate assessment and is closely associated with transplant outcomes. Exercise-based prehabilitation may improve transplant candidacy, yet real-world implementation is limited. Our institution implemented a structured Frailty Program (FP) to assess frailty and introduce prehabilitation interventions. This study describes the program's evolution and evaluates the impact of prehabilitation. Between April 2021 and April 2025, 185 consecutive allo-HCT candidates were included in 3 sequential FP phases. In the first phase (No-Prehab, n = 76), patients underwent frailty assessment only. In the second phase (Pilot-Prehab, n = 59), patients received a home-based, nonsupervised prehabilitation program. In the third phase (Tele-Prehab, n = 50), patients participated in the HCT Pre-App Program, a structured digital telemedicine intervention supervised by rehabilitation physicians. Frailty was assessed at first consultation and HCT admission using the HCT Frailty Scale. Median prehabilitation duration was 6 weeks. Adherence was high across frailty groups (76%-88%), with no adverse events. Prehabilitation improved frailty status: fit patients increased from 22% to 42% in the Pilot-Prehab cohort (P = .009) and from 34% to 56% in the Tele-Prehab cohort (P = .001). Tele-Prehab independently increased the odds of being fit at admission (odds ratio [OR], 3.86; P = .001) and reduced frailty incidence (OR, 0.17; P = .031). One-year OS and NRM were comparable across cohorts (OS, 74.5%, 84.5%, and 78.1%; P = .367) with a trend toward lower NRM among prehabilitated patients (NRM, 14.6%, 5.1%, and 5.6%; P = .075). Frailty is dynamic in allo-HCT candidates, and results support how home-based digital prehabilitation improves fitness before transplantation.
15 Apr 2026
Read appraisal →JMIR cancer
Physical Activity Monitoring in Adolescents and Young Adults With Cancer: Observational Feasibility Study
BACKGROUND: There is a growing interest in characterizing the relationship between long-term physical activity (PA) habits and cancer-related outcomes such as treatment-related toxicities, recurrence, and complications. Wearable devices can provide critical information to achieve this goal; however, inferences are significantly influenced by device wear adherence. OBJECTIVE: This study aimed to assess the feasibility of using wearable devices to monitor short- and long-term PA in adolescent and young adult (AYA) patients with sarcoma during and after chemotherapy in a free-living environment and evaluate the ability to accurately capture changes in PA over 3 years. METHODS: A total of 63 AYA patients with sarcoma were provided with a Fitbit Charge 3 to track daily steps, sedentary time, and heart rate for up to 3 years. RESULTS: On average, during the first 30 days of follow-up, 57.1% (36/63) of patients wore their device at least 10 hours per day, and only 23.8% (15/63) of patients wore their devices thereafter. Patients spent a mean of 80% (SD 11%) of their day in a sedentary state. Despite low adherence, daily step count trends increased over time. CONCLUSIONS: This study highlights the adherence challenges met with longitudinal PA monitoring in AYA patients with sarcoma. Wearer discomfort, lost devices, and lack of data uploading compliance contributed to data missingness and attrition. Caution is warranted when relying on wearable activity trackers to inform program decisions, accurately assess PA outcomes, and monitor program adherence longitudinally without consideration of wearer bias. Alternative methods that would be more broadly accepted by AYA patients for effective long-term monitoring should be considered.
15 Apr 2026
Read appraisal →Blood advances
Treatment and survival outcomes for patients with follicular lymphoma and POD24: a systematic review and meta-analysis
Follicular lymphoma with progression of disease within 24 months (POD24) is associated with poor prognosis and represents clinical challenges. Therefore, we performed a systematic review and pooled analysis of patients with POD24. Twenty-one trials involving 1242 participants were included, assessing the overall response rate (ORR), complete response (CR), duration of response, and progression-free survival. In some trials, we compared pooled response rates between POD24 and non-POD24 populations with the same treatment regimen. Four trials evaluated chimeric antigen receptor (CAR) T-cell therapy in patients with POD24. Pooled analysis showed an ORR of 91.2% (95% confidence interval [CI], 83.7-98.7) with significant heterogeneity (P = .0414; I2 = 68.61%) and a CR of 75.7% (95% CI, 55.1-96.4) with significant heterogeneity (P< .0001; I2 = 93.99%). The specific response rates for different bispecific antibodies in POD24 were pooled analysis, the ORR was 81.6% (95% CI, 75.9-87.3) with no heterogeneity (P = .6958; I2 = 0%), and the CR was 65.7% (95% CI, 57.1-74.3) with moderate heterogeneity (P = .2148; I2 = 34.99%). For anti-CD19 antibody-drug conjugates (ADCs)/monoclonal antibodies (mAbs), the ORR and CR rate for loncastuximab plus rituximab and tafasitamab plus R2 (lenalidomide + rituximab) were 100% and 79.3%, and 87.5% and 43.2%, respectively. Phosphatidylinositol 3-kinase inhibitors and anti-CD20 mAb-containing regimens were also analyzed in pooled analyses. Our results demonstrated that anti-CD19 CAR T-cell therapy achieved the highest CR rate. Additionally, bispecific antibodies, anti-CD19 ADCs/mAbs, and the combination of lenalidomide with obinutuzumab or rituximab also exhibited excellent efficacy. Notably, lenalidomide plus obinutuzumab showed superior efficacy compared with R2.
15 Apr 2026
Read appraisal →Biomedical physics & engineering express
A multi-task cross-attention strategy to segment and classify polyps
Polyps are the main biomarkers for diagnosing colorectal cancer. Their early detection and accurate characterization during colonoscopy procedures rely on expert observations. Nevertheless, such a task is prone to errors, particularly in morphological characterization. This work proposes a multi-task representation capable of segmenting polyps and stratifying their malignancy from individual colonoscopy frames. The approach employs a deep representation based on multi-head cross-attention, refined with morphological characterization learned from independent maps according to the degree of polyp malignancy. The proposed method was validated on the BKAI-IGH dataset, comprising 1200 samples (1000 white-light imaging and 200 NICE samples) with fine-grained segmentation masks. The results show an average IoU of 83.5% and a recall of 94%. Additionally, external dataset validation demonstrated the model's generalization capability. Inspired by conventional expert characterization, the proposed method integrates textural and morphological observations, allowing both tasks, polyp segmentation and the corresponding malignancy stratification. The proposed strategy achieves the state-of-the-art performance in public datasets, showing promising results and demonstrating its ability to generate a polyp representation suitable for multiple tasks.
14 Apr 2026
Read appraisal →European radiology experimental
Impact of deep learning image reconstruction on ADC quantification and histogram metrics: a phantom study
OBJECTIVE: Recently, deep learning (DL)-based reconstruction methods have been introduced into clinical magnetic resonance imaging (MRI) systems to enhance image quality and reduce acquisition time. However, their effects on apparent diffusion coefficient (ADC) maps remain unclear. We investigated whether DL-based image reconstruction influences ADC quantification and histogram-based ADC metrics using a calibrated diffusion-weighted imaging (DWI) phantom. MATERIALS AND METHODS: A phantom containing vials with known ADC values was scanned on a 3-T system using full (fFOV) and reduced (rFOV) field-of-view DWI sequences. Each acquisition was performed using conventional (DL-OFF) and three DL-based strength levels (low, medium, high). Median ADC values were analyzed for repeatability (coefficient of variation (CV)) and accuracy. Histogram changes and first-order radiomic features were assessed using the Wasserstein distance, Friedman, and Wilcoxon tests. RESULTS: ADC estimates showed high repeatability (CV 0.1-1.2%) and good accuracy (deviation -2 to 7%) across all DL levels and sequences. DL reconstruction progressively reduced histogram dispersion, particularly in high-ADC vials. Wasserstein distances increased with DL strength, confirming a progressive effect on ADC value distributions, while median ADC values remained unchanged. Entropy and interquartile range decreased significantly (p < 0.001), whereas kurtosis and skewness increased, with differences showing less stable and sequence-dependent statistical significance. CONCLUSION: DL-based reconstruction maintained accurate and repeatable ADC quantification while reducing the dispersion of ADC values. The effect was more evident for high-ADC regions and the rFOV sequence, resulting in narrower distributions of ADC values. Further investigations comparing different DL-based solutions are warranted to assess the generalizability of these findings in clinical settings. RELEVANCE STATEMENT: Over the past decade, ADC histogram analysis has proven valuable for quantifying tumor heterogeneity, differentiating tumor grade, and evaluating early treatment response. Deep learning reconstruction narrows ADC distributions and reduces dispersion, supporting its potential in oncologic DWI, while highlighting the need for patient-based validation studies. KEY POINTS: DL reconstruction preserved ADC accuracy in both full FOV and reduced FOV DWI. ADC repeatability remained high across DL levels for both DWI sequences. Histogram dispersion progressively reduced across DL levels, particularly in high-ADC vials. Entropy and interquartile ranges decreased progressively with increasing DL strength.
14 Apr 2026
Read appraisal →Epigenetics
The role of R-loops-mediated epigenetic regulation in genome stability maintenance and disease pathogenesis: a systematic review.
This systematic review synthesizes current evidence linking R-loops - associated processes with epigenetic regulation, genome stability, and human disease, integrating findings across DNA/RNA/protein modifications and 3D genome organization, and highlighting key mechanistic and technical gaps relevant to future studies, including longer-term translational exploration. Following PRISMA guidelines (Registration No.: INPLASY202560055), PubMed and ScienceDirect databases were searched through 18 January 2026. Two independent reviewers conducted blinded screening via the Rayyan platform. From 1290 initial articles, 71 studies met inclusion criteria. Methodological rigour was assessed using a modified SYRCLE risk-of-bias tool. Thematic synthesis identifies R-loops as recurrent intermediates linking transcription, chromatin regulation, and genome maintenance; feedback-like relationships are reported in selected systems. They spatially impede DNMTs to sustain promoter hypomethylation while recruiting TET enzymes for demethylation, and RNA modifications (e.g. m5C/m6A) may tune hybrid stability and, in some contexts, influence effector recruitment. R-loops facilitate the recruitment of histone-modifying complexes (PRC1/PRC2, G9a/GLP), thereby influencing chromatin landscapes and 3D genome architecture - including associations with TAD boundary insulation and with LLPS-related repair compartments in specific contexts. Dysregulation of R-loop homeostasis links to disease-related epigenetic alterations and genome instability with distinct disease-, locus- and cell-state specificity, and its context-dependent roles in diverse disorders involve transcriptional silencing, DNA damage, replication-transcription conflicts, repair pathways, chromatin regulation and mitochondrial genome maintenance. Integrating DRIP-seq with epigenomic profiling and functional tests supports mapping of context-specific relationships.
14 Apr 2026
Read appraisal →Blood advances
MPN-FIT: a randomized controlled pilot trial of supervised exercise in myeloproliferative neoplasms
Myeloproliferative neoplasms (MPN) are associated with a high symptom burden and impaired quality of life (QoL), often unaided by available treatments. Physical activity has demonstrated benefits in other cancers; however, its potential has yet to be explored in MPN. This randomized controlled trial aimed to evaluate the feasibility, acceptability, and efficacy of a supervised exercise program for patients with MPN based on longitudinal assessment of symptom burden, QoL, and clinical/inflammatory markers (C-reactive protein, erythrocyte sedimentation rate, ferritin, lactate dehydrogenase [LDH], and serum cytokines). Patients with MPN (n = 55) were randomized 3:2 to a 12-week home-based exercise intervention (flexibility/resistance/aerobics) supervised by a kinesiologist vs waitlist control. Measures included patient-reported outcome questionnaires, peripheral blood sampling, and postintervention interviews with participants. Forty-seven patients completed the trial (FIT group, n = 26; control group, n = 21). Median age was 66 years (range, 27-86), and 60% were female. All feasibility benchmarks were met, with 88% of participants satisfied and 92% with intent to continue an exercise program on a regular basis. A significant reduction in LDH was observed in FIT patients compared with controls (-14.5 U/L vs +4.0 U/L; P = .03). Patients interviewed described positive effects of the intervention on symptoms and would unanimously recommend the program to others with MPN. In this pilot study, supervised exercise was feasible, acceptable, and showed potential benefits on inflammatory/disease markers.
14 Apr 2026
Read appraisal →Annals of plastic surgery
Immediate Lymphatic Reconstruction to Prevent Postmastectomy Lymphedema: A National Analysis of Outcomes and Regional Trends
BACKGROUND: Lymphedema is a chronic disease characterized by swelling, inflammation, adipose deposition, and fibrosis. In the United States, lymphedema occurs most frequently as a sequela of oncologic lymphadenectomy. Axillary lymph node dissection (ALND) for breast cancer has been associated with a 5%-40% incidence of upper limb lymphedema. In immediate lymphatic reconstruction (ILR), surgeons perform lymphovenous anastomosis (LVA) with ALND to prevent the development of future lymphedema. Although early evidence supports a protective effect of ILR, there have been few large-scale studies on the topic. We leveraged a large claims database to better characterize the outcomes of ILR and national trends related to its adoption. METHODS: Adult female patients with breast cancer who underwent axillary lymph node dissection (ALND) between 2007 and 2022 were identified within the Merative MarketScan Research Databases and stratified according to whether they underwent ILR. Adjusted odds of undergoing ILR, both overall and regionally, and of experiencing postmastectomy lymphedema syndrome (PMLS) were calculated. RESULTS: Of all patients undergoing mastectomy and ALND, 1.3% received ILR. ILR was associated with decreased odds of developing PMLS (OR, 0.81; P = 0.02). The frequency of ILR procedures increased in all regions between 2017 and 2022, most dramatically in the Midwest and East US. More recent surgery year, younger age, complete mastectomy, and delayed ALND elevated the odds of undergoing ILR ( P ≤ 0.01). CONCLUSIONS: The popularity of ILR has grown rapidly since 2017, with significant regional variability. Patients with breast cancer who undergo ILR at the time of ALND are significantly less likely to develop lymphedema.
13 Apr 2026
Read appraisal →Human mutation
Use of Single-Cell Data and scPagwas Analysis to Identify T Cell Subsets and Construct a Prognostic Model for Clear Cell Renal Cell Carcinoma
BACKGROUND: Clear cell renal cell carcinoma (KIRC), the most prevalent pathological renal cell carcinoma (RCC) subtype, makes up approximately 75%-84% of total cases. KIRC is characterized by high heterogeneity, high metastasis rates, and a poor prognosis. Its incidence rate has continued to rise in recent years. We sought to construct new prognostic models to optimize treatment decisions, improve clinical benefits, and explore potential therapeutic targets. METHODS: This study integrated various omics data including single-cell RNA seq (GSE171306), TCGA-KIRC, GWAS, and validation datasets (GSE29609 and E-MTAB-1980). The scPagwas algorithm combines GWAS with scRNA-seq to identify immune subgroups with high feature correlation. Key genes are identified through the combination of weighted correlation network analysis (WGCNA) with differentially expressed genes (DEGs). We built a clinical prognostic model by using machine learning algorithms and validated it through survival rate and receiver operating characteristic (ROC) analysis. We used cancer drug sensitivity genomics data to analyze drug sensitivity and performed molecular docking to identify potential therapeutic drugs. RESULTS: Using single-cell RNA seq data, we identified T cell subsets as characteristic cell subsets in KIRC through scPagwas analysis. In single-cell analysis, key genes in T cell subsets and genes with PCC values > 0.05 were combined with the core genes in DEGs and WGCNA modules, thus yielding 86 intersecting genes. These genes were significantly enriched in immune-related pathways. We established a clinical prognostic model containing seven risk genes. Low-risk patients exhibited substantial survival advantages. Time-dependent ROC analysis indicated the prognostic model's excellent clinical predictive value. Functional enrichment, immune infiltration, and somatic mutation analyses highlighted different biological mechanisms among risk populations. The SHAP values of the XGBoost and LightGBM machine learning algorithms indicated DOCK8 as a potential biomarker. Drug prediction and molecular docking predicted five potential drugs targeting DOCK8 (finasteride, nocodazole, palonosetron, pifithrin alpha, and topiramate). CONCLUSION: Our systematic analysis of the immune microenvironment, key genes, and prognosis of KIRC highlighted the critical roles of T cell subsets. We additionally established an effective clinical prognostic model. Our findings provide new insights and potential targets for the precise diagnosis and targeted KIRC therapy.
11 Apr 2026
Read appraisal →Biomedical physics & engineering express
The impact of data consistency on deep learning models for nasopharyngeal cancer organ auto-segmentation
Purpose. To investigate how annotation consistency influences deep learning-based auto-contouring performance for organs-at-risk (OARs) in nasopharyngeal cancer radiotherapy.Methods. We evaluated CT scans from 1,301 nasopharyngeal carcinoma patients: 65 contoured by Physician A, 76 by Physician B, and 1,160 by heterogeneous multi-physician teams. Three cohorts (50 samples each for Physicians A/B; 1,000 for multi-physician) with standardized U-Net training protocols generated Models A, B, and C. Model C underwent physician-specific fine-tuning. Performance was quantified via Dice similarity coefficients (DSC) against ground-truth contours across 14 critical OARs.Results. Each model achieved peak accuracy on physician-matched test data. Critically, small-consistency models (A/B) outperformed large-heterogeneous Model C on target cohorts (Model A: 0.777 versus Model C's 0.743 on Test A; Model B: 0.806 versus 0.765 on Test B). Physician-specific fine-tuning closed institutional data gaps, boosting Model C's DSC to 0.795 (+7.08% versus original) on Test A and 0.814 (+6.32%) on Test B-surpassing both original Model C and dedicated small-data models (Model A: +2.39%; Model B: +0.97%).Conclusion. Annotation consistency supersedes dataset scale as the primary performance driver for OAR auto-contouring. Small high-consistency datasets enable optimal native model accuracy, whereas fine-tuning large pre-trained models with targeted physician data closes domain adaptation gaps and delivers state-of-the-art segmentation, advancing precision radiotherapy for head and neck oncology..
11 Apr 2026
Read appraisal →Journal of controlled release : official journal of the Controlled Release Society
Redox-active microneedles for disease management.
Reactive oxygen and nitrogen species are essential to several biological functions, from maintaining cellular homeostasis to driving disease progression and aging. As such, the precise modulation of cellular redox states has emerged as a promising medical strategy. In parallel, microneedle technology has rapidly advanced over the past two decades, offering minimally invasive, targeted, and painless delivery of therapeutics. This review presents a comprehensive analysis of the emerging field of redox-active microneedles (RAMs), a novel class of wearable medical devices with broad potential for treating infected wounds, cancer, psoriasis, diabetes, arthritis, hair loss, skin aging, and potentially many other conditions. By integrating redox biology with advanced microneedle-based drug delivery systems, RAMs are poised to transform personalized and precision medicine, an evolution supported by a growing number of patents, clinical trials, and rapid advancements in catalytic, photodynamic, and nanomedicine research.
11 Apr 2026
Read appraisal →Journal of clinical oncology : official journal of the American Society of Clinical Oncology
Phase II Study of BCMA Chimeric Antigen Receptor T-Cell Therapy in Patients With Newly Diagnosed Multiple Myeloma Ineligible for or Not Proceeding to Autologous Stem-Cell Transplantation (CAREMM-001)
PURPOSE: Patients with newly diagnosed multiple myeloma (NDMM) who are ineligible for or not proceeding to autologous stem-cell transplantation (ASCT)-often because of age or frailty-have limited opportunities to receive multiple effective lines of therapy, underscoring the need for novel frontline strategies. METHODS: In this phase II, open-label, single-arm trial (ClinicalTrials.gov identifier: NCT05860036), patients received 3-4 cycles of protocol-allowed induction, followed by B-cell maturation antigen (BCMA) CAR-T infusion, and subsequent consolidation and lenalidomide maintenance. The primary end point was the rate of minimal residual disease (MRD) negativity (10-5) at Month three postinfusion. RESULTS: Between April 4, 2023, and December 26, 2024, 43 patients were screened, 40 were enrolled, and 36 received infusion (median age, 68 years [46-75]). In the infused cohort, the MRD negativity rate at Month three postinfusion was 100% (36 of 36; 95% CI, 90.3 to 100.0). With a median follow-up of 15.8 months postinfusion (range, 4.3-26.0), no MRD recurrence was observed. The complete response rate (CRR) increased from 33.3% (12 of 36; 95% CI, 18.6 to 51.0) preinfusion to 69.4% (25 of 36; 95% CI, 51.9 to 83.7) at Month 3% and 94.4% (34 of 36; 95% CI, 81.3 to 99.3) at last follow-up. The most common grade 3 to 4 adverse events were transient cytopenia, including lymphopenia (100%), neutropenia (88.9%), leukopenia (80.6%), thrombocytopenia (19.4%), and anemia (8.3%). Cytokine release syndrome occurred in 52.8% of patients (all grade 1 to 2), immune effector cell-associated neurotoxicity in 5.6% (all grade 1), and infections in 30.6% (grade ≥3 in 19.4%). No deaths or disease progressions occurred by cutoff. CONCLUSION: Frontline BCMA CAR-T therapy induces deep, rapid, and durable remissions with a manageable safety profile in the NDMM population ineligible for or not proceeding to ASCT. These findings support its investigation as a potentially practice-changing strategy for this population.
11 Apr 2026
Read appraisal →Acta dermato-venereologica
Predicting Melanoma Impact on the Swedish Healthcare System from the Adult Population Using Machine Learning on Registry Data
Melanoma incidence has increased in Western countries over the past 50 years, leading to significant healthcare costs. In Sweden, comprehensive healthcare registries enable large-scale prediction studies using machine learning. Several machine learning models were evaluated to predict melanoma diagnoses using Swedish registry data, assessing the added value of diagnostic and medication data beyond demographics. The study included all adults in Sweden with continuous residency for 9.5 years (n = 6,036,186). The outcome was a melanoma diagnosis, including melanoma in situ, recorded within 5 years after the index date (31 December 2014). Predictors included age, sex, income, education, marital status, region of birth, diagnoses, and dispensed drugs. Models tested were logistic regression, gradient boosting, random forests, and a neural network. A total of 38,582 individuals (0.64%) developed melanoma. The gradient boosting model using all predictors performed best, with an area under the receiving operating characteristics curve (AUC) of 0.735 (95% confidence interval [CI], 0.725-0.746). When diagnosis and medication data were excluded, AUC dropped to 0.681 (95% CI: 0.670-0.691). The findings highlight that including healthcare codes improves predictive performance, and demonstrate the utility of Swedish registries for computational phenotyping. This approach may support early detection of melanoma and targeted follow-up.
10 Apr 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
A trifold prehabilitation model prior to induction chemotherapy in patients with leukemia: a randomized controlled trial
PURPOSE: A trifold prehabilitation program that integrates exercise, nutritional support, and psychological care has not yet been incorporated into routine care for leukemia patients prior to induction chemotherapy. This study aimed to evaluate the safety and feasibility of this trifold intervention and to further examine its potential impact on physical fitness, nutritional risk, and psychological health. METHODS: In a single-blind, parallel-group randomized trial, 86 newly diagnosed leukemia patients at a tertiary hospital in China were randomly assigned to a 12-week prehabilitation group (n = 43) or usual care (n = 43). Primary outcomes were recruitment, attrition, and safety. Secondary outcomes included the 6-min walk test (6MWT), exercise self-efficacy, cancer-related fatigue (CRF), anxiety, depression, and nutrition. Assessments occurred at baseline (T0), pre-chemotherapy (T1), and 1 (T2) and 3 months (T3) post-chemotherapy. RESULTS: The recruitment rate was 60.14%, with an attrition rate of 20.93%, and no serious adverse events occurred. Compared with controls, the intervention group showed significantly greater improvements in 6-min walk distance and cancer-related fatigue from T1 to T3 (all p < 0.05). Improvements in exercise self-efficacy were significant at T2 and T3 but not at T1. Nutritional status improved at T3 (p = 0.033). Anxiety and depression levels were consistently lower across all follow-up points (p < 0.01). CONCLUSIONS: This study demonstrates that prehabilitation is safe, feasible, and potentially beneficial for physical, nutritional, and psychological outcomes in leukemia patients. TRIAL REGISTRATION: China Clinical Trials Registration Platform: ChiCTR2500105061, date of registration: June 27, 2025 (retrospectively registered).
10 Apr 2026
Read appraisal →Blood
A decade of ibrutinib for CLL with and without TP53 aberration: final report on an investigator-sponsored phase 2 study
Bruton tyrosine kinase inhibitors improve outcomes for patients with chronic lymphocytic leukemia (CLL). Long-term data with continuous therapy are limited. With a median follow-up of 10.0 years, we report final results on 84 patients with TP53 aberrations (deletion of chromosomal arm 17p or TP53 mutation) or ≥65 years of age treated with 420 mg of single-agent ibrutinib daily until progression or unacceptable toxicity. A total of 52 (61.9%) patients were previously untreated; 56 (66.7%) had unmutated immunoglobulin heavy-chain variable region; and 53 (63.1%) had TP53 aberrations, including 34 treatment naïve patients. As of 31July 2024, 9 (10.7%) patients continued ibrutinib, 39 (46.4%) discontinued ibrutinib for progressive disease, 31 (36.9%) for adverse events, and 5 (5.9%) withdrew consent. The median progression-free survival (PFS) was 7.2 years; median overall survival (OS) was not reached. In patients with and without TP53 aberrations, median PFS was 5.6 years and not reached, and 10-year OS was 51.3% and 75.3%, respectively. The estimated 10-year PFS and OS for patients with TP53-aberrant CLL treated in first line was 38.6% and 65.7%, respectively. Minimal residual disease (MRD) was quantified by peripheral blood flow cytometry annually. Undetectable MRD (uMRD; at 10-4) was achieved in 13 (15.5%) patients after a median of 5 years. Twelve patients maintained uMRD, the longest observation ongoing at 8.0 years. Seventeen (42.5%) patients with best response of high MRD (>10-2) remained progression-free for >5 years. These results highlight durable benefits and deepening responses with ibrutinib, including in high-risk CLL. Whether patients maintaining uMRD for years can safely discontinue therapy should be assessed prospectively. This trial was registered at www.clinicaltrials.gov as NCT01500733.
10 Apr 2026
Read appraisal →Biomedical physics & engineering express
Efficient machine learning models leveraging DCE-MRI morphological and dynamic features allow accurate breast lesion classification.
We propose an ensemble learning approach to classify malignant versus benign breast lesions by leveraging morphological and dynamic features derived from Magnetic Resonance Images (MRI). The analysis was performed on 164 breast lesions of the publicly available 'Advanced MRI Breast Lesions' dataset from The Cancer Imaging Archive, containing T2-weighted and Dynamic Contrast-Enhanced (DCE)-MRI sequences, along with the segmentation masks of suspicious lesions. After extracting radiomic features using the Pyradiomics Python package, we computed dynamic features from DCE-MRI kinetic curves, which describe the contrast agent wash-in and wash-out. These features have been defined as the derivatives of image intensity measures, such as mean and standard deviation, computed within the masks on the five DCE-MRI time steps. We trained and evaluated an eXtreme Gradient Boosting (XGBoost) classifier, experimenting with different combinations of features and segmentation masks in a stratified 5-fold cross-validation. The best model trained on T2-weighted MRI morphological features achieved an Area Under the Curve (AUC) score of 0.83 ± 0.04 (95% C.I. 0.826-0.835) on the independent test set consisting of 20 lesions, while the model using only dynamic features performed an AUC of 0.91 ± 0.03 (95% C.I. 0.907-0.915). Despite being obtained on a small test sample, these results show the potential of features derived from DCE images for breast lesions classification.
10 Apr 2026
Read appraisal →European thyroid journal
Promise and pitfalls of AI chatbots in complex decision-making for thyroid nodules and papillary thyroid cancer
INTRODUCTION: Artificial intelligence (AI) chatbots are increasingly used in medicine, but their reliability in scenarios with multiple management options is unclear. Indeterminate thyroid nodules and low- and low-to-intermediate-risk papillary thyroid carcinoma (PTC) represent such cases. METHODS: In a nationwide web-based survey, 201 members of the Hellenic Endocrine Society evaluated 12 clinical vignettes on indeterminate thyroid nodules and low- and low-to-intermediate-risk PTC. Their responses were compared with those generated by four conversational AI models (ChatGPT, Gemini, Copilot, and DeepSeek) at two time points, 11 months apart. DeepSeek was assessed only at the second time point. Chatbot outputs were assessed for agreement with endocrinologists' predominant answers, concordance with the most guideline-consistent options (American and European Thyroid Association recommendations), temporal stability, and inter-model agreement. RESULTS: Alignment between chatbots and endocrinologists' predominant responses was limited, reaching at most 25% across scenarios. In contrast, concordance with the most guideline-consistent options was higher, up to 83% (10/12 scenarios), depending on the model and time point. Across 12 scenarios, ChatGPT, Gemini, and Copilot changed their responses in 4, 7, and 5 scenarios, respectively, with some updates moving closer to, and others further from, guideline-based answers. Inter-model agreement ranged from 33 to 67%, indicating substantial variability among chatbots. CONCLUSION: AI chatbots show evolving but inconsistent performance in complex thyroid management scenarios. While guideline concordance can be relatively high, substantial variability across models, limited temporal reproducibility, and poor alignment with clinical practice highlight the need for ongoing longitudinal evaluation before safe integration into clinical decision-making.
9 Apr 2026
Read appraisal →JMIR cancer
Acceptance, Perceived Usefulness, and Data Sharing in Mobile Health Apps Among Patients With Breast Cancer: Cross-Sectional Survey Study
BACKGROUND: Mobile health (mHealth) apps promise to enhance patient empowerment, enable real-time health monitoring, and support self-management. Patients with breast cancer stand to benefit particularly from these capabilities, given the demanding pre- and posttreatment procedures they face. However, the effective adoption of these tools is challenged by issues including accessibility, usability limitations, and privacy concerns. OBJECTIVE: This study aimed to investigate the current usage behavior and attitudes toward mHealth apps among patients with breast cancer in Germany, focusing on acceptance, usefulness, empowerment, and data-sharing. Specifically, we aimed to identify the incentives and barriers influencing use and the intention to use mHealth apps at the time of this writing to guide their further implementation and use. METHODS: A cross-sectional online survey was conducted between November 2023 and June 2024 using a structured questionnaire developed in German. Participants with a self-reported breast cancer diagnosis were recruited via university hospital channels, patient associations, and social media. Topics included technology and health app usage, perceived usefulness, personal empowerment, and attitudes toward data sharing. The survey comprised both closed- and open-ended questions to capture quantitative patterns and qualitative insights into participants' experiences and perspectives. RESULTS: We collected responses from 90 participants with breast cancer. Participants demonstrated high engagement with digital technology; 97% (87/90) reported daily smartphone use, and 70% (63/90) owned a smartwatch or fitness tracker. Approximately 67% (60/90) of respondents had used health apps in the past year, with many relying on general fitness and lifestyle apps to support their health management. Most participants (62/90, 69%) indicated that health apps contribute to a better understanding of their personal health. Health apps were generally perceived as useful, with users highlighting their ability to provide continuous support and timely, reliable health information (n=31). This, in turn, was seen as instrumental in promoting self-management and empowerment. Respondents also showed a conditional willingness to share personal health data for research, particularly when clear benefits for treatment advancements were evident (n=82). Nonetheless, concerns were raised regarding data privacy, app usability, the need for multiple apps to cover all necessary features, and the potential increase in fear of illness (cyberchondria). CONCLUSIONS: The findings underscore the potential of mHealth apps to empower patients with breast cancer by improving health literacy and supporting self-management. To maximize their impact, future digital health tools must align closely with patient needs by incorporating adaptive, interactive features and integrating seamlessly with clinical care. Addressing key barriers-particularly data privacy and usability issues-is essential for broader adoption. Longitudinal studies are warranted to evaluate the long-term effects of health app usage on patient empowerment, quality of life, and clinical outcomes.
9 Apr 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Digital and AI-enhanced psychosocial interventions in breast cancer patients: a systematic review and meta-analysis
PURPOSE: Digital and AI-supported psychosocial interventions are increasingly implemented to address psychological distress among women with breast cancer. However, their effectiveness and sustainability remain uncertain. This review aimed to systematically evaluate randomised controlled trials (RCTs) assessing the impact of digital and AI-enhanced psychosocial interventions on stress, anxiety, depression, and health-related quality of life (HRQoL). METHODS: Following PROSPERO registration and PRISMA 2020 guidelines, comprehensive searches of multiple databases were conducted up to June 2025. Eligible studies included RCTs involving breast cancer populations and reporting validated psychosocial outcomes. Risk of bias was assessed using the Cochrane RoB 2.0 tool, and certainty of evidence was evaluated with GRADE. Random-effects meta-analyses using standardised mean differences (Hedges' g) were conducted to pool outcomes measured with different instruments. RESULTS: Thirty-five RCTs (n ≈ 7551 participants) met inclusion criteria. Pooled analyses indicated modest but consistent reductions in stress, anxiety, and depressive symptoms, alongside improvements in HRQoL. Interventions grounded in cognitive behavioural therapy, mindfulness, or psychoeducation produced the most reliable outcomes. AI-assisted and blended formats tended to show stronger effects compared with static or minimally interactive platforms. However, heterogeneity in intervention design, short follow-up periods, reliance on self-reported outcomes, and small single-centre samples limited certainty. Cultural adaptation and representation from low- and middle-income countries were minimal. CONCLUSION: Digital and AI-supported psychosocial interventions show promise in reducing distress and improving HRQoL among women with breast cancer. Evidence certainty remains moderate, constrained by methodological limitations. Larger, multicentre trials with longer follow-up and diverse populations are needed to establish sustainability, generalisability, and clinical integration. RELEVANCE: These findings suggest that digital and AI-enhanced psychosocial interventions may serve as adjuncts to conventional care, with potential to improve access to supportive oncology services. Further high-quality, long-term studies are needed before informing guidelines, policy, or widespread implementation. TRIAL REGISTRATION: PROSPERO (CRD420251074813; registered 16 June 2025).
9 Apr 2026
Read appraisal →BMJ open
Identification of immunomodulators associated with atrial fibrillation reporting to better understand AF immunologic mechanisms: a Vigibase retrospective disproportionality analysis and a literature review
OBJECTIVES: Growing evidence exists about the pivotal role of immune mechanisms in the physiopathology of atrial fibrillation (AF). Drugs that modulate the immune system (immunomodulators) may contribute to the development of AF. We aimed to identify immunomodulators that are associated with AF to better define their safety profile, and elucidating their mechanisms of action could yield novel insights into AF's immune physiopathology. DESIGN: A descriptive and disproportionality analysis of claims data. SETTINGS: World pharmacovigilance database VigiBase until 1 March 2025. PRIMARY AND SECONDARY OUCTOMES: First, we ascertained the association of immunomodulators with AF over-reporting with a disproportionality analysis evaluating the multivariable-adjusted reporting odds ratio (aROR) for AF reporting performed for 141 immunomodulators in VigiBase. Then, a literature review was done to explore the underlying mechanisms of AF through immunomodulator mechanisms. RESULTS: A total of 6 148 556 reports encompassing at least one of the 141 immunomodulators were identified in Vigibase. Our primary analysis revealed 20 immunomodulators associated with AF over-reporting. The three immunomodulators with the greatest signal were: recombinant interleukin-11 with an aROR=20.91 (99.96% CI 12.08 to 36.17), efgartigimod alfa with an aROR=6.75 (99.96% CI 3.96 to 11.52) and recombinant interleukin-2 with an aROR=6.15 (99.96% CI 3.62 to 10.45). A derivative literature review posited a hypothetical immune 'vicious circle' promoting AF, involving T helper cells, macrophages and natural killer cells which could lead to electrophysiologic and histologic atrial remodelling. CONCLUSIONS: Twenty Food and Drug Administration (FDA)-labelled immunomodulators are associated with AF overreporting in Vigibase with a substantial signal on recombinant IL-11. These data contribute substantively to the prevailing understanding of the safety profile of these immunomodulators. Moreover, these findings support a multidirectional interaction between the immune system and AF development and might lead to considering future therapeutic targets. TRIAL REGISTRATION NUMBER: NCT06095791.
9 Apr 2026
Read appraisal →Journal of computer-aided molecular design
Computer-aided discovery of CDK16 inhibitors: a docking-augmented machine learning regression modelling approach
Cyclin-dependent kinase 16 (CDK16), a serine/threonine protein kinase, is a critical regulator of cell cycle progression, vesicle trafficking, and apoptosis. Its dysregulation is implicated in the progression of aggressive cancers, including triple-negative breast, lung, and prostate cancer, where its overexpression correlates with poor prognosis. Despite its therapeutic promise, CDK16 remains an understudied kinase lacking selective inhibitors, underscoring the need for innovative discovery approaches. This study introduces a novel computational framework that leverages multiple docked poses to augment datasets for regression-based machine learning (ML) models targeting CDK16 inhibition. This data augmentation strategy incorporated docking scores, ligand-receptor contact fingerprints (LRCFs), and conformation-sensitive physicochemical descriptors as input features. To our knowledge, this is the first application of docked pose augmentation for a regression-based drug discovery model. A systematic evaluation of eight ML algorithms across multiple docking score consensus levels identified Gradient Boosted Trees as the optimal learner. The Genetic Function Algorithm was integrated to select a minimal set of descriptors, which improved model generalizability. The resulting validated ML-QSAR model guided the generation of a robust pharmacophore model, which was used for virtual screening of the NCI and OpnMe databases. Subsequent in vitro LanthaScreen kinase assays confirmed two novel and potent CDK16 inhibitors: compound H_28 (OpnMe code: BI-831266) with an IC50 of 3.5 µM, and H_33 (OpnMe code: BI-1282) with an IC50 of 5.8 µM. These findings highlight the efficacy of integrating docked pose augmentation with regression modeling and experimental validation, offering a robust framework for accelerating the discovery of targeted anticancer agents.
7 Apr 2026
Read appraisal →JMIR medical informatics
Deep Learning for Content-Based Medical Image Retrieval in Picture Archiving and Communication Systems for Brain Tumor Detection: Algorithm Development and Validation
BACKGROUND: Advances in medical imaging have led to massive archives, yet navigating these datasets remains challenging due to the limitations of traditional text-based search engines. While content-based medical image retrieval (CBMIR) offers a visual feature-based solution to enhance clinical workflows and research, its operational integration into picture archiving and communication systems (PACS) remains a significant bottleneck. Despite the progress in deep learning for feature extraction, CBMIR tools are rarely integrated and effectively implemented within existing radiology information systems due to complex protocol barriers. OBJECTIVE: To address these challenges, this study develops a CBMIR system meticulously designed to cater to 7 distinct types of brain tumors as seen in brain magnetic resonance images. Our system is tailored to assist radiologists and health care professionals in efficiently retrieving pertinent historical medical images, thereby providing quantitative decision support for radiologists and facilitating evidence-based case comparison, with the potential to improve retrieval efficiency and clinical workflow, rather than directly enhancing diagnostic accuracy. METHODS: The dataset used in this study was collected from a single medical center and is not publicly available. The core innovation is a state-of-the-art deep learning-based feature extraction algorithm specifically engineered for the CBMIR system. We use GoogLeNet as the primary architecture, incorporating generalized mean pooling to capture nuanced local features and an embedding layer for dimension reduction. Crucially, we address the integration gap by harmonizing 2 open-source projects to successfully embed the CBMIR system into a functional PACS environment via standard protocols. RESULTS: The image dataset contains 658 participants with 15,873 images collected from 2000 to 2017. The empirical findings of our research demonstrate the performance and robustness of the proposed CBMIR system. Our system achieves a remarkable mean average precision score of 89.16% and an equally impressive Precision@10 score of 94.08%. These metrics affirm the system's efficacy in retrieving relevant medical images. Furthermore, we successfully integrate the CBMIR system into a PACS by successfully harmonizing 2 open-source projects. CONCLUSIONS: This study presents the design and implementation of a PACS-integrated CBMIR system for brain magnetic resonance imaging, and the experimental results demonstrate that the system can achieve efficient and accurate image retrieval within a clinical workflow.
7 Apr 2026
Read appraisal →JMIR dermatology
Community-Based Teledermatology for Urgent Suspected Skin Cancer: Health Economic Cost-Comparison and Discrete Event Simulation Study
BACKGROUND: The increasing incidence and financial burden of skin cancer place immense pressure on the UK's National Health Service (NHS). Systemic challenges, including dermatologist shortages and long waiting lists, complicate timely assessment of skin lesions for patients under the urgent suspected cancer pathway. While teledermatology offers an innovative solution compared to traditional face-to-face appointments, standard teledermatology models still face limitations in addressing health care access barriers. Community-based decentralized models may reduce such barriers, but the cost and operational impact of such specific models remain largely underresearched. OBJECTIVE: This study evaluated the differences in financial cost to the NHS and patient waiting times at the Northern Care Alliance NHS Foundation Trust by comparing a community-based teledermatology model using Pathpoint eDerma against the Trust's standard-of-care for patients in the urgent suspected skin cancer pathway. METHODS: This study used an ambidirectional design involving 2 distinct analyses. The cost comparison analysis (CCA) compared costs incurred under the teledermatology model (intervention arm, n=563) against the Trust's standard care, represented by a synthetic comparator arm (n=4011). The discrete event simulation (DES) modeled the operational impact on patient waiting times over a 1-year period. Data for the intervention arm were collected prospectively from December 2022 to May 2023 for CCA and up to November 2023 for DES, while comparator data were collected retrospectively from September 2021 to December 2022. Publicly available resource costs were incorporated to ensure the robustness of the analyses. RESULTS: The community-based teledermatology model was associated with significant improvements in both cost to the NHS and patient waiting times. The CCA revealed a mean cost saving of £45 (£1=US $1.24) per referral (95% CI £22-£60; P<.001). This cost saving was associated with a 26% reduction in the proportion of patients requiring a full diagnostic biopsy, falling from 48% (1925/4011) in standard care to 22% (124/563) in the teledermatology model as well as time savings in face-to-face clinics and administration. Furthermore, the DES demonstrated that, on average, the teledermatology pathways decreased the time to reach a clinical diagnosis by 9.90 (95% CI 9.64-10.16) days; to communicate a diagnosis to patients by 54.18 (95% CI 50.76-57.61) days; and to reach a histopathological diagnosis by 62.8 (95% CI 59.76-65.83) days compared to standard care. CONCLUSIONS: The implementation of the community-based teledermatology model appears to be a highly effective, cost-efficient strategy associated with shortened patient journeys. The intervention showed a faster initial triage phase, but the study identified the histopathology process as the next major systemic constraint that could deter further pathway efficiency. Achieving timely diagnosis for all patients, including those requiring diagnostic biopsies, will necessitate continued strategic investment in innovative technologies to accelerate this downstream process.
7 Apr 2026
Read appraisal →Lifetime data analysis
Q-learning via deep learning-based Buckley-James method for non-linear censored data
In healthcare, personalized treatment strategies are vital for improving patient outcomes, especially under right-censored survival data. We propose Dynamic Deep Buckley-James Q-Learning, a novel counterfactual Q-learning algorithm that integrates deep learning with the Buckley-James method to simultaneously address censoring and nonlinear modeling challenges. By explicitly capturing complex, nonlinear interactions between covariates and treatment effects, the algorithm robustly estimates optimal dynamic treatment regimes. Leveraging a counterfactual framework, we define and estimate potential survival outcomes under hypothetical treatment sequences, enabling unbiased Q-function estimation even in the presence of time-dependent covariates and right censoring. The algorithm maximizes the expected imputed survival reward under these counterfactual scenarios. Simulation studies and real-world data analysis demonstrate its superior performance in predictive accuracy and treatment decision-making, offering a powerful framework for individualized care in complex clinical settings.
6 Apr 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
A community based participatory research approach to evaluate barriers and facilitators for behavioral weight loss intervention implementation in a predominantly black community.
PURPOSE: Breast cancer survivors with overweight or obesity have worse breast cancer specific survival and outcomes as compared to those with average weight by Yung and Ligibel (Clin Adv Hematol Oncol 14:790-797, 2016) and Druesne-Pecollo et al. (Breast Cancer Res Treat 135:647-654, 2012). Our pilot Group-basEd Telehealth behavioral Weight Los (GET-WEL) Program showed that fewer Black breast cancer survivors (BBCS) enrolled and lost less weight than White breast cancer survivors by Allison et al. (Obesity Science and Practice 10:e70023, 2024). This study is aimed at using a community-based participatory research approach to assess barriers and facilitators of implementing a behavioral weight loss intervention among BBCS. METHODS: Eight BBCS from a predominantly Black community were invited to participate in semi-structured interviews that were voice recorded, transcribed, coded, and analyzed via comparative thematic analysis. RESULTS: Thematic analyses revealed multiple barriers within participants. These included lack of affordable healthy food access, safety concerns with regards to outdoor activities, lack of affordable fitness center memberships, time constraints related to competing work/life obligations, and steep learning curves with technology use. Most BBCS preferred an integrated community-based coach to guide their weight loss interventions via a combination of both virtual and in-person sessions. CONCLUSION: Our results indicate that a multimodal approach including nutrition education, reducing physical activity barriers, limiting time constraints by implementing both in-person and virtual platforms, and assisting with technology courses, is necessary to improve the equitable implementation of weight loss interventions. BBCS recommended utilizing established community facilities and leveraging known community members such as nutrition counselors and physical trainers to increase successful implementation.
6 Apr 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
User experiences of digital health interventions among older adults with cancer: a systematic review of qualitative data
PURPOSE: The aim of this review was to synthesize and analyze the results of several qualitative studies to understand the perceptions of older adults with cancer using digital health interventions to manage cancer in a digital context. METHODS: Six databases were systematically queried to identify qualitative studies of digital health interventions in older cancer populations, published from the inception of the databases until December 10, 2024. Studies that met the inclusion and exclusion criteria were selected, and the quality of the literature was assessed according to established guidelines. The results were synthesized using a thematic synthesis approach. RESULTS: A total of 12 papers were included. Three analytic themes emerged, (1) positive experiences, (2) barriers to use, (3) user expectations. The first theme was with three descriptive themes, including "improved self-management," "safe and accessible support," and "better access to health information"; the second theme with three descriptive themes, including "inadequate content design," "technical difficulties," and negative user perceptions and behaviors"; the third theme with two descriptive themes: "enhanced functionality" and "optimized user experience." CONCLUSIONS: This study found that older adults with cancer recognize the benefits of digital health Interventions in improving their health. However, their experiences are influenced by various factors. Patients expressed diverse needs regarding the functionality, information support, and social support aspects of digital health interventions.
6 Apr 2026
Read appraisal →BMJ open respiratory research
Associations between respiratory diseases and lung cancer risk: a secondary analysis of the large-scale prospective UK biobank cohort
INTRODUCTION: Respiratory diseases are significant risk factors for lung cancer; however, the association between acute respiratory infections and lung cancer incidence requires further exploration. METHODS: We performed a secondary analysis of the prospective UK Biobank cohort, including participants aged 37-73 years recruited from 22 assessment centres across the UK between 2006 and 2010. Cox proportional hazards models estimated HRs for incident lung cancer according to respiratory disease status, which was defined based on linked hospital inpatient records. Mediation analysis explored potential biomarkers, and Mendelian randomisation assessed causal relationships. RESULTS: During a mean follow-up of 10.44 years (4 790 738 person-years), 2189 participants developed lung cancer. Among 107 007 individuals with respiratory diseases, 1322 cases occurred (incidence rate 27.6 per 10 000 person-years), compared with 867 cases among 351 876 participants without respiratory diseases (7.9 per 10 000 person-years). Overall, respiratory diseases were associated with increased lung cancer risk (HR 2.97, 95% CI 2.75 to 3.21). Acute respiratory infections, including acute nasopharyngitis (HR 3.41; 95% CI 1.83 to 6.34), influenza (HR 3.90; 95% CI 2.53 to 6.01), viral pneumonia (HR 9.86; 95% CI 6.55 to 14.85) and bacterial pneumonia (HR 6.28; 95% CI 4.40 to 8.96), showed strong associations with lung cancer incidence. In subtype analyses, squamous cell carcinoma exhibited the highest risk elevation (HR 3.65; 95% CI 3.06 to 4.36). Mediation analysis indicated that neutrophil counts partially mediated these associations (proportion mediated up to 8%). CONCLUSION: Acute respiratory infections were associated with higher lung cancer incidence, providing hypothesis-generating evidence that may inform future risk stratification research.
5 Apr 2026
Read appraisal →PloS one
Utilizing the transformer mechanism to predict cervical lymph node metastasis in patients with papillary thyroid carcinoma
RATIONALE AND OBJECTIVE: The status of cervical lymph node metastasis(LNM) in Papillary thyroid carcinoma(PTC) can affect the patient's treatment plan and prognosis. This study aims to develop and validate the application value of Vision transformer (ViT) model in preoperatively predicting cervical LNM in PTC. MATERIALS AND METHODS: A total of 540 PTC patients were retrospectively reviewed from two hospitals from April 20,2022 to August 20,2023.The ViT model is built based on the two-dimensional rectangular ultrasound image of the primary thyroid tumor, and at the same time, to compare its performance, a deep learning model of the traditional Convolutional neural network (CNN) framework, a ultrasound radiomics combined model(Clinical-Rad model), and clinical model are built. RESULTS: The ViT model demonstrated an AUC of 0.807 (95% CI: 0.709-0.905) in the internal validation cohort and 0.809 (95% CI: 0.720-0.900) in the external validation cohort. The ViT model's AUC ranged from 0.807-0.814 across all cohorts, significantly exceeding the clinical model (AUC: 0.595-0.669, P<0.001). While the AUC of the ViT model in the training cohort was slightly lower than that of the combined ultrasound radiomics model (0.814 vs 0.828, P=0.491), it showed significantly higher AUC values in the internal (0.807 vs 0.718, P=0.049) and external validation cohorts (0.809 vs 0.691, P<0.001). Compared to the clinical and combined radiomics models, the ViT model exhibited stable and superior predictive performance for PTC cervical lymph node metastasis.In the internal validation cohort, Doctor C's net reclassification improvement (NRI) with the ViT model was 0.106 (P=0.022), and the integrated discrimination improvement (IDI) was 0.106 (P=0.023). Doctor D showed NRI and IDI values of 0.113 (P=0.022) and 0.106 (P=0.024), respectively. In the external validation cohort, Doctor C's NRI and IDI were 0.090 (P=0.024) and 0.106 (P=0.024), while Doctor D had values of 0.011 (P=0.013) and 0.106 (P=0.013). The ViT model enhanced the diagnostic capabilities of both Doctor C, with less clinical experience, and Doctor D, with extensive experience. CONCLUSION: The deep learning model based on the Transformer mechanism shows good performance in predicting LNM in PTC patients, which is superior to the clinical model, Clinical-Rad model, and traditional CNN model.
5 Apr 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
A systematic review of the implementation of cancer-specific holistic needs assessment (HNA) in adult clinical practice, and applicability to the brain tumour population
PURPOSE: This review aimed to identify and review any brain tumour-specific holistic needs assessment (HNA) and the clinical implementation of any HNAs in an adult cancer population. METHODS: A systematic review and narrative synthesis. Five electronic databases were searched from 2008 to 2023 and updated in January 2025. Reference lists of systematic reviews were screened for relevant studies. Two independent reviewers performed study selection, critical appraisal, and data extraction. RESULTS: Four HNA tools implemented in clinical practice, across 9 studies, were included. One study was adapted for brain tumour patients. Evidence of widespread implementation in clinical practice was limited and varied evidence, relating to the timing, location and mode of delivering. Patients were more likely to report on physical rather than psychosocial needs, with limited evidence of patient and staff satisfaction with the use of HNA. CONCLUSION: There is limited and varied evidence of HNAs being successfully implemented in brain tumour and the wider cancer healthcare settings. Tailored interventions for the needs of people with brain tumour have the potential to address the complexities this population faces. However, clear evidence-based guidance to develop relevant HNAs for successful implementation in clinical settings is needed.
4 Apr 2026
Read appraisal →Regional anesthesia and pain medicine
Anesthetic approaches and 2-year recurrence rates in non-muscle invasive bladder cancer: a randomized clinical trial
BACKGROUND: The effect of anesthesia methods on non-muscle invasive bladder cancer (NMIBC) recurrence post-resection remains uncertain. We aimed to compare the oncological outcomes of spinal anesthesia (SA) and general anesthesia (GA) in patients with NMIBC. METHODS: This prospective randomized controlled trial recruited 287 patients with clinical NMIBC at Seoul National University Hospital from 2018 to 2020. The patients underwent transurethral resection of the bladder tumor within 4 weeks of randomization. Intrathecal hyperbaric bupivacaine (0.5%) and a mixture of propofol (1-2 mg/kg) and fentanyl (50-100 μg/kg) were used as induction agents in the SA and GA groups, respectively, with desflurane or sevoflurane used for maintaining anesthesia. The primary and secondary outcome measures were disease recurrence and disease progression, respectively, at 2 years after resection. Cumulative incidence of outcomes was compared between the two groups using time-to-event analyses. RESULTS: 15 patients required alternative anesthesia owing to clinical needs such as SA failure or significant obturator reflex, resulting in a modified intention-to-treat (ITT) population of 272 patients. Time-to-event analysis showed a significantly lower recurrence of NMIBC in the SA group than in the GA group, in both ITT (27.4% vs 39.8%) and modified ITT populations (26.8% vs 39.6%). Disease progression occurred more frequently in the GA than in the SA group (15.2% vs 7.8%), although the difference was not statistically significant. CONCLUSIONS: A notable reduction in the 2-year recurrence rate was observed in patients who underwent SA than in those who underwent GA. Thus, SA may be considered the preferred anesthetic approach. TRIAL REGISTRATION NUMBER: NCT03597087.
4 Apr 2026
Read appraisal →The New England journal of medicine
Perioperative Enfortumab Vedotin and Pembrolizumab in Bladder Cancer
BACKGROUND: Patients with muscle-invasive bladder cancer who are ineligible for cisplatin-based chemotherapy proceed directly to radical cystectomy with pelvic lymph-node dissection. Perioperative therapy may improve outcomes in this population. METHODS: In this phase 3, open-label trial, participants with muscle-invasive bladder cancer who were ineligible for or declined cisplatin-based chemotherapy were randomly assigned to perioperative (neoadjuvant and adjuvant) enfortumab vedotin, an antibody-drug conjugate directed at nectin-4, plus pembrolizumab and surgery (9 total cycles of enfortumab vedotin [1.25 mg per kilogram of body weight on days 1 and 8] plus 17 total cycles of pembrolizumab [200 mg on day 1 every 3 weeks], with surgery after 3 cycles) or surgery alone (control). The primary end point was event-free survival. Key secondary end points were overall survival and pathological complete response (absence of viable tumor after surgical resection). Other secondary end points included safety. RESULTS: A total of 344 participants underwent randomization (170 in the enfortumab vedotin-pembrolizumab group and 174 in the control group). At data cutoff, median follow-up was 25.6 months (range, 11.8 to 53.7). Surgery was performed in 87.6% of participants in the enfortumab vedotin-pembrolizumab group and in 89.7% in the control group. At 2 years, estimated event-free survival was 74.7% in the enfortumab vedotin-pembrolizumab group and 39.4% in the control group (hazard ratio for an event or death, 0.40; 95% confidence interval [CI], 0.28 to 0.57; two-sided P<0.001); estimated overall survival was 79.7% and 63.1% (hazard ratio for death, 0.50; 95% CI, 0.33 to 0.74; two-sided P<0.001). A pathological complete response had occurred in 57.1% and 8.6% of the participants (estimated difference, 48.3 percentage points; 95% CI, 39.5 to 56.5; two-sided P<0.001). Adverse events occurred in all participants in the enfortumab vedotin-pembrolizumab group (grade ≥3, 71.3%; grade ≥3 drug-related, 45.5%) and in 64.8% in the control group (grade ≥3, 45.9%). CONCLUSIONS: Perioperative enfortumab vedotin plus pembrolizumab and surgery led to significantly better event-free and overall survival outcomes and a greater percentage of participants with pathological complete response than surgery alone in a predominantly cisplatin-ineligible population with muscle-invasive bladder cancer. Safety was also assessed. (Funded by Merck Sharp and Dohme, a subsidiary of Merck [Rahway, NJ]; KEYNOTE-905 ClinicalTrials.gov number, NCT03924895.).
4 Apr 2026
Read appraisal →Quintessence international (Berlin, Germany : 1985)
The association between concurrent oral cancer and periodontal disease: a systematic review and meta-analysis
OBJECTIVE: Since emerging studies have reported a positive association on oral cancer and periodontal disease, the present study aimed to evaluate the odds ratio of having concurrent oral cancer in patients with periodontal disease. METHOD AND MATERIALS: A systematic review following the PRISMA guidelines was conducted with three electronic databases. The inclusion criteria were structured according to the PECOS framework: (P) Human subjects aged over 18; (E) Patients with periodontal disease; (C) Individuals without periodontal disease; (O) The odds of patients developing oral and/or head and neck cancers; (S) Case-control studies. After screening, 18 clinical studies from 13 different regions, comprising a total of 31,504 participants, were included. RESULTS: A meta-analysis was performed and revealed that the odds ratio for developing oral cancer in patients with periodontal disease, compared to those without, was 3.35 (95% CI 2.63-4.26; P .001). After adjusting for potential confounding factors, the adjusted odds ratio was found to be 2.32 (95% CI 1.71-3.15; P .001). Case definition also has an impact, with odds ratio of 2.02 if using missing teeth compared to odds ratio of 2.68 by using American Academy of Periodontology clinical periodontal parameters. CONCLUSION: Clinical considerations for this specific group of patients should draw new attention for a strategic interdisciplinary care.
4 Apr 2026
Read appraisal →Yonsei medical journal
Deep Learning for Automated Tumor Segmentation of Rectal Cancer on T2-Weighted Magnetic Resonance Images
PURPOSE: To develop a deep learning (DL)-based automated segmentation model for rectal cancer on T2-weighted (T2W) magnetic resonance (MR) images. MATERIALS AND METHODS: A total of 458 patients who underwent baseline rectal MR imaging were retrospectively enrolled. An experienced radiologist labeled the tumor on each slice of the selected T2W axial images covering the entire tumor mass, and tumor volume was measured during labeling. Another radiologist labeled the rectum on the same images. Attention U-Net was trained on the T2W images from the training dataset to classify each voxel as tumor or non-tumor. Segmentation with or without rectum guidance was compared using evaluation metrics, including the Dice similarity coefficient (DSC), in the test dataset. RESULTS: The tumor segmentation DSC without rectum guidance was 73.35% (71.97-74.71); that with the predicted rectum was 71.79% (70.53-73.05); and that with the overlaid rectum was 75.52% (74.32-76.62), which was the highest among the three segmentation models. Tumor volume showed a positive correlation with tumor segmentation accuracy with the predicted rectum (r=0.333, p=0.001; overlaid rectum, r=0.319, p=0.002) and without rectum guidance (r=0.219, p=0.036). CONCLUSION: A DL-based automated segmentation model can predict RC with over 70% accuracy, which was further improved with overlaid rectum, and larger tumor volume.
4 Apr 2026
Read appraisal →Annals of surgical oncology
A Deep Learning-Based Multimodal Clinico-Histology-Genomic Prognostic Model in Prostate Cancer
BACKGROUND: Prostate cancer remains a leading cause of cancer mortality, yet current risk stratification systems inadequately integrate multidimensional tumor characteristics. This study aims to develop a deep learning-based multimodal prognostic model that combines genomic signatures, histomorphological features from whole-slide imaging (WSI), and clinical parameters to improve risk stratification and therapeutic decision-making. MATERIALS AND METHODS: We constructed a deep learning framework using two independent cohorts: The Cancer Genome Atlas (TCGA) for training and the Prostate, Lung, Colorectal and Ovarian (PLCO) trial for external validation. The model sequentially extracted 768-dimensional histopathological features from hematoxylin and eosin (H&E)-stained WSIs, predicted genomic scores, and histopathological scores, and integrated these with clinical variables via Cox regression to generate a multimodal prognostic score. Performance was evaluated through Kaplan-Meier analysis, Harrell's concordance index, and multivariate Cox regression. RESULTS: The prognostic score demonstrated superior prognostic accuracy (C-index: 0.774) compared with unimodal scores (genomic, histopathological, clinical) and NCCN risk stratification (C-index: 0.706-0.746, p < 0.05). High-risk patients identified by the multimodal model had significantly shorter progression-free intervals (HR = 9.088, 95% CI 6.033-13.691, p < 0.0001) and prostate cancer-specific mortality (HR = 8.787, 95% CI 3.238-23.847, p < 0.0001). Subgroup analysis within NCCN high-risk patients revealed distinct survival trajectories (p < 0.001). Gene set enrichment linked multimodal scores to tumor-relevant pathways. CONCLUSIONS: This integrative model eliminates reliance on genomic testing by computationally inferring genomic features from histopathology, while significantly enhancing prognostic precision. By stratifying heterogeneous patient populations and refining existing NCCN classifications, the prognostic score offers clinical potential for personalized risk assessment and optimized therapeutic strategies.
4 Apr 2026
Read appraisal →Journal of dentistry
A deep learning method for diagnosis of oral potentially malignant disorders
OBJECTIVES: This study aimed to develop and validate a two-stage deep learning method for diagnosing oral potentially malignant disorders (OPMDs). We also compared its diagnostic performance with that of clinicians at different levels of seniority and evaluated its utility as a clinical decision support tool. METHODS: A two-stage deep learning method was developed. CLA OPMD-OOML was designed to differentiate OPMDs from other oral mucosal lesions (OOML), while CLA OPMDs was designed to classify specific OPMD subtypes. The method was trained on an internal dataset (ZJUSS, n = 3305), and its generalizability was evaluated on two external multicenter datasets (WCHS and CS-SJTU, n = 1756). In a blinded, two-step comparative study, nine clinicians (junior, intermediate, and senior groups) performed diagnoses independently and then with AI assistance. RESULTS: The method significantly outperformed all clinician groups, achieving higher F1 scores in both CLA OPMD-OOML (89.9 % vs. 78.4-82.6 % for clinicians) and CLA OPMDs (92.2 % vs. 77.7-80.0 % for clinicians). It showed robust generalizability on the two external datasets, with F1 scores of 87.3 % and 86.9 % for CLA OPMD-OOML and 75.9-83.0 % for CLA OPMDs. With AI assistance, the diagnostic precision of junior and intermediate clinicians increased by 10.8 % and 5.9 %, respectively, raising the junior group's performance to the level of senior clinicians. CONCLUSIONS: The developed two-stage deep learning method demonstrated diagnostic performance comparable to or exceeding that of experienced clinicians in classifying OPMDs from clinical images. It functions as a powerful assistive tool that substantially enhances the diagnostic capabilities of junior and intermediate clinicians. CLINICAL SIGNIFICANCE: This AI method has the potential to serve as a reliable tool for large-scale early screening of OPMDs, particularly in regions with limited access to specialist dental care. It can also serve as a valuable training and decision support system for junior clinicians, helping to standardize diagnostic accuracy and improve patient outcomes through timely and accurate detection. A supplemental appendix to this article is available online.
4 Apr 2026
Read appraisal →The Journal of pathology
Integrating artificial intelligence (AI) into colorectal cancer reporting
Artificial intelligence (AI) and deep learning (DL) are transforming cancer research and clinical care, with histopathology playing a central role in this transformation. In colorectal cancer (CRC), the second leading cause of cancer mortality world-wide, multimodal and vision-language models (VLMs) hold particular promise for enhancing the standardisation of histopathology reporting, the understanding of disease biology, and the discovery of novel prognostic indicators. Despite the availability of guidelines and reporting templates for essential prognostic indicators, variability remains in how key features such as TNM staging or tumour deposits are assessed and reported in routine clinical practice. AI-based tools have the potential to support refined extraction of established and extended features directly from whole-slide images. In parallel, recent studies have shown that DL models applied to pathology slides and associated AI-based biomarkers can outperform traditional histopathological prognostic indicators and uncover novel parameters, including tumour-adipocyte interactions, tumour-stroma ratio, and immune cell patterns at the invasive margin. Here, we review recent advances in both domains: AI-assisted standardisation of CRC pathology reporting and AI-driven identification of novel prognostic biomarkers. We highlight the need to refine and standardise CRC reporting practices and propose that a harmonised approach combining established pathology features with AI-derived prognostic indicators could refine risk assessment and improve outcomes for CRC patients. © 2026 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
4 Apr 2026
Read appraisal →Annals of oncology : official journal of the European Society for Medical Oncology
Deep learning discriminates thymic epithelial tumors' histological subtypes using digital pathology.
BACKGROUND: Thymic epithelial tumors (TETs) are rare malignancies that pose significant diagnostic challenges due to their heterogeneous histological patterns and substantial interobserver variability in classification. Despite standardized World Health Organization (WHO) classification criteria, diagnostic concordance remains suboptimal, particularly in nonexpert settings, where second-opinion reviews lead to diagnostic reclassification in up to 57% of cases. Deep learning may offer a tool to reduce diagnostic variability and improve the consistency of histological classification. MATERIALS AND METHODS: We trained a deep learning-based model using hematoxylin-eosin (H&E) whole-slide images from The Cancer Genome Atlas as a training dataset. The model incorporated a novel hierarchical loss function designed to reflect clinically relevant tumor groupings based on treatment strategies and patient outcomes. We validated the model on 112 consecutive cases from the University of Chicago, with diagnoses confirmed by an expert thoracic pathologist. Model performances were evaluated using both a three-group hierarchical scheme and the six-class WHO classification. RESULTS: In the clinically relevant hierarchical three-group classification (As: A + AB; Bs: B1 + B2 + B3; and thymic carcinoma), the model achieved an accuracy of 91.1% with Cohen's κ = 0.859, indicating almost perfect agreement. In the six-class classification (A, AB, B1, B2, B3, and thymic carcinoma), the accuracy was 77.7% with κ = 0.716. The model demonstrated 100% sensitivity and 94.6% accuracy for thymic carcinoma detection. Notably, 60% of misclassifications occurred within the same clinical management group, thereby limiting their impact on therapeutic decision making. CONCLUSION: This deep learning model demonstrates strong potential as a diagnostic tool for TETs classification, particularly in settings with limited thoracic pathology expertise. The high sensitivity for thymic carcinoma detection and robust performance across different tissue processing conditions suggest its clinical applicability for improving diagnostic consistency and supporting pathological decision making in both specialized and nonspecialized settings.
4 Apr 2026
Read appraisal →Abdominal radiology (New York)
Artificial intelligence in gastric cancer: a systematic review of machine learning and deep learning applications
BACKGROUND: Gastric cancer (GC) remains a major global health concern, ranking as the fifth most prevalent malignancy and the fourth leading cause of cancer-related mortality worldwide. Although early detection can increase the 5-year survival rate of early gastric cancer (EGC) to over 90%, more than 80% of cases are diagnosed at advanced stages due to subtle clinical symptoms and diagnostic challenges. Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has shown great promise in addressing these limitations. OBJECTIVES: This systematic review aims to evaluate the performance, applications, and limitations of ML and DL models in GC management, with a focus on their use in detection, diagnosis, treatment planning, and prognosis prediction across diverse clinical imaging and data modalities. METHODS: Following the PRISMA 2020 guidelines, a comprehensive literature search was conducted in MEDLINE, Web of Science, and Scopus for studies published between 2004 and May 2025. Eligible studies applied ML or DL algorithms for diagnostic or prognostic tasks in GC using data from endoscopy, computed tomography (CT), pathology, or multi-modal sources. Two reviewers independently performed study selection, data extraction, and risk of bias assessment. RESULTS: A total of 59 studies met the inclusion criteria. DL models, particularly convolutional neural networks (CNNs), demonstrated strong performance in EGC detection, with reported sensitivities up to 95.3% and Area Under the Curve (AUCs) as high as 0.981, often exceeding expert endoscopists. CT-based radiomics and DL models achieved AUCs ranging from 0.825 to 0.972 for tumor staging and metastasis prediction. Pathology-based models reported accuracies up to 100% for EGC detection and AUCs up to 0.92 for predicting treatment response. Cross-modality approaches combining radiomics and pathomics achieved AUCs up to 0.951. Key challenges included algorithmic bias, limited dataset diversity, interpretability issues, and barriers to clinical integration. CONCLUSION: ML and DL models have demonstrated substantial potential to improve early detection, diagnostic accuracy, and individualized treatment in GC. To advance clinical adoption, future research should prioritize the development of large, diverse datasets, implement explainable AI frameworks, and conduct prospective clinical trials. These efforts will be essential for integrating AI into precision oncology and addressing the increasing global burden of gastric cancer.
4 Apr 2026
Read appraisal →European radiology
Predictive value of artificial intelligence-based quantitative CT feature analysis for diagnosing the pathological types of pulmonary nodules
OBJECTIVES: Accurate preoperative classification of pulmonary nodules (PNs) is critical for guiding clinical decision-making and preventing overtreatment. This study aims to evaluate the predictive performance of artificial intelligence (AI)-based quantitative computed tomography (CT) feature analysis in differentiating among four pathological types of PNs: atypical adenomatous hyperplasia and adenocarcinoma in situ (AAH + AIS), minimally invasive adenocarcinoma (MIA), invasive adenocarcinoma (IAC), and lung inflammatory nodules (IN). MATERIALS AND METHODS: A total of 462 pathologically confirmed PNs were analyzed. Radiomic features, including CT attenuation metrics, 3D morphometrics, and texture parameters such as entropy and skewness, were extracted using a deep learning-based AI platform. Logistic regression models were constructed using both single- and multi-variable strategies to evaluate the classification accuracy of these features. Moreover, the inclusion of IN as a separate category significantly enhanced the clinical utility of AI in differentiating benign mimickers from malignant nodules. The combined model, which integrated AI-derived features with traditional CT signs, was used to assess the diagnostic performance of the radiomic features in differentiating the four pathological types of nodules. RESULTS: The combined model demonstrated superior diagnostic performance, with area under the curve (AUC) values of 0.936 for IAC, 0.884 for AAH + AIS, and 0.865 for IN. Although MIA showed lower classification accuracy (AUC = 0.707), key features such as entropy, solid component ratio, and total volume effectively distinguished invasive from non-invasive lesions. CONCLUSION: These findings highlight the potential of AI-enhanced radiomics for supporting non-invasive, objective, and individualized diagnosis of PNs. KEY POINTS: Question Can artificial intelligence (AI)-based quantitative CT analysis reliably differentiate benign inflammatory nodules from the spectrum of lung adenocarcinoma subtypes, a common diagnostic challenge? Findings An integrated model combining AI-driven radiomic features and traditional CT signs demonstrated high accuracy in differentiating invasive adenocarcinoma (AUC = 0.936), pre-invasive lesions (AUC = 0.884), and inflammatory nodules (AUC = 0.865). Clinical relevance AI-enhanced radiomics provides a non-invasive, objective tool to improve preoperative risk stratification of pulmonary nodules, potentially guiding personalized management and reducing unnecessary surgeries for benign inflammatory lesions that mimic malignancy.
4 Apr 2026
Read appraisal →The Journal of investigative dermatology
A Metabolism-Driven Prognostic Model and PSMD14-SP1-GYS1 Axis Reveal Therapeutic Vulnerabilities in Melanoma
Melanoma is a highly aggressive cutaneous malignancy characterized by a strong propensity for metastasis and therapy resistance, with its progression being closely linked to metabolic reprogramming. This study integrated multiomics data (The Cancer Genome Atlas, Gene Expression Omnibus, European Nucleotide Archive) and advanced machine learning to develop prognostic and immunotherapy prediction models for melanoma, focusing on 114 metabolism-related pathways. Cox regression identified 70 genes linked to survival, with functional enrichment revealing key metabolic pathway alterations. A metabolism-related prognostic model (MRPM) was constructed using 101 combinations of machine learning algorithms, demonstrating superior predictive accuracy across 4 cohorts. High-risk patients showed worse survival and immunotherapy response in melanoma and other cancers. Tumor microenvironment analysis revealed MRPM's negative correlation with immune infiltration and positive association with tumor purity. Single-cell sequencing highlighted MRPM gene enrichment in melanocytes. Mechanistically, GYS1 (the key gene in MRPM) emerged as a pivotal prognostic gene that promotes melanoma proliferation and metastasis. Regulatory studies uncovered SP1's transcriptional control of GYS1- and PSMD14-mediated stabilization of SP1 through K48-linked ubiquitination removal. In vivo validation confirmed that PSMD14 knockdown suppressed tumor growth through SP1-GYS1 axis disruption. This work establishes MRPM as a robust predictive tool and elucidates the PSMD14-SP1-GYS1 regulatory network as a potential therapeutic target in melanoma metabolism.
4 Apr 2026
Read appraisal →Annals of plastic surgery
Predicting Surgical Outcomes in Breast Reconstruction With Machine Learning: A Systematic Review
INTRODUCTION: The applications of artificial intelligence (AI) in plastic surgery have grown considerably in recent years. As large patient datasets become more accessible, surgeons are increasingly leveraging machine learning (ML), a subset of AI, to predict patient outcomes and guide surgical decision-making. This review evaluates the relative performance of ML prediction models in breast reconstruction. METHODS: A systematic review was conducted utilizing PubMed, Scopus, and EMBASE according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Studies using ML to predict patient outcomes in breast reconstruction were included. The type of ML model and the specific outcome measures were reported. Performance of the models was reported as area under the receiver operating characteristic curve and compared using descriptive statistics, multivariate linear regression, and random-effects meta-regression in RStudio. RESULTS: Our search yielded 1025 citations, of which 24 were assessed for eligibility. Fourteen studies met the inclusion criteria and were sought for data extraction. There were 19 ML models and 11,013 patients assessed across 92 testing conditions. Models were trained on varying patient demographics, comorbidities, and operative characteristics, whereas outcomes assessed included various surgical complications or patient satisfaction using BREAST-Q. The median area under the receiver operating characteristic curve of all models was 0.71 (interquartile range = 0.16). When adjusting for the number of patients, number of predictors, ML model category, and outcome of interest, models predicting BREAST-Q performed higher with skin necrosis used as the reference outcome (β = 0.13, P < 0.01). After adjusting for number of patients and predictors, models that employed strategies to mitigate class imbalance were associated with higher model discrimination (β = 0.038; 95% CI, 0.002-0.075; P = 0.041). CONCLUSION: Machine learning applications for risk prediction and surgical planning are growing rapidly. The models evaluated in this review demonstrated the ability to predict a variety of outcomes, with models predicting BREAST-Q, various surgical outcomes, and those reporting class imbalance methods leading to higher model discrimination. Notably, covariate adjustment and study heterogeneity may have impacted these associations. As ML models are increasingly integrated into plastic surgery practice, standardized reporting practices are essential to promote reproducibility and cross-study comparison.
4 Apr 2026
Read appraisal →Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
Multicenter Clinical Validation of an Artificial Intelligence Diagnostic Classification Model for Laryngoscopy Images
OBJECTIVE: To develop and externally validate a computer-aided diagnosis (CADx) model using artificial intelligence (AI) for classifying laryngeal lesions from laryngoscopy images into high-risk (HR), low-risk (LR). STUDY DESIGN: Retrospective multicenter development of a CADx model and external validation on independent cohorts. SETTING: Multicenter tertiary referral hospitals (Italy, India, China, Greece, and Spain). METHODS: Over 20,000 images derived from laryngoscopic examinations were retrieved. Images were annotated based on histopathology or expert consensus. A deep learning model was trained using an internal dataset and evaluated on 2 external datasets to assess generalizability. The CADx model classifies only images containing visible lesions, discriminating between LR and HR categories. Diagnostic performance was measured using standard metrics, including accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). Model performance was compared with physicians of varying expertise and ChatGPT-4o. RESULTS: The computer-aided diagnosis model achieved a similar performance across internal and external datasets in distinguishing HR from LR lesions, with accuracy/AUC of 0.90/0.89 internally, 0.85/0.85 on the Greek dataset, and 0.88/0.88 on the Spanish dataset. The model's accuracy was statistically noninferior to that of otolaryngologists and expert laryngologists, and superior to general practitioners and ChatGPT-4o. CONCLUSION: This is a large multicenter clinical validation of a CADx model for laryngeal endoscopy, demonstrating generalizability and performance comparable to clinicians in discriminating between LR and HR lesions. The model's success supports its potential role in augmenting diagnostic capabilities, especially in resource-limited settings. A prospective multicenter clinical trial is underway to assess real-world clinical implementation.
2 Apr 2026
Read appraisal →J Med Internet Res
Use of Health and Welfare Technology in Palliative Care: State-of-the-Art Review
BACKGROUND: As more individuals live longer with complex conditions, the need for effective palliative care (PC) grows. It has been stated that access to PC should be integrated early and delivered in a timely manner to patients with life-threatening illnesses. Health and welfare technologies (HWTs) offer tools to enhance care delivery, particularly in home and rural settings. Although there is a profound lack of evidence regarding the impact when used in PC, it is necessary to critically assess the current state of knowledge regarding impacts and consequences of technologies, ensuring that their integration considers broader implications for patients, caregivers, and health care systems in PC. OBJECTIVE: This review explores health and welfare technology used in PC, aiming to inform practice and improve care quality. METHODS: This state-of-the-art review included empirical studies describing the use of HWT in PC for adult patients. We used a thematic synthesis approach to compare studies and provide a synthesis of the key points. RESULTS: Based on the inclusion criteria, 94 studies were included. PC is both a clinical specialty and an overall approach to care that focuses on improving quality of life and relieving suffering for patients and families facing serious illness, based on needs and not prognosis. HWT shows potential to increase access and continuity of care, for symptom management to support patients to remain at home and prevent frequent emergency visits. It can have the potential to build and remain relationships between patients, their families, and the health care team, as well as for interprofessional collaboration and support. However, there are challenges to overcome that might affect the quality of care when using technology. CONCLUSIONS: HWT shows potential as a complement to usual PC. Our findings point toward the importance of caution in choosing when to use HWT in PC, and for which patients.
1 Mar 2026
Read appraisal →Int J Surg
Artificial Intelligence in melanoma research: a bibliometric analysis
BACKGROUND: As one of the most lethal skin cancers, melanoma has encountered many obstacles in diagnosis and therapy. Artificial intelligence (AI) can help improve early diagnosis, prognosis, and treatment of melanoma. However, there is a lack of detailed and accurate bibliometric analysis of the field. METHODS: All publications were extracted from Web of Science Core Collection based on AI and melanoma terms. Bibliometric analysis was conducted on 1476 articles/reviews by using VOSviewer, CiteSpace, and bibliometrix for co-authorship, citation, keyword, and journal analysis. RESULTS: There was a sudden increase in publications each year after 2017 and reached 260 in 2024. The United States (337 publications) and China (292 publications) ranked top in publication productivity. Germany was the top institution and author country. IEEE Access (57 publications) and Diagnostics (54 publications) were core journals. The two most prominent research hotspots were AI-assisted diagnosis and AI-integrated immunotherapy according to the keyword analysis. CONCLUSION: AI in melanoma research has exploded since 2017. It is recommended that future research focus on various datasets, explainable AI, and cross-disciplinary cooperation to promote the transformation of achievements into clinical practice.
1 Feb 2026
Read appraisal →J Clin Oncol
Genomics Define Malignant Transformation in Myeloma Precursor Conditions
Multiple myeloma (MM) is consistently preceded by monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). While these precursor conditions are asymptomatic, they are not entirely benign and carry a lifelong risk of progression to MM. Unlike other cancers defined by pathology, malignant transformation from MGUS or SMM to MM has so far relied on demonstration of clinical end-organ damage as morphology and cytogenetics cannot reliably distinguish them. In this study, using genomic data from 374 patients with MGUS or SMM (277 training, 97 validation), to our knowledge, we demonstrate for the first time the ability to identify malignant transformation in MGUS and SMM. We introduce the concept of genomic MM and genomic MGUS to differentiate the subsets of MGUS and SMM that are biologically malignant with genomic features indistinguishable from MM from the subset that is premalignant and unlikely to progress to malignancy. Importantly, we find that most SMM has biological features of malignant transformation indistinguishable from MM. As expected, this subset that we consider having genomic MM is associated with a high risk of progression to MM although some patients remained progression-free beyond 5 years. Conversely, 60% of MGUS and 10% of SMM have no evidence of malignant transformation (genomic MGUS), with no progression during follow-up. Integration of genomic features with the 2/20/20 International Myeloma Working Group model significantly improved the prediction of progression among genomic MM. These findings support the use of genomic criteria to refine the classification and the risk stratification in myeloma precursor conditions.
1 Jan 2026
Read appraisal →Cancer Res
Artificial Intelligence and Multimodality Data Integration Decipher Tertiary Lymphoid Structure Maturity in Gastric Cancer
UNLABELLED: Tertiary lymphoid structures (TLS) are critical components of the tumor microenvironment in gastric cancer, but clinical assessment of TLSs is challenging. The development of automated annotation tools for histopathologic slide analysis could facilitate the identification of TLSs and enhance our understanding of the mechanisms driving TLS maturation. In this study, we generated a transformer-based deep learning model that enables quantitative characterization of TLS maturity from whole-slide images. Application of the model to a large gastric cancer cohort (n = 253) showed that higher TLS maturity correlated with improved patient survival. Integration of single-cell RNA sequencing data from 17 patients with gastric cancer combined with multiplex IHC, flow cytometry, and functional coculture assays identified a key immune circuit in mature TLSs involving CD8+ tissue-resident memory T cells, which recruit activated B cells via the CXCL13-CXCR5 axis to enhance tissue-resident memory T-cell cytotoxicity through granzyme B upregulation. Overall, this study established a clinically applicable artificial intelligence tool and uncovered key immune interactions that regulate TLS maturation and antitumor immunity in gastric cancer. SIGNIFICANCE: A deep learning model demonstrates that higher tertiary lymphoid structure maturity predicts improved gastric cancer patient survival and identifies a key immune circuit, offering a clinically applicable tool that could guide treatment. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI.
1 Dec 2025
Read appraisal →Ann Med
Artificial intelligence-assisted diagnosis of early gastric cancer: present practice and future prospects
Gastric cancer (GC) occupies the first few places in the world among tumors in terms of incidence and mortality, causing serious harm to human health, and at the same time, its treatment greatly consumes the health care resources of all countries in the world. The diagnosis of GC is usually based on histopathologic examination, and it is very important to be able to detect and identify cancerous lesions at an early stage, but some endoscopists' lack of diagnostic experience and fatigue at work lead to a certain rate of under diagnosis. The rapid and striking development of Artificial intelligence (AI) has helped to enhance the ability to extract abnormal information from endoscopic images to some extent, and more and more researchers are applying AI technology to the diagnosis of GC. This initiative has not only improved the detection rate of early gastric cancer (EGC), but also significantly improved the survival rate of patients after treatment. This article reviews the results of various AI-assisted diagnoses of EGC in recent years, including the identification of EGC, the determination of differentiation type and invasion depth, and the identification of borders. Although AI has a better application prospect in the early diagnosis of ECG, there are still major challenges, and the prospects and limitations of AI application need to be further discussed.
1 Dec 2025
Read appraisal →Blood
Artificial intelligence in hematology
Artificial intelligence (AI) and its subdiscipline, machine learning (ML), have the potential to revolutionize health care, including hematology. The diagnosis and treatment of hematologic disorders depend on the integration of diverse data sources, such as imaging, pathology, omics, and laboratory parameters. The increasing volume and complexity of patient data have made clinical decision-making more challenging. AI/ML hold significant potential for enhancing diagnostic accuracy, risk stratification, and treatment response prediction through advanced modeling techniques. Generative AI, a recent advancement within the broader field of AI, is poised to have a profound impact on health care and hematology. Generative AI can enhance the development of novel therapeutic strategies, improve diagnostic workflows by generating high-fidelity images or pathology reports, and facilitate more personalized approaches to patient management. Its ability to augment clinical decision-making and streamline research represents a significant leap forward in the field. However, despite this potential, few AI/ML tools have been fully implemented in clinical practice due to challenges related to data quality, equity, advanced infrastructure, and the establishment of robust evaluation metrics. Despite its promise, AI implementation in hematology faces critical challenges, including bias, data quality issues, and a lack of regulatory frameworks and safety standards that keep pace with rapid technological advancements. In this review, we provide an overview of the current state of AI/ML in hematology as of 2025, identify existing gaps, and offer insights into future developments.
1 Nov 2025
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