Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 22 appraisals
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 →Journal of robotic surgery
Robotic versus laparoscopic and open surgery for endometrial cancer: a systematic review of randomized trials and pooled analysis of conversion rates
To summarize randomized evidence assessing robotic surgery in relation to conventional laparoscopic and open abdominal approaches for endometrial cancer treatment, with particular attention to perioperative outcomes and conversion to open surgery. Randomized controlled trials evaluating robot-assisted surgical treatment of endometrial cancer were identified across major biomedical databases up to December 2025. Eligible studies compared the robotic approach against laparoscopic or open abdominal surgery. Quantitative pooling was undertaken only when outcome reporting was sufficiently consistent across studies. Eight randomized trials including 647 patients met the inclusion criteria. Overall, 322 patients underwent robotic surgery, 244 conventional laparoscopy, and 81 laparotomy. Most perioperative endpoints were reported heterogeneously, limiting formal pooling. Operative time varied across trials when robotics was compared with laparoscopy and was generally longer than laparotomy. Intraoperative blood loss and postoperative hospitalization did not show consistent differences between the two minimally invasive approaches. Compared with laparotomy, the robotic approach was linked to reduced postoperative stay. Conversion to laparotomy occurred less frequently after robotic surgery than after laparoscopy (0.7% vs. 8.4%; OR 0.17; p = .03). Complication reporting was inconsistent, although trials comparing robotics with laparotomy generally favored the robotic approach. Direct procedural costs were higher for robotics, whereas indirect costs favored robotics in the single study evaluating them. The robotic approach resulted in a lower need for open conversion compared with conventional laparoscopy. Other perioperative outcomes appeared broadly comparable between the two minimally invasive approaches, while comparisons with laparotomy suggested shorter hospital stay and fewer postoperative complications, although these findings should be interpreted cautiously because of the limited and heterogeneous randomized evidence.
21 July 2026
Read appraisal →Journal of robotic surgery
Mapping the evolution of deep learning and computer vision in robotic surgery: a bibliometric analysis of surgical video intelligence, instrument perception, and clinical translation.
Deep learning and computer vision are increasingly embedded in robotic surgery, yet the development and translational direction of this research domain remain incompletely characterized. We conducted a bibliometric and visualization analysis of publications retrieved from the Web of Science Core Collection using Bibliometrix/Biblioshiny, VOSviewer, and CiteSpace. A total of 1,186 documents published between 2010 and 2026 across 356 sources were included. Scientific output increased rapidly, with an annual growth rate of 16.09% and a peak of 216 publications in 2025. The field involved 5,296 authors, and international collaboration accounted for 30.69% of publications. IEEE Robotics and Automation Letters was the most productive and locally influential source. China and the United States were the leading contributors, with China showing the most rapid recent expansion and the United States retaining the highest citation impact. Citation-burst and keyword analyses identified U-Net, residual learning, transformer architectures, and foundation-model-enabled segmentation as major methodological drivers. The conceptual structure evolved from image guidance, registration, and navigation toward surgical video intelligence, instrument perception, workflow understanding, autonomous assistance, and clinical translation. Instrument perception emerged as a central link between algorithmic development and operative application. Future progress will require diverse multi-institutional datasets, external and prospective validation, integrated scene understanding, and rigorous evaluation of intelligent assistance within real robotic surgical workflows.
21 July 2026
Read appraisal →Journal of chemical information and modeling
Characterizing the MSMP-CCR2 Interaction through Molecular Dynamics Simulations and Machine Learning Approaches
The MSMP (MicroSeminoProtein, Prostate-associated) protein is overexpressed in several cancers, including prostate, ovarian, and breast cancers. Its overexpression is particularly prevalent in tumors resistant to hormonal therapy, as well as to antiangiogenic treatments (e.g., anti-VEGF therapies). In hypoxic tumor microenvironments, characteristic of solid tumors, MSMP expression increases and facilitates tumor growth. MSMP binds to the transmembrane receptor CCR2 (C-C chemokine receptor type 2), a GPCR present on monocytes and lymphocytes. This interaction stabilizes an active conformation of CCR2, enabling downstream MAP kinase signaling pathways to reactivate androgen synthesis and promote tumor progression. In this study, we employed molecular modeling, molecular dynamics simulations, and machine learning techniques to elucidate the structural basis of the MSMP-CCR2 interaction. High-resolution models of the MSMP-CCR2-G protein complex were generated using AlphaFold2 and refined with MD simulations. Comparative analyses with the CCL2-CCR2-G protein complex and the unbound CCR2-G protein complex revealed key conformational rearrangements that modulate and stabilize the active state of the receptor. Binding free energy calculations revealed similar energetic profiles for MSMP and CCL2, despite distinct binding modes. Residue-level energy decomposition identified critical residues at the MSMP-CCR2 interface, offering insights into the receptor's activation mechanism. Additionally, machine learning models classified molecular dynamics trajectories, highlighting key structural features associated with stable MSMP-CCR2-G protein states. This integrative approach identified residues essential for maintaining a stable conformation of the MSMP-CCR2-G protein complex, providing a basis for targeting MSMP-CCR2 interactions in therapeutic development.
14 July 2026
Read appraisal →JMIR research protocols
Applications of Large Language Models in Ovarian Cancer Management: Protocol for a Systematic Review and Meta-Analysis
BACKGROUND: Ovarian cancer (OC) is a highly fatal gynecologic malignancy with complex management challenges and limited long-term survival for advanced stages. Large language models (LLMs)-including systems such as GPT-4, Claude, Google Gemini, and others-are emerging artificial intelligence (AI) tools capable of performing health care-related tasks such as diagnostic support, treatment planning, report generation, and patient communication. However, their applications in OC care have not yet been comprehensively assessed. OBJECTIVE: This protocol outlines a systematic review and meta-analysis aimed at evaluating the use, performance, and clinical impact of LLMs in OC management. We will examine how LLMs have been applied across various domains (eg, diagnosis, prognosis, treatment planning, and patient engagement), the metrics used to assess their performance (eg, accuracy, sensitivity, and area under the curve), and their strengths and limitations. METHODS: This review will be conducted in accordance with PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols) guidelines. A comprehensive search strategy will be implemented across biomedical, technical, and Chinese-language databases (eg, PubMed, Embase, Web of Science, IEEE Xplore, and China National Knowledge Infrastructure) from inception to December 31, 2025. Eligible studies include clinical evaluations, validation studies, and real-world implementation reports involving LLMs in OC care. Two independent reviewers will perform screening, data extraction, and quality appraisal using validated tools (eg, version 2 of the Cochrane risk-of-bias tool for randomized trials, Risk of Bias in Nonrandomized Studies of Interventions, Quality Assessment of Diagnostic Accuracy Studies 2, and Prediction Model Study Risk of Bias Assessment Tool+AI). Outcomes of interest include model performance metrics, clinical process impacts, safety concerns, and usability. Meta-analyses will be conducted where feasible using random-effects models in R (meta, metafor, and mada packages), including bivariate models for sensitivity and specificity. RESULTS: The review is currently in progress. The PROSPERO registration has been completed, and the literature search and selection process is underway. Study selection, data extraction, and quality assessment are expected to be completed by mid-2026. Final results will include pooled performance metrics (eg, accuracy, F1-score, and area under the curve), qualitative insights into clinical integration, and identification of limitations such as reporting bias or insufficient external validation. CONCLUSIONS: This systematic review will provide the first comprehensive synthesis of evidence on the application of LLMs in OC care. It will identify promising use cases, highlight safety and reporting challenges, and inform future research directions. The findings are expected to support evidence-based integration of LLMs into gynecologic oncology workflows while promoting transparency and methodological rigor in AI evaluation.
12 July 2026
Read appraisal →Cancer causes & control : CCC
Post-diagnosis physical activity in relation to mortality among gynecological cancer survivors
PURPOSE: Physical activity may play a supportive role in cancer survivorship. However, evidence on the association between post-diagnosis physical activity and mortality among women with gynecological cancer remains limited and inconsistent. METHODS: We conducted a systematic review of the literature published between 1949 and January 2026. Eligible observational studies were identified, and random-effects meta-analyses were performed to estimate pooled hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between post-diagnosis physical activity and all-cause mortality among women diagnosed with gynecological cancer. RESULTS: A total of ten eligible studies on endometrial, ovarian, and cervical cancer were included, collectively reporting 3,867 deaths. High levels of post-diagnosis physical activity, compared with low levels, were associated with lower mortality (HR: 0.65; 95% CI 0.54-0.78). This inverse relationship was evident in both endometrial and ovarian cancer survivors (endometrial cancer: HR: 0.60; 95% CI 0.43-0.83; ovarian cancer: HR: 0.71; 95% CI 0.58-0.86). Medium levels of physical activity tended to be inversely associated with mortality (HR: 0.88; 95% CI 0.76-1.02). CONCLUSION: Higher levels of physical activity after a gynecological cancer diagnosis were associated with improved survival. The results suggest that physical activity may represent a modifiable lifestyle factor with the potential to improve long-term outcomes among gynecological cancer survivors. IMPLICATIONS FOR CANCER SURVIVORS: This supports the potential value of integrating physical activity into survivorship care, although further high-quality prospective studies are needed to strengthen causal inference.
6 July 2026
Read appraisal →Medical image analysis
Medical hierarchical image classification via dual-geometry image-text learning
Hierarchical image classification is a fundamental challenge in medical image analysis, as tree-structured taxonomies inherently reflect biological and clinical relationships, spanning the general categorisation of disease entities and fine-grained cellular distinctions. Existing approaches primarily rely on multi-task learning and fine-grained detection, often requiring intricate model design and complex training strategies. In this paper, we aim to exploit the negative curvature property of hyperbolic space, which allows efficient representation of hierarchical structures. We propose a dual-geometry image-text framework, termed H2CL. Specifically, we introduce a lightweight classifier head on top of image backbones to extract both Euclidean and hyperbolic features, which are then combined to simultaneously preserve taxonomic consistency from an etiological perspective and enhance instance discrimination from a morphological perspective. Furthermore, a text branch is incorporated to integrate label semantics, where an entailment loss is employed to jointly model image-text alignment and inter-sample relationships. Extensive experiments on cervical cell, skin lesion, and gallbladder disease datasets demonstrate that our framework consistently outperforms advanced methods. Compared to the standard Swin Transformer, H2CL achieves an average accuracy improvement of 7% across all three datasets at the fine-grained level, with similarly consistent gains observed when integrated with other backbone models. The source code is publicly available at https://github.com/MCPathology/H2CL.
4 July 2026
Read appraisal →Physics in medicine and biology
Deep learning-based dose prediction to enhance planning efficiency in cervical brachytherapy with hybrid applicators
Objective.This study aimed to develop a dose prediction model for hybrid applicators in cervical cancer brachytherapy.Approach.A total of 216 previous treatment plans were collected and divided into three datasets: training (161 plans), testing (40 plans), and clinical evaluation (15 plans). The treatment plans included three hybrid applicator types: tandem and ovoids combined with interstitial needles, tandem and ring combined with interstitial needles, and tandem and cylinder combined with interstitial needles. The model was validated on the testing dataset using mean absolute error (MAE) for voxel-wise dose evaluation and on the clinical evaluation dataset using dice similarity coefficient (DSC) and dose statistic differences.Main results.The model achieved a voxel-wise MAE of 0.45 ± 0.27 Gy and showed no statistically significant differences (p> 0.05) in all key dose-volume histogram parameters when compared to clinical plans. The DSC values ranged from 0.74 to 0.77. Small dosimetric differences between the optimized and manually generated plans were observed for the high-risk clinical target volume (HR-CTV) D98%, HR-CTV D90%, and bladder D2cc.Significance.The developed dose prediction model demonstrates significant potential for application in hybrid applicator brachytherapy. This model can provide guidance for dose delivery during dwell time optimization. As a reference for optimization, it has the potential to streamline the planning process, reduce planning time, and improve consistency in adaptive workflows.
3 July 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 →Cancer causes & control : CCC
HPV in breast cancer: prevalence and comparison with healthy tissue-a systematic review and meta-analysis
INTRODUCTION: Breast cancer (BC) is the most common cancer among women worldwide, with over 2.3 million new cases annually. Recent studies suggest that Human Papillomavirus (HPV), a known oncogenic virus, may be involved in BC development. This study investigates HPV prevalence in BC samples and its potential role in tumorigenesis. METHODS: Random-effects meta-analyses were conducted to estimate raw proportions and odds ratio (OR), with 95% confidence intervals (CIs). Heterogeneity was assessed using I2. Statistical significance was set at p < 0.05. Analyses were performed in R 4.5.0 RESULTS: Our meta-analysis encompassed 82 studies and evaluated 7,683 breast cancer (BC) tissue samples to assess the presence of HPV. The overall prevalence of HPV in BC specimens was estimated at 23% (95%CI: 19%-28%). When stratified by continent, Oceania exhibited the highest regional prevalence at 38%. Comparative analysis between BC tissues and healthy controls revealed a significantly increased likelihood of HPV detection in the cancer group (OR 5.06; P < 0.001). This association remained statistically robust in both case-control (OR 6.34; P < 0.001) and cross-sectional designs (OR 2.83; P < 0.001). Among continents, South America demonstrated the most pronounced association (OR 11.66; P = 0.005). Subgroup analysis based on economic classification indicated that countries with low-income settings had the highest HPV prevalence (34%; 95%CI: 9%-73%). Evaluation by BC subtype revealed that luminal B had the highest HPV-positive rate (44%; 95%CI: 27%-61%). CONCLUSION: This meta-analysis reveals a global presence of HPV in BC and suggests a possible link. Further well-designed studies are needed to confirm its role in tumorigenesis.
26 June 2026
Read appraisal →International journal of hyperthermia : the official journal of European Society for Hyperthermic Oncology, North American Hyperthermia Group
The role of capacitive hyperthermia as an adjunct treatment in oncology: a systematic review of randomized phase III trials.
INTRODUCTION: Capacitive hyperthermia (cHT) has been established for superficial tumors but the efficacy of cHT for intermediate or deep-seated tumors remains a subject of debate. This review is an evaluation of the current clinical evidence on the effectiveness of cHT in the treatment of tumors that are located deeper within the body. METHODS: A systematic review was conducted in accordance with the PRISMA guidelines of Phase III randomized controlled trials that included cancer patients of all ages and sexes with confirmed malignant tumors undergoing radiotherapy (RT) and/or chemotherapy (CT), with or without additional cHT. The literature search was conducted in PubMed and the Cochrane Register of Controlled Trials (CENTRAL). The primary endpoints included overall survival (OS), tumor response (TR; encompassing complete response (CR)), local control (LC), and cHT treatment-related toxicity (TRT). RESULTS: A total of nine randomized phase III trials were included in the study. The combination of cHT with RT and/or CT was found to improve both LC and OS in three out of five trials conducted in patients with cervical cancer. The analysis of both head and neck cancer trials demonstrated a marked increase in TR rates in cases where cHT was employed. Conversely, no such benefit was observed in non-small cell lung cancers and in a large multicenter cervical cancer trial. The treatment was well tolerated, with mild to moderate adverse events being the most common. CONCLUSION: It has been demonstrated that the combination of cHT with either RT or CT may improve TR and LC and, in certain cases, OS when compared with RT with or without CT alone. The occurrence of significant side effects is rare.
24 June 2026
Read appraisal →Cell systems
Fusing imaging and metabolic modeling via multimodal deep learning in ovarian cancer
Integrating genotype (e.g., transcriptomics), phenotype (e.g., imaging), and tumor microenvironment (e.g., metabolomics) is crucial to elucidating the molecular basis of ovarian cancer. However, there is a lack of robust multimodal integration methods when only a limited number of common samples is available. Here, we generate patient-specific metabolic models starting from transcriptomics data and integrate them with imaging data. We show that this multimodal integration-never attempted before-improves survival estimation and enables a mechanistic interpretation of the predictions. We assess the robustness of our approach with different combinations of transcriptomics, fluxomics, and 3D computerized tomography (CT) imaging data, correctly stratifying patients based on risk. Fusing metabolic modeling with imaging and transcriptomics significantly improves model accuracy compared with widely used transcriptomics-imaging approaches and elucidates critical metabolic reactions. Our approach is general and can be applied to other cancer types where coupled imaging-transcriptomics data are available. A record of this paper's transparent peer review process is included in the supplemental information.
19 June 2026
Read appraisal →Archives of gynecology and obstetrics
Comparing clinical decision-making between colposcopists and large language models in cervical dysplasia management: a pilot prospective multicenter study
PURPOSE: This prospective multicenter study aimed to compare the decision-making abilities of board-certified colposcopists and two commercially available large language models (LLM), ChatGPT-4o and ChatGPT-5, in cervical dysplasia management. METHODS: Twenty-three anonymized real-life patient cases with multiple-choice (MC) questions regarding treatment decisions were used to assess answer quality. Ten board-certified colposcopists and the two LLMs addressed the MC questions. The gold standard was defined by two guideline authors. LLMs were prompted to justify their responses. Concordance rates were calculated and compared across all questions and histopathological subgroups, including cervical intraepithelial neoplasia (CIN), unspecific histopathological results, and cervical cancer cases. RESULTS: Clinicians and LLMs achieved similar overall concordance rates compared to the gold standard (69.6% for clinicians, 69.6% for ChatGPT-4o, and 65.2% for ChatGPT-5). ChatGPT-5 outperformed clinicians in precancerous lesions (81.8% vs. 66.4%), while clinicians excelled in complex cases with unspecific histopathology (86% vs. 60%). Clinicians showed a tendency to overtreat low-grade lesions (CIN I), opting for more intensive surveillance. ChatGPT-4o performed better than ChatGPT-5 in cervical cancer cases, though both models struggled with these scenarios. CONCLUSION: This study highlights the potential of LLMs as decision support tools in cervical dysplasia management, particularly for straightforward cases like precancerous lesions. However, clinicians remain superior in handling complex or ambiguous cases. The tendency of clinicians to overtreat low-grade lesions may offer the potential to test the implementation of a decision support tool for those cases. While LLMs show promise, exploring open-ended clinical scenarios and integrating retrieval-augmented generation could enhance their practical application.
19 June 2026
Read appraisal →Journal of ovarian research
Deep learning-based prediction of gene expression from histopathology identifies NR5A1 as a candidate biomarker and druggable target in high-grade serous ovarian carcinoma
BACKGROUND: High-grade serous ovarian cancer (HGSOC) is the most lethal ovarian cancer subtype, responsible for ~ 70% of ovarian cancer-related deaths and marked by late-stage diagnosis and frequent platinum resistance. Although transcriptomic profiling enables molecular stratification and prediction of therapeutic response; routine clinical use of this approach is limited by cost and logistical constraints. Computational pathology analysis offers a scalable alternative by inferring transcriptional states directly from routine hematoxylin and eosin (H&E) whole-slide images (WSIs). METHODS: Paired H&E WSIs and RNA-sequencing data from the TCGA-OV cohort, including 1,371 diagnostic H&E WSIs retrieved for preprocessing and quality control, were used to develop a self-supervised virtual-transcriptomics framework based on Momentum Contrast v2 (MoCo v2) and multi-output Random Forest regression. Model performance was assessed using patient-level five-fold cross-validation. Candidate genes were evaluated by reverse transcription quantitative polymerase chain reaction (RT-qPCR) in an independent cohort of 10 HGSOC tumors, including 4 platinum responders and 6 non-responders. RESULTS: The model predicted expression of approximately 6,400 protein-coding genes, achieving a genome-wide mean Pearson correlation of r = 0.36, with more than 300 genes showing stronger image-expression coupling (r > 0.44). RT-qPCR analysis of 18 candidate genes revealed substantial inter-patient heterogeneity. NR5A1 exhibited the highest expression variability (coefficient of variation [CV] = 1.486) and significantly higher expression in platinum-responsive tumors than in non-responders (mean 2⁻ΔCt = 0.263 vs. 0.013; p < 0.05). Exploratory in silico docking and molecular dynamics (MD) analyses suggested structurally stable binding interactions between Steroidogenic Factor-1 (SF-1/NR5A1) and the natural plant compound cubebin. CONCLUSION: This study demonstrates that histological architecture contains measurable transcriptomic information that can support scalable biomarker prioritization from routine diagnostic histology in HGSOC. NR5A1 represents a hypothesis-generating candidate biomarker and structurally tractable target for future experimental studies. Future validation in larger, multi-center cohorts will be essential to confirm model robustness, biological relevance, and potential clinical utility.
14 June 2026
Read appraisal →The Cochrane database of systematic reviews
Bone marrow-sparing radiotherapy in women with cervical cancer treated with radiochemotherapy
This is a protocol for a Cochrane Review (intervention). The objectives are as follows: Primary objective To evaluate the benefits and harms of bone marrow-sparing radiotherapy compared with non-bone marrow-sparing radiotherapy in women with locally advanced cervical cancer treated with concurrent chemoradiotherapy. Secondary objectives To investigate whether effects differ according to clinical, histopathological, and technical characteristics, focusing on: acute haematologic toxicity; acute gastrointestinal and genitourinary toxicity; and bone marrow dose-volume parameters associated with acute haematologic toxicity, including exploration of dose-volume ranges relevant for clinical implementation.
13 June 2026
Read appraisal →Biomedical physics & engineering express
RADIANT: A fully configurable radiotherapy dose prediction framework
Objective.Treatment planning is a multi-disciplinary effort that requires medical decision-making, specialized training, and access to specialized software. Recently, deep learning has arisen as a powerful method for predicting patient-specific dose distributions. This work presents the radiotherapy dose inference and analysis toolkit (RADIANT), an open-source, fully configurable framework for 3D radiotherapy dose prediction. Built upon the Medical Imaging Segmentation Toolkit, RADIANT supports diverse network architectures, loss functions, and training strategies.Approach.We demonstrate its capabilities on cervical and prostate treatment plans generated with the radiation planning assistant, and on head and neck cancer plans from the American Association of Physicists in Medicine OpenKBP challenge data. For cervical cancer, we trained and compared nnU-Net, FMG-Net, W-Net, ddU-Net, and Swin UNETR using clinical metrics such as dose score, homogeneity index, and percent errors inD95,D98, andD99. Benchmarking was also performed on the OpenKBP dataset using dose score and dose-volume histogram (DVH) score for comparison with top challenge submissions.Results.Our results indicate that RADIANT provides a scalable platform for the rapid development and benchmarking of dose prediction models for selected cancer sites (cervix, prostate, head and neck). For cervical cancer, the best configuration (nnU-Net architecture, mean absolute error loss, cosine learning rate scheduler) achieved a dose score of 1.31 and sub-1%errors inD95andD98on the training set, and a dose score of 1.20 on the test set. The same configuration performed best on prostate data with a dose score of 1.96 on a test set. On the head and neck OpenKBP data, RADIANT achieved a dose score of 2.702 and a DVH score of 1.495, competitive with top challenge results.Significance.This work provides an open-source, fully configurable framework for deep learning-based dose prediction. RADIANT facilitates reproducible experimentation across data preprocessing, model configuration, training, and evaluation for multiple cancer sites and anatomical structures.
30 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 →Journal of radiation research
Feasibility of online adaptive SBRT boost with 1.5-T MR-Linac for cervical cancer patients unsuitable for brachytherapy: a planning study
This study aimed to evaluate the effectiveness of online adaptive treatment (ADT) planning for stereotactic body radiotherapy (SBRT) boost on a 1.5-T MR-Linac in cervical cancer patients ineligible for brachytherapy, compared with the conventional position-alignment (PA) assumed dose distributions. This retrospective analysis included 15 treatment plans from five patients, each receiving 21.0 Gy in three fractions. Daily magnetic resonance imaging (MRIs) were used to compare target and organ-at-risk (OAR) doses between ADT and PA dose distributions. The planning target volume (PTV) was defined as the high-risk clinical target volume (HR-CTV) plus a 3 mm margin. OARs included the bladder, bowel bag, and planning organ-at-risk volume (PRV) for rectum and sigmoid. Reference plans prioritized OAR constraints while maximizing target coverage; the intended PTV D90% range was 18.9-21.6 Gy. PA doses were reconstructed by dose warping based on alignment of the anterior rectal wall between daily and pretreatment MRIs, whereas ADT plans were reoptimized on the daily MRIs. HR-CTV decreased by 1.6%-41.5% between the planning MRI and the first treatment fraction. ADT plans improved PTV D90%, achieved better target coverage and met all OAR constraints, whereas PA plans showed dose constraint exceedance across the three fractions, with mean excess doses of 6.89 ± 4.39 Gy to PRV-sigmoid and 5.76 ± 3.33 Gy to PRV-rectum. In conclusion, 1.5-T MR-Linac based SBRT boost with daily online adaptation enhanced tumor coverage and reduced OAR doses, supporting MR-guided online adaptive radiotherapy as a promising option for patients ineligible for brachytherapy.
27 May 2026
Read appraisal →Clinical cancer research : an official journal of the American Association for Cancer Research
Allogeneic B7-H3-Targeted CAR Vδ1T-cell Therapy in Advanced Solid Tumors: A Phase I Study
PURPOSE: The aim of the study was to evaluate the safety, pharmacokinetics, and preliminary clinical activity of UTAA06, an "off-the-shelf" allogeneic B7-H3-targeted chimeric antigen receptor (CAR) Vδ1T-cell therapy, in patients with pretreated, advanced B7-H3-positive solid tumors. PATIENTS AND METHODS: In this first-in-human, phase I, dose-escalation study (NCT06372236), 10 patients with advanced solid tumors (including gastric, colorectal, hepatocellular, ovarian, and neuroendocrine cancers) were enrolled. Following lymphodepletion chemotherapy (cyclophosphamide and fludarabine), patients received UTAA06 infusion across three dose levels (5 × 108, 8 × 108, or 1 × 109 cells). The primary endpoint was safety. Secondary endpoints included pharmacokinetics and antitumor efficacy. RESULTS: UTAA06 demonstrated a manageable safety profile; no GVHD was observed, and cytokine release syndrome was limited to two transient grade 1 events. A single dose-limiting toxicity (grade 3 pneumonitis) was reported in one patient at the 5 × 108 cell dose level. Although UTAA06 demonstrated signals of biological activity, including transient reductions in serum tumor markers in 50% of patients, no objective response by RECIST v1.1 criteria was observed. Further analysis identified that the limited CAR T-cell persistence was likely driven by subclinical host-versus-graft rejection. CONCLUSIONS: This study provides clinical proof of concept for allogeneic B7-H3-targeted CAR-Vδ1T cells as a safe platform with low risk of GVHD and demonstrable biological activity in solid tumors. However, clinical efficacy was constrained by limited cellular persistence caused by host immune rejection. Future strategies are required to enhance the durability and therapeutic potential of this allogeneic approach.
17 May 2026
Read appraisal →Journal for immunotherapy of cancer
Safety and immunogenicity of a tri-antigen vaccine targeting IGFBP-2, HER2, and IGF-IR in participants with non-metastatic breast cancer
BACKGROUND: Ductal carcinoma in situ (DCIS) is a preinvasive form of breast cancer. Current treatment consists of surgery, radiation, and often systemic therapy exposing patients to unnecessary health risks. Vaccines targeting DCIS may be a way to intercept preinvasive lesions and prevent the development of invasive breast cancer. METHODS: We developed a Th1 selective multiantigen, polyepitope plasmid-DNA vaccine encoding segments of IGFBP-2, HER2, and IGF-IR, all antigens expressed in hormone receptor positive and negative DCIS. We then performed a Phase I study in participants with non-metastatic breast cancer with no evidence of disease. The primary objective was to assess the safety of 3 monthly intradermal doses (150, 300, or 600 µg) of the tri-antigen vaccine with granulocyte macrophage colony-stimulating factor as an adjuvant. 32 participants were enrolled, 10 per dose level. Toxicity evaluations occurred monthly with vaccination and at 1 and 6 months after the last vaccine. Blood was collected at baseline and at 1 and 6 months after the last immunization to assess cellular immune responses. Participants were followed annually for 5 years for long-term toxicity. RESULTS: There was no significant difference in adverse events (AEs) across dose levels and all related AEs were grades 1 or 2. All doses were immunogenic; responders included 70% of participants at the 150 µg dose level, 67% at the 300 µg dose, and 40% at the 600 µg dose level. All participants at the 300 µg dose retained significant Th1-antigen-specific immune response at 6 months after end of immunizations. T-cells derived from vaccine immunologic responders exhibited gene expression profiles that indicated an increased metabolic fitness as compared with immunologic non-responders. CONCLUSIONS: The tri-antigen vaccine appears safe and immunogenic. The intermediate dose (300 µg) was chosen as the Phase II dose due to the long-term persistence of immunity after vaccination. The vaccine will be studied in Phase II trials for the treatment of DCIS. TRIAL REGISTRATION NUMBER: NCT02780401.
11 May 2026
Read appraisal →Menopause (New York, N.Y.)
Deciphering the predictors of endometrial nonbenign lesions in asymptomatic postmenopausal women via explainable machine learning
OBJECTIVES: Timely identification of endometrial nonbenign lesions led to improved outcomes, but there was a lack of effective predictive models for asymptomatic endometrial thickening. The aim of this study was to develop a strong machine learning (ML) model for assessing the risk of endometrial malignancy in asymptomatic patients after menopause. METHODS: This retrospective study was designed to collect data from 971 postmenopausal asymptomatic women with endometrial thickening. The bootstrap resampling method was used for model training, internal validation, and external validation. With 41 easily accessible characteristics, multifactor regression and least absolute shrinkage and selection operator regression were performed for feature selection. Nine ML algorithms were applied to build a model. To explain the final model and rank feature importance, the SHapley Additive exPlanation (SHAP) method was utilized. Meanwhile, a nomogram was developed to facilitate model interpretation. RESULTS: The comprehensive methodologies identified parity, Doppler flow signals, endometrial thickness, cancer antigen 125, and D-dimer as significant predictors. The logistic regression (LR) model demonstrated superior performance compared with other ML algorithms, achieving an accuracy of 88%, a sensitivity of 78%, a specificity of 98%, and an area under the receiver operating characteristic curve of 0.81. Furthermore, individualized predictions of endometrial malignancy were visualized through a force plot generated by SHAP analysis. A nomogram based on the LR model was subsequently constructed, showing area under the receiver operating characteristic curve values of 0.82, 0.82, and 0.81 for the training, internal validation, and external validation cohorts, respectively. The calibration curve demonstrated excellent consistency. CONCLUSIONS: We developed an LR-based nomogram model and interpreted using the SHAP method, which provided visual insights for detecting endometrial nonbenign lesions in asymptomatic postmenopausal women. This approach would aid clinicians in providing individualized treatment and help avoid unnecessary invasive surgeries.
5 May 2026
Read appraisal →Lancet (London, England)
Overall survival with relacorilant and nab-paclitaxel in patients with platinum-resistant ovarian cancer (ROSELLA): a phase 3 randomised controlled trial
BACKGROUND: Relacorilant is a selective glucocorticoid receptor antagonist that increases the sensitivity of many cancer cell types to chemotherapy. The efficacy and safety of relacorilant plus nab-paclitaxel were assessed in the phase 3 ROSELLA (GOG-3073, ENGOT-ov72, APGOT-Ov10, and LACOG-0223) trial; the combination showed significant improvement in progression-free survival among patients with platinum-resistant ovarian cancer compared with nab-paclitaxel monotherapy. Results of the final overall survival analysis are reported here. METHODS: In this open-label phase 3 trial, patients were randomly assigned 1:1 to receive relacorilant (150 mg orally the day before, day of, and day after nab-paclitaxel infusion) plus nab-paclitaxel (80 mg/m2 intravenously on days 1, 8, and 15 of each 28-day cycle) or nab-paclitaxel monotherapy (100 mg/m2 intravenously on the aforementioned schedule). Patients, aged 18 years or older, with one to three lines of previous anticancer therapy and platinum-resistant disease (progression <6 months from their last dose of platinum) were eligible. The trial was conducted at 117 hospitals and community oncology centres in 14 countries across Australia, Europe, Latin America, North America, and South Korea. Progression-free survival, assessed by blinded independent central review, and overall survival (time from randomisation to death from any cause) were dual primary endpoints. Additional prespecified endpoints included safety, second progression-free survival (time from randomisation to disease progression on subsequent anticancer therapy or death due to any cause, whichever occurred first), and patient-reported outcomes. This trial is registered at ClinicalTrials.gov, NCT05257408, and is ongoing. FINDINGS: Between Jan 5, 2023, and April 8, 2024, 381 patients were randomly assigned to the relacorilant combination group (n=188) or the nab-paclitaxel monotherapy group (n=193). All patients had received bevacizumab; 167 (44%) had received three previous lines of therapy, and 234 (61%) had received a poly(ADP-ribose) polymerase inhibitor. At a median follow-up of 24·8 months (95% CI 23·6-25·7), the addition of relacorilant to nab-paclitaxel resulted in a statistically and clinically significant improvement in overall survival compared with nab-paclitaxel monotherapy (hazard ratio for death 0·65 [95% CI 0·51-0·83]; p=0·0004); 18-month overall survival was 46% and 27%, respectively. The median overall survival in the relacorilant combination group was extended by 4·1 months compared with the nab-paclitaxel monotherapy group (16·0 [95% CI 13·0-18·3] vs 11·9 months [10·0-13·8]). Subsequent anticancer treatments were similar across study groups. Adverse events were similar in both groups when adjusted for duration of study treatment. Neutropenia (121 [64%]), anaemia (115 [61%]), fatigue (101 [54%]), and nausea (82 [44%]) were the most common adverse events in the relacorilant combination group. No new safety signals were observed with additional follow-up since the primary analysis. INTERPRETATION: The addition of relacorilant to nab-paclitaxel led to significantly longer overall survival in patients with platinum-resistant ovarian cancer, without the need for biomarker selection. The findings support relacorilant plus nab-paclitaxel as a potential new standard treatment option for patients with platinum-resistant ovarian cancer. FUNDING: Corcept Therapeutics.
21 Apr 2026
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