Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 240 appraisals
Cancer medicine
Applications of Artificial Intelligence in Cancer Diagnosis and Treatment
Driven by changes in lifestyle and environmental factors, the global incidence of cancer is steadily increasing, which has established it as a leading cause of mortality worldwide. The current paradigm for cancer diagnosis and treatment relies on conventional methods, such as imaging, endoscopy, and tissue biopsy, which present significant limitations regarding sensitivity in early screening, diagnostic specificity, and personalized treatment. Consequently, the development of more efficient and accurate technologies remains a major objective in modern oncology research, and artificial intelligence (AI) has emerged as a particularly promising solution. Through machine learning and deep learning algorithms, AI is reshaping cancer care by enabling automated detection of minute lesions during screening and quantitative analysis of pathological features for diagnosis. It may also advance tumor theranostics through multimodal data integration for treatment stratification, response prediction, and image-guided or targeted therapeutic decision-making, whereas providing data-driven recommendations for personalized treatment. Despite these prospects, medical AI development faces several key issues, including data bias, model explainability, clinical reliability and generalizability, emerging limitations of foundation models and generative AI, and regulatory and ethical issues that need to be addressed. By reviewing recent advances in AI across screening, diagnosis, theranostics, and treatment, we aim to clarify where these methods are already useful, where evidence remains limited, and why closer collaboration among clinicians, engineers, and data scientists is needed for clinical translation. We hope this review serves as a practical reference for researchers and clinicians evaluating how AI may be integrated into oncology in a more standardized, clinically responsible way.
1 Aug 2026
Read appraisal →Acta oncologica (Stockholm, Sweden)
User perceptions of machine learning models as decision support for colorectal cancer multidisciplinary team conferences (AID-SIM-2): a qualitative simulation study.
BACKGROUND: Multidisciplinary team (MDT) conferences are considered a cornerstone of decision-making in cancer diagnostics and care. However, the current literature has not demonstrated improved patient outcomes based on the decisions of the MDT conferences. AIM: We aimed to evaluate how four different decision-support modalities impacted the decision-making process and the internal discussions in the MDTs in a multicenter simulation study, with a focus on user perceptions. METHODS: Four colorectal cancer centers with MDTs participated. We performed four simulations in each center. Each simulation used a different decision-support tool: (1) Current standard, (2) Current standard plus a prediction model, (3) A structured data presentation tool, and (4) A structured data presentation tool plus the prediction model. Clinician- and model-estimated risks were compared, the treatment suggestions from each site were compared, questionnaires about user perceptions were conducted after Simulations 2, 3, and 4 using a Google Form link, and a semi-structured interview was conducted at each site after the last simulation. RESULTS: Similar distributions of risk groups between clinicians and models were found; however, distinct discrepancies in predictions arose, particularly with higher-risk patients, highlighting the need for standardization for more complex clinical cases. The primary perceived benefit of decision support was increased standardization of care, independent of the individual physicians' personal views. However, participants emphasized the necessity of clinician autonomy to overrule tool suggestions when identifying clinical nuances not captured by the model. CONCLUSIONS: The colorectal cancer MDTs expressed a positive view regarding the use of prediction models and other forms of decision-support in their workflow. While clinicians and prediction models had similar risk score distributions, they diverged in the assessment of specific individual patients.
1 Aug 2026
Read appraisal →Journal of breast imaging
Contrast-Enhanced Mammography for Breast Cancer Screening: A Systematic Review and Meta-Analysis
OBJECTIVE: To evaluate pathway-specific cancer detection and summarise screening outcomes (recall/biopsy/PPV3) for contrast-enhanced mammography when reported, and to compare them with those for low-energy mammography and breast MRI. METHODS: This systematic review and meta-analysis followed PRISMA guidelines. PubMed (MEDLINE), Embase, Scopus, and Web of Science were searched from January 1, 2011, through August 31, 2025, for studies evaluating contrast-enhanced mammography in screening or surveillance populations (including supplemental/add-on screening and surveillance of patients with a personal history of breast cancer). Diagnostic, recall, and problem-solving studies were excluded. Screening pathways were categorized as: (1) direct comparison with low-energy mammography, (2) direct comparison with MRI, and (3) add-on CEM to mammography-based screening. Random-effects meta-analyses were performed when designs were comparable. Comparisons between CEM and MRI were summarized qualitatively because of heterogeneity and limited cancer events across studies. Single-arm screening studies were summarized descriptively. Risk of bias was assessed using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). RESULTS: Fourteen studies met eligibility criteria for qualitative synthesis; seven screening and surveillance studies contributed to the meta-analysis. Compared with low-energy mammography, CEM detected 9.3 additional cancers per 1,000 women screened (risk difference [RD], +9.3 per 1,000; 95% CI, 4.0-14.6; P < 0.01; I2 = 0%). Three studies directly compared CEM with MRI in screening settings and were summarized qualitatively because of heterogeneity and limited cancer events. CONCLUSION: In screening and surveillance populations, contrast-enhanced mammography increases cancer detection compared with low-energy mammography. Evidence from available comparative studies suggests that cancer detection with CEM may be broadly comparable to MRI in selected screening contexts; however, the limited number of studies and heterogeneous designs preclude definitive comparative conclusions. These pathway-specific estimates may inform clinical implementation and the design of prospective screening trials.
30 July 2026
Read appraisal →Human vaccines & immunotherapeutics
Artificial intelligence in immune checkpoint inhibitor research: A bibliometric analysis of the landscape
This bibliometric analysis examines the transformative role of artificial intelligence (AI) in immune checkpoint inhibitor (ICI) research. Using VOSviewer, CiteSpace, and Bibliometrix, we analyzed 1,938 publications from the Web of Science Core Collection (2015-2026), revealing a dramatic rise in AI-related ICI studies, led by China and the USA. Key findings demonstrate AI's integration in predicting treatment response, optimizing dosing strategies, and managing immune-related adverse events. Through keyword co-occurrence and citation analyses, we identify critical AI applications including digital pathology and tumor microenvironment characterization. This comprehensive overview provides valuable insights into research trends and emerging frontiers for AI-driven innovations in cancer immunotherapy.
30 July 2026
Read appraisal →Journal of chemical information and modeling
Protein Language Model-Based Fitness Estimates Facilitate Resistance Mutation Identification
Drug resistance is a major challenge in cancer therapy. Cancer cells with pre-existing or acquired mutations that confer resistance to a given drug treatment outgrow the susceptible cell population and cause cancer recurrence after an initial successful treatment response. Knowledge about resistance mutations before they occur in the clinic could prevent unnecessary patient treatment with ineffective drugs, in clinical trials as well as clinical practice, or potentially speed up the development of follow-up compounds. Here, we focused on on-target amino acid mutations that confer resistance to an inhibitor compound with a known binding mode. We evaluated whether a combination of physics-based free energy perturbation (FEP) affinity estimates and protein language model-based protein fitness estimates could improve the in silico identification of resistance mutations. Validation was done with data from deep mutational scanning (DMS) experiments that tested for resistance to single amino acid mutations. A public data set testing ERK2 resistance against the inhibitor SCH772984 and an internal data set testing resistance of an EGFR_exon20 mutant against a Bayer small molecule inhibitor were used. Our results show that protein fitness estimates can facilitate the identification of resistance mutations by filtering mutations with a low estimated fitness. Even though FEP has flagged such mutations as affinity-decreasing and thus potentially resistant, they were not resistant according to the DMS experiment and therefore correctly filtered out. This indicates that protein language model-based protein fitness estimates could be a computationally efficient method to filter mutations without having to model the negative impact of mutations on native function or protein stability, which is error-prone and computationally expensive.
28 July 2026
Read appraisal →Journal of chemical information and modeling
ProphDR: An Interpretable Deep Learning Model for Predicting Cancer Drug Response via Multi-Omics and Cross-Attention Mechanisms
Predicting cancer drug responses (CDRs) accurately remains a significant challenge due to the complexity of tumor biology and the limitations of existing "black-box" machine learning models. To address this, we propose ProphDR, an interpretable deep learning framework that integrates multiomics data and drug structural information using a hierarchical attention mechanism. ProphDR incorporates a Criss-Cross Gene-level Multiomics Integration (CGMI) module to capture gene-level features and a cross-attention (CA) module to model drug-gene interactions. Evaluated on datasets from GDSC and CCLE, ProphDR achieves state-of-the-art performance in predicting ln(IC50) values (PCC = 0.938, RMSE = 0.978) and classifying drug sensitivity (AUC = 0.981). It also demonstrates strong generalizability in cold-start scenarios involving unseen drugs or cell lines. Crucially, ProphDR generates biologically interpretable attention maps that highlight key pharmacophores and resistance-related genes such as ERBB2 (HER2), consistent with established mechanisms in NSCLC and BRCA. These insights bridge genomic features with phenotypic outcomes, offering valuable guidance for target prioritization and drug repurposing. ProphDR represents a robust and explainable AI tool for advancing precision oncology.
28 July 2026
Read appraisal →Science advances
Interpretable dynamic quantitative vascular morphometry features using SHAP for anti-angiogenic therapy response prediction
Anti-angiogenic therapy benefits vary, with response rates of 40 to 70%, highlighting the need for early biomarkers to identify responders. We developed an automated machine learning framework that uses delta quantitative vascular morphometry features from standard contrast-enhanced CT to evaluate treatment response. This workflow combines automated tumor and vessel segmentation with feature extraction from routine scans for clinical use. Shapley additive explanations (SHAP)-based attributions identify key vascular and clinical features, providing meaningful, imaging-visible evidence aligned with therapy targets beyond traditional radiomics. Using baseline and follow-up CTs from 163 patients with lung cancer, we built three models using fivefold cross-validation, with the delta-merge model achieving high accuracy (area under the receiver operating characteristic curve = 0.842 internally, 0.806 externally). SHAP analysis uncovered an "arterial-dominant, venous-adaptive" pattern, where arterial involvement and venous recovery distinguish responders. This automated workflow and visualization support early, imaging-based response assessment and personalized treatment.
27 July 2026
Read appraisal →Medicine
Survival benefit of concurrent beta-blocker use in cancer patients treated with radiotherapy: A systematic review and meta-analysis
BACKGROUND: Both preclinical and retrospective studies have implicated β-adrenergic signaling in cancer progression, leading to interest in β-blockers (BBs) as adjunctive anticancer agents. There is evidence that they may enhance the sensitivity of cancer cells to radiotherapy and other conventional therapies. We conducted a systematic review and meta-analysis to explore the radiosensitizing effects of BBs. METHODS: PubMed, the Cochrane Library, Embase, Web of Science, the China National Knowledge Infrastructure, the China biology medicine database, Wan fang Data, and the VIP database were searched for articles in both English and Chinese from the establishment of the databases to September 8, 2025, to identify studies comparing outcomes according to beta-blocker use (yes vs no) in patients with solid tumors treated with radiotherapy. The primary endpoint was overall Survival (OS), Secondary objectives included 1, 2, 5-Year Survival, disease-free survival, distant metastasis-free survival, locoregional progression-free survival and progression-free survival (PFS). RESULTS: 8 studies (3165 patients), including 7 retrospective cohort studies and 1 randomized controlled trial, were analyzed. The most common cancer was non-small cell lung cancer (n = 5). BB use was associated with significantly improved OS (hazard ratio [HR] 0.73, 95% confidence interval: 0.64-0.83). Benefits were also observed for disease-free survival (HR 0.69) and Distant Metastasis-Free Survival (HR 0.62), indicating reduced recurrence and metastasis. CONCLUSION: In this meta-analysis, the use of BBs during or around radiotherapy may be associated with longer OS in cancer patients, suggesting that it may enhance the efficacy of radiotherapy and provide a survival benefit. Alternatively, the effect of β-blockers on survival may shift from a nonspecific effect to an intriguing cancer-specific effect over time. Beta-blockers are an intriguing option to explore in prospective studies of patients with solid tumors undergoing radiotherapy.
26 July 2026
Read appraisal →New biotechnology
Harnessing AI to decode protein kinases: Structural, functional, and therapeutic design perspectives.
Recent advances in Artificial Intelligence (AI) are reshaping kinase research by uncovering complex regulatory mechanisms and accelerating drug discovery. These advances have enabled the capture of elusive allosteric transitions and transient cryptic pockets by exploring dynamic conformational landscapes, revealing features critical for understanding ligand interactions and enzymatic regulation. AI-driven structure prediction tools, such as AlphaFold (AF)-based models, offer high-resolution insights into kinase conformations and complex assembly, thereby clarifying receptor activation in signaling pathways. Moreover, cutting-edge AI frameworks provide fresh perspectives on substrate prediction, cofactor mapping, and mutation-induced structural changes. Integrative strategies that combine Machine Learning (ML), molecular docking, Molecular Dynamics (MD) simulation, and experimental validation have further streamlined the discovery of novel kinase inhibitors and related studies. Collectively, these AI-enhanced approaches deepen our understanding of kinase-mediated signaling-from developmental processes to oncogenic transformation-and highlight the powerful synergy between computational and experimental research in advancing therapeutic innovations.
25 July 2026
Read appraisal →JMIR cancer
AI-Enhanced Predictive Analytics to Optimize Tele-Oncology Implementation in Rural Settings: Scoping Review
BACKGROUND: Tele-oncology addresses geographic barriers to cancer care, but implementation challenges persist in rural settings. AI-enhanced predictive analytics offer opportunities for optimizing deployment through personalized, data-driven strategies; however, evidence in rural tele-oncology contexts remains limited, and critical equity considerations remain underexamined. OBJECTIVE: This scoping review aimed to map evidence on AI-enhanced predictive analytics in tele-oncology implementation, with particular attention to rural and underserved populations, to identify research gaps and inform implementation science priorities. METHODS: We searched 5 databases (PubMed, Embase, CINAHL, Web of Science, and IEEE Xplore) using 4 concept domains (tele-oncology, rural implementation barriers, AI or predictive analytics, implementation science) from January 2015 through November 2025. Two independent reviewers screened 330 unique records (title or abstract; Cohen κ=0.78), with the principal investigator resolving conflicts. Of 138 full-text reviews (κ=0.82), 4 studies met inclusion criteria. Data extraction captured study characteristics, AI applications, implementation factors, and outcomes. We used narrative thematic analysis to map findings into three themes: (1) the current tele-oncology implementation landscape in rural and underserved settings, (2) potential AI applications addressing implementation challenges, and (3) implementation considerations for AI systems themselves. RESULTS: Four included studies (1 pilot feasibility study, 1 proof-of-concept validation study, 1 cross-sectional predictive study, and 1 platform development study; published 2019-2025) demonstrated limited evidence at the intersection of AI, tele-oncology, and rural health equity. Patient characteristics predicted telehealth modality preferences with 86.2% accuracy, revealing that male patients exhibited 66% increased odds of video selection versus female patients (P=.004), and urban residents showed 101% increased odds compared to rural counterparts (P<.001). Liu et al demonstrated that disadvantaged populations engaged with AI-generated health literacy content 2.52-fold more frequently than nondisadvantaged counterparts. However, all 4 studies documented substantial implementation barriers (patient, provider, organizational, and system levels) persisting despite technological sophistication. Organizational threshold effects, where remote monitoring interventions succeeded with adequate provider capacity but failed under resource constraints-suggest that algorithmic innovations cannot overcome structural limitations in rural facilities. No studies explicitly examined algorithmic bias, cross-population validation, or potential harms in rural contexts. Geographic concentration in high-resource countries (United States n=2, Greece n=1, and Singapore n=1) and limited oncology-specific focus underscore structural gaps in knowledge generation for underserved populations. CONCLUSIONS: Current evidence remains insufficient to support definitive practice recommendations. The observed evidence gap may reflect broader structural inequities in knowledge generation: populations with the greatest implementation challenges appear to remain substantially underrepresented in AI and digital health literature. Future research should prioritize comparative effectiveness studies in authentic rural contexts with implementation science outcomes, equity-centered cross-population validation, specification of translation mechanisms linking AI predictions to implementation strategies, health economic analyses, and mechanistic research on sociotechnical integration factors, ensuring technological innovation reduces rather than perpetuates disparities in cancer care.
19 July 2026
Read appraisal →Molecular biology reports
Metabolic signatures of triple-negative breast cancer
Triple-negative breast cancer (TNBC), being one of the most aggressive subtypes of breast malignancies, is characterized by poor prognosis and limited treatment options. As a leading cause of cancer-related mortality among women, TNBC poses unique clinical challenges due to the absence of effective targeted therapies. Conventional treatment strategies, including chemotherapy, often suffer from significant drawbacks such as drug resistance and intolerable side effects, underscoring an urgent need for innovative approaches to improve therapeutic outcomes. A defining hallmark of TNBC is its markedly altered cellular metabolism, which not only supports tumor growth and survival but also contributes to therapy resistance. Elucidating these metabolic alterations could provide critical insights into potential vulnerabilities that may be exploited for therapeutic intervention. This review provides a comprehensive overview of the major metabolic pathways that are dysregulated in TNBC and their relevance to disease progression and therapeutic intervention. Finally, we explore recent advances in metabolomics-driven precision medicine, highlighting the integration of artificial intelligence and machine learning approaches, covering aspects such as data processing, feature selection, and model construction that can accelerate the advancement of personalized treatment strategies for TNBC.
18 July 2026
Read appraisal →International journal of cancer
Drug repurposing in oncology: Bridging computational discovery to clinical application.
Drug repurposing, the identification of new therapeutic applications for existing drugs, has emerged as a pragmatic and cost-efficient strategy to accelerate oncology drug discovery. Faced with rising development costs, protracted timelines, and high attrition rates associated with traditional de novo drug development, repurposing leverages known pharmacokinetics, safety profiles, and manufacturing processes to expedite clinical translation. This review synthesizes current advances in computational and experimental methodologies and mechanistic insights that drive drug repurposing for cancer therapy. In silico strategies, including molecular docking, machine learning, transcriptomic-proteomic signature reversal, and network-based modeling, have enabled rapid identification of repurposable agents by mining multi-omics and historical pharmacological data. Experimental pipelines spanning high-throughput screening, phenotypic assays, biochemical validations, and functional animal models remain essential to establish efficacy and delineate mechanisms of action. Notably, repurposed drugs exhibit anticancer activity by modulating key pathways, including phosphatidylinositol 3-kinase/Ak strain transforming/mechanistic target of rapamycin, mitogen-activated protein kinase/extracellular signal-regulated kinase, wingless-related integration site/β-catenin signaling, as well as redox homeostasis and DNA response. Despite their promise, repurposed candidates face barriers including limited intellectual property protections, dose optimization challenges, and regulatory uncertainty. Moreover, clinical translation is often hindered by insufficient mechanistic understanding and a lack of predictive biomarkers. Integration of multi-omics datasets, explainable artificial intelligence, patient-derived organoids, and clustered regularly interspaced short palindromic repeats-based genetic screens now offers unprecedented precision in identifying context-specific drug effects and synthetic lethal interactions. With cancer causing one in six global deaths and marked by therapeutic resistance and molecular heterogeneity, drug repurposing provides a scalable solution. This approach bridged preclinical insight with clinical application, potentially transforming cancer therapeutics through rational, data-driven innovation.
15 July 2026
Read appraisal →Cancer cell
OpenIO: An open framework for AI-native immunotherapy
We propose Open Immune Oncology (OpenIO), a framework integrating generative AI and omics to advance precision oncology. By leveraging biological scaling laws and foundation models, we aim to transition immunotherapy from empirical screening to rational, AI-native engineering of therapeutic interventions.
15 July 2026
Read appraisal →JMIR cancer
Internet of Things-Enhanced Mathematical Oncology: Conceptual Framework for Adaptive Cancer Care Modeling
The Internet of Things (IoT) is transforming various industries, including health care. IoT-based systems are increasingly prevalent in consumer health applications, while intelligent or smart devices equipped with sophisticated sensors are gaining recognition for their potential to improve clinical care practice and decision-making. Cancer care is a particularly promising area for IoT applications, enabling real-time and personalized interventions. However, empirical research on the effects of IoT in this field is limited due to the complexities inherent in cancer as a dynamic disease and the paucity of IoT-generated data available for research. This presents an opportunity to apply mathematical modeling to understand the effects of IoT under various scenarios. These analytical and "in silico" mathematical approaches are instrumental with limited data. Such models support the analysis of treatment uncertainty and patient response while balancing patient preferences, clinical outcomes, and system-level constraints. Grounded in mathematical oncology and health informatics, this paper proposes a conceptual framework that integrates real-time IoT data as dynamic inputs into adaptive mathematical models to simulate cancer dynamics. By exploring applications across multiple levels of analysis, the study demonstrates how IoT-enhanced mathematical models could inform implementation and optimize oncology services, addressing a critical gap in current research.
15 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 →European respiratory review : an official journal of the European Respiratory Society
Prediction of PD-L1 expression in nonsmall cell lung cancer using artificial intelligence models based on radiomics: a systematic review and meta-analysis
BACKGROUND: Since 1992, the incidence of lung cancer has shown an overall downward trend, with survival rates significantly improving in recent years due to the application of immune checkpoint inhibitors (ICIs). Assessing programmed death ligand 1 (PD-L1) expression status in lung cancer patients is crucial for predicting the efficacy of ICIs and formulating treatment plans. Therefore, there is an urgent need to assess PD-L1 expression status in lung cancer patients, particularly those with nonsmall cell lung cancer (NSCLC). While numerous studies have demonstrated promising results using artificial intelligence (AI) models based on radiomics, current understanding in this area remains incomplete. METHOD: We conducted a search of medical databases and screened studies that meet the criteria. We performed feature extraction and data extraction from these studies, analysed them using R software, and generated forest plots. We conducted subgroup analyses based on imaging modality and algorithm model. RESULTS: A total of 35 studies were included in the analysis. For the Tumor Proportion Score (TPS)1 group, the pooled area under the curve (AUC) for the validation set was 0.782. For the TPS50 group, the pooled AUC for the validation set was 0.798. Subgroup analysis revealed that machine learning performed better when predicting TPS50, while deep learning was superior for predicting TPS1. When predicting TPS, prediction models constructed using 2-fluoro-2-deoxy-d-glucose (18F) positron emission tomography/computed tomography (CT) and contrast-enhanced CT outperformed those based on noncontrast CT. Conversely, when predicting a TPS, the noncontrast-CT-based prediction model significantly outperformed the other two groups. CONCLUSIONS: Imaging modality and algorithm model are key factors influencing AI prediction models for PD-L1 expression in NSCLC patients.
10 July 2026
Read appraisal →Journal of gastrointestinal cancer
Radiomics Models as Tools for Predicting Genetic Mutations in Colorectal Cancer: A Systematic Review and Meta-Analysis
BACKGROUND: The evaluation of genetic mutations is crucial for personalized therapy in colorectal cancer (CRC), but the invasive tissue biopsy is subject to sampling bias and other complications. Radiomics has emerged as a non-invasive tool to predict these mutations from standard medical images. In this systematic review and meta-analysis, we aimed to evaluate the diagnostic accuracy and methodological quality of radiomics models for predicting key genetic mutations in CRC. METHODS: A comprehensive search of PubMed, Scopus, Web of Science, and Embase was conducted in accordance with PRISMA guidelines. Studies evaluating radiomics models for predicting genetic mutations in CRC patients using pre-operative CT, MRI, or PET/CT were included. A meta-analysis of diagnostic accuracy was performed to calculate the pooled sensitivity, specificity. Methodological quality was assessed using the Radiomics Quality Score (RQS) and QUADAS-2 tools. RESULTS: Sixteen studies were included in the quantitative analysis. The pooled sensitivity and specificity were 0.75 (95% CI, 0.67-0.81) and 0.78 (95% CI, 0.70-0.85), respectively, with an overall AUC of 0.79. Subgroup analyses revealed that radio-clinical models integrating both clinical and radiomics features achieved superior sensitivity compared to models with only radiological input. However, the overall methodological quality of the included studies was low, with a mean RQS of 45%. CONCLUSION: Conventional radiomics models demonstrate promising results for the non-invasive prediction of genetic mutations in CRC, with sensitivity enhanced by the integration of clinical data. Despite this potential, significant methodological shortcomings and heterogeneity across studies highlight the need for standardized protocols and large-scale, prospective validation before these models can be translated into routine clinical practice.
10 July 2026
Read appraisal →Journal of gastrointestinal cancer
Tumour-Associated Neutrophils in Colorectal Cancer: a Systematic Review, Meta-analysis, and Comprehensive Literature Review
PURPOSE: Neutrophils are at the forefront of innate immune response. The prognostic impact of tumour-associated neutrophils (TANs) remains unclear. This study aimed to evaluate the prognostic role of TANs in colorectal cancer (CRC). METHODS: A systematic review and meta-analysis were conducted using PubMed, Embase, and Scopus to identify studies correlating TANs with time-to-event survival analysis in patients with stages I-IV CRC from the date of inception until April 2024. Articles were included if they involved the identification of TANs through hematoxylin-eosin (HE) or immunohistochemistry (IHC) and reported survival analysis. Data was collected from the tumour core (TC) and invasive margin (IM). The hazard ratios (HRs) were extracted from the study results. Heterogeneity was evaluated using Cochran's Q and I². Primary and secondary endpoints were overall survival (OS) and disease-free survival (DFS). RESULTS: Of 4,292 citations found, 18 studies fulfilled eligibility criteria, encompassing 7,406 patients. IHC was used to identify TANs in 12 trials, while six performed HE. Five studies included patients with disease stages I-III, and 13 had stages I-IV. High TANs at the IM were associated with improved OS (HR = 0.64, 95% Confidence Interval [CI] 0.50-0.81, I2 = 48%) and DFS (HR = 0.47, 95% CI 0.25-0.88, I2 = 32%). At the TC, high TANs showed a trend towards better OS (HR = 0.82, 95% CI 0.61-1.10, I2 = 83%) and DFS (HR = 0.48, 95% CI 0.21-1.07, I2 = 93%), although not statistically significant and with high heterogeneity. CONCLUSION: In this systematic review and meta-analysis, high TANs at the invasive margin (IM), the tumour-host interface, was associated with improved prognosis in CRC. In contrast, no statistically significant association was observed for high TANs at the tumour core (TC).
9 July 2026
Read appraisal →Journal of the American Heart Association
Social Determinants of Health and Cardiovascular Disease-Related Outcome Disparities Among Breast Cancer Survivors: A Systematic Review
BACKGROUND: Breast cancer is the most commonly diagnosed malignancy worldwide. Breast cancer survivors face an increased risk of cardiovascular disease (CVD), a leading cause of death in this group. Social determinants of health (SDoHs), operationalized as economic, environmental, and psychosocial factors, play an important role in CVD disparities. However, few studies have examined how SDoHs are associated with CVD disparities in this population, and no systematic review has addressed their multilevel influences. METHODS: This systematic review summarizes the current evidence on the relationships between SDoHs at different levels and CVD disparities among breast cancer survivors. Using the 2024 American College of Cardiology/American Heart Association framework, SDoHs were categorized at the individual, interpersonal, or community levels. Studies published in peer-reviewed journals between January 1, 2010, and November 30, 2025, were identified through searching electronic databases (PubMed, Embase, Cumulative Index to Nursing and Allied Health Literature, Web of Science, PsycINFO) and citations. RESULTS: Of 6550 unique records, 37 articles that addressed the impact of SDoHs on CVD outcomes were selected. Most (n=31) were conducted in the United States and used retrospective designs (n=29). Most (n=30) focused on individual-level SDoHs such as race, income, or education; 2 on interpersonal-level SDoHs (ie, psychosocial stress); and 13 on community-level SDoHs, including neighborhood socioeconomic status and residential area. Black race, lower neighborhood socioeconomic status, and rural residence were associated with a higher incidence of CVD and increased cardiovascular death. CONCLUSIONS: This review highlights the urgent need to address SDoHs and emphasizes the importance of multilevel interventions to reduce CVD disparities among breast cancer survivors.
9 July 2026
Read appraisal →Journal of neuro-oncology
Feasibility evaluation of tumor treating fields for brainstem gliomas
PURPOSE: Tumor treating fields (TTFields) are FDA-approved for supratentorial glioblastoma, but feasibility in infratentorial tumors remains poorly defined. This simulation study evaluated TTFields dose in brainstem gliomas using patient-specific modeling with scalp-only transducer arrays. METHODS: MRI and CT imaging from seven patients with brainstem gliomas were used for TTFields planning with MAXPOINT® (Novocure, Switzerland). Clinical target volume (CTV) was defined as enhancing tumor on T1 post-contrast MRI (Gross tumor volume, GTV) plus a 3 mm peritumoral expansion. The platform optimized scalp-only array layouts, and finite element calculations generated maps of local minimum field intensity (LMiFI, V/cm) and local minimum power density (LMiPD, mW/cm³). Values were compared against a standard, unplanned layout using one-sided paired t-tests, with LMiFI ≥ 1.0 V/cm as a therapeutic reference. RESULTS: MAXPOINT-optimized layouts achieved significantly higher LMiFI than the standard layout across all regions (all p ≤ 0.019). GTV median LMiFI was 1.1 vs. 1.0 V/cm (p = 0.019). CTV median LMiFI was 1.1 vs. 1.0 V/cm (p = 0.002). Brainstem median LMiFI was 1.3 vs. 1.1 V/cm (p = 0.002). Posterior fossa showed the largest difference: median LMiFI 1.5 vs. 1.2 V/cm (p < 0.001). All optimized LMiFI values met or exceeded the therapeutic reference. LMiPD was also significantly higher with MAXPOINT® across all regions (all p ≤ 0.006). CONCLUSION: This simulation study demonstrates that patient-specific modeling can achieve favorable TTFields intensities in brainstem gliomas using scalp-only arrays, supporting prospective clinical evaluation for infratentorial tumors.
8 July 2026
Read appraisal →Journal of hematology & oncology
Emerging artificial intelligence advances in oncology: latest updates from the 2026 AACR annual meeting.
Advances presented at the 2026 American Association for Cancer Research (AACR) Annual Meeting highlight a shift from standalone artificial intelligence (AI) models to integrated, agentic systems across oncology. Platforms such as Synapse enable large-scale data coordination, supporting interoperable and reproducible research. Building on this foundation, conversational and multi-agent AI tools (e.g., DrBioRight, GP CoPilot, Isabl AI Agent) allow natural language interaction with multimodal cancer data, lowering technical barriers. Agentic frameworks for real-world data (RWD) transformation, including clinical document abstraction, cohort extraction, and social determinants of health (SDOH) analysis, demonstrate high accuracy and scalability, while self-critical systems improve reliability. Clinically, AI shows growing impact through validated imaging biomarkers, enhanced trial matching, and scalable cohort identification. In parallel, multi-agent systems are accelerating therapeutic discovery, including CAR-T development and immunotherapy target identification. Collectively, these advances position AI as an active, collaborative partner in cancer research and precision oncology.
5 July 2026
Read appraisal →The Journal of international medical research
Diagnostic performance of machine learning-based radiomics models for predicting epidermal growth factor receptor mutation status in lung adenocarcinoma in Chinese patients: A systematic review and meta-analysis
ObjectiveThis systematic review and meta-analysis evaluates the diagnostic performance of machine learning-based radiomics models for predicting epidermal growth factor receptor mutation status in Chinese patients with lung adenocarcinoma.MethodsFollowing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines and prospectively registered in the International Prospective Register of Systematic Reviews (CRD420251273027), a systematic search of PubMed, Embase, Web of Science, the Cochrane Library, Scopus, China National Knowledge Infrastructure, Wanfang, VIP, and Chinese Biomedical Literature Database was conducted from inception to 31 October 2025. Two reviewers independently screened studies, extracted data, and assessed bias using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. A bivariate random-effects model was used to synthesize the data. Subgroup analyses were conducted for three factors: (a) imaging modality (computed tomography vs. positron emission tomography-computed tomography); (b) algorithm type (deep learning vs. conventional machine learning); and (3) validation strategy (external vs. internal).ResultsThirteen studies encompassing 6628 patients were included. The pooled sensitivity was 71% (95% confidence interval: 68-74), the pooled specificity was 81% (95% confidence interval: 78-84), and the summary area under the curve was 0.85 (95% confidence interval: 0.82-0.88). Deep learning models significantly outperformed conventional machine learning models (area under the curve: 0.871 vs. 0.798; P = 0.012). Computed tomography-based models yielded higher accuracy than positron emission tomography-computed tomography-based models (area under the curve: 0.879 vs. 0.828; P = 0.038). Models validated on independent external cohorts demonstrated superior performance compared with those relying solely on internal validation (area under the curve: 0.922 vs. 0.841; P = 0.006). Imaging modality was a significant source of heterogeneity (P < 0.05). No threshold effect or publication bias was detected.ConclusionMachine learning-based radiomics models exhibit promising diagnostic accuracy for the noninvasive prediction of epidermal growth factor receptor mutations in Chinese patients with lung adenocarcinoma. Computed tomography-based deep learning models subjected to independent external validation represent the current optimal approach. However, the retrospective nature and substantial heterogeneity of the included studies necessitate large-scale, prospective, multicenter trials with standardized workflows before clinical translation.
4 July 2026
Read appraisal →The American journal of pathology
Deep Learning-Enhanced Biomarker Interpretation on Cytology Cell Blocks: Foundations and Emerging Opportunities in Spatial Pathobiology
Cytology cell block specimens are essential for diagnosing patients with advanced-stage malignancy and often represents the only available strategy for therapeutic biomarker evaluation. The use of cell blocks preserves tumor cells, captures high-grade or metastatic populations, and retains meaningful microenvironmental context, making them well suited for IHC analysis. With the rapid expansion of computational pathology, deep learning-assisted biomarker interpretation in cell blocks is emerging as a promising frontier for improving reproducibility, reducing interobserver variability, and enabling quantitative assessment of spatial tumor-immune interactions. Because many treatment-defining biomarkers are routinely assessed on cytology cell blocks, this mini-review highlights artificial intelligence-based applications for PD-L1, HER2, ER/PR, Ki-67, ALK/ROS1, BRAF V600E, and p16 markers that directly inform decisions about immunotherapy, targeted therapy, and hormone therapy. Also reviewed are emerging predictive models that convey biomarker status directly from morphology, extending the utility of artificial intelligence beyond conventional IHC interpretation. Finally, spatial pathobiology-related opportunities afforded by cell block preparations are discussed, and future directions for integrating artificial intelligence-enabled analysis into cytology workflows are outlined. Together, these advances position cytology cell blocks as an important platform for computational biomarker interpretation and morphology-driven precision oncology.
2 July 2026
Read appraisal →BMJ supportive & palliative care
Time toxicity of patients with cancer enrolled in clinical trials: a systematic review and meta-analysis
PURPOSE: Time toxicity-the time patients spend receiving treatment, managing side effects, attending follow-ups and undergoing rehabilitation-is increasingly recognised as an important burden of cancer care. While time toxicity has been studied in routine clinical settings, it has rarely been quantified among patients with cancer enrolled in pharmacotherapy clinical trials. This systematic review aims to quantify time toxicity in this population. DESIGN: We searched Ovid MEDLINE, Embase and Cochrane CENTRAL from inception to 12 June 2025. Studies were included if they reported time toxicity among patients with cancer enrolled in clinical trials. Time toxicity was defined as calendar days with healthcare contact. Primary outcomes were (1) total days, (2) planned days and (3) unplanned days per month. Study quality was assessed using the Joanna Briggs Institute's (JBI) Checklist for Cohort Studies. RESULTS: Four retrospective studies reporting on an aggregate of 1809 patients were included. Two studies had low risk for bias and two had unclear risk of bias. Patients experienced time toxicity a mean of 7.6 days per month, with the highest burden in the first month of trial participation. Planned and unplanned days averaged 2.2 and 2.0 days per month, respectively. The ratio of planned to unplanned days remained consistent over time. CONCLUSIONS: Time toxicity is a substantial, under-recognised burden in cancer pharmacotherapy trials-approximately two times that experienced by non-participants. Its measurement and mitigation should be prioritised to improve patient-centred care, support shared decision-making and inform trial design, policy and consent processes. REGISTRATION: Study protocol preregistration: https://doi.org/10.17605/OSF.IO/BVY7C.
1 July 2026
Read appraisal →Scientific reports
Oral cancer in the era of precision medicine: molecular targets and technological innovations
This topical Collection presents a series of studies that examine molecular mechanisms, diagnostic approaches, and therapeutic strategies relevant to oral cancer. The included investigations explore topics ranging from metastasis-associated pathways and immune evasion to emerging detection technologies such as deep learning-based impedance spectroscopy and volatile organic compound analysis. Together, these studies reflect the growing convergence of molecular oncology, digital diagnostics, and translational clinical research in shaping the future of oral cancer management. This Collection represents an ongoing effort to consolidate current knowledge and guide future research in oral squamous cell carcinoma.
28 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 →Journal of chemical information and modeling
GraFSyn: An Interpretable Deep Learning Framework for Anticancer Drug Synergy via Graphlet Fingerprints
Predicting drug synergy is important for accelerating the discovery of effective anticancer combination therapies. Synergy intrinsically depends on the precise interactions of key chemical substructures within specific cellular environments. However, current molecular graph-based computational methods typically rely on implicit atom-level feature aggregation, which may obscure the topological representation of critical chemical substructures and limit structural traceability. Therefore, we present Graphlet Fingerprint-based Synergy prediction (GraFSyn), a deep learning framework for anticancer drug synergy prediction that uses graphlet fingerprints to encode drugs as explicit connected substructure units, preserving predefined chemical substructures and their topological identity. We further introduce a Dynamic Multi-Scale Convolution (DMSC) module to learn informative representations from high-dimensional and sparse graphlet features. The framework also includes an interaction module to capture context-dependent interactions between drug substructures and cell line gene expression. On the Merck and AstraZeneca benchmark data sets, GraFSyn achieved ROC-AUC/PR-AUC values of 0.972/0.912 and 0.823/0.906, respectively, outperforming representative baseline methods. In addition, attributed signals can be mapped back to specific pharmacophoric regions, supporting substructure-level analysis of synergistic interactions. In general, GraFSyn provides an accurate and structurally traceable approach for anticancer drug combination screening.
23 June 2026
Read appraisal →JMIR cancer
Digital Intervention for Electronic Patient-Reported Outcomes in Advanced Cancer: Mixed Methods Study
BACKGROUND: Digital health interventions are increasingly being integrated into oncological care to support patients in managing treatment-related symptoms and psychological distress. In a randomized controlled pilot trial, we investigated the feasibility and preliminary efficacy of a digital health app (SOFIA) among patients with cancer, including those in palliative care. SOFIA consists of an electronic patient-reported outcome (ePRO) assessment and coaching component. We showed good feasibility and high acceptability of SOFIA in routine clinical care. OBJECTIVE: This pilot study aimed to evaluate user experiences and behavior with SOFIA. We applied a mixed methods design, combining a qualitative exploration of patients' experiences and a quantitative analysis of app usage patterns. The integrated findings are intended to inform the refinement and further development of digital health interventions to better address the needs, preferences, and engagement behaviors of this patient population. METHODS: Patients randomly assigned to the intervention group participated in semistructured interviews at 2 time points during the 12-week intervention period: midway through the intervention (at week 6) (T1) and postintervention (at week 12) (T2). Qualitative data were analyzed using content analysis. User data were descriptively analyzed with Python and the Pandas library (version 2.2.3). RESULTS: Our qualitative analysis of 29 patients revealed benefits regarding the ePRO assessment (empowerment, support, user-friendliness, and facilitation of patient-physician communication), as well as some criticism (inflexible design and insufficient use by physicians). Benefits of the coaching tool included the availability of helpful information and design aspects such as clarity and user-friendliness. Quantitative data from 32 active users of the app showed that 16 (50%) of patients read articles, 28 (87.5%) started journeys, and 15 (46.9%) completed exercises at any point during the study. CONCLUSIONS: The results of this mixed methods study may provide important indications for digital interventions, including ePRO assessment, for patients with cancer. From the patients' perspective, key features include intuitive design, relevant symptom items, reliable reminder functions, the translation of ePROs into clinically actionable information, clear symptom visualizations, seamless integration into electronic health records, and effective physician engagement. Both our qualitative and quantitative data show the importance of therapy-specific content. These results might increase acceptance and usage, and thereby also the clinical benefit, of future digital interventions for patients with cancer.
20 June 2026
Read appraisal →Journal of neuro-oncology
AI for prognosis and treatment stratification in glioblastoma neurosurgery: a systematic review.
BACKGROUND: Glioblastoma (GBM) remains one of the most lethal adult primary brain tumors, and neurosurgical decision-making increasingly depends on integrating imaging, molecular, perioperative, and post-treatment data. Artificial intelligence (AI) methods have been proposed for several clinically relevant GBM tasks, but the literature remains heterogeneous and difficult to translate into practice. METHODS: We performed a PROSPERO-registered systematic review of AI, machine learning, and deep learning studies using MRI-derived and/or multimodal perioperative data in GBM for prognosis, risk stratification, treatment-response assessment, post-treatment classification, recurrence/progression prediction, and molecular prediction. Risk of bias was assessed using PROBAST-informed criteria. RESULTS: Thirty studies were included. Survival-focused tasks predominated (20/30, 66.7%), with radiomics plus conventional machine learning as the most common model family (13/30, 43.3%), followed by deep learning (8/30, 26.7%) and hybrid deep learning plus radiomics approaches (4/30, 13.3%). Validation was predominantly internal, and external validation was uncommon (5/30, 16.7%). CONCLUSIONS: AI shows promise for prognosis and treatment stratification in GBM neurosurgery, but current evidence is limited by heterogeneity, incomplete external validation, and inconsistent methodological reporting. CLINICAL TRIAL NUMBER: Not applicable.
20 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 →The American journal of cardiology
Sacubitril/Valsartan and Prevention of Chemotherapy-Induced Cardiac Dysfunction: Meta-analysis of Randomized Trials
Chemotherapy-related cardiac dysfunction (CTRCD) is a major limitation of cardiotoxic cancer therapies. Although global longitudinal strain (GLS) allows early detection of myocardial injury, preventive strategies remain scarce. Angiotensin receptor-neprilysin inhibitors (ARNIs) may offer cardioprotection, but current evidence is limited. We conducted a systematic review and meta-analysis of randomized controlled trials evaluating Sacubitril/Valsartan versus control in patients undergoing chemotherapy. PubMed, Embase, and Cochrane databases were searched. Risk ratios (RRs) and mean differences (MDs) with 95% confidence intervals (CIs) were computed for binary and continuous outcomes. Four randomized controlled trials comprising 412 participants were included; 42.7% received ARNI therapy, with follow-up ranging from 6 to 18 months. Compared with control, ARNI significantly preserved left ventricular systolic function (MD 1.47%, 95% CI 0.59-2.34) and attenuated GLS deterioration (MD -0.93%, 95% CI -1.49 to -0.38). However, ARNI did not significantly reduce the incidence of CTRCD (RR 0.40, 95% CI 0.08-1.97) or all-cause mortality (RR 0.63, 95% CI 0.08-5.01). ARNI increased the risk of hypotension but had no significant effects on NT-proBNP or dyspnea. In conclusion, ARNI therapy improves GLS and left ventricular ejection fraction during chemotherapy but has not yet demonstrated reductions in CTRCD or mortality. Hypotension remains a key safety consideration.
16 June 2026
Read appraisal →Science advances
PathTIGR: A pathway topology-informed graph representation learning framework for immunotherapy response prediction
Immunotherapy has revolutionized cancer treatment, yet substantial inter-patient response heterogeneity limits therapeutic benefit to specific patient subsets. Here, we present PathTIGR, a pathway topology-informed graph representation learning framework that systematically integrates biological pathway network topology knowledge with genome variation information for immunotherapy response prediction. PathTIGR uses a three-component design: (i) pathway graph encoder with multihead attention embeding pathway topology knowledge and cancer genomic variants to pathway representation, (ii) transformer module capturing pathway regulatory dependencies, and (iii) multilayer perceptron synthesizing pathway-level representations to predict immunotherapy response. This architecture enables PathTIGR to capture complex molecular interactions underlying immunotherapy response. Comprehensive validation across multiple independent immunotherapy cohorts demonstrates that PathTIGR achieves superior predictive performance compared to established biomarkers and state-of-the-art deep learning approaches while maintaining biological interpretability through identification of key signatures underlying response heterogeneity. PathTIGR represents an interpretable graph-based learning framework that enhances immunotherapy response prediction and elucidates molecular determinants of therapeutic efficacy, thereby facilitating the advancement of precision cancer immunotherapy.
14 June 2026
Read appraisal →Med (New York, N.Y.)
Sintilimab and lenvatinib plus hepatic arterial infusion chemotherapy and embolization as conversion therapy for uHCC: A phase 2 trial
BACKGROUND: Transarterial chemoembolization (TACE) or systemic treatments are recommended for unresectable hepatocellular carcinoma (uHCC) but have low conversion-to-resection rates. This single-arm phase II trial (PLATIC) aims to investigate the conversion efficacy of PD-1 inhibitor sintilimab, lenvatinib plus TACE and hepatic arterial infusion chemotherapy (TACE-HAIC) in uHCC. METHODS: 57 uHCC patients received 200 mg of sintilimab and lenvatinib (8/12 mg for body weight <60 kg or ≥60 kg) and TACE-HAIC every 3-4 weeks. The primary endpoint was conversion-to-resection rate. A historical-based cohort treated with TACE-HAIC was included as control. FINDINGS: After a median of three treatment cycles, 44 (77.2%; 95% confidence interval [CI], 0.64-0.87]) patients underwent conversion resection. The objective response rates were 80.7% (95% CI, 0.68-0.90) according to modified Response Evaluation Criteria in Solid Tumors (mRECIST) and 43.8% (95% CI, 0.31-0.58) according to RECIST 1.1. Grade ≥ 3 treatment-related adverse events (TRAEs) occurred in 37 (64.9%) patients; no treatment-related deaths were reported. Among the 44 patients receiving resection, the median progression-free survival (PFS) was 17.3 months, and the median overall survival (OS) was unavailable. After inverse probability of treatment weighting analysis, the PLATIC cohort exhibited longer PFS (hazard ratio [HR] = 0.55, 95% CI, 0.33-0.94; p = 0.020) and OS (HR, 0.50, 95% CI, 0.28-0.92; p = 0.029) than the historical cohort. CONCLUSIONS: Sintilimab, lenvatinib, and TACE-HAIC demonstrated favorable conversion-to-resection rates and acceptable TRAEs in uHCC. FUNDING: Supported by grants from the National Natural Science Foundation of China (no. 82172815 and 82272887) and Cancer Innovative Research Program of Sun Yat-sen University Cancer Center (no. PT22040201).
14 June 2026
Read appraisal →Signal transduction and targeted therapy
Liposomal doxorubicin plus nab-paclitaxel with/without chemoradiotherapy in head and neck adenoid cystic carcinoma: single-arm phase II study
Given no standard therapies, this single-arm, open-label, multicenter phase II study was conducted in unresectable locally advanced or recurrent/metastatic adenoid cystic carcinoma of the head and neck (LA or R/M ACCHN). Patients received chemotherapeutic nanomedicines (20 mg/m2 liposomal doxorubicin plus 120 mg/m2 nab-paclitaxel on days 1 and 8) for three 21-day cycles. Subsequently, patients eligible for radiotherapy received concurrent chemoradiotherapy (cCRT; helical tomotherapy or intensity-modulated radiotherapy and nab-paclitaxel), whereas those ineligible for radiotherapy continued 2-3 cycles of original chemotherapy. Between May 27, 2021, and November 13, 2024, 31 enrolled patients were evaluable for efficacy and safety. The primary endpoint objective response rate (ORR) was 90.3% (95% CI, 74.2-98.0%), with 15 (48.4%) complete responses and 13 (41.9%) partial responses, meeting the prespecified primary endpoint. The median duration of response was 18.5 (95% CI, 0-45.8) months. With a median follow-up of 15.7 months by data cutoff (June 25, 2025), the median progression-free survival (PFS) was 25.7 (95% CI, 6.5-44.8) months; the 1-year PFS was 77.1%. The median overall survival (OS) was not reached (NR), with a 1-year OS of 90.0%. Patients who received cCRT had a prolonged PFS (NR vs. 12.8 months) and OS (NR vs. 21.2 months) compared to those who did not. Grade 3 treatment-related adverse events (TRAEs) occurred in 8 (25.8%) patients. No ≥grade 4 TRAEs or treatment-related deaths occurred. This study demonstrated tumor responses among the highest reported to date, with a manageable safety profile in unresectable LA or R/M ACCHN. Chemotherapeutic nanomedicines, especially sequential cCRT for appropriate patients, may represent an effective and safe option.
14 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 →Med (New York, N.Y.)
Neoadjuvant mFOLFOXIRI with or without cadonilimab versus mFOLFOX6 in locally advanced colorectal cancer: A randomized phase 2 trial (OPTICAL-2)
BACKGROUND: The efficacy of neoadjuvant chemotherapy combined with dual immune checkpoint blockade in proficient mismatch repair/microsatellite-stable (pMMR/MSS) locally advanced colorectal cancer (LACRC) remains uncertain. METHODS: In this open-label, randomized phase 2 trial (ClinicalTrials.gov: NCT05571644), eligible patients were randomly assigned (1:1:1) to receive 6 cycles of neoadjuvant mFOLFOXIRI plus cadonilimab, mFOLFOXIRI, or mFOLFOX6, followed by surgery and adjuvant chemotherapy. The primary endpoint was pathological complete response (pCR) in the intention-to-treat population. Secondary endpoints included major pathological response (MPR), safety, locoregional recurrence, disease-free survival, and overall survival. FINDINGS: Between July 2023 and August 2024, 123 patients were randomly assigned (41 per group). Neoadjuvant mFOLFOXIRI plus cadonilimab significantly improved pCR compared with mFOLFOX6 (26.8% versus 9.8%; odds ratio [OR], 3.4; 95% confidence interval [CI], 1.04 to 13.24; p = 0.046), whereas mFOLFOXIRI alone did not (14.6% versus 9.8%; OR, 1.6; 95% CI, 0.4 to 6.7; p = 0.50). MPR rates were 68.3%, 46.3%, and 43.9%, respectively, and were significantly higher with mFOLFOXIRI plus cadonilimab than with mFOLFOX6 (p = 0.026). Downstaging to ypStage 0-I occurred in 66%, 45%, and 41% of patients in the three groups, respectively, and was significantly higher in the mFOLFOXIRI plus cadonilimab group (p = 0.027). Grade ≥3 adverse events were manageable, with liver enzyme elevation and neutropenia being the most common. CONCLUSIONS: Neoadjuvant mFOLFOXIRI plus cadonilimab significantly improved pCR compared with mFOLFOX6 in LACRC, with a manageable safety profile, supporting further investigation. FUNDING: This work was funded by the National Science Fund for Distinguished Young Scholars (82425045) and the National Natural Science Foundation of China (82272800).
14 June 2026
Read appraisal →Journal of neuro-oncology
Symptomatic radiation necrosis following intracranial brachytherapy: a systematic review and meta-analysis
PURPOSE: To estimate the pooled incidence of symptomatic radiation necrosis (RN) following intracranial brachytherapy and explore associations between clinical and technical variables and RN risk. METHODS: PubMed, Embase, Web of Science, and the Cochrane Library were searched from 1954 - 2024 in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Eligible human studies reported symptomatic RN after intracranial brachytherapy. RN ascertainment was secondarily classified as clear, partial, or unclear based on reproducibility of diagnostic criteria. A random-effects generalized linear mixed model was used to pool incidence rates, with subgroup analyses by tumor type, implant technique, isotope, dose rate, and prior radiation. Study quality was assessed using National Institutes of Health tools. RESULTS: Eighty-three studies encompassing 3,666 patients were included. The pooled incidence of symptomatic RN was 5.67% (95% CI: 3.84%-8.29%; I2 = 75.7%). RN ascertainment was clear in 25 studies, partial in 43, and unclear in 15. In sensitivity analyses, pooled symptomatic RN incidence was 8.71% among studies with clear RN definitions and 7.48% among studies with clear or partial definitions. Subgroup RN rates were 3.72% for low-grade gliomas, 8.44% for high-grade gliomas, 2.07% for brain metastases, and 8.98% for meningiomas. Isotope-specific rates were 5.83% for Iodine-125, 7.61% for Iridium-192, and 2.07% for Cesium-131. No statistically significant subgroup differences were observed. CONCLUSIONS: Symptomatic RN occurs in approximately 5.7% of patients following intracranial brachytherapy, within the range reported for other focal radiation therapies. Subgroup findings were not statistically significant and should be interpreted in the context of clinical and methodological heterogeneity.
13 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 →Journal of clinical oncology : official journal of the American Society of Clinical Oncology
HER2CLIMB-05: A Phase III Study of Tucatinib Versus Placebo in Combination With Trastuzumab and Pertuzumab as First-Line Maintenance Therapy for HER2+ Metastatic Breast Cancer
PURPOSE: The HER2CLIMB-05 study (ClinicalTrials.gov identifier: NCT05132582) is investigating the efficacy and safety of adding tucatinib to trastuzumab and pertuzumab as first-line (1L) maintenance therapy in patients with human epidermal growth factor receptor 2-positive (HER2+) metastatic breast cancer (MBC). METHODS: Patients with centrally confirmed HER2+ MBC without evidence of progression post induction therapy and no or asymptomatic brain metastases (BM) were enrolled. Patients were randomly assigned 1:1 to tucatinib (300 mg) or placebo twice a day combined with trastuzumab/pertuzumab. The primary end point is investigator-assessed progression-free survival (PFS); secondary end points include overall survival (OS), PFS per blinded independent central review, CNS-PFS, and safety. RESULTS: Between March 2022 and July 2024, 654 patients were randomly assigned to tucatinib (n = 326) and placebo (n = 328) arms. All patients were female (median age, 54 years), 69.3% had de novo MBC, 52.6% were hormone receptor-positive, and 12.4% had presence/history of baseline BM. In this primary analysis, PFS was statistically significantly improved with addition of tucatinib versus placebo (hazard ratio, 0.641 [95% CI, 0.514 to 0.799]; P < .0001; median PFS: 24.9 v 16.3 months); a PFS benefit was seen regardless of the presence/absence of BM or hormone receptor status. OS data remain immature. The most common treatment-emergent adverse events (TEAEs) in the tucatinib arm were diarrhea (72.7%), nausea (33.1%), and elevated liver enzymes (ALT: 28.2%; AST: 25.8%), of which 6.1%, 0.9%, 13.5%, and 7.1%, respectively, were grade ≥3. In the tucatinib arm, 13.5% discontinued tucatinib because of TEAEs. CONCLUSION: Tucatinib addition to trastuzumab and pertuzumab demonstrated improvement in PFS with no new safety signals identified and may be an option for 1L maintenance therapy in patients with HER2+ MBC.
12 June 2026
Read appraisal →Journal of neuro-oncology
Laser interstitial thermal therapy (LITT) for brain metastases: a systematic review and pooled analysis of Efficacy/Safety and the role of combination treatments
PURPOSE: Laser interstitial thermal therapy (LITT) has been used for brain metastases (BM), particularly for recurrent lesions and radiation necrosis after stereotactic radiosurgery (SRS). As SRS is increasingly used, differentiating tumor recurrence from radiation necrosis has become a frequent clinical problem. Recently, LITT has also been combined with other treatments to improve outcomes. However, prior reviews have primarily focused on LITT monotherapy and have provided limited evaluation of emerging multimodal strategies. We aim to summarize the current literature on LITT for BM, with particular attention to local control, safety, factors associated with outcomes, and the potential role of combination treatment strategies. METHODS: PubMed, Scopus, and Web of Science were searched following PRISMA guidelines. We included studies reporting local control outcomes following LITT for BM or radiation necrosis secondary to previously treated BM. We used a combination of the following terms: ("laser interstitial thermal therapy" OR "LITT" OR "laser ablation") AND ("brain" OR "cerebral" OR "intracranial") AND ("metastases" OR "metastasis"). Graphical data were extracted using WebPlotDigitizer. A proportional meta-analysis was conducted to estimate pooled LTC rates at 6 and 12 months following treatment. RESULTS: A total of 254 studies were identified after duplicate removal, of which 30 met the inclusion criteria. These included 26 studies on LITT monotherapy and 4 on combination therapies, comprising 972 patients. Median age ranged from 51 to 69 years and median tumor volume from 2.2 to 8.5 cm. SRS was the most common prior treatment. Among patients treated with LITT monotherapy, pooled 6- and 12-month LTC rates were 74.4% and 67.9%, respectively. In subgroup analyses, corresponding rates were 74.0% and 67.9% for brain tumor metastases, and 76.6% and 65.7% for radiation necrosis. Studies of combination therapy reported local control rates of 76% for LITT+SRT (stereotactic radiotherapy), 94.7% for LITT+ICB (immune checkpoint blockade), and 100% at 12 months in both studies for LITT+cSRS (consolidation SRS). Transient neurological deficits were the most common adverse events, whereas permanent deficits were less frequent and procedure-related complications were reported in 20 patients. Significant predictive factors for better local control included greater extent of ablation, smaller pre-treatment tumor volume, pathology consistent with radiation necrosis and post-LITT chemotherapy. CONCLUSIONS: Current evidence suggests that LITT may provide favorable local control with an acceptable safety profile in selected patients, and combination approaches may further improve outcomes. However, the available evidence remains largely retrospective and heterogeneous, and further prospective studies are needed to better define the role of LITT and combination treatment strategies.
12 June 2026
Read appraisal →Journal of clinical oncology : official journal of the American Society of Clinical Oncology
Neoadjuvant Taxane Plus Trastuzumab and Pertuzumab With or Without Carboplatin in Human Epidermal Growth Factor Receptor 2-Positive Breast Cancer: The Randomized Noninferiority Phase III neoCARHP Trial
PURPOSE: The neoCARHP aimed to investigate the efficacy and safety of investigator-selected taxane (docetaxel, paclitaxel, or nab-paclitaxel) plus trastuzumab and pertuzumab, with carboplatin (TCbHP) or without carboplatin (THP), in stage II and III human epidermal growth factor receptor 2 (HER2)-positive breast cancer. METHODS: The neoCARHP was a multicenter, randomized, phase III, noninferiority study. Eligible patients were women age 18 years or older with previously untreated, stage II and III, HER2-positive invasive breast cancer. Patients were randomly assigned (1:1) to receive six 3-week cycles of TCbHP or THP. The primary end point was pathologic complete response (pCR) rate in the breast and axilla (ypT0/is ypN0) in the modified intention-to-treat (mITT) population (all randomly assigned patients receiving at least one dose of study treatment). Safety was evaluated in all patients who received any study treatment. RESULTS: Between April 30, 2021, and August 27, 2024, 774 patients were randomly assigned and 766 were included in the mITT population (382 in THP and 384 in TCbHP). pCR was achieved in 245 (64.1% [95% CI, 59.1 to 69.0]) patients in the THP group and 253 (65.9% [60.9-70.6]) in the TCbHP group (absolute difference, -1.8% [95% CI, -8.5 to 5.0]; odds ratio, 0.93 [95% CI, 0.69 to 1.25]; Pnoninferiority = .0089). The THP group had fewer grade 3 and 4 adverse events (20.7% v 34.6%) and serious adverse events (1.3% v 4.7%) than the TCbHP group. The most common grade 3 and 4 adverse events with THP were neutropenia (6.8% v 16.4% with TCbHP), leukopenia (5.5% v 14.8%), and diarrhea (2.6% v 4.2%). No treatment-associated deaths occurred. CONCLUSION: THP provided noninferior pCR rates and improved tolerability compared with TCbHP. Omitting carboplatin may be applicable in HER2-positive breast cancer.
12 June 2026
Read appraisal →Journal of clinical oncology : official journal of the American Society of Clinical Oncology
Amivantamab Monotherapy in Chemorefractory RAS/BRAF Wild-Type Metastatic Colorectal Cancer: Results From OrigAMI-1, an Open-Label, Phase Ib/II Study
PURPOSE: Amivantamab, an EGFR-MET bispecific antibody with immune cell-directing activity, is approved in non-small cell lung cancer (NSCLC). Effective treatments are limited for chemorefractory metastatic colorectal cancer (mCRC). METHODS: OrigAMI-1 (ClinicalTrials.gov identifier: NCT05379595) is a phase Ib/II study evaluating amivantamab monotherapy in chemorefractory (2-3 prior lines) mCRC. Participants had centrally confirmed RAS/BRAF/EGFR ectodomain wild-type status, without ERBB2/HER2 amplification. Participants with left-sided mCRC without (cohort A) or with (cohort B) prior anti-EGFR antibody treatment, or right-sided mCRC (cohort C) regardless of prior anti-EGFR treatment, received intravenous amivantamab 1,050 mg (1,400 mg for ≥80 kg) once every 2 weeks. The primary end point was objective response rate (ORR) per RECIST v1.1. RESULTS: By October 31, 2024, 94 participants received amivantamab monotherapy (median follow-up, 11.9 months). The median age was 60 years, and 65% of participants were male, with a median of 2 prior lines (94%, prior bevacizumab). In left-sided cohorts, the ORR was 29% (5 of 17) in cohort A and 19% (10 of 54) in cohort B; the median duration of response (DoR) was 9.0 months and 6.1 months, and the median progression-free survival (PFS) was 5.7 months and 4.6 months, respectively. In the right-sided cohort, the ORR was 22% (10 of 23; 43% had prior anti-EGFR), the median DoR was 9.8 months, and the median PFS was 3.7 months. Most frequent treatment-related grade ≥3 adverse events (AEs) were rash (7%), dermatitis acneiform (4%), and hypoalbuminemia (4%). One participant discontinued amivantamab because of a treatment-related AE. CONCLUSION: Amivantamab monotherapy demonstrated promising, durable antitumor activity in chemorefractory mCRC, regardless of prior anti-EGFR therapy and the primary tumor location. The amivantamab safety profile in mCRC is consistent with experience in NSCLC. Amivantamab plus chemotherapy is currently being explored in two phase III studies in first-line and second-line mCRCs.
11 June 2026
Read appraisal →Journal of clinical oncology : official journal of the American Society of Clinical Oncology
Durvalumab in Combination With Neoadjuvant Chemotherapy in Early Triple-Negative Breast Cancer: Long-Term Analysis From the GeparNuevo Trial
The phase II GeparNuevo trial investigated whether adding durvalumab to neoadjuvant chemotherapy (NACT) only in patients with early triple-negative breast cancer cT1b-cT4a-d would improve pathologic complete response (pCR) rate and patient survival. Hundred and seventy-four patients were randomly assigned to receive durvalumab or placebo concurrently with nab-paclitaxel once per week and followed by dose-dense epirubicin and cyclophosphamide. With 86.4 months of median follow-up compared with the previously reported 43.7 months, durvalumab showed sustained significant improvements in long-term outcomes as defined by STEEP compared with placebo regarding not only invasive disease-free survival (iDFS; hazard ratio [HR], 0.56 [95% CI, 0.32 to 0.99]; stratified log-rank P = .0431), but also distant disease-free survival (DDFS; HR, 0.41 [95% CI, 0.21 to 0.80]; P = .0069) and overall survival (OS; HR, 0.33 [95% CI, 0.14 to 0.79]; P = .0085). All analyses were stratified by stromal tumor-infiltrating lymphocytes (sTILs) at baseline (low [≤10%], intermediate [11%-59%], high [≥60%]). In exploratory subgroup analysis, patients with nodal involvement at baseline demonstrated a greater iDFS benefit (HR, 0.33 [95% CI, 0.144 to 0.771]; P = .01; Pinteraction = 0.045). sTILs in residual disease (RD) could be assessed in 39/71 patients without pCR. Post hoc analyses by sTILs high (>10%) versus low (≤10%) in RD showed estimated 7-year iDFS rates of 92.3% (95% CI, 56.6 to 98.9) and 51.4% (95% CI, 29.2 to 69.7), respectively. Hence, adding durvalumab to dose-dense NACT without adjuvant continuation of checkpoint inhibition improved long-term survival outcomes, irrespective of the extent of pathologic response. This underscores the necessity to re-evaluate the adjuvant continuation of checkpoint inhibition.
11 June 2026
Read appraisal →Blood advances
A phase 1/2 study of duvelisib plus venetoclax in patients with relapsed/refractory CLL/SLL or Richter transformation
In this phase 1/2 study we evaluated duvelisib plus venetoclax in patients with relapsed/refractory (R/R) chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL) and Richter transformation (RT). Venetoclax was added after 1 week of duvelisib. After 1 year of therapy, patients with a complete response (CR) and undetectable minimal residual disease (uMRD) could discontinue therapy; patients with detectable MRD continued venetoclax monotherapy until 2 separate occasions of uMRD. Thirty five patients with CLL/SLL and 9 with RT enrolled. Among patients with CLL, 77% had unmutated immunoglobulin heavy chain variable region, 46% had TP53 aberrancy, and patients had a median of 2 prior therapies, including 60% with prior Bruton tyrosine kinase inhibitor. In phase 1 the maximum tolerated dose was not reached, and the recommended phase 2 dose was 25mg twice daily duvelisib plus 400mg daily venetoclax. 91% of patients experienced ≥grade 3 neutropenia, with febrile neutropenia in 6%. Common nonhematologic toxicities were diarrhea (63%, 14% ≥grade 3) and elevated aspartate aminotransferase (51%, 9% ≥grade 3). The best CR and overall response rates were 60% and 91%, respectively. At cycle 13, peripheral blood and bone marrow uMRD at 10-4 were 43% and 40%, respectively. The median progression-free survival (PFS) for patients with CLL/SLL was 46 months, and the 3-year PFS was 67% (95% confidence interval, 0.51-0.86). Four patients with RS achieved a response (3 CRs). Overall, duvelisib plus venetoclax was active in R/R CLL/SLL and RT, although serious adverse events occurred, including immune-mediated toxicities. This trial was registered at www.clinicaltrials.gov as NCT03534323.
9 June 2026
Read appraisal →Blood advances
Ropeginterferon alfa-2b for prefibrotic myelofibrosis and lower-risk myelofibrosis requiring cytoreduction
There is currently no consensus on the optimal treatment of early myelofibrosis (MF). Ropeginterferon alfa-2b (ropeg) is a next-generation monopegylated interferon alfa-2b developed specifically to treat myeloproliferative neoplasms. We conducted a phase 2 study in patients with prefibrotic primary MF (pre-PMF) and Dynamic International Prognostic Scoring System low-/intermediate-1-risk fibrotic PMF or secondary MF (SMF) who required cytoreduction. Ongoing treatment with hydroxyurea was substituted with ropeg (week 0, 250 μg; week 2, 350 μg; week 4 onward, 500 μg every 2 weeks). The primary end points were safety and clinicohematologic complete response (CHCR). Seventy-one patients (40 men and 31 women) with a median age of 60 years (range, 31-86) were enrolled and followed up for a median of 119 weeks (range, 10-131). At weeks 24, 48, and 104, CHCR was achieved in 63.8% (pre-PMF, 74%; fibrotic PMF, 20%; fibrotic SMF, 42.9%), 63.5% (pre-PMF, 69.6%; fibrotic PMF, 25%; SMF, 53.9%), and 70% of patients (pre-PMF, 78.4%; fibrotic PMF, 33.3%; SMF, 50%), respectively. Thrombo-hemorrhagic events were not observed after stopping hydroxyurea. At weeks 24, 48, and 104, reduction in JAK2V617F variant allele frequency (VAF) was observed in 53.1%, 70%, and 67.6% of patients, and reduction in CALR mutant VAF was observed in 52.6%, 42.9%, and 40% of patients, respectively. In 50 evaluable cases at week 104, 13 patients (26%) had reduced bone marrow reticulin fibrosis. There were 3 treatment discontinuations (4.2%) due to adverse events. In conclusion, ropeg was safe and induced CHCR associated with significant molecular responses in patients with early MF. This trial was registered at www.clinicaltrials.gov as NCT04988815.
9 June 2026
Read appraisal →Neurology
Hematopoietic Stem Cell Transplant and Brain Volume Changes in Adults With Sickle Cell Disease
BACKGROUND AND OBJECTIVES: Adults with sickle cell disease (SCD) are at risk of decline in brain health and cognition, even without clinical stroke. SCD-related changes on MRI include brain atrophy and T2 fluid attenuated inversion recovery (FLAIR) white matter hyperintensity (WMH) growth. Hematopoietic stem cell transplant (HSCT) can be curative, but conditioning regimens risk new complications, particularly for adults with accumulated organ damage. We tested whether successful HSCT for adults with SCD was associated with reduced brain atrophy or WMH growth. METHODS: In this cohort study, adults with severe SCD underwent HSCT as part of 5 research protocols at the National Institutes of Health Clinical Center, including autologous gene therapy, HLA-matched donor HSCs, and haploidentical HSCs. Serial brain MRI scans acquired before and after transplant for up to 12 years included 3D T1-weighted and axial FLAIR sequences. Longitudinal brain volumes, segmented using FreeSurfer and corrected for total intracranial volume (TICV), were used to measure brain parenchymal fraction (BPF). A deep learning algorithm segmented WMH volumes from FLAIR sequences. Volumes were log2-transformed as a percentage of TICV. We tested whether HSCT success (persistent engraftment vs graft failure) or change in hemoglobin (Hb) (post-pre-HSCT) was associated with the rate of brain atrophy or WMH growth using mixed-effects linear regression. Random effects included scanner, participant intercept, and slope, while fixed effects included age and sex. RESULTS: Seventy-seven participants (mean age 32 ± 9 years, 43% female) who underwent HSCT were followed for a median 5.3 years (interquartile range [IQR] = 4.6). For 65 participants with successful HSCT, BPF declined nonsignificantly (-0.055%/year; 95% CI -0.12, 0.006; p = 0.08). Twelve participants with graft failure had a 7-fold greater BPF decline (-0.42%/year; 95% CI -0.57, -0.26; p < 0.001; group difference p < 0.001). A smaller change in Hb after HSCT was associated with steeper BPF decline (0.035%/year per unit Hb; 95% CI 0.017, 0.053; interaction p < 0.001). WMH growth was similar in both groups (0.038% vs 0.030% per year; interaction p = 0.50) and was associated with older age and baseline WMHs, but not treatment success. DISCUSSION: Successful HSCT was associated with preserved brain volume, but not WMH growth, another brain health biomarker. Whether these HSCT-related brain structural changes translate to cognitive preservation remains to be determined. TRIAL REGISTRATION INFORMATION: ClinicalTrials.gov NCT02140554 registered 2014-05-14, first enrollment February 2015; ClinicalTrials.gov NCT00061568; registered 2004-07-16, first enrollment July 2004; ClinicalTrials.gov NCT02105766; registered 2014-04-01, first enrollment April 2014; ClinicalTrials.gov NCT03077542; registered 2017-03-10, first enrollment April 2017; ClinicalTrials.gov NCT00977691; registered 2009-12-14, first enrollment December 2009. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that in adults with sickle cell disease, successful hematopoietic stem cell transplant is associated with slower declines in BPFs over a median period of 5 years, compared with patients with transplant failure.
9 June 2026
Read appraisal →Blood advances
Phase 1 study of zanubrutinib plus lenalidomide for patients with relapsed/refractory diffuse large B-cell lymphoma
Patients with relapsed/refractory (R/R) diffuse large B-cell lymphoma (DLBCL) typically have a poor prognosis. In preclinical studies, lenalidomide and a Bruton tyrosine kinase (BTK) inhibitor demonstrated synergistic antitumor effects. Zanubrutinib, a next-generation BTK inhibitor, has greater selectivity to minimize off-target binding. BGB-3111-110 was a phase 1 multicenter dose-escalation/-expansion study. Patients with R/R DLBCL received zanubrutinib 160 mg twice daily plus lenalidomide (15, 20, or 25 mg once daily) until progression or unacceptable toxicity. Primary end points were safety, recommended phase 2 dose (RP2D), and overall response rate (ORR; Lugano 2014 criteria). Sixty-six patients were enrolled and treated. Patients had a median of 2 previous therapies, 83% had stage III/IV disease, and ∼67% had non-geminal center B-cell-like or activated B-cell-like DLBCL. No dose-limiting toxicities occurred; the lenalidomide RP2D was 25 mg once daily when combined with zanubrutinib 160 mg twice daily. Grade ≥3 treatment-emergent adverse events (TEAEs) occurred in 74%; the most common (>20%) grade ≥3 TEAEs were decreased neutrophil count (58%) and decreased white blood cell count (29%). TEAEs led to 7 treatment discontinuations (11%) and 2 deaths (3%). At the RP2D, ORR and complete response rate were 58% and 42%, respectively; median time to response was 2.8 months. Median duration of response was 14.9 months. Median progression-free survival was 5.5 months (95% confidence interval [CI], 2.9-11.1); the 12-month event-free rate was 34% (95% CI, 21-48). Zanubrutinib plus lenalidomide demonstrated acceptable tolerability and antitumor activity in patients with R/R DLBCL. This trial was registered at www.clinicaltrials.gov as NCT04436107.
9 June 2026
Read appraisal →Blood advances
FDG-PET medullary total tumor volume highlights high-risk patients with newly diagnosed multiple myeloma in CASSIOPEIA trial.
This study aimed to assess the prognostic value of medullary total metabolic tumor volume (mTMTV) derived from fluorodeoxyglucose-positron emission tomography/computed tomography ([18F]FDG-PET/CT) compared with conventional PET-derived features and biological/chromosomal abnormalities in patients with newly diagnosed multiple myeloma (NDMM) treated with daratumumab for induction/consolidation and/or maintenance and enrolled in CASSIOPET, a companion study of CASSIOPEIA, with long-term follow-up. Automated bone/liver CT-based segmentation were applied to the baseline [18F]FDG-PET images, with mTMTV being defined using the median liver background as the cut-off, including focal lesions and diffuse bone marrow (BM) involvement. Both univariate/multivariate Cox and machine learning (ML)-based survival models were performed. A total of 195 patients were included, 81% of them PET-positive. Multivariate analysis demonstrated independent prognostic value of mTMTV for progression-free survival (PFS) (P< .001) and overall survival (OS) (P< .001), complementary to Revised International Staging System (R-ISS) (P = .008 and P< .001, respectively). The ML model confirmed these findings, achieving concordance index of 0.609 and 0.659 and identifying mTMTV as the most informative feature for PFS and OS. Adding R-ISS, BM maximum standardized uptake value (SUVmax) and anemia to mTMTV accounted for >60% of the ML model explanation for PFS and adding R-ISS, the number of focal lesions and BM SUVmax for >60% of the model for OS. Combining R-ISS and mTMTV enabled the creation of 2 new-risk subgroups. In conclusion, this prospective study demonstrated the prognostic relevance of [18F]FDG-PET/CT-based parameters in the initial workup of patients with NDMM in the era of anti-CD38-based therapy. mTMTV was found to have strong independent prognostic value, complementary to R-ISS and refining risk stratification. This trial was registered at www.clinicaltrials.gov as #NCT02541383.
9 June 2026
Read appraisal →Bioorganic chemistry
In silico discovery and biological evaluation of a novel selective HDAC8 inhibitor featuring 1H-pyrazolo[3,4-b]pyridine scaffold
Histone deacetylase 8 (HDAC8), a pivotal epigenetic regulator orchestrating chromatin remodeling and transcriptional control, has emerged as a compelling anticancer target owing to its frequent dysregulation in malignancies. The development of isoform-selective inhibitors, however, confronts substantial challenges because of the pronounced structural conservation across HDAC isoforms. This study developed a high-efficiency drug screening pipeline primarily leveraging artificial learning (AI) models and molecular simulation techniques, and further evaluated the biological activity of computationally prioritized compounds through enzymatic inhibition assays and cellular experiments. The structurally novel compound Cmpd.16 was successfully identified, exhibiting promising inhibitory activity against HDAC8 (IC50 = 0.16 ± 0.01 μM) and moderately inhibited HCT116 cell proliferation mainly through the induction of apoptosis and G2/M phase cell cycle arrest. Subsequently, molecular dynamics simulations were employed to investigate the binding conformation of Cmpd.16 within the active pocket, and the key residues identified through free energy decomposition further elucidated the drug-target binding mode. Collectively, Cmpd.16, featuring 1H-pyrazolo[3,4-b]pyridine scaffold, was discovered via an efficient screening workflow, offering valuable insights to guide the rational design of HDAC8-targeted agents.
7 June 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Developing the multidisciplinary and multispecialty workforce and the systems needed to sustain high-quality care from diagnosis through long-term survivorship
With the growing population of individuals living with and beyond cancer-who often have a variety of complex health needs across their lifetimes-coordinated multidisciplinary, multispecialty expert care is needed to ensure comprehensive survivorship cancer care beginning at diagnosis. Guided by the July 2023 US National Academies of Sciences, Engineering, and Medicine workshop, we describe the evidence and propose steps to (1) identify and promote clinician competencies through education and training; (2) improve communication and care coordination, including leveraging the electronic health record and digital health innovations; (3) examine health system interventions and financial strategies to facilitate multidisciplinary, multispecialty survivorship care; and (4) identify policy, payment, and advocacy challenges and opportunities to achieve equitable, accessible, high-quality care. Although the recommendations are centered on the United States, their applicability to other countries should also be explored.
1 June 2026
Read appraisal →Radiation oncology (London, England)
HyperSightTM-CBCT based monitoring of sarcopenia during definitive radiotherapy of prostate cancer: a longitudinal feasibility study
BACKGROUND: Sarcopenia and treatment-related muscle loss are associated with increased toxicity and reduced resilience in cancer patients. HyperSight™ cone-beam CT (CBCT) provides improved soft-tissue contrast compared with conventional CBCT and may enable reliable longitudinal skeletal-muscle monitoring during image-guided radiotherapy (IGRT). This study evaluated the feasibility of HyperSight™ CBCT-based muscle assessment in prostate cancer patients and explored the clinical relevance of intratreatment muscle decline. METHODS: Eighty-eight patients with non-metastatic prostate cancer were treated on an Ethos® system with HyperSight™ CBCT. Patients received ultrahypofractionated (UHF) or normo-/moderately hypofractionated (NF/MHF) radiotherapy; 28% received androgen-deprivation therapy. Cross-sectional muscle area (CSA) and radiodensity (HU) were assessed on planning CT (PCT) and serial CBCTs (first, second, last fraction [FF, SF, LF]); HU values were z-standardized to subcutaneous fat (zHU). Agreement between PCT and CBCT was evaluated using concordance correlation coefficients (CCC), regression, and Bland-Altman analysis. Longitudinal changes were analyzed using mixed-effects models, and associations with acute genitourinary/gastrointestinal (GU/GI) toxicity (RTOG ≥ 2) using regression models. RESULTS: HyperSight™ CBCT showed excellent agreement with PCT for CSA (CCC = 0.981). The NF/MHF cohort showed a significant longitudinal CSA decline (FF-LF: -153.0 mm², p = 0.021), whereas no significant within-cohort change was observed in UHF. Muscle density (zHU) decreased significantly in both cohorts (NF/MHF: p < 0.001; UHF: p = 0.0017). Weight loss and age were independently associated with zHU change, while no independent predictors of CSA decline were identified. In the NF/MHF cohort, CSA loss > 5% was associated with acute GU toxicity in univariate analysis (OR 4.90, p = 0.033; Fisher's exact p = 0.014), but not after adjustment for age, weight loss, ECOG performance status, and ADT; no associations were found for GI toxicity or in the UHF cohort. CONCLUSIONS: HyperSight™ CBCT enables quantitative skeletal-muscle assessment during prostate radiotherapy and detects intratreatment reductions in muscle density, which may represent an early imaging marker of muscle decline. Muscle loss showed an exploratory association with acute GU toxicity in NF/MHF patients in univariate analysis, but not after adjustment. These findings support the feasibility of CBCT-based muscle monitoring and warrant further evaluation in larger cohorts. CLINICAL TRIAL NUMBER: Not applicable.
1 June 2026
Read appraisal →Cancer chemotherapy and pharmacology
A randomized, open-label, two-way crossover clinical trial to evaluate the food effect on pharmacokinetics and safety of DHP107 in patients with advanced solid tumors: the FEEL study
PURPOSE: The phase 1, randomized, open-label, two-way crossover FEEL study assessed the effect of food on the pharmacokinetics of DHP107, an oral paclitaxel with a novel lipid formulation designed to be systemically absorbed without Cremophor EL, in patients with advanced solid tumors. METHODS: Patients were randomized 1:1 to DHP107 (200 mg/m2 orally twice daily on Days 1, 8, 15 every 28 days for two cycles) treatment sequences: fasted-fed (first DHP107 dose administered after overnight fast; n = 13) and fed-fasted (first DHP107 dose administered after a high-fat meal; n = 12). The primary objective was to evaluate the effect of food on DHP107 pharmacokinetics. The secondary objective was DHP107 safety; efficacy was also assessed (both in all patients regardless of dosing sequence). RESULTS: Mean systemic exposures were similar for paclitaxel and its metabolites under fed and fasted conditions. Geometric mean ratios (fed:fasted) for paclitaxel were: maximum concentration (Cmax), 101.00% (95% CI 91.93-110.95%); area under the curve (AUC0-72), 102.92% (95% CI 95.04-111.45%); and AUC0-inf 104.76% (95% CI 96.44-113.80%), meeting criteria for no food effect. Common adverse events across the study period included diarrhea (fasted-fed 61.5%; fed-fasted 41.7%), vomiting (fasted-fed 30.8%; fed-fasted 8.3%), and neutropenia (fasted-fed 23.1%; fed-fasted 16.7%). Two fed-fasted patients (16.7%) had partial responses and six had stable disease (50.0%); seven fasted-fed patients had stable disease (53.8%). CONCLUSION: This study suggests that the pharmacokinetics of DHP107 are unaffected by food and DHP107 can be taken with or without food. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov ID: NCT04675528; 107CS-7.
31 May 2026
Read appraisal →Journal of gastrointestinal cancer
Durable Survival Outcomes After Liver Transplantation for Colorectal Liver Metastases: A Systematic Review and Single-arm Meta-analysis of Prospective Clinical Trials
BACKGROUND: Liver transplantation (LT) has emerged as a potential treatment option for selected patients with colorectal liver metastases (CRLM), but existing meta-analyses are limited by heterogeneous populations and mixed treatment modalities. METHODS: A systematic review and single-arm meta-analysis were conducted in accordance with PRISMA guidelines. Studies reporting outcomes following LT for CRLM were included. Primary outcomes were overall survival (OS) and progression-free survival (PFS) at predefined time points. Secondary outcomes included adverse events. Pooled proportions were calculated using random-effects models with logit transformation and Hartung-Knapp adjustment. Heterogeneity was assessed using the I² statistic. RESULTS: Four prospective studies (1 RCT, 3 prospective cohorts) comprising 118 patients were included. Studies were conducted in Norway (SECA-I, n = 21; SECA-II, n = 15), the United Kingdom (n = 50), and a European multicentre RCT (TransMet, n = 32). Median age ranged from 55 to 58 years; all patients had liver-only metastases with resected primary tumors and had received pre-transplant chemotherapy. The pooled 3-year OS was 69% (95% CI, 52-82%; I² = 0.0%), and the pooled 5-year OS was 62% (95% CI, 36-83%; I² = 14.6%). The pooled 3-year PFS was 33% (95% CI, 7-76%), with substantial heterogeneity (I² = 62.8%). The pooled incidence of adverse events was 41% (95% CI, 25-60%; I² = 0.0%). Sensitivity analyses confirmed the robustness of pooled estimates. The retrospective literature also supported the finding that LT can achieve meaningful long-term survival in carefully selected patients. CONCLUSIONS: In carefully selected patients with CRLM, LT is associated with durable long-term survival and an acceptable safety profile, supporting further prospective evaluation and refinement of selection criteria.
31 May 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Concurrent de novo glioblastoma and meningiomatosis: a case report and systematic review of clinical, molecular, and topographical characteristics
INTRODUCTION: The co-occurrence of glioblastoma (GBM) and meningioma in a single patient is an exceptionally rare clinical phenomenon, often associated with prior irradiation or genetic syndromes. This study presents a unique case of concurrent de novo GBM and meningiomatosis and provides a systematic review of the literature to characterize this rare association. CASE PRESENTATION AND METHODS: A 78-year-old female with a known history of untreated meningiomatosis presented with refractory seizures and aphasia. Imaging revealed multiple stable extra-axial lesions and a new, distinct intra-axial right temporal lesion. The patient underwent a temporal lobectomy with gross total resection. Histopathology confirmed WHO grade 4 GBM (IDH-wildtype). Following multidisciplinary review, she completed adjuvant radiotherapy. At 6-month follow-up, she remained clinically stable with no recurrence. A systematic review was conducted, identifying 34 cases of concurrent GBM and meningioma. Data regarding demographics, topography, molecular profiles, and survival were analyzed. RESULTS: The median age was 63 years (range 30-86), with a slight male predominance (52.9%). Tumors were synchronous in 88.2% of cases. Topographically, 47.1% were "collision" or adjacent tumors, while 32.3% occurred in different hemispheres. Most meningiomas were WHO grade I (79.4%), while 97.1% of glial tumors were grade 4 GBM. Molecular analysis revealed classic independent drivers: 22q loss/NF2 mutations in meningiomas and 10q loss/EGFR amplification in GBMs. Median overall survival was 7 months (range 0.5-24 months), with modern trimodal therapy (Stupp protocol) showing a trend toward extended survival compared to older cohorts (Fig. 1). CONCLUSION: The coexistence of GBM and meningioma appears mostly coincidental, driven by distinct molecular pathways rather than a common progenitor. Despite the benign nature of the associated meningioma, prognosis is dictated by the aggressive GBM component. Early surgical intervention and modern adjuvant therapy remain essential for optimizing survival in these complex cases.
31 May 2026
Read appraisal →Journal of neuro-oncology
Predicting progression-free survival in glioblastoma with neuroimaging and machine learning
PURPOSE: Glioblastoma (GBM) is the most prevalent and aggressive form of malignant glioma. Reliable estimation of progression-free survival (PFS) prior to medical intervention could strengthen clinical decision-making and improve patient care. Here, we utilize machine learning (ML) to predict PFS in GBM patients using resting state network (RSN) connectivity before medical intervention. METHODS: GBM patients (N = 45, mean age 62.1 ± 10.3 years, mean PFS 9.5 ± 5.6 months, 62.2% male) were retrospectively recruited from Washington University Medical Center. All patients completed structural neuroimaging and resting-state functional MRI before surgery. Deep neural networks were trained on resting-state functional connectivity to predict PFS. Feature selection identified the 15 strongest predictive features prior to training. RESULTS: Sex (p = 0.0037), overall survival (p = 0.0003), MGMT promoter methylation status (p = 0.0064), presentation of weakness (p = 0.0037), and presentation of memory impairment (p = 0.045) were significantly associated with PFS. Tumor frequency and spatial correlation analyses associated dorsal attention, visual, frontal-parietal, and default mode networks with shorter PFS. Conversely, right-temporal lobe tumors were associated with better outcomes. RSN spatial maps revealed widespread alterations in association networks in GBM patients relative to controls. MRMR feature selection identified thalamic and association network connectivity, including somatomotor, ventral and dorsal attention, and default mode/parietal memory as the strongest predictors of PFS. Using leave-one-out validation, the model predicted PFS with an RMSE of 1.26 months, MAE of 1.08 months, and R² of 0.96 (p < 0.001). CONCLUSIONS: Our findings indicate that GBM alters functional brain organization on a widespread scale, and these global effects are informative of patient outcomes.
31 May 2026
Read appraisal →Journal of the Egyptian National Cancer Institute
Artificial intelligence in cancer immunotherapy: current trends in predicting response and personalizing treatment
Artificial intelligence (AI) can transform cancer immunotherapy by enabling more accurate prediction of treatment responses, the discovery of specific biomarkers, and the development of personalised treatment plans. Traditional single-marker biomarkers (PD-L1, TMB, MSI) lack consistency across tumour types and cannot be used to assess tumour heterogeneity or the dynamic tumour microenvironment (TME). This review synthesises developments in multimodal AI models that combine genomics, transcriptomics, radiomics, digital pathology (pathomics), circulating biomarkers, and clinical evidence to create composite predictive signatures with significantly better discriminatory value. AUCs over 0.8 have been seen in a few retrospective studies with deep learning and ensemble models on whole-slide images, CT/MRI/PET radiomics, spatial and single-cell omics, and multi-omics fusion models, but prospective and multicentre validation is scarce, and external validation often shows deterioration in performance. AI is also used to enhance the translational pipelines of adoptive cell therapies (e.g., CAR-T) by improving patient selection, manufacturing (e.g., digital twins), and early toxicity prediction (e.g., CRS, ICANS). Nevertheless, clinical implementation remains hindered by data heterogeneity, bias, poor longitudinal validation, limited reproducibility, and a lack of transparency in most models, even though prospective, multicenter validation and explainable AI are crucial for clinician trust and regulatory acceptance. New systems such as federated learning, foundation models, spatial omics, digital twins, and wearable monitoring represent paths to generalizable, privacy-preserving, and actionable systems in clinical practice. To achieve the potential of AI, the generation of data will need to be standardized, reporting must be transparent, interdisciplinary, and regulatory frameworks must be strengthened focusing on the practical use of AI and patient safety. By taking these steps, AI could be shifted to prospective clinical decision support, which uses AI to meaningfully enhance personalization and outcomes in cancer immunotherapy based on a retrospective research tool.
30 May 2026
Read appraisal →American Society of Clinical Oncology educational book. American Society of Clinical Oncology. Annual Meeting
Artificial Intelligence in Oncology: Clinical Applications, Challenges, and Opportunities
Artificial intelligence (AI) is reshaping cancer research and clinical oncology by enabling large-scale analysis of complex biomedical data. Although early AI efforts focused on single-modality tasks such as imaging interpretation or pathology classification, contemporary oncology increasingly requires integrated reasoning across heterogeneous inputs, including imaging, digital pathology, and genomics. Multimodal artificial intelligence (MMAI) models represent a major evolution by fusing these data types into unified predictive systems capable of informing diagnosis, risk stratification, treatment selection, and disease monitoring. This manuscript reviews the state of AI in oncology and highlights emerging foundation models within the field. There will be a focus on histopathology, and how this can be incorporated into risk assessment models. There will also be examples of MMAI utility in radiology and spatial proteomics. Additionally, we will discuss ethical and legal implications of MMAI in modern oncology, and recommendations for responsible application within clinical oncology practice.
30 May 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Cancer patients' experiences of "time toxicity": a qualitative meta-synthesis
OBJECTIVE: To systematically review and synthesize qualitative studies on cancer patients' experiences of time toxicity, providing evidence to inform policy making. METHODS: We searched PubMed, Web of Science, Cochrane Library, Embase, CINAHL, CNKI, Wanfang, VIP, SinoMed, and other databases for qualitative studies on cancer patients' experiences of time toxicity, from database inception to November 2025. Included studies were appraised using the 2016 edition of the JBI quality appraisal criteria for qualitative research from the Joanna Briggs Institute (Australia). Results were synthesized using an aggregation approach for meta-synthesis. RESULTS: Ten studies were included. Forty-one themes were extracted, which were grouped into 11 categories and further consolidated into 4 synthesized findings: (1) multidimensional sources of time toxicity; (2) negative impacts of time toxicity; (3) coping strategies adopted to address time toxicity; and (4) positive experiences from investing time in treatment. CONCLUSIONS: Time toxicity has multidimensional effects on cancer patients' daily rhythms, psychological states, and social roles, and requires coordinated attention from healthcare professionals, society, and policy makers. Future efforts should build systemic support through integrating healthcare resources, optimizing service processes, and strengthening social support to reduce patients' time toxicity.
30 May 2026
Read appraisal →Journal of neuro-oncology
Predicting late radiation-associated neurocognitive and endocrine toxicity in patients with brain tumors
PURPOSE: Neurocognitive and endocrine dysfunction are potential complications of cranial irradiation. However, risk factors are poorly understood, impeding accurate prognostication and exploration of potential preventive interventions. The objective of this study was to evaluate the prognostic value of various vascular and genotypic risk factors for the development of radiation-related toxicities. METHODS: This single-institution retrospective cohort study included patients with metastatic and malignant primary brain tumors who received cranial irradiation as part of their initial tumor-directed therapy. Demographic and treatment characteristics were collected, as well as putative vascular and genotypic risk factors. Univariate, multivariate, and machine-learning analyses were performed using five pre-specified measures of radiation-related toxicity. The primary outcome was change in mini-mental status exam (MMSE). RESULTS: Eighty patients (53% male, mean age 55.7, 53% primary and 44% metastatic brain tumors) were included. Elevated homocysteine and ApOE4 genotype were the strongest predictors of MMSE decline in the multivariate model (OR 3.96 [6.5-200, p < 0.001 and 2.85 [1.92-27.6], p = 0.004). Elevated homocysteine was associated white matter change on MRI and both physician and patient assessment. ApoE4 allele was associated with new endocrine deficiency, and physician assessment. An online nomogram provides risk predictions for each of the five late toxicity outcomes: ( https://afranklin22.shinyapps.io/PRMMSERadiationRiskFactors/ ) CONCLUSION: Two pre-treatment laboratory values (elevated homocysteine and ApOE genotype) were strongly associated with post-radiation neurocognitive and endocrine dysfunction using a variety of domains. Our predictive algorithm can aid clinicians in stratifying baseline risk and should be validated in prospective trials and with additional metrics of ND.
30 May 2026
Read appraisal →Scientific reports
Automated RECIST tumor response classification through prompt-guided large language models
This study investigates whether an entirely offline, general-purpose large language model (LLM) can reliably automate the classification of routine radiology reports according to RECIST (Response Evaluation Criteria in Solid Tumors) guidelines, focusing on how different prompting strategies support accurate, privacy-preserving tumor response assessment without additional model fine-tuning. An offline, in-house implementation of LLaMA-3.3 (70B) was used to classify real-world CT imaging reports from oncology patients. Reports were authored following RECIST-structured reporting but had outcome labels programmatically withheld prior to processing. Three prompting strategies-zero-shot, few-shot, and chain-of-thought prompting-were tested to guide the model in assigning RECIST categories: Baseline (BL), Complete Response (CR), Partial Response (PR), Stable Disease (SD), and Progressive Disease (PD). Model outputs were benchmarked against original expert labels using accuracy, precision, recall, and F1 scores. Across all tested prompting strategies, the LLaMA-3.3 model achieved strong classification performance. The best results were obtained with chain-of-thought prompting, reaching micro F1 scores of 0.81 across all RECIST categories. Overall model predictions aligned well with human expert assessments. Operating entirely offline within hospital infrastructure, the system preserved full compliance with stringent data privacy requirements. Prompt-driven large language models can accurately classify tumor response categories from real-world radiology reports in a scalable, reproducible, and privacy-preserving manner. Offline LLM deployment, combined with optimized prompting strategies, offers a promising approach for automating structured oncology report interpretation, potentially enhancing consistency and efficiency in clinical decision support workflows.
29 May 2026
Read appraisal →PLoS medicine
Sequential chemo-immunotherapy followed by standard versus reduced thoracic radiotherapy for older and/or frail stage III non-small-cell lung cancer: A randomized open-label cohort trial
BACKGROUND: The appropriateness of concurrent chemoradiotherapy (cCRT) for older or clinically vulnerable stage III unresectable non-small-cell lung cancer (NSCLC) patients remains contentious. Furthermore, the survival implications of de-escalating thoracic radiotherapy (RT) intensity in this population have not been conclusively elucidated. METHODS AND FINDINGS: We conducted a phase II randomized, open-label, two-cohort (non-comparative) trial at a tertiary hospital in China (NCT05557552). Between September 30, 2022 and April 30, 2024, we enrolled 56 older and/or frail patients with stage III NSCLC who were ineligible for cCRT. The primary endpoint was the 1-year progression-free survival (PFS) rate estimated using the Kaplan-Meier method. Secondary endpoints included objective response rate (ORR), overall survival (OS), and safety. In the intention-to-treat (ITT) set, which included all 56 randomized patients who received at least one dose of study treatment, the 1-year PFS was 84.3% (95% confidence interval [CI] [70.3%, 98.3%]) in the standard RT group and 70.7% (95% CI [54.3%, 87.1%]) in the reduced RT group. In the per-protocol set (53 patients), the 1-year PFS was 82.9% (95% CI [68.9%, 98.8%]) in the standard RT group and 73.4% (95% CI [58.3%, 92.4%]), with a median follow-up of 24 months. Among 56 patients in the safety analysis set, 71.4% of patients experienced grade 3/4 adverse events (AEs) in the standard RT group and 53.6% in the reduced RT group. One patient (3.6%) in the reduced RT and three patients (10.7%) in the standardized RT experienced grade 5 AEs. The main limitations are the non-comparative design, small sample size, and lack of power to establish non-inferiority or superiority. CONCLUSION: The current study suggested that reduced RT combined with sequential chemo-immunotherapy might be feasible for older/frail patients intolerant to cCRT, showing numerically similar survival outcomes. These exploratory findings warrant confirmation in larger, adequately powered randomized trials. TRIAL REGISTRATION: The trial had been registered on ClinicalTrials.gov on Sep 30, 2022. ClinicalTrials.gov NCT05557552.
29 May 2026
Read appraisal →The New England journal of medicine
Zanidatamab with and without Tislelizumab in HER2-Positive Gastroesophageal Cancer
BACKGROUND: Zanidatamab, a dual human epidermal growth factor receptor 2 (HER2)-targeted bispecific antibody, plus chemotherapy both with and without tislelizumab (anti-programmed death 1), showed encouraging efficacy and safety as first-line therapy in phase 2 studies involving patients with HER2-positive gastroesophageal adenocarcinoma. METHODS: In an open-label, phase 3 trial, we randomly assigned, in a 1:1:1 ratio, patients with previously untreated, centrally confirmed HER2-positive advanced gastroesophageal adenocarcinoma to receive zanidatamab and tislelizumab plus chemotherapy, zanidatamab plus chemotherapy, or trastuzumab plus chemotherapy. The two primary end points were progression-free survival and overall survival. RESULTS: At a median follow-up of 25.9 months, progression-free survival was longer with zanidatamab-tislelizumab-chemotherapy (median among 302 patients, 12.4 months) and zanidatamab-chemotherapy (median among 304 patients, 12.4 months) than with trastuzumab-chemotherapy (median among 308 patients, 8.1 months) (hazard ratio for progression or death with zanidatamab-tislelizumab-chemotherapy, 0.63 [95% confidence interval {CI}, 0.51 to 0.78]; hazard ratio with zanidatamab-chemotherapy, 0.65 [95% CI, 0.52 to 0.81]; P<0.001 for both comparisons). Overall survival was longer with zanidatamab-tislelizumab-chemotherapy than with trastuzumab-chemotherapy (median, 26.4 vs. 19.2 months; hazard ratio for death, 0.72; 95% CI, 0.57 to 0.90; P = 0.004). At this interim analysis, overall survival did not differ significantly between zanidatamab-chemotherapy (median, 24.4 months) and trastuzumab-chemotherapy (hazard ratio, 0.80; 95% CI, 0.64 to 1.01; P = 0.06). The incidence of grade 3 or higher adverse events was 83.3% with zanidatamab-tislelizumab-chemotherapy, 73.8% with zanidatamab-chemotherapy, and 74.5% with trastuzumab-chemotherapy; diarrhea was the most common such event, in 24.8%, 20.0%, and 12.9% of patients, respectively. CONCLUSIONS: Zanidatamab plus chemotherapy, both with and without tislelizumab, led to longer progression-free survival than trastuzumab plus chemotherapy among patients with HER2-positive advanced gastroesophageal adenocarcinoma. At this interim analysis, overall survival was longer with zanidatamab-tislelizumab-chemotherapy than with trastuzumab-chemotherapy; further analyses are planned to assess zanidatamab-chemotherapy. Diarrhea was a common adverse event. (Funded by Jazz Pharmaceuticals and others; HERIZON-GEA-01 ClinicalTrials.gov number, NCT05152147.).
29 May 2026
Read appraisal →Clinical & experimental metastasis
Local control and dose selection for lung cancer brain metastases treated with radiosurgery: an artificial intelligence model
Clinical and radiological outcomes after Stereotactic radiosurgery (SRS) for lung cancer brain metastases are heterogeneous, and prescription dose selection is often guided by experience rather than individual patient and tumor specific features. A data-driven approach that links dose to quantitative local control and adverse effect predictions could improve planning and follow-up strategies. We performed a retrospective single-center cohort study of lung cancer brain metastases treated with GKRS at the University of Pittsburgh Medical Center (2014-2024). Using routinely available clinical, tumor, and dosimetric variables, we trained a tumor-level Random Survival Forest to predict Local Control and Dose Selection for Lung Cancer Brain Metastases after GKRS treatment. Internal validation used leakage-resistant patient-level grouped cross-validation. Performance was assessed using Harrell's concordance index (C-index) and integrated Brier score (IBS). A connected dose-sweep decision layer evaluated predicted local control across a clinically feasible margin dose grid and selected the dose associated with the most favorable predicted profile at prespecified horizons. The survival model demonstrated good internal performance (C-index 0.83; IBS 0.15). A baseline dose imitation model predicted historical margin dose with low error (OOF MAE 1.59 Gy). The integrated decision layer generated individualized local control trajectories and returned horizon-specific local control probabilities and model-based prescription dose recommendations. An Artificial Intelligence framework integrating time-to-event prediction with outcome-linked dose sweeping can provide individualized GKRS decision support for lung cancer brain metastases by delivering quantitative local control forecasts and model-based dose recommendations.
28 May 2026
Read appraisal →Acta oncologica (Stockholm, Sweden)
Prospective evaluation and clinical outcomes of adaptive radiotherapy for locally advanced non-small cell lung cancer (LA-NSCLC)
BACKGROUND AND PURPOSE: Locoregional tumor control is strongly associated with survival for non-small cell lung cancer, emphasizing the importance of precise radiotherapy delivery. This study aims to evaluate a traffic light adaptation protocol regarding target coverage, protocol performance and patient outcomes. MATERIAL AND METHODS: From 2019 to 2022, we prospectively enrolled inoperable non-small cell lung cancer patients. We acquired daily cone-beam computed tomography and traffic light registrations, classifying anatomical changes and need of action. Using repeated computed tomography (rCT) in week 1 and 3 for dosimetric evaluation, we recalculated clinical target volume (CTV) D98% and V95% with and without adaptation, and compared traffic light registrations with the actual dose distribution. Protocol performance was evaluated by the sensitivity and specificity in detecting CTV V95% < 98%. RESULTS: Among 45 patients with complete registrations, baseline shift and tumor shrinkage were most common changes, leading to corrections from bone to tumor match in 37.8% and replanning in 13.3% of patients. Among 38 patients with at least one rCT, 15.8% would have received insufficient CTV coverage without adaptation. Protocol sensitivity, specificity and balanced accuracy were 83.3, 88.6, and 85.7%, respectively. For 38 stage II-III patients, median overall survival was 43.5 months (95% confidence interval [CI]: 32.9-54.1), median time to locoregional failure 27.4 months (95% CI: 0-61.8) and median estimated time to distant failure 30.7 months (CI not estimated due to censoring). INTERPRETATION: The protocol identified relevant changes, improving target coverage without increasing organ at risk doses. With appropriate training, protocol performance was good, and clinical outcomes were consistent with international results.
28 May 2026
Read appraisal →Acta oncologica (Stockholm, Sweden)
Change in skeletal muscle mass during systemic cancer treatment: a systematic review and meta-analysis
BACKGROUND AND PURPOSE: Loss of skeletal muscle mass (SMM) is common during systemic cancer treatment, but the magnitude and variability across cancer and treatment types remain uncertain. We aimed to describe changes in SMM during systemic cancer treatment supported by pooled quantitative estimates. PATIENTS/MATERIAL AND METHODS: We systematically searched PubMed, Embase, and Web of Science until April 2025 for longitudinal studies reporting SMM during chemotherapy and/or immunotherapy (± targeted therapy) in patients with cancer (PROSPERO CRD42022308388). Standardized mean changes (SMC) were pooled in random-effects meta-analyses using the restricted maximum-likelihood estimator with Hartung-Knapp adjustment. Heterogeneity was assessed using I². Risk of bias was assessed with the NIH Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. RESULTS: Seventy-eight studies (n = 10,502; 52% male; median age 64 years [interquartile range, IQR: 34-77]) were included. Meta-analysis across cancers showed an association between systemic cancer treatment and decline in SMM (59 studies; n = 6,373; SMC = -0.24, 95% confidence interval [CI]: -0.29 to -0.20; I² = 92%), corresponding to -5% over a median interval of 90 (IQR: 71-129) days among studies (62%) reporting assessment intervals. Declines were most pronounced during chemotherapy (± targeted therapy). INTERPRETATION: Declines in SMM are frequently observed during systemic cancer treatment, particularly during chemotherapy (± targeted therapy), although effect sizes were generally small per Cohen's thresholds. However, substantial heterogeneity limits interpretation of a single pooled estimate. Prospective studies with standardized methods are needed to clarify trajectories, mechanisms and clinical implications of SMM loss.
28 May 2026
Read appraisal →Journal of radiation research
Feasibility of nonspinal bony landmark-based image registration in CyberKnife ® robotic radiotherapy for pelvic and femoral bone tumors
In stereotactic body radiotherapy for bone metastases using the CyberKnife system, spinal anatomy is generally employed as the alignment reference. However, when treating pelvic tumors such as those in the pubic bone or proximal femur, the spinal reference may be anatomically distant from the target, potentially compromising visualization and localization accuracy. This study evaluated the feasibility of using nonspinal bony landmarks, specifically the pubic bone and femoral neck, as reference points for image registration. Intrafractional positional deviations in six degrees of freedom and the frequency of intrafractional corrections were analyzed according to treatment site, including cases treated with conventional spine-based alignment. Alignment accuracy was visually and numerically verified by the treatment operator before beam delivery. Across all treatment sites, the mean translational and rotational deviations during treatment remained within ±0.5 mm and ± 0.5°, respectively. Furthermore, no substantial differences were observed in the mean frequency of intrafractional corrections among the different alignment approaches. These findings demonstrate that nonspinal tumor-adjacent bony landmarks can provide clinically acceptable accuracy comparable to conventional spine-based registration. To our knowledge, this is the first study to systematically assess the clinical feasibility of this approach in CyberKnife-based pelvic radiotherapy. The proposed method may serve as a practical and reliable alternative to spine-based alignment, particularly when the spine is remote from the target.
27 May 2026
Read appraisal →Blood advances
Phase 1b study of ABBV-744, a novel, selective BET inhibitor, as monotherapy for patients with myelofibrosis
Janus kinase inhibitors (JAKi) are standard of care for patients with myelofibrosis (MF) but can be associated with treatment-limiting cytopenias and do not modify underlying disease. Novel treatments targeting other clinically relevant pathways are needed. Inhibitors of bromodomain (BD) and extraterminal domain (BET) proteins are a promising class of drugs that have demonstrated the ability to modulate key pathways involved in inflammation, fibrosis, and apoptosis. ABBV-744 is a novel small molecule that targets the BDII of BET proteins and has previously been shown to be well tolerated when administered daily at doses of 120 and 180 mg to patients with acute myeloid leukemia. We report the outcomes of a multicenter, open-label phase 1b study of ABBV-744 in patients with MF who received ≥1 previous lines of therapy, including a JAKi. The primary objective was safety, including dose-limiting toxicities (DLTs). Secondary end points included a reduction in spleen volume of ≥35% (SVR35), ≥50% reduction in total symptom score (TSS50), objective response rate, and pharmacokinetics. All 21 patients experienced at least 1 treatment-emergent adverse event, and 11 patients experienced a DLT. The most common events leading to DLTs were thrombocytopenia (33%) and anemia (24%). SVR35 was attained in 24% of patients at week 12 and 33% at week 24. TSS50 was reported for 29% of patients at 12 weeks and 19% at week 24. These outcomes are noteworthy in this heavily pretreated population considering the advanced disease state and limited treatment options for this patient population. This trial was registered at www.clinicaltrials.gov as NCT04454658.
27 May 2026
Read appraisal →Journal of computer-aided molecular design
In silico discovery of novel small-molecule PD-L1 inhibitors through a multi-stage computational workflow integrating machine learning and molecular dynamics
Cancer immunotherapy targeting the PD-1/PD-L1 pathway has transformed modern oncology; however, developing small-molecule inhibitors as viable alternatives to monoclonal antibodies remains a major challenge. In this study, an integrated computational framework is established in which machine learning, molecular docking, molecular dynamics simulations, and binding free-energy calculations are combined to enable the discovery and optimization of novel PD-L1 inhibitors. A validated ML-QSAR model was constructed using the XGBoost algorithm (R2_train = 0.925, R2_test = 0.743) on a dataset of 74 known inhibitors with consistent assay conditions. Through virtual screening of FDA-approved drugs, Pralatrexate was subsequently identified as a promising repurposing candidate, demonstrating a higher predicted binding affinity than the reference inhibitors. Structure-based modification of Pralatrexate yielded the derivative D1, which exhibited improved computational binding properties across all evaluation methods. Molecular dynamics simulations indicated that the D1-PD-L1 complex achieved greater stability than both the free protein and the reference complex, with reduced RMSD fluctuations and preserved key interactions with tyrosine residues Tyr56(A/B). MM-GBSA calculations further confirmed D1's superior binding affinity (-86.21 kcal/mol vs. - 73.65 kcal/mol for the reference), and predicted IC50 values suggested enhanced inhibitory potential. This multi-stage computational workflow effectively integrates machine learning predictions with atomic-level binding analyses, providing a robust platform for accelerated drug discovery. The optimized derivative D1 thus represents a promising candidate for experimental validation and further development as a potential cancer immunotherapeutic agent.
26 May 2026
Read appraisal →Journal of neuro-oncology
Clinical utility of liquid biopsy in distinguishing true progression from radiation necrosis and pseudoprogression in malignant brain tumor.
BACKGROUND: Distinguishing true tumor progression from pseudoprogression (PsP) and radiation necrosis (RN) following radiotherapy or radiosurgery for intracranial tumors remains a significant clinical challenge. Conventional imaging modalities, including MRI and PET, often lack sufficient diagnostic accuracy, while histopathological confirmation, although definitive, is invasive and not always feasible. Liquid biopsy (LB) has emerged as a promising noninvasive diagnostic approach. METHODS: A systematic review was conducted across PubMed, Embase, Scopus, and Cochrane databases. A total of 964 records were identified, including PubMed (n = 322), Embase (n = 280), Scopus (n = 297), and Cochrane (n = 65), along with three clinical trials identified from trial registers. After removal of 157 duplicates using Covidence, 807 studies were screened by title and abstract. Of these, 749 were excluded, and 58 articles underwent full-text review. Ultimately, 11 studies met the inclusion criteria and were included in the analysis. RESULTS: A diverse range of LB-based biomarkers has been investigated, including immune cell-based markers (e.g., HLA-DRneg/low and VNN2+ CD14+ monocytic myeloid-derived suppressor cells), immune-related proteins (e.g., CXCL11 and MUC-16), circulating tumor cells, cell-free DNA, mitochondrial DNA, B1-SINE elements, microvesicles, and RNA analytes. Plasma was the most used biofluid, with limited evaluation of urine and cerebrospinal fluid. Analytical techniques varied widely, including flow cytometry, PCR-based assays, immunostaining-FISH, and multiplex protein platforms. Several biomarkers showed statistically significant differences among RN, PsP, and tumor recurrence; however, these findings were largely derived from small, heterogeneous cohorts with inconsistent reporting of diagnostic performance metrics. Composite indices, such as the DR-VNN2 index (DVI) and the Necrosis Prediction Index (NPI), showed potential for improving diagnostic differentiation. CONCLUSIONS: LB-based biomarkers show promise for differentiating RN and PsP from tumor recurrence; however, current evidence is limited by small sample sizes, methodological heterogeneity, and lack of standardized diagnostic criteria. Larger prospective studies and validation of composite biomarker models are required to establish their clinical utility. CLINICAL TRIAL NUMBER: Not Applicable.
26 May 2026
Read appraisal →Journal of medical Internet research
Sequencing AI Automation and Data Interoperability in Oncology Using a Scenario-Planning Framework Coupled With Discrete-Event Simulation: Proof-of-Concept Study
BACKGROUND: As oncology workflows integrate increasingly autonomous artificial intelligence (AI) agents, health systems face uncertainty regarding operational impacts. Traditional linear forecasting methods fail to capture second-order effects such as governance saturation, induced demand, and bottleneck migration. To navigate this complexity, the emerging field of medical futures studies requires methodologies that bridge qualitative strategic foresight with quantitative operational modeling. These system-level dynamics directly influence timely diagnosis, treatment delays, and overall health system resilience. OBJECTIVE: This study aimed to develop a proof-of-concept framework coupling qualitative scenario planning with computational discrete-event simulation to stress-test oncology AI adoption strategies. METHODS: We defined a strategic state space using 2 orthogonal axes, AI automation intensity and data interoperability, resulting in 4 distinct futures scenarios. We translated these qualitative narratives into a quantitative discrete-event simulation model of a 3-year operational horizon. The model quantified system performance (referral-to-treatment interval [RTTI] and throughput), volatility, and resource constraints across different adoption trajectories. RESULTS: The scenario-planning phase yielded 4 operational archetypes (analog oncology, automation islands, interconnected clinicians, and AI-orchestrated care) with distinct constraints, risks, and failure modes. In the simulation, the fully integrated scenario maximized capacity (1244, SD 21.4 patients per year) and halved the mean RTTI to 14.9 (SD 0.3) days, a magnitude comparable to major pathway redesign interventions. Isolated automation without data infrastructure led to reduced system performance, increasing RTTI by 26% (37.1, SD 1.3 days) and reducing throughput to 647 (SD 10.1) patients per year due to administrative governance saturation. The model illustrated a structural bottleneck migration: successful upstream AI adoption shifted binding constraints from diagnostic scanners to downstream chemotherapy infusion units, whereas missing data interoperability resulted in governance constraints. Pathway optimization analysis indicated that a coordinated strategy prioritizing early improvements in data interoperability reduced transition volatility compared to an automation-first approach. CONCLUSIONS: Integrating qualitative scenario planning with quantitative simulations enabled a systematic evaluation of oncology AI adoption strategies. As a proof of concept, it offers a replicable framework for health leaders to model future scenarios of digital transformation in times of high uncertainty. Subsequent work should expand this methodology to incorporate financial and health equity dimensions, establishing simulation-based scenario planning as an important tool in medical futures studies.
26 May 2026
Read appraisal →Blood advances
A phase 1 trial of romidepsin, azacitidine, dexamethasone, and lenalidomide in relapsed or refractory T-cell lymphoma
Targeted therapies can induce responses in patients with relapsed/refractory T-cell lymphoma (R/R TCL) but are not curative. Genetic mutations associated with epigenetic and transcriptional dysregulation are common in many TCL subtypes, and drugs that modulate the epigenome and that target transcriptional regulators have synergistic activity in TCL. We hypothesized that fixed duration treatment with multiagent combinations of these drugs could produce deep responses leading to durable remissions. In a phase 1 trial, we tested escalating doses of lenalidomide added to romidepsin, azacitidine, and dexamethasone (RAdR) for patients with R/R TCL. The primary objective was to identify the maximum tolerated dose (MTD) of lenalidomide that could safely be used in RAdR. Secondary end points included response rate, survival, and markers of immune activation. Twenty-six patients enrolled and 21 were evaluable for response. Adverse events were predominantly gastrointestinal and hematologic. Grade 3/4 thrombocytopenia and neutropenia occurred in 19% and 21% of cycles, respectively. The MTD of lenalidomide was 20 mg. Among 9 patients treated at the MTD the objective response rate was 89% and complete response rate 22%. At 1 year, the estimated rate of progression-free survival was 14% and overall survival was 66%. Cell line models of anaplastic large cell lymphoma were interrogated to explore the activity of drug combinations in responding genetic subtypes. RAdR demonstrated activity in patients with highly refractory TCL but did not induce durable responses. This novel combination effectively bridged some patients with refractory TCL to consolidation such as allogeneic transplantation. This trial was registered at www.ClinicalTrials.gov as NCT04447027.
26 May 2026
Read appraisal →Blood advances
Selinexor plus ruxolitinib in JAK inhibitor-naïve patients with myelofibrosis: a multicenter, open-label, phase 1 study
The phase 1 portion of SENTRY/XPORT-MF-034 (NCT04562389) evaluated the exportin 1 inhibitor selinexor (40 mg/60 mg once weekly) plus ruxolitinib in JAK inhibitor-naïve patients with myelofibrosis (n = 24). Primary end points were maximum tolerated selinexor dose, recommended clinical trial dose, and safety. No dose-limiting toxicities were reported. Common adverse events were nausea, fatigue, anemia, thrombocytopenia, constipation, vomiting, and headache. Nausea was transient, mainly grade 1, and managed by prophylactic antiemetics. Four deaths occurred, all unrelated to study treatment. Selinexor 60 mg in combination with ruxolitinib was identified as the recommended phase 3 dose based on safety, efficacy, and exposure-response analyses. Overall safety profiles and grades of clinically significant adverse events were similar and generally manageable regardless of selinexor dose. A greater proportion of patients achieved spleen volume reduction of ≥35% (SVR35) and total symptom score reduction of ≥50% (TSS50) at week 24 in the selinexor 60-mg group (79% and 58%) than the 40-mg group (38% and 25%). Among evaluable patients who received suboptimal ruxolitinib ≤5 mg twice daily in the selinexor 60-mg group, SVR35 was observed in 100% (6/6) and TSS50 in 75% (3/4) of patients. The trial was registered at ClinicalTrials.gov as NCT04562389.
26 May 2026
Read appraisal →Journal of gastrointestinal cancer
Trend of Adjuvant Radiation in Resected Pancreatic Ductal Adenocarcinoma: Evaluation of RTOG 0848 with a Large National Database
PURPOSE: Adjuvant radiation (AR) remains a controversial treatment modality for pancreatic ductal adenocarcinoma (PDAC). This study aimed to evaluate the findings of the RTOG 0848 trial assessing the benefits of adjuvant radiation in node-negative disease utilizing a national database. METHODS: Utilizing the National Cancer Database, all patients diagnosed with non-metastatic PDAC, who underwent pancreatectomy, were included (2004-2019). The trend of adjuvant radiation utilization was evaluated. Overall survival in a cohort of patients mimicking the RTOG 0848 protocol was examined as were clinicodemographic and pathologic predictors of adjuvant radiation. RESULTS: Overall rates of adjuvant radiation decreased from 45 to 12%, while neoadjuvant radiation rates simultaneously increased from 4 to 12%. Positive margins and nodal disease were the strongest predictors of adjuvant radiation receipt (OR 1.7[ 95% CI: 1.6-1.7] p < 0.001 and OR: 1.1 [95% CI: 1.1-1.2] p < 0.001; respectively). Both high-risk pathologic groups experienced a decline in the use of adjuvant radiation over time, even after exclusion of patients who received neoadjuvant radiation. After application of the RTOG 0848 criteria, improved overall survival was observed in patients with node-negative and positive margins (adjuvant radiation: 28.6 [95% CI: 25.4-31.8] vs 23.0 [95% CI: 19.4-26.7] months; p = 0.005). In the margin positive cohort, adjuvant radiation was significantly associated with reduced mortality (HR: 0.791 [95% CI: 0.7-0.9]; p < 0.001). CONCLUSION: Evaluation of RTOG 0848 in the setting of real-world data indicates a role for adjuvant radiation for patients with margin-positive, node-negative pancreatic cancer. This highlights the essential role of multidisciplinary discussions regarding patients with PDAC care.
26 May 2026
Read appraisal →Head and neck pathology
Digital Pathology in Head and Neck Squamous Cell Carcinoma: Translational Advances and Clinical Integration for Pathologists, Oncologists, and Surgeons
PURPOSE: Artificial intelligence (AI)-enabled digital pathology has advanced rapidly in head and neck squamous cell carcinoma (HNSCC), but its readiness for clinical implementation remains uncertain. This review evaluates the translational maturity of AI-based digital pathology applications in HNSCC and their proximity to routine clinical use. METHODS: A structured literature search of PubMed and Scopus identified studies published between January 2020 and March 2026. Studies were included if they applied AI or machine learning to histopathologic or whole-slide images in HNSCC or related oral malignancies and reported diagnostic, prognostic, biomarker, or treatment-response outcomes. Given the narrative scope of this review, representative studies aligned with key clinical domains were selected for qualitative synthesis. A six-stage translational maturity framework was used to categorize applications based on development stage, validation, and clinical integration. RESULTS: Of 444 identified records, 358 unique studies were screened, from which representative HNSCC-specific AI digital pathology studies were selected for detailed analysis. Most applications-including diagnostic classification, dysplasia grading, prognostic modeling, and therapy-response prediction-remain at early translational stages (Stage 0-1), typically limited to retrospective, single-center cohorts with minimal external validation. Biomarker quantification and HPV prediction show relatively greater maturity, with some studies approaching Stage 2, reflecting external validation and early prospective evaluation. However, calibration, decision-curve analysis, and outcome-linked endpoints are rarely reported. No HNSCC-specific AI pathology tools have demonstrated outcome-linked deployment or achieved regulatory-grade implementation, and evidence for workflow integration, clinical decision impact, and patient-centered outcomes remains limited. CONCLUSION: AI-enabled digital pathology in HNSCC remains an early-stage field. Despite promising technical performance, most applications lack the prospective validation, workflow integration, and outcome-based evidence required for clinical adoption. Progress will depend on multi-institutional prospective studies, standardized reporting of clinical utility, and demonstration that AI-assisted decisions improve patient management and outcomes.
26 May 2026
Read appraisal →Neurosurgical review
The role of preoperative thrombocytic factors on survival in patients with glioblastoma: a meta-analysis and synopsis of the literature
Glioblastoma (GBM) is the most common malignant primary brain tumour and is associated with poor prognosis. Platelet-related factors have been linked to outcomes in GBM, but evidence remains inconsistent. This review evaluates their prognostic value for survival. We conducted a systematic review and meta-analysis of studies on preoperative thrombocytic factors in GBM. PubMed, MEDLINE, and Embase were searched from inception to January 2025 using MeSH terms. Eligible studies included observational cohort studies of adults (≥ 16 years) with WHO grade 4 diffuse astrocytoma (GBM), ≥ 30 participants, and ≥ 3 months' follow-up after resection. Random-effects meta-analysis using restricted maximum likelihood with Hartung-Knapp adjustment was used to pool hazard ratios (HRs) of high vs. low parameters on overall survival (OS). Study quality was assessed with the Newcastle-Ottawa Scale. 21 studies were included. 13 (n = 2609) reported HRs of high vs. low platelet: lymphocyte ratio (PLR, median threshold = 150), with high PLR significantly associated with worse OS (pooled HR = 1.46, 95% CI [1.23, 1.74]). Eight studies (n = 1921) assessed platelet count (PC, median threshold = 208*109/L), showing high PC was a borderline predictor of worse OS (pooled HR = 1.38, 95% CI [1.00, 1.90]). Four studies (n = 1234) examined mean platelet volume (MPV, median cut-off = 9.05 fL), which qualitatively demonstrated worse OS in patients with high MPV or high MPV: PC ratio. One study assessed platelet distribution width (PDW, threshold = 14.7 fL), finding worse OS in the high PDW group. High preoperative PLR is associated with poor prognosis in GBM, while high PC demonstrates borderline increased risk, which may help refine risk stratification and guide future mechanistic studies.
25 May 2026
Read appraisal →Journal of neuro-oncology
Therapeutic effects of selumetinib on diffuse neurofibroma and optic pathway glioma in neurofibromatosis type 1.
PURPOSE: Selumetinib is approved for the treatment of inoperable plexiform neurofibromas (PN) in patients with neurofibromatosis type 1 (NF1). However, its efficacy in treating NF1-associated diffuse neurofibromas (NF1-DN) or optic pathway gliomas (NF1-OPG) remains unclear. We evaluated the efficacy and safety of selumetinib in these subgroups. METHODS: This was a sub-analysis of a Korean phase II open-label trial focusing on non-target treatment effects on NF1-DN and NF1-OPG. A total of 88 pediatric and adult patients with NF1-PN (59 children and 29 adults) in this trial had been treated for at least 2 years (~ 26 cycles, 28-day cycle) with oral selumetinib (20 or 25 mg/m², or 50 mg/dose every 12 h). Tumor volume, quality of life (QoL), and visual acuity were assessed. RESULTS: Among the 88 included patients, NF1-DN was diagnosed in 25 (28%), and NF1-OPG in 3 (3%). All NF1-DN patients exhibited disfigurement, two experienced pain, and a partial response (PR; ≥20% tumor reduction at a single time) was achieved in 9 of these cases (36%). The median time to PR was 6 cycles (range, 6-12), and the median time to best response was 18 cycles (range, 6-26), with a median volume change of - 11.9% (range, - 55.4% to + 36.3%). Confirmed PR (cPR; PR sustained for > 6 cycles) was observed in 6 NF1-DN patients (24%), stable disease (SD) was observed in 9 of these patients (36%), and progressive disease (PD) in 10 cases (40%). In a paired comparison, cPR was significantly lower for NF1-DN than for NF1-PN (24% vs. 88%, P < 0.001), and the median best volume reduction was also smaller (- 11.9% vs. -42.1%, P < 0.001). For the 3 NF1-OPG patients, visual impairment was present in all cases at baseline. One patient achieved PR at cycle 12 (- 36.1% from baseline) and complete response (CR) at cycle 26 but the visual acuity did not improve in this case. SD was maintained in the other two patients. Within these subgroups, QoL improved in 16 patients (57%) by cycle 26. No serious adverse events or treatment discontinuations were reported. CONCLUSIONS: Selumetinib shows therapeutic potential against NF1-DN, although with a lower response than that observed against NF1-PN. The NF1-OPG findings herein for this drug were exploratory only because of the small number of cases, but they may provide an additional clinical context for evaluating the broader effects of selumetinib across NF1-associated lesions. TRIAL REGISTRATION INFORMATION: KCT0003700, cris.nih.go.kr/cris/search/detailSearch.do/19,080, first submission March 4, 2019; first patient enrolled May 14, 2019.
25 May 2026
Read appraisal →Journal of gastrointestinal cancer
Optimal First-line Immune-related Therapy Selection for Advanced Gastric Cancer: A Systematic Review and Network Meta-analysis
PURPOSE: Although immune checkpoint inhibitors combined with chemotherapy improve survival in advanced gastric cancer (AGC), comparative evidence across first-line regimens remains insufficient. This study aimed to systematically evaluate their relative efficacy and safety. METHODS: PubMed, Embase, the Cochrane Library, Web of Science, and major conferences were searched up to February 28, 2025. Eligible RCTs comparing ICI-related regimens as first-line AGC treatment were included. Primary outcomes were overall survival (OS) and progression-free survival (PFS). RESULTS: Eleven RCTs involving 8,999 patients, six integrated strategies, and twelve specific regimens were included. Dual-target ICI-chemo and single ICI-chemo showed significant survival benefits over chemotherapy, whereas dual ICIs±chemo and single ICI alone did not. Dual-target ICI-chemo was associated with significantly better survival than both dual ICIs±chemo and single ICI±chemo. Among the specific regimens, cadon-chemo achieved the most favorable OS and PFS. No significant OS differences were observed among the single ICI-chemo regimens. In PD-L1-positive subgroups, both dual-target ICI-chemo and single ICI-chemo significantly improved survival compared with chemotherapy, with cadon-chemo being most effective in CPS ≥ 10 and CPS ≥ 1, and SHR1701-chemo in CPS ≥ 5. In the CPS < 5 subgroup, dual-target ICI-chemo significantly improved OS, while single ICI-chemo showed a trend toward benefit. However, no ICI-based strategy demonstrated benefit in CPS < 1. CONCLUSIONS: Dual-target ICI-chemo appears to be the most effective first-line strategy for AGC based on current indirect evidence, surpassing single ICI-chemo and dual ICI-based regimens. These findings support personalized therapy selection but await confirmation by head‑to‑head trials. Future studies should address CPS < 1 and explore optimized combinations to enhance outcomes.
25 May 2026
Read appraisal →Computer assisted surgery (Abingdon, England)
An automatic detection model for spread through air spaces in postoperative pathological sections based on deep learning in NSCLC
Spread through air spaces (STAS) is recognized as an aggressive pattern of invasion in lung cancer and has been associated with poorer survival outcomes. However, STAS is frequently overlooked or misdiagnosed during routine pathological diagnosis. We analyzed 129 pathological slides from 91 STAS-positive patients with non-small cell lung cancer (NSCLC). An artificial intelligence framework consisting of a tumor region segmentation algorithm and an object detection algorithm was developed. The segmentation algorithm was first used to isolate the main tumor region, followed by STAS detection using an improved object detection model. The segmentation module achieved a Jaccard similarity of 0.846. The proposed object detection algorithm demonstrated superior performance with a precision of 0.738, recall of 0.747 and average precision (AP) of 0.784 and F1 score of 0.742, outperforming other object detection methods. Both segmentation and detection results met diagnostic requirements, with higher accuracy observed in lung adenocarcinoma (LUAD) than in lung squamous cell carcinoma (LUSC). In external validation, the model achieved a precision of 0.670, recall of 0.705 and AP of 0.732 and F1 score of 0.687. Additionally, the AI-derived STAS counts showed substantial agreement with pathologists (interclass correlation coefficient = 0.703). Kaplan-Meier survival analysis revealed that the number of STAS events was significantly associated with disease-free survival (DFS) in stage I LUAD (p < .05). We propose a deep learning-based artificial intelligence framework for automated STAS detection and quantification in digital whole-slide images of NSCLC. This model holds promising potential to assist pathologists in achieving more comprehensive and accurate diagnosis of STAS.
25 May 2026
Read appraisal →PloS one
Timely Integration of specialist palliative care into oncology care for patients receiving radiotherapy for bone metastases: A study protocol for the TIPZO-RT Randomized Controlled Trial
BACKGROUND: Patients living with advanced cancer often benefit from palliative care. Timely referral to specialist palliative care improves quality of life and reduces potentially inappropriate end-of-life care. Despite these benefits, specialist palliative care is frequently introduced late and inconsistently. This study evaluates whether systematically offering a consultation with the hospital palliative care consultation team (PCCT) to all patients referred for radiotherapy for symptomatic bone metastases improves satisfaction with care. PATIENTS AND METHODS: The Timely Integration of Palliative Care in Oncology care for patients referred for palliative RadioTherapy (TIPZO-RT) trial follows the Trials within Cohorts design and is embedded within the PRospective Evaluation of interventional StudiEs on boNe meTastases (PRESENT+) cohort. Following cohort enrollment, 246 patients will be randomized (1:1) to either the intervention or control group. Patients in the intervention group are offered a PCCT consultation, which they may accept or refrain from. Patients in the control group are not informed about the trial and continue to receive usual care. After four weeks, patient satisfaction with care (affective behavior, EORTC Satisfaction with Cancer Care core questionnaire (EORTC PATSAT-C33)) will be compared between the groups. Secondary outcomes include symptom burden, quality of life, overall survival, and palliative care utilization. Additionally, in the intervention group, patients' experiences with the consultation are evaluated. DISCUSSION: Integrating palliative care into oncological care for patients with advanced cancer is essential to deliver comprehensive, patient-centered care that addresses physical, psychosocial and spiritual needs. This pragmatic study may provide evidence to support timely integration of specialist palliative care for all patients with bone metastases who may benefit from specialist palliative care. Using the Trials within Cohorts design, this study generates real-world evidence on the acceptance or need for a consultation with the PCCT, while minimizing disappointment or response bias, as patients in the control group are not informed. TRIAL REGISTRATION: ClinicalTrials.gov ID NCT06805396. Registered on 25-03-2025.
24 May 2026
Read appraisal →Medicine
Survival prediction of colorectal cancer using 101 machine learning methods based on immune-related genes: A machine learning study
Colorectal cancer (CRC) is characterized by high incidence and mortality rates. Early identification of high-risk CRC patients and timely intervention are crucial for reducing mortality rates. This study aims to develop a prognostic model incorporating immune-related genes using 101 machine learning algorithms. We utilized data from 630 CRC patients in The Cancer Genome Atlas Program database and the Gene Expression Omnibus database as a validation set. We identified immune-related genes from the ImmPort repository and conducted differential gene expression analysis. We developed 101 machine learning models, including random survival forest, elastic net, LASSO, Ridge regression, stepwise Cox, Coxboost, partial least squares regression for Cox, supervised principal component analysis, generalized boosted modeling, and survival support vector machines. The model with the highest mean concordance index was selected as the optimal prognostic model. The Ridge regression model demonstrated the most robust performance with a mean concordance index of 0.67. Patients with higher immune-related gene risk score (IRGRS) had significantly lower survival rates (log-rank test, P < .001). Univariate and multivariate Cox regression analyses confirmed IRGRS as an independent prognostic factor. Nomograms successfully predicted overall survival at 1, 2, and 3 years. IRGRS correlated with immune cell infiltration levels and stromal activity in the tumor microenvironment. High IRGRS scores were associated with higher tumor immune dysfunction and exclusion scores, suggesting potential differences in response to immune checkpoint inhibitors. In addition, high IRGRS patients exhibited greater sensitivity to certain chemotherapy drugs. The IRGRS score is a potent predictor of survival in CRC patients. This study offers a new perspective for CRC prognosis and supports the development of personalized treatment strategies.
24 May 2026
Read appraisal →Studies in health technology and informatics
Digital GO: A Clinical Decision-Support Dashboard for Gynecologic Oncology Tumor Board
Multidisciplinary Tumor Board (MTB) meetings are essential for coordinated oncologic decision-making but are often hindered by the complexity and fragmentation of patient data. This study presents the development of an interactive digital dashboard designed to streamline data access and visualization during ovarian cancer MTB sessions. The system leverages structured knowledge extracted from unstructured clinical reports using in-house large language model (LLM) pipelines. These outputs are harmonized within a cloud environment through dedicated processing workflows and mapped into a domain ontology, to ensure interoperability. The resulting dashboard, implemented using a dedicated functional module, enables both cohort-level exploration and patient-specific visualization, including longitudinal event timelines. Preliminary results demonstrate the feasibility of integrating LLM-based data extraction with a scalable visualization framework, offering a step toward the digital transformation of multidisciplinary oncology workflows.
24 May 2026
Read appraisal →BMC cancer
Efficacy and risk analysis of monotherapy and dual immunotherapy in dMMR/MSI-H metastatic colorectal cancer: a meta-analysis based on randomized controlled trials
BACKGROUND: Immune checkpoint inhibitors (ICIs) have transformed the treatment landscape for metastatic colorectal cancer (mCRC) exhibiting DNA mismatch repair deficiency (dMMR) or microsatellite instability-high (MSI-H). However, the comparative efficacy and safety of monotherapy versus dual immunotherapy remain inadequately quantified. This meta-analysis aims to evaluate the benefits and risks of these strategies by quantitatively synthesizing data from randomized controlled trials (RCTs) to inform clinical decision-making. DESIGN: We systematically searched major databases (PubMed, Web of Science, Cochrane, Embase) from inception to September 2025 for relevant RCTs. Outcomes included objective response rate (ORR), progression-free survival (PFS), overall survival (OS), duration of response (DOR), and adverse events. Statistical analyses were performed using RevMan 5.3 and Stata 18.0. RESULTS: Six RCTs (1,460 patients) were included. Pooled analysis showed that PD-1/PD-L1 monotherapy significantly improved ORR compared with chemotherapy (OR = 1.52, 95% CI: 1.02-2.27, P = 0.04), based on two RCTs (n = 429), and demonstrated a significantly lower incidence of grade ≥ 3 adverse events (OR = 0.14, 95% CI: 0.08-0.23, P < 0.00001; 2 studies). Dual immunotherapy with nivolumab plus ipilimumab significantly improved ORR versus monotherapy (OR = 1.77, 95% CI: 1.25-2.49, P = 0.001), based on one RCT (n = 582), with a significantly increased toxicity risk (OR = 1.73, 95% CI: 1.22-2.44, P = 0.002). For PFS, KEYNOTE-177 (HR = 0.59, 95% CI: 0.45-0.79) and CheckMate 8HW (HR = 0.62, 95% CI: 0.48-0.81) showed significant benefits, while SAMCO-PRODIGE 54 and CheckMate 9 × 8 did not demonstrate statistically significant PFS improvement. OS hazard ratios across four studies ranged from 0.74 to 1.03, with all 95% confidence intervals crossing 1.0, indicating no statistically significant survival benefit in any individual study. DOR was consistently longer with immunotherapy, with median not reached for pembrolizumab versus 10.6 months for chemotherapy in KEYNOTE-177. CONCLUSIONS: Effect estimates for PFS, OS, and DOR generally favored immunotherapy; however, confidence intervals were wide and statistical significance was not consistently observed across trials. PD-1/PD-L1 monotherapy demonstrated a favorable safety profile and a statistically significant improvement in ORR compared with chemotherapy (OR = 1.52, 95% CI: 1.02-2.27, P = 0.04). Dual immunotherapy significantly improved ORR compared with monotherapy (OR = 1.77, 95% CI: 1.25-2.49, P = 0.001), but this benefit was accompanied by significantly increased toxicity (OR = 1.73, 95% CI: 1.22-2.44, P = 0.002) and did not consistently translate into statistically significant PFS or OS benefits across trials. These findings highlight the need for personalized risk-benefit assessment. Future research should focus on biomarker-driven patient selection and strategies to overcome resistance.
23 May 2026
Read appraisal →Journal of medical Internet research
Benchmarking Large Language Models and Prompt Engineering Strategies in Microsatellite Instability Cancers: Evaluation Study
BACKGROUND: The reliability of general-purpose large language models (LLMs) for complex clinical tasks in specialized domains such as microsatellite instability (MSI) cancers remains critically uncharacterized. The absence of a domain-specific benchmark to evaluate and guide the optimization of their capabilities across diverse clinical tasks poses unevaluated risks to patient safety. OBJECTIVE: This study aimed to develop and validate Microsatellite Instability Cancer Benchmark (MSIC-Bench), a novel, two-tiered benchmark for MSI cancer, covering both consensus and frontier knowledge. Using this framework, we aimed to systematically assess LLM performance across various prompting strategies, identify task-specific weaknesses, and reveal effective pathways for performance improvement. METHODS: We developed MSIC-Bench, a 511-question benchmark derived from clinical guidelines and a curated knowledge base. Three state-of-the-art LLMs (GPT-4o, [OpenAI], Gemini 2.5 Pro [Google], and Claude Opus 4 [Anthropic]) were evaluated using 4 prompting strategies, including vanilla, chain-of-thought, reflection of thoughts, and retrieval-augmented generation (RAG), under both multiple-choice and open-ended modalities. Performance was assessed on accuracy, safety (honesty), error composition, and token usage. RESULTS: LLMs demonstrated a significant "scaffolding effect," with accuracy dropping substantially in open-ended scenarios. For non-RAG strategies, the primary failure mode was an internal knowledge deficit. The integration of RAG proved to be the most effective intervention. A domain-aligned RAG strategy not only significantly improved accuracy in complex decision-making tasks but also fundamentally shifted the system's primary bottleneck from knowledge deficits to retrieval failures. In terms of safety, RAG induced a favorable shift from high-risk fabrications to safer refusals, though this introduced a safety-utility trade-off in the form of false refusals. Notably, our hybrid-RAG configuration, which combined both knowledge sources, demonstrated the most robust and generalizable performance across all tasks. CONCLUSIONS: Current LLMs lack the specialized knowledge required for reliable application in MSI oncology. A well-designed RAG architecture is the pivotal intervention to address this gap. However, its success is not automatic; it transforms the nature of system failure, making retrieval precision and knowledge base quality the new critical determinants of performance and safety. Our findings establish a clear directive for developing trustworthy clinical artificial intelligence: focus must shift toward optimizing the retrieval component and curating high-quality, comprehensive knowledge sources. MSIC-Bench provides a robust framework to guide these future efforts.
22 May 2026
Read appraisal →Postgraduate medical journal
Next-generation cancer therapies: translating experimental advances into clinical practice
Recent advancements in cancer therapy have led to the emergence of innovative approaches that offer new hope to patients. This review provides a comprehensive overview of the latest cutting-edge technologies and strategies in cancer treatment, including clinically approved therapies and experimental modalities in preclinical or early clinical development, with a focus on their potential to improve patient outcomes. We examined next-generation therapies, including personalized immunotherapies, targeted molecular treatments, gene editing approaches, and artificial intelligence-driven strategies. By evaluating the current landscape of these therapies, we highlighted their benefits, limitations, and future directions. The integration of these advanced modalities into clinical practice holds promise for enhancing their efficacy, reducing side effects, and ultimately transforming cancer care.
22 May 2026
Read appraisal →Journal of medical Internet research
Explainable AI in Cancer Imaging: Scoping Review of Methods, Modalities, and Clinical Integration
BACKGROUND: Explainable artificial intelligence (xAI) is increasingly used in medical imaging to enhance transparency, clinical interpretability, and trust in artificial intelligence (AI)-assisted diagnostics, particularly in oncology. Evidence on how explainability is implemented, validated, and reported in cancer imaging remains fragmented. OBJECTIVE: This scoping review aimed to systematically map research applying xAI methods to radiologic cancer imaging, summarize methodological and clinical trends, and identify persistent gaps in validation and integration. METHODS: We conducted a structured search of PubMed and Scopus (final search executed on October 20, 2025), covering studies published from 2017 to December 2024. Eligible peer-reviewed articles using machine learning or deep learning were analyzed with a focus on xAI components. Data from 371 studies were extracted into predefined categories covering cancer type, imaging modality, AI model, xAI method, terminology, validation, code availability, and decision support system integration. RESULTS: Studies focused primarily on breast (112/371, 30.2%), lung (87/371, 23.5%), and brain (56/371, 15.1%) cancers, with prostate, thyroid, and liver cancers also represented. The primary imaging modalities were computed tomography (139/371, 37.5%) and magnetic resonance imaging (104/371, 28%). Deep learning was used in 70.1% (260/371) of studies, classical machine learning in 18.1% (67/371), hybrid pipeline methods for 10% (37/371), and emerging concept-, prototype-, or causal-based approaches accounted for 1.9% (7/371) of studies. Post hoc xAI methods were dominant (305/371, 82.2%), with visualization (163/371, 53.4%), and feature relevance (111/371, 36.4%) as the most common subcategories. Hybrid post hoc or inherent approaches comprised 12.1% (45/371) and intrinsically interpretable methods 5.7% (21/371). Data sources were mostly public (149/371, 40.2%) or mixed (100/371, 26.9%); 22.9% (85/371) used private institutional datasets, and 7.8% (29/371) did not report data sources. Among validated studies, expert or user-based validation was most common (104/193, 53.9%), followed by mixed methods (74/193, 38.3%), while quantitative metrics (10/193, 5.2%) and clinical knowledge-based (8/193, 4.1%) validation remained rare. Only 17.5% (65/371) of studies provided code and 12.1% (45/371) reported decision support system integration, with few achieving actual clinical deployment. CONCLUSIONS: This scoping review maps xAI implementation across multiple cancer imaging modalities, revealing methodological inconsistency and insufficient validation. Most research emphasizes visualization over quantitative interpretability, and few models are reproducible or clinically implemented. These findings provide an evidence base for researchers, clinicians, and regulators to prioritize standardization of xAI reporting, quantitative validation, and user-centered frameworks to advance trustworthy AI in oncology imaging.
22 May 2026
Read appraisal →European radiology experimental
Predicting resistance to neoadjuvant chemotherapy in osteosarcoma using machine learning with clinical data and T2-weighted MRI radiomics
OBJECTIVES: Identifying patients at risk of chemoresistant osteosarcoma enables risk-adapted management. This study aimed to predict chemoresistant osteosarcoma using baseline clinical and magnetic resonance (MRI)-derived radiomics features, with histological response as the reference standard and external validation included. MATERIALS AND METHODS: This retrospective single-center study included 115 patients with osteosarcoma from an institutional registry as the internal cohort, divided into training and test sets, and 49 patients from another institution as an external validation cohort. Tumor and peritumoral regions were manually segmented on baseline fat-suppressed T2-weighted MRI. Radiomics features were extracted using PyRadiomics, followed by two feature selection methods to identify potential predictors. Six machine learning models with varying feature combinations were trained to classify histologic chemoresistance in the internal training set. Model performance was assessed in the internal test set, and the best model was externally validated. RESULTS: The support vector machine model combining eight tumor radiomics features and four clinical-imaging parameters (presence of tumor necrosis > 50% on contrast-enhanced MRI, age, body mass index, and presence of metastasis at presentation) demonstrated the best performance. In the internal test set, it achieved a sensitivity of 83.3%, a specificity of 72.7%, an area under the receiver operating characteristic curve (AUROC) of 0.84, and a positive likelihood ratio of 3.06. External validation yielded a sensitivity of 88.5%, a specificity of 47.8%, and an AUROC of 0.77. CONCLUSION: A model combining tumor radiomics and clinical parameters at diagnosis showed strong performance in predicting chemoresistant osteosarcoma, with results confirmed by external validation. This approach may support personalized treatment strategies in high-grade osteosarcoma. RELEVANCE STATEMENT: The validated model may support early, individualized osteosarcoma management. KEY POINTS: Baseline T2-weighted MRI radiomics and clinical data can predict chemoresistant osteosarcoma. Tumor radiomics combined with clinical features achieved strong predictive accuracy. The support vector machine model reached an AUROC of 0.84 for the internal testing and of 0.77 for the external validation. The validated model may support early, individualized osteosarcoma management.
21 May 2026
Read appraisal →Journal of the Egyptian National Cancer Institute
Preclinical efficacy of drug delivery systems in colon cancer therapy: a systematic review and meta-analysis of in vivo animal studies
BACKGROUND: Drug delivery systems (DDS) offer a promising strategy to enhance the therapeutic index of chemotherapeutic agents in colon cancer by improving tumor targeting, circulation time, and controlled drug release. Despite extensive preclinical research, a quantitative synthesis evaluating the efficacy of DDS and the influence of design parameters remains lacking. METHODS: We conducted a systematic review and meta-analysis of preclinical in vivo studies assessing DDS-based chemotherapy in murine models of colon cancer. A comprehensive search of PubMed, EMBASE, Scopus, Google Scholar, Cochrane CENTRAL, and ClinicalTrials.gov was performed through October 15, 2025. Outcomes included tumor growth inhibition, with subgroup analyses examining the effects of DDS platform, chemotherapeutic agent, targeting strategy, ligand type, and route of administration. RESULTS: Twenty-three studies comprising 25 experiments and 539 animals were included. Overall, DDS-based therapies significantly reduced tumor growth compared with controls ((WMD: - 557.7; 95% CI: - 716.8 to - 398.5; I²=88%; p < 0.001) and free-drug administration ((WMD: - 276.3; 95% CI: - 367.6 to - 185.1; I²=78%; p < 0.001). Both targeted and non-targeted DDS significantly reduced tumor growth compared with free drug treatment, while targeted DDS showed significantly greater efficacy than non-targeted systems (WMD: - 240.3; 95% CI: - 399.4 to - 81.25; I²=59%; p = 0.003). Although no statistically significant differences were observed between DDS platforms or chemotherapeutic agent subgroups, micelle-based systems and DDS formulations incorporating SN-38 or doxorubicin tended to show greater tumor growth reduction. Intravenous administration demonstrated significantly greater efficacy than intraperitoneal delivery, while hyaluronic acid- and aptamer-based targeting strategies achieved the largest tumor growth inhibition. Risk-of-bias assessment indicated moderate methodological quality, with variability in reporting of randomization, blinding, and sample size calculations. CONCLUSIONS: DDS-based chemotherapy consistently improves antitumor efficacy in preclinical colon cancer models, with targeting strategy, platform type, chemotherapeutic agent, and administration route influencing outcomes. These findings support the rational design of DDS platforms and underscore their translational potential. Rigorous preclinical study design and standardized reporting of efficacy and safety are essential to facilitate clinical translation.
21 May 2026
Read appraisal →Journal of clinical oncology : official journal of the American Society of Clinical Oncology
Tissue-Free Circulating Tumor DNA Assay and Patient Outcome in a Phase III Trial of FOLFOX-Based Adjuvant Chemotherapy (Alliance N0147)
PURPOSE: Detection of molecular residual disease using circulating tumor DNA (ctDNA) may enable postoperative risk stratification and guide adjuvant therapy. We evaluated the prognostic value of a tissue-free, epigenomic ctDNA assay in patients with stage III colon cancer (CC) enrolled in a phase III adjuvant chemotherapy trial. METHODS: Plasma samples were collected after surgery and before adjuvant infusional fluorouracil, leucovorin, and oxaliplatin alone or combined with cetuximab. ctDNA was analyzed using a tissue-free assay; in ctDNA-positive patients, tumor fraction (TF) was quantified and genotyping was performed with a 739-gene panel. Associations with disease-free survival (DFS), time to recurrence (TTR), and overall survival (OS) were assessed using multivariable Cox models adjusted for covariates. RESULTS: Among 2,260 evaluable patients, 461 (20.4%) were ctDNA-positive with significantly higher detection in advanced T-/N-stage, high-grade, obstruction/perforation, and BRAFV600E tumors. At a median follow-up of 6.1 years, ctDNA positivity was independently associated with shorter TTR (hazard ratio [HR], 5.96 [95% CI, 5.11 to 6.96]), DFS (HR, 5.03 [95% CI, 4.36 to 5.81]), and OS (HR, 4.45 [95% CI, 3.76 to 5.27]; all P < .0001). The 5-year DFS was 27.7% (95% CI, 23.8 to 32.2) v 77.1% (95% CI, 75.1 to 79.1) in ctDNA-positive versus ctDNA-negative patients, and adverse prognostic impact was greater in lower T/N stage, low-risk, and dMMR subsets (interaction P = .0012-.041). Among ctDNA-positive patients, TF was nearly double in those who recurred or died (P = .0002) and stratified patients for TTR, DFS, and OS (all adjusted P < .002). Genotyping identified mutations in FLT1 (OR, 8.99) and PREX2 (OR, 7.73) genes that were most strongly associated with recurrence (P < .03). CONCLUSION: Evaluation of ctDNA in resected stage III CC using a tissue-free assay provided robust and independent prognostic value. Higher ctDNA burden, dMMR, and specific mutations defined poor prognostic groups among ctDNA-positive patients.
20 May 2026
Read appraisal →Journal of clinical oncology : official journal of the American Society of Clinical Oncology
Randomized Phase II/III Trial Comparing Hepatectomy, Followed by mFOLFOX6 With Hepatectomy Alone for Liver Metastasis From Colorectal Cancer: Long-Term Results of JCOG0603
JCOG0603 demonstrated improved disease-free survival (DFS) with adjuvant mFOLFOX6 after hepatectomy compared with hepatectomy alone in colorectal liver-only metastasis (CRLM), but the overall survival (OS) data were immature. Here, we report OS after long-term follow-up. Eligible patients with colorectal adenocarcinoma and an unlimited number of liver metastases were randomized to adjuvant mFOLFOX6 or hepatectomy alone. DFS was the primary endpoint, and OS was a secondary endpoint. Between March 2007 and January 2019, 151 and 149 patients were randomized to adjuvant chemotherapy and hepatectomy alone, respectively. At a median follow-up of 7.7 years for disease-free surviving patients, 54 (35.8%) and 51 (34.2%) patients had died in the respective arms, (hazard ratio [HR], 1.07 [95% CI, 0.73 to 1.57]). Five-year OS was 73.4% (95% CI, 65.5 to 79.7) and 80.1% (95% CI, 72.6 to 85.7) and 7-year OS was 69.4% (95% CI, 61.2 to 76.2) and 72.4% (95% CI, 64.2 to 79.1), respectively. One patient in the adjuvant chemotherapy arm possibly died of protocol treatment-related toxicity, and one in the hepatectomy-alone arm died of post-protocol treatment complications. Five-year DFS was respectively 49.7% (95% CI, 41.5 to 57.3) and 40.5% (95% CI, 32.5 to 48.3) in the adjuvant chemotherapy and hepatectomy-alone arms (HR, 0.72 [95% CI, 0.54 to 0.97]). Long-term OS did not differ with adjuvant mFOLFOX6 compared with hepatectomy alone in resectable CRLM. Adjuvant mFOLFOX6 may delay recurrence but did not improve long-term survival.
20 May 2026
Read appraisal →JAMA
Tucidinostat Plus R-CHOP vs R-CHOP in MYC/BCL2 Double-Expressor Diffuse Large B-Cell Lymphoma: A Randomized Clinical Trial
IMPORTANCE: Epigenetic dysregulation is associated with the pathogenesis and progression of diffuse large B-cell lymphoma (DLBCL). MYC/BCL2 double-expressor lymphoma (DEL), a distinct population of DLBCL defined by MYC and BCL2 coexpression, refers to poor prognosis after standard rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) immunochemotherapy. Tucidinostat (or chidamide), an oral, selective histone deacetylase inhibitor, has shown promising activity in DEL. OBJECTIVE: To evaluate efficacy and safety of tucidinostat plus R-CHOP vs R-CHOP alone as first-line treatment for patients with DEL. DESIGN, SETTING, AND PARTICIPANTS: This randomized, double-blind, placebo-controlled phase 3 trial enrolled patients from May 21, 2020, through July 25, 2022, with follow-up to June 26, 2025. The trial was conducted at 40 study centers in China; a total of 423 eligible patients were enrolled. INTERVENTIONS: Patients were randomly assigned in a 1:1 ratio to receive oral tucidinostat (20 mg on days 1, 4, 8, and 11 of each 21-day cycle) or matching placebo, plus 6 cycles of R-CHOP. Patients with a complete response after combination therapy received either tucidinostat or placebo maintenance up to 24 weeks. MAIN OUTCOMES AND MEASURES: The primary end point was event-free survival. Secondary end points included complete response rate, progression-free survival, disease-free survival, overall survival, and tolerability. RESULTS: Among 423 patients randomized (median age, 63 years; 47.5% male), the median follow-up duration from randomization was 41.3 months. The tucidinostat group demonstrated a 28% lower risk of disease progression, relapse after complete response, death, or initiation of new therapy for residual disease compared with the placebo group (stratified hazard ratio, 0.72 [95% CI, 0.54-0.96]; P = .02), with a 2-year event-free survival rate of 60.3% vs 50.5%, respectively. The complete response rate was 73.0% vs 61.8% (difference, 11.1% [95% CI, 2.3%-20.0%]), respectively. Increased toxicity associated with treatment was observed in the tucidinostat group but generally manageable with supportive care. CONCLUSIONS AND RELEVANCE: Tucidinostat plus R-CHOP significantly improved event-free survival, with manageable toxicity in patients newly diagnosed with DEL. This trial is the first to demonstrate the benefit of an epigenetic modulator in DLBCL, offering a new first-line therapeutic approach dually targeting MYC and BCL2 oncoprotein for this high-risk population. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04231448.
20 May 2026
Read appraisal →BMJ open
What are the views of cancer care administrators and clinicians in England on the use of a machine learning clinical decision support system (ML-CDSS) to predict patients' risk of hepatic and renal deterioration during chemotherapy? A qualitative study.
OBJECTIVE: To explore administrators' and clinicians' views on the factors that influence their use and adoption of a machine learning clinical decision support system (ML-CDSS) to predict patients' risk of hepatic and renal deterioration during chemotherapy. METHODS AND ANALYSIS: This was a qualitative study that used purposive sampling. 18 participants with administration and clinical backgrounds working in cancer care in England were recruited. Qualitative data were collected by conducting semi-structured interviews and a focus group. Data were analysed thematically using the framework method to identify key themes. RESULTS: Participants acknowledged that monitoring blood chemistry is a core component of chemotherapy as it helps clinicians assess patient fitness and treatment response. The ML-CDSS was perceived as a potentially valuable tool for identifying patients at increased risk of hepatic and renal deterioration, supporting clinical decision-making and enhancing care efficiency. However, several concerns were raised regarding its potential implementation in practice. Participants questioned clinicians' willingness and capacity to integrate the tool into their existing workflows. Participants also believed it was important to demonstrate the ML-CDSS's sensitivity, specificity and validity in accurately predicting patients' risk to build clinicians' trust in the tool, demonstrating evidence of its efficacy and effectiveness in practice. CONCLUSION: Administrators and clinicians recognised the potential benefits of the ML-CDSS to enhance the delivery of chemotherapy by identifying patients at risk for hepatic and renal deterioration. Successful adoption in practice depends on building trust with the tool by being transparent in its development, its effectiveness and impact. Future work should demonstrate the ML-CDSS being used in practice to generate real-world evidence.
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 →Cell reports methods
A network-based deep learning model integrating subclonal architecture for therapy response prediction in cancer
Predicting treatment response remains challenging in oncology, particularly given the growing diversity of therapeutic options. Despite efforts using gene expression signatures, or integrative multi-omics frameworks, robust and interpretable biomarkers remain limited. We present SubNetDL, a deep learning framework that integrates subclonal mutation profiles and protein-protein interaction networks via network propagation. Unlike condition-specific approaches, SubNetDL leverages somatic mutations alone and is applicable across diverse cancer types and treatment modalities. Applied to 10 TCGA cancer-drug combinations, SubNetDL achieved consistently strong performance (median area under the receiver operating characteristic curve [AUROC] = 0.74) and successfully generalized to two independent immunotherapy datasets (median AUROC = 0.77). Importantly, it identified candidate biomarker genes with treatment-specific relevance. SubNetDL prioritized genes that were not central in the network, highlighting its ability to capture context-specific patterns beyond traditional metrics. In conclusion, our approach offers a robust and interpretable framework for identifying predictive biomarkers and stratifying patients based on mutation profiles and network context.
20 May 2026
Read appraisal →Current oncology reports
Impact of 18-FDG PET/CT on Multidisciplinary Decision-Making and Management in Abdominal Malignancies: A Systematic Review and Meta-Analysis
PURPOSE: To systematically review and assess the impact of 18 F-FDG PET/CT on multidisciplinary team (MDT) decision-making and management in adults with abdominal malignancies. MATERIALS & METHODS: We searched Ovid MEDLINE, Ovid Epub, Cochrane CENTRAL, EMBASE, and Scopus upto August 31, 2024, for studies including 18-FDG PET/CT and MDT management in abdominal malignancies in adult patients and describing pre- and post-PET/CT treatment plans or MDT decisions. Data extraction followed by independent two reviewer assessment using ROBINS-I framework. A random-effects meta-analysis of proportions was performed for studies reporting management change attributable to PET/CT. RESULTS: Fourteen studies met inclusion criteria, of which seven provided sufficient quantitative data for meta-analysis. A random-effects meta-analysis of the seven eligible quantitative studies showed that 21.8% of patients (95% CI: 10.6%-33.0%, p < 0.001; range: 8-36%) experienced a management change directly attributable to PET-based imaging. Between-study heterogeneity was substantial (I² ≈ 85%, p < 0.001). Reported management changes included cancellation or addition of planned surgery, modification of surgical extent, change in treatment intent between curative and palliative strategies, and alteration of systemic therapy or radiotherapy plans. Evidence on downstream outcomes such as survival, quality of life, and cost-effectiveness was limited and heterogeneous, precluding quantitative synthesis. CONCLUSION: PET/CT leads to a change in MDT management in roughly one in five patients with abdominal malignancies, often through re-staging and identification of occult metastases that alter treatment intent or surgical strategy. The magnitude of impact varies widely by clinical context and tumor type. 1. PET/CT identifies occult metastases and clarifies disease extent, allowing MDTs to avoid futile surgeries and appropriately shift between curative and palliative strategies. 2. Approximately one in five patients experience a meaningful change in management after PET/CT, demonstrating its significant influence on real-world treatment planning. 3. Incorporating PET/CT into MDT discussions enhances diagnostic confidence and supports more coordinated, evidence-based care across surgical, oncologic, and radiologic teams. By highlighting which patients benefit most, the findings support more efficient use of PET/CT, emphasizing the need for standardized outcome measures and prospective data to refine patient selection.
19 May 2026
Read appraisal →Radiation oncology (London, England)
Comparative study between PET-CT simulation and CT for head and neck cancer treatment and their potential effect on the thyroid gland: a prospective comparative study
BACKGROUND: Radiotherapy (RT) plays a key role in multidisciplinary treatment of Head and neck cancers (HNCs). Over the past two decades, advances have improved RT's tumor control and toxicity profiles. Precise delineation of gross tumor volume and organs at risk using CT and PET-CT is crucial for safe and effective RT. Additionally, RT increases the risk of thyroid dysfunction, especially hypothyroidism. AIM: The present study compared PET-CT simulation and CT-alone planning in head and neck cancer (HNC) treatment, emphasizing their effects on thyroid gland function. METHODS: The patients were divided into two groups according to the simulation method (PET-CT or CT), two fresh blood samples (5 mL/ sample) were collected from each patient, before and after radiotherapy. TSH, FT3, and FT4 (Thyroid biomarkers) were assessed by electrochemiluminescent immunoassay (ECLIA). RESULTS: PET-CT simulation demonstrated a significant improvement in GTVp delineation accuracy by 71.8% compared with CT. After radiotherapy, TSH levels show minor decline changes, whereas FT3 and FT4 levels declined significantly, with greater declines observed in stages II and III. The most common tumor site was the larynx (60% in PET-CT, 75% in CT), followed by the tongue, nasopharynx, and parotid gland. CONCLUSION: PET-CT simulation offered superior GTVp delineation accuracy over CT, improving HNCs radiotherapy planning. Immediately after RT, radiotherapy caused significant declines in serum FT3 and FT4 levels, with only a minor, non-significant TSH declining, particularly in stage II/III disease. Notably FT4 is little more affected in PET-CT simulation patient group, highlighting the need for thyroid function monitoring. These results advocate PET-CT integration in planning and routine endocrine monitoring for treatment complications.
19 May 2026
Read appraisal →Journal for immunotherapy of cancer
Clinical outcomes and spatial transcriptomic profiles of CD19/20 CAR-T therapy in relapsed or refractory B-cell non-Hodgkin's lymphoma
BACKGROUND: Relapsed or refractory (R/R) B-cell non-Hodgkin's lymphoma (B-NHL) remains a major therapeutic challenge despite CD19-directed chimeric antigen receptor (CAR) T-cell therapies, with treatment failure often driven by antigen escape and limited CAR-T persistence. Dual targeting of CD19 and CD20 may mitigate antigen loss. We conducted an expanded phase I/II study of bispecific CD19/20 CAR-T cells and incorporated spatially resolved single-cell transcriptomics to evaluate tumor-intrinsic and microenvironmental factors of clinical response. METHODS: Patients with R/R B-NHL received CD19/20 CAR-T cells following lymphodepletion. Efficacy, safety, CAR-T expansion/persistence, and B-cell reconstitution were assessed. Pretreatment tumor biopsies from five patients with divergent outcomes underwent spatial single-cell transcriptomic profiling. RESULTS: 32 patients were treated, including 24 with diffuse large B-cell lymphoma. Among 31 evaluable patients, the best overall response rate was 74%, including 58% achieving complete remission. Median progression-free and overall survival were 6.8 and 22.1 months, respectively. Response rates were higher in patients with normal lactate dehydrogenase. CAR-T expansion peaked on days 7-17, and persistence exceeded 500 days in long-term responders. Cytokine release syndrome occurred in 53% (12% grade ≥3) and immune effector cell-associated neurotoxicity syndrome in 9% (all grade 3), with no lasting deficits. Spatial profiling identified two dominant tumor architectures: (1) a B-cell-dominant phenotype, in which durable remission was associated with heightened apoptotic competence in malignant B cells, and (2) a fibroblast-enriched and monocyte/macrophage-enriched phenotype, in which response correlated with a chemokine-rich, T-cell-permissive microenvironment. CONCLUSIONS: Bispecific CD19/20 CAR-T therapy produced durable clinical activity with manageable toxicity. Spatial single-cell analysis reveals distinct tumor-intrinsic and microenvironmental features associated with CAR-T responsiveness, providing a spatially informed framework for understanding heterogeneous therapeutic outcomes. TRIAL REGISTRATION NUMBER: NCT04723914.
18 May 2026
Read appraisal →Lancet (London, England)
Addition of autologous stem-cell transplantation to an ibrutinib-containing first-line treatment in patients aged 18-65 years with mantle cell lymphoma (TRIANGLE): 4·5-year follow-up of a three-arm, randomised, open-label, phase 3 superiority trial of the European MCL Network
BACKGROUND: Adding ibrutinib to standard, first-line immunochemotherapy improves failure-free survival in adult patients aged 18-65 years with mantle cell lymphoma, according to the first results from the TRIANGLE trial. With prolonged follow-up, we investigated whether the addition of autologous stem-cell transplantation (ASCT) to an ibrutinib-containing regimen improves failure-free survival, and evaluated effects on overall survival. METHODS: We conducted a three-arm, randomised, open-label, phase 3 superiority trial (TRIANGLE) in 165 secondary or tertiary clinical centres, with experience in mantle cell lymphoma treatment and the capability to perform ASCT or an association with such a centre, in 13 European countries and Israel. Patients aged 18-65 years with untreated, stage II-IV mantle cell lymphoma and suitable for ASCT were randomly assigned (1:1:1) to control group A or experimental groups A + I or I. Randomisation was done using computer-generated random numbers and stratified by study groups and Mantle Cell Lymphoma International Prognostic Index risk groups. Treatment in group A consisted of six alternating, 21-day cycles of R-CHOP (intravenous rituximab 375 mg/m2 on day 0 or 1, cyclophosphamide 750 mg/m2 on day 1, doxorubicin 50 mg/m2 on day 1, vincristine 1·4 mg/m2 on day 1 [up to a maximum of 2 mg], and oral prednisone 100 mg on days 1-5) and R-DHAP or R-DHAOx (intravenous rituximab 375 mg/m2 on day 0 or 1, intravenous or oral dexamethasone 40 mg on days 1-4, high-dose intravenous cytarabine 2 × 2 g/m2 for 3 h every 12 h on day 2, plus either intravenous cisplatin 100 mg/m2 over 24 h on day 1 [R-DHAP] or intravenous oxaliplatin 130 mg/m2 on day 1 [R-DHAOx]), followed by ASCT. In group A + I, oral ibrutinib (560 mg daily) was added on days 1-19 of R-CHOP cycles and as 2-year maintenance after ASCT. In group I, ibrutinib was given the same way, but ASCT was omitted. Rituximab maintenance was allowed in all treatment groups according to national guidelines. Three pairwise, one-sided, log-rank tests for the primary outcome (failure-free survival) were statistically monitored. The primary analysis was by intention to treat and included all randomly assigned patients, ignoring protocol deviations. Safety was assessed in randomly assigned patients who started any trial treatment component of the respective treatment phase. The trial is registered with ClinicalTrials.gov (NCT02858258) and is complete. FINDINGS: Between July 29, 2016, and Dec 28, 2020, 870 patients (662 [76%] were male, 208 [24%] were female) were randomly assigned to group A (n=288), group A + I (n=292), or group I (n=290). After median follow-up of 54·9 months (95% CI 54·4-56·0), group A + I did not show superiority over group I, with 4-year failure-free survival of 82% (95% CI 78-87) versus 81% (76-86; hazard ratio [HR] 0·86 [one-sided 98·33% CI 0·00-1·27]; one-sided p=0·21). Group A + I remained superior to group A (82% [78-87] vs 70% [65-76]; HR 0·63 [one-sided 98·33% CI 0·00-0·89]; one-sided p=0·0026) and, as before, group A did not show superiority over group I (70% [65-76] vs 81% [76-86]; HR 1·45 [one-sided 98·33% CI 0·00-2·02]; one-sided p=0·99). 4-year overall survival was 88% (95% CI 84-92) in group A + I versus 81% (76-85) in group A (HR 0·59 [95% CI 0·38-0·92], p=0·0036) and 90% (87-94) in group I versus 81% (76-85) in group A (0·57 [0·36-0·90], p=0·0019). During maintenance or follow-up, the most common grade 3-5 adverse events were haematological disorders, reported in 127 (54%) of 234 patients in group A + I versus 74 (28%) of 269 in group I and 56 (23%) of 240 patients in group A, and infections, reported in 80 (34%) of 234 patients in group A + I versus 71 (26%) of 269 in group I and 37 (15%) of 240 patients in group A. Infections and infestations were the most common fatal adverse events during maintenance or follow-up, occurring in four (2%) of 234 patients in group A + I and five (2%) of 269 patients in group I. INTERPRETATION: After a prolonged follow-up of 55 months, both ibrutinib-containing groups showed relevant improvements not only in failure-free survival-a modified form of progression-free survival-but also in overall survival. In contrast, the addition of ASCT to an ibrutinib-containing regimen had no supplementary benefit but increased toxicity. Induction treatment with ibrutinib and R-CHOP plus R-DHAP (or R-DHAOx), followed by 2 years of maintenance treatment with ibrutinib, should be considered as a new standard of care for younger patients with mantle cell lymphoma. FUNDING: Janssen.
18 May 2026
Read appraisal →Signal transduction and targeted therapy
HILL: the efficacy and safety of hepatic arterial infusion chemotherapy with the FOLFOX regimen combined with lenvatinib and the PD-L1 inhibitor durvalumab in unresectable hepatocellular carcinoma: a prospective, single-arm, phase 2 clinical trial
Despite numerous available treatment options, the overall prognosis for patients with unresectable hepatocellular carcinoma (uHCC) remains poor. This study aimed to evaluate the efficacy and safety of combining hepatic arterial infusion chemotherapy with FOLFOX (FOLFOX-HAIC), lenvatinib, and the PD-L1 inhibitor durvalumab in this population, with all uHCC patients receiving the triple-combination regimen as initial therapy. The primary objective was progression-free survival (PFS), and secondary outcomes included objective response rate (ORR), disease control rate (DCR), overall survival (OS), and safety. From August 2021 to September 2023, 40 uHCC patients were enrolled, with a median follow-up of 23.1 months (95% confidence interval [CI], 19.0-23.8); 3 deaths and 12 disease progressions were recorded. The median PFS was 15.8 months (95% CI, 8.3-23.3) with a 6-month PFS rate of 77.5% (95% CI, 63-90%), and 1- and 2-year OS rates were 97.5% and 94.0%, respectively. Per modified RECIST criteria, ORR was 75.0% (9 CR/pCR, 21 PR) and DCR was 95.0% (8 SD); median time to response (TTR) was 2.2 months and median duration of response (DOR) was 10.4 months. Seven patients (17.5%) underwent R0 conversion surgery, 3 (42.9%) achieving pCR. Safety was favorable: 85.0% had grade 1-2 adverse events, with grade 3 elevated alanine aminotransferase and thrombocytopenia each occurring in 2 patients (5.0%). No grade 4 adverse events were observed. Collectively, the triple-combination therapy of FOLFOX-HAIC, lenvatinib, and durvalumab exhibits promising therapeutic efficacy, favorable disease control, and a well-tolerated safety profile, providing a potential new treatment option for patients with uHCC. Trial number: NCT04961918.
18 May 2026
Read appraisal →Clinical cancer research : an official journal of the American Association for Cancer Research
PET/CT-Guided Neoadjuvant Tislelizumab plus Chemotherapy/Chemoradiotherapy for Resectable Esophageal Squamous Cell Carcinoma: RATIONALE-213 Final Analysis
PURPOSE: The aim of this study is to assess positron emission tomography/computed tomography (PET/CT)-guided neoadjuvant treatment with tislelizumab plus chemotherapy/chemoradiotherapy in patients with resectable esophageal squamous cell carcinoma (ESCC). PATIENTS AND METHODS: RATIONALE-213, a multicenter, open-label, two-cohort phase II study, enrolled 70 patients with resectable ESCC. After one induction chemotherapy cycle, patients were categorized as PET responders (PET maximum standardized uptake value decrease ≥35%) or PET nonresponders (<35%). Subsequently, all patients received three cycles of tislelizumab 200 mg intravenously. In addition, PET responders received chemotherapy and PET nonresponders received chemoradiotherapy as neoadjuvant treatment followed by surgery. The primary endpoint was the pathologic complete response (pCR) rate. RESULTS: As of October 25, 2024 (median follow-up 25.5 months), pCR rates were 30% [95% confidence interval (CI), 11.9-54.3] in PET responders and 34.4% (95% CI, 18.6-53.2) in PET nonresponders. Higher pCR rates were associated with higher baseline programmed cell death ligand 1 tumor area positivity scores in both cohorts. One-year disease-free survival rates were 79% and 74.2%, and event-free survival rates were 87.1% and 67.8%, respectively. Any-grade and grade ≥3 treatment-related adverse events (TRAE) occurred in 93.3% and 50% of PET responders and 97.5% and 82.5% of PET nonresponders, with no new safety signals. The most common grade ≥3 TRAE was decreased neutrophil count (36.7% and 70%, respectively). CONCLUSIONS: Tislelizumab with chemotherapy/chemoradiotherapy as neoadjuvant treatment for resectable ESCC yielded promising pCR rates and favorable survival outcomes with a tolerable safety profile in both PET/CT-guided responders and PET nonresponders.
18 May 2026
Read appraisal →European journal of cancer (Oxford, England : 1990)
Invasive lobular and ductal breast cancers: a systematic review and metanalysis in association with BRCA1/2 mutation status
BACKGROUND: Invasive lobular breast carcinoma (ILC) differs from invasive ductal breast carcinoma (IDC) with respect to genetic alterations and risk factors. We aimed to quantify differences in the prevalence of germline BRCA1/2 mutations between these two patients' populations. MATERIALS AND METHODS: We conducted a systematic literature search to extract data on the association between BRCA1/2 mutation status and breast cancer (BC) histological subtype (ILC and IDC). Summary odds ratios (SOR) and 95% CI were calculated using random effects univariate and bivariate models and between-study heterogeneity investigated through meta-regression and subgroup analyses. RESULTS: Twelve studies, published between 1998 and 2025, were considered, including 8004 BC women. BRCA1 mutations were significantly less frequent in patients with ILC than IDC (SOR=0.32, 95%CI (0.16-0.65)) whereas no association was found overall for BRCA2 (SOR=1.28, 95%CI (0.95-1.72)). The difference between the association with BRCA1 and BRCA2 was statistically significant (p-value<0.001). There was no indication of publication bias in BRCA1 (Egger's and Begg's test p-value=0.23, 0.12). The between study heterogeneity was 29% in BRCA1 and 0% in BRCA2. Excluding papers with high risk of bias, BRCA2 mutations were more frequent in patients with ILC than in IDC (SOR=2.56, 95%CI (1.15-5.7)). This association was primarily driven by the bivariate random-effects model, which accounts for the correlation between BRCA1 and BRCA2 estimates. CONCLUSIONS: In studies with lower risk of bias, germline BRCA2 mutations were more frequent in patients with ILC than IDC. Conversely, BRCA1 mutations are more frequently observed in IDC, particularly among younger patients and those with a positive family history of BC.
17 May 2026
Read appraisal →Clinical cancer research : an official journal of the American Association for Cancer Research
Pembrolizumab Monotherapy in Sorafenib-Treated and Treatment-Naïve Advanced Hepatocellular Carcinoma: Long-Term Follow-up of the Open-Label, Phase II KEYNOTE-224 Study
PURPOSE: In the phase II KEYNOTE-224 study, pembrolizumab showed durable antitumor activity and manageable safety in participants with sorafenib-treated (cohort 1) or treatment-naïve (cohort 2) advanced hepatocellular carcinoma (HCC). We present data after a median follow-up of ∼7 years for cohort 1 and ∼5 years for cohort 2. PATIENTS AND METHODS: Adults with advanced HCC received pembrolizumab 200 mg intravenously every 3 weeks until disease progression or unacceptable toxicity for ≤35 cycles. The primary endpoint was objective response rate per Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST v1.1) by blinded independent central review. Secondary endpoints included overall survival (OS), progression-free survival (PFS), and safety. RESULTS: Overall, 155 participants received ≥1 dose of pembrolizumab (cohort 1, n = 104; cohort 2, n = 51). The median follow-up was 83 months (range, 79.3-87.3) for cohort 1 and 58.8 months (range, 55.3-60.8) for cohort 2. The median OS was 13.2 months [95% confidence interval (CI), 9.7-15.3] and 16.9 months (95% CI, 8.3-23.1), respectively; 24- and 48-month OS rates were 31%/17% and 34%/20%, respectively. The median PFS was 4.9 months (95% CI, 3.5-7) and 4.3 months (95% CI, 2.1-7.8), respectively. Treatment-related adverse events occurred in 76 participants [73.1%; grade 3-5, 27 (26%)] in cohort 1 and 28 participants [54.9%; grade 3-5, 8 (15.7%)] in cohort 2. CONCLUSIONS: Pembrolizumab continued to show durable response and manageable safety in participants with advanced HCC with or without prior systemic therapy, with long-term effects on OS lasting beyond 48 months in some participants despite receiving study treatment for ≤2 years.
17 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 →European journal of cancer (Oxford, England : 1990)
Aflibercept and 5-FU vs. FOLFOX as 1st line treatment for older adults or frail elderly patients with metastatic colorectal cancer - The randomized phase 2 AIO / IKF ELDERLY trial (AIO-KRK-0117 / IKF 629)
BACKGROUND: Fluoropyrimidines + angiogenesis inhibitor or dose-reduced doublets are used for (frail) elderly patients with metastatic colorectal cancer (mCRC). This non-comparative trial evaluated two 1st line regimens for patients not eligible for full-dose combinations. METHODS: This open-label, multicenter, phase II study randomized untreated mCRC patients ≥ 70 years ± frailty 1:1 to dose reduced mFOLFOX7 in Arm A or aflibercept (4 mg/kg) plus dose reduced mLV5FU2 in Arm B. Primary endpoint was the 6-months PFS rate (PFS@6), secondary endpoints included safety, overall survival (OS), overall response rate (ORR), patient reported outcomes and overall treatment utility (OTU). FINDINGS: The full analysis population comprised 120 patients (62 Arm A, 58 Arm B; 63% male), with a median age of 78.5 years. 82% frail patients were included. PFS@6 was 68% [95%CI 56%;80%] in Arm A and 46% [32%;59%] in Arm B. Median PFS was 7.9 and 5.5 months, median OS was 20.4 and 19.0 months and ORR was 47% and 22% in Arm A and B, respectively. At 3 months, 52% in Arm A and 35% in Arm B had a good OTU. Most frequent grade ≥ 3 AEs in Arm B were hypertension (41%) and proteinuria (10%). More patients in arm B had treatment-related SAEs grade ≥ 3 (17% in arm B and 5% in arm A). INTERPRETATION: Both regimens were feasible in this (frail) elderly population. Dose-reduced mFOLFOX7 and, to a lesser extent, 5-FU/aflibercept showed good PFS and 3-month OTU. Although most patients were regarded frail, survival rates were promising, most likely due to effective further-line treatments.
17 May 2026
Read appraisal →Clinical cancer research : an official journal of the American Association for Cancer Research
Efficacy and Safety of Belantamab Mafodotin with Bortezomib plus Dexamethasone in Patients with Relapsed/Refractory Multiple Myeloma: The DREAMM-6 Arm B Trial
PURPOSE: The phase I/II DREAMM-6 arm B study (NCT03544281) explored belantamab mafodotin combined with bortezomib/dexamethasone (BVd) in relapsed/refractory multiple myeloma (RRMM). PATIENTS AND METHODS: Adults with RRMM were enrolled sequentially in two belantamab mafodotin (intravenous) dose-escalation (DE) cohorts (2.5 then 3.4 mg/kg every 3 weeks). Additional patients enrolled sequentially to eight dose-expansion cohorts: 1.9, 2.5, or 3.4 mg/kg every 3 weeks; 2.5 or 3.4 mg/kg every 3 weeks split dose (days 1 and 8); 1.9 or 2.5 mg/kg every 6 weeks; or 2.5 mg/kg in cycle 1 stepped down to 1.9 mg/kg every 6 weeks thereafter. Patients received bortezomib twice weekly and dexamethasone four times weekly. Endpoints were dose-limiting toxicities (DLT; DE), adverse events (AE), serious AEs (SAE; DE and expansion), overall response rate (ORR; expansion), and pharmacokinetics. RESULTS: One hundred seven patients (median 4 prior lines of therapy) received BVd (n = 12-18/cohort). The median follow-up was 15.2 to 25.4 months. No DLTs occurred during DE. The most common grade 3/4 AE was keratopathy (53%). Protocol-defined ocular events (change in best corrected visual acuity and/or corneal examination findings) were reported in 93% of patients (grade 3/4: 77%). Twenty-eight (26%) patients experienced any study treatment-related SAEs; 3 of 7 fatal SAEs had a treatment-related primary cause. The ORR was 70% (95% confidence interval, 60.5-78.6). Higher initial exposures had higher probabilities of response and ocular events; lower exposures resulted in fewer deep responses, with small differences in ocular events. CONCLUSIONS: BVd demonstrated manageable safety and clinical activity across all dosing cohorts in heavily pretreated RRMM, supporting the 2.5 mg/kg every 3 weeks dose.
17 May 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Effects of activity pacing-related interventions on cancer-related fatigue and classification of behavior change techniques: A systematic review and meta-analysis
PURPOSE: Although physical activity is recommended for managing cancer-related fatigue (CRF), activity pacing (AP) and energy conservation are also implemented to balance activity and rest. This systematic review assessed the effectiveness of AP-related interventions and behavior change techniques (BCTs) employed in AP-related interventions for CRF management. METHODS: We searched PubMed, CINAHL, CENTRAL, and Ichu-shi databases for articles published up to February 28, 2026. After assessing the risk of bias, we performed meta-analyses for quantitative synthesis. We also identified and classified BCTs using data from the Behavior Change Technique Taxonomy version 1. RESULTS: Among the 1257 identified studies, 10 (7 randomized controlled trials [RCTs] and 3 non-RCTs) met the inclusion criteria. The effect size was small but not significant (standardized mean differences [SMD] = - 0.36; 95% confidence intervals [CI]: - 0.73 to 0.00; 5 studies; 617 participants), with substantial heterogeneity (I2 = 66%). Compared with controls, the intervention showed a moderate, statistically significant effect in the subgroup of patients undergoing chemotherapy (SMD = - 0.58; 95% CI: - 0.87 to - 0.29; three studies; 197 participants with breast cancer). The primary BCTs used in AP-related interventions were "goal setting," "social support," and "graded tasks." CONCLUSION: AP-related interventions may provide some benefit in improving CRF, particularly during chemotherapy. However, the certainty of the current evidence is extremely low. Although these interventions represent useful approaches, further RCTs are needed, and digitization of AP-related intervention programs incorporating effective BCTs may be beneficial.
17 May 2026
Read appraisal →Clinical cancer research : an official journal of the American Association for Cancer Research
Circulating Tumor DNA Dynamics and Clinical Outcomes in Patients with Advanced Colorectal Cancer Treated with Cetuximab-Based Induction and Maintenance Treatment
PURPOSE: We investigated whether circulating tumor DNA (ctDNA) changes may be useful to assess clinical outcomes in patients with metastatic colorectal cancer (mCRC) randomized in the TIME-PRODIGE-28 trial comparing biweekly maintenance with cetuximab alone with observation after 4-month fluorouracil, folinic acid, and irinotecan (FOLFIRI) plus cetuximab induction chemotherapy. EXPERIMENTAL DESIGN: ctDNA samples were collected at four time points from baseline until disease progression during the first chemotherapy-free interval and analyzed using next-generation sequencing and methylation marker approaches. Progression-free survival (PFS) and overall survival (OS) from randomization were analyzed according to ctDNA kinetics and EGFR-MAPK pathway alterations. RESULTS: Among 139 randomized patients, 104 (74.8%) had paired samples available. Patients with negative baseline ctDNA remaining negative after 4-month induction chemotherapy had significantly longer PFS from randomization (9.6 months) as compared with patients with a ctDNA decrease of ≥80% (3.4 months) or a ctDNA decrease of <80% (2.1 months; P = 0.013). Patients with EGFR-MAPK pathway alterations identified either in tissue or baseline ctDNA had worse PFS and OS from randomization. Acquired alterations found in 17 of 63 (26.9%) patients at disease progression during the first chemotherapy-free interval were associated with worse OS from reintroduction of the full induction chemotherapy (14.9 vs. 19.4 months; P = 0.025). CONCLUSIONS: Our findings show the prognostic impact of both ctDNA kinetics and EGFR-MAPK pathway alteration dynamics following induction chemotherapy with FOLFIRI-cetuximab in patients with mCRC. Prospective studies evaluating ctDNA-guided treatment strategies are needed to validate the clinical utility of ctDNA monitoring to improve patient selection for first-line treatment de-escalation and anti-EGFR-based maintenance regimens, including treatment adaptation over time.
16 May 2026
Read appraisal →International immunopharmacology
NY-ESO-1 in Triple-Negative Breast Cancer: Systematic Review, Meta-Analysis, and Immunotherapeutic Implications
Breast cancer remains one of the most common types of cancer, including the subtype triple-negative breast cancer (TNBC) which is challenging to treat due to its aggressiveness. Cancer-testis antigens (CTAs), especially New York Esophageal Squamous Cell Carcinoma 1 (NY-ESO-1), have become promising immunotherapeutic targets attributable to their restricted expression profile in normal tissues but overexpressed in TNBC, leading to immunogenicity. This review provides comprehensive discussion of NY-ESO-1 including its structural basis of NY-ESO-1 recognition by T cell receptors (TCRs) and antibodies, epigenetic regulation via DNA methylation or histone modifications, and expression patterns or clinical relevance in TNBC. A systematic meta-analysis of 12 studies comprising 1,545 TNBC patients showed a pooled NY-ESO-1 expression prevalence of 16.1% (95% CI: 11.4-22.2%), with heterogeneity (I2=83.7%) attributable to antibody clone used for NY-ESO-1 detection (E978: 15.1% vs. D8.38: 32.6%, p<0.0001) and scoring methods (composite scoring: 15.9% vs. dual scoring: 16.9% vs. H-score: 10.1% vs. simple threshold: 32.6%, p<0.001). Structural analyses reveal NY-ESO-1157-165 recognition by TCRs, antibodies, and engineered binding scaffolds. We examine preclinical and clinical evidence for NY-ESO-1-targeted therapies, including adoptive cell therapy and peptide vaccines, which show manageable safety profiles and induce immunological responses. Epigenetic regulation is a therapeutic avenue, whereby hypomethylating agents and histone deacetylase inhibitors can upregulate NY-ESO-1 expressions that promote tumor immunogenicity. Nonetheless, challenges persist such as NY-ESO-1 expression heterogeneity, lack of TNBC-specific clinical trials, and inadequate immunogenicity. Future research should standardize NY-ESO-1 detection protocols to identify TNBC patients for NY-ESO-1-targeted immunotherapy, prioritize TNBC-specific trials and combinations with epigenetic priming agents.
16 May 2026
Read appraisal →Blood
Evolution of tumor subclones and T-cell dynamics underlie variable ibrutinib responses in Waldenström macroglobulinemia
To elucidate the molecular basis underlying differential responses and resistance to ibrutinib in Waldenström macroglobulinemia (WM), we conducted a prospective phase 2 trial of ibrutinib monotherapy in treatment-naïve patients. A total of 74 sequential bone marrow (BM) aspirates from 17 patients, collected from baseline through 48 treatment cycles, were profiled using single-cell multiomics. BM cells were segregated primarily into B-cell/plasma cell and T-cell compartments. Longitudinal clonal tracking of malignant B cells/plasma cells identified 3 distinct evolutionary patterns: evolution (early clone contraction with late clone expansion and increasing genomic complexity), devolution (early clone expansion with late clone contraction and genomic simplification), and no evolution (stable clonal architecture). The evolution pattern was strongly associated with disease progression, whereas devolution correlated with durable clinical response. Transcriptomic profiling of resistant clones enabled development and validation of the Waldenström ibrutinib prediction (WIP) score, which predicted treatment response at baseline. Within the WIP signature, LYN emerged as a key regulator; LYN knockdown or inhibition significantly increased WM cell sensitivity to ibrutinib, suggesting a rational combination strategy. In parallel, GZMB+ CD8+ effector-memory T cells expanded after treatment in patients with progressive disease and coexisted with tumor evolution. These cells exhibited persistently impaired cytotoxic programs (eg, GNLY), a dedifferentiated memory-like state, elevated PDCD1 expression, and reduced T-cell receptor diversity. Together, this study provides, to our knowledge, the first single-cell framework of tumor clonal evolution and T-cell dysfunction under ibrutinib in WM, introduces the WIP score as a predictive biomarker for treatment response, and identifies actionable tumor-intrinsic and immune mechanisms driving resistance. This trial was registered at www.ClinicalTrials.gov as NCT02604511.
16 May 2026
Read appraisal →Computer methods and programs in biomedicine
Explainable machine learning framework for the molecular classification of triple negative breast cancer
BACKGROUND AND OBJECTIVE: The difference in molecular characteristics of Triple negative breast cancer (TNBC) aids in distinguishing between its four prominent subtypes- basal-like 1, basal-like 2, mesenchymal, and luminal androgen receptor. This study presents the first integrative framework that combines explainable AI with machine learning approaches to classify TNBC subtypes. Unlike conventional models, our approach offers interpretability while enabling biomarker prioritization by identifying key hub genes that drive subtype-specific predictions. METHODS: In the experiment 783 cases (BL1 (160), BL2 (75), M (151), LAR (106), non-TNBC (291) reported in Gene Expression Omnibus (GEO) and Genomic Data Commons (GDC) data portal were used for the analysis. The proposed framework comprises modules for the identification of gene signatures for the four-subtype followed by the classification model based on eight different machine learning algorithms. Random Forest classifier was found to be best model with 96 % testing accuracy, which was elected for Explainable framework using Shapley Additive Explanations. RESULTS: Explainable biomarker module could provide a set of 47 biomarkers which is relevant in distinguishing the four types on triple negative breast cancer. The biomarkers could have the potential to be considered for TNBC prognosis in clinical setting. CONCLUSION: Key findings highlight the hub genes CDC20, CDCA2, PIMREG, KIF2C, and CENPW, implicating pathways such as ubiquitin-proteasome signaling and microtubule dynamics. These insights pave the way for biomarker-driven therapies and precision medicine in triple negative breast cancer.
16 May 2026
Read appraisal →European journal of cancer (Oxford, England : 1990)
Integrating liquid biopsies and artificial intelligence for early cancer detection: A systematic review and meta-analysis
INTRODUCTION: The latest generation of liquid biopsies incorporates multi-omic features, including genomics, methylomics, and fragmentomics. Machine learning (ML) approaches have been proposed to synthesize these complex biological data for the development of diagnostic classifiers. This study aims to evaluate the integration of ML with circulating cell-free DNA (cfDNA) analysis for early cancer detection. METHODS: Medline, Embase, Cochrane, and Web of Science were searched in July 2025. Eligible studies combined ML and cfDNA features to distinguish cancer patients (stages I-III) from non-cancer controls. Summary diagnostic performance metrics and their 95% confidence intervals (CI) were calculated. RESULTS: The study included 109 articles permitting analyses for lung (n = 34), liver (n = 29), colorectal (n = 28), pancreatic (n = 16), breast (n = 17), esophageal (n = 12), ovarian (n = 13), gastric (n = 9), head and neck (n = 4), and mixed (n = 27) cancer types. Specificity was consistently high across all tumor types and stages (94%-99%). Sensitivity ranged from 72% to 92% for stage I-III, 44-91% for stage I, 71-98% for stage II and 83-99% for stage III. In the pooled study population, neural networks (90%, 95% CI: 81%-95%), random forest (86%, 95% CI: 77%-92%) and heterogeneous ensemble learning (85%, 95% CI: 79%-89%) demonstrated the highest sensitivity. The stratified analysis by classifier feature revealed 86% (95% CI: 80%-90%) sensitivity for fragmentation and 81% (95% CI: 76%-85%) for methylation, with 92%-96% specificity. CONCLUSION: ML and cfDNA profiling show potential for early cancer detection, with ensemble methods, neural networks and random forests achieving the best overall performance. Fragmentomic features provide the highest sensitivity.
16 May 2026
Read appraisal →JMIR research protocols
A Natural Language Processing Framework for Structuring and Visualizing Clinical Trial Eligibility Criteria at Scale: Protocol for a Quantitative Study
BACKGROUND: Eligibility criteria are essential to clinical trial design, guiding recruitment, and ensuring patient safety and scientific rigor. However, criteria are often lengthy, heterogeneous, and inconsistently formatted, which hinders large-scale interpretation and slows patient-trial matching. Manual review is time-consuming and error-prone. Advances in natural language processing and large language models (LLMs) offer opportunities to standardize and analyze eligibility text at scale. OBJECTIVE: This study aims to develop and evaluate a scalable system that uses LLM-enabled natural language processing and unsupervised learning to identify, normalize, categorize, and visualize clinical trial eligibility criteria, with the goal of improving patient-trial matching and revealing domain-level trends. METHODS: We designed a three-part pipeline: (1) representation of eligibility text using embeddings, followed by clustering to group semantically similar criteria; (2) dual-layer zero-shot LLM summarization for concept normalization, refinement, and deduplication of cluster exemplars; and (3) an interactive, web-based visualization interface to explore criteria distributions and trends by disease domain and over time. The pipeline was applied to 53,872 oncology trials (breast, lung, and gastrointestinal cancer) indexed on ClinicalTrials.gov. Outputs include cluster labels, normalized criterion summaries, and per-domain frequency profiles. Feasibility was assessed via successful end-to-end processing and inspection of face validity for cluster coherence and domain-specific patterns. RESULTS: The system successfully processed all 53,872 trials and generated stable clusters of inclusion and exclusion concepts. The LLM summarization layers produced concise, nonredundant labels that improved the interpretability of clustered criteria. The visualization interface enabled rapid exploration of cross-trial patterns and temporal trends within breast, lung, and gastrointestinal oncology, facilitating identification of common inclusion requirements and potential barriers to enrollment. A public, open-source demonstration instance allows for interactive exploration of these clusters and summaries. Benchmarking through human validation on a random sample of eligibility criteria found the system to be 94% (470/500) accurate, reflecting its ability to consistently categorize criteria correctly in congruence with human judgment. CONCLUSIONS: A combined embeddings-clustering-LLM pipeline can standardize heterogeneous eligibility text and surface domain-level patterns at scale. This framework provides a foundation for accelerating patient-trial matching and informing future trial design. While the current implementation was evaluated on ClinicalTrials.gov oncology trials, the approach is readily generalizable to additional diseases and alternative modeling configurations.
16 May 2026
Read appraisal →Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation
Health-related quality of life domains relevant to people in Europe undergoing cancer treatment: a systematic review of qualitative research
PURPOSE: To identify and synthesize evidence from European qualitative studies on cancer-related quality of life outcomes, needs, experiences, preferences, and concerns of people undergoing cancer treatment in the last decade. METHODS: Systematic review ( https://www.crd.york.ac.uk/PROSPERO , CRD42024575065) of European studies using qualitative methodology, assessing constructs related to HRQoL, and involving adults receiving cancer treatment. The search was performed in PubMed and Scopus from January 2013 to July 2024. Titles, abstracts, and full texts screening, data extraction and risk of bias assessment were conducted independently by two researchers. The main outcomes were the themes reported in each study. The thematic analysis was performed by organizing the themes of the studies into categories. RESULTS: Out of 18,256 articles initially identified, 36 met the inclusion criteria: 21 with generic and 15 with specific objectives. Five categories encompassing 110 themes were identified from the generic studies: Psychological Function (n = 41), Clinical Management (n = 26), Symptoms and Physical Function (n = 18), Social Function (n = 16), and Life Disruption (n = 9). Eleven studies with specific objectives focused on clinical management with all their themes fitting within the categories identified in the generic studies. CONCLUSIONS: Results showed the predominance of psychological function and clinical management themes. Symptoms and physical function, social function, and life disruption maintained their importance within the classical HRQoL framework. The emergence of clinical management is consistent with the growing patient-centered care approach, suggesting the need to integrate this content into the evaluation of patients undergoing cancer treatment. LIMITATIONS: most European countries were not represented, and publication bias could hide traditional domains.
15 May 2026
Read appraisal →Blood advances
Early mortality with bispecific antibody therapy in RRMM: an IMWG immunotherapy database real-world analysis
Recent therapeutic advances, including bispecific antibodies (bsAbs), have improved outcomes for patients with relapsed/refractory multiple myeloma (RRMM). However, early mortality (EM) remains a significant challenge, even in the era of novel immunotherapies. We conducted a multicenter, retrospective analysis of real-world patients with RRMM treated with commercial bsAbs to evaluate the incidence and causes of EM. Between May 2022 and June 2025, a total of 441 patients were treated across 7 countries. EM, defined as death within 12 months of therapy initiation, occurred in 148 patients (34%), with a median time from bsAb initiation to death of 2.87 months (interquartile range, 1.38-6.85). The leading cause of EM was progressive disease (72%), followed by infection (13%); regardless of the cause of death, 82% had active disease at the time of death. Our findings highlight that despite encouraging efficacy, EM remains high among real-world bsAb recipients, emphasizing the need for improved disease control strategies and supportive care interventions.
13 May 2026
Read appraisal →Blood advances
Belantamab mafodotin, carfilzomib, lenalidomide, and dexamethasone for relapsed or refractory multiple myeloma
Belantamab mafodotin (belamaf), an antibody-drug conjugate targeting B-cell maturation antigen, has demonstrated single-agent activity in relapsed/refractory multiple myeloma (RRMM) but is associated with ocular toxicity. This phase 1/2 study evaluated the safety and preliminary efficacy of belamaf administered every 8 weeks in combination with carfilzomib, lenalidomide, and dexamethasone (KRd-b) in patients with RRMM. Eligible patients had received at least 1 previous line of therapy and had not progressed on full-dose lenalidomide. In the dose-escalation phase (3+3 design), belamaf was administered at 1.4 or 1.9 mg/kg every 8 weeks. The recommended phase 2 dose was 1.9 mg/kg, with no dose-limiting toxicities at this level. Among 19 patients treated, the overall response rate was 89.5%, and 78.9% achieved very good partial response or better, with minimal residual disease negativity in all complete responders. At a median follow-up of 19.3 months, 24-month progression-free survival and overall survival rates were 74.3% and 85.1%, respectively. Keratopathy occurred in 94.7% of patients but was mostly grade 1 to 2, reversible, and manageable without permanent discontinuation. Grade ≥3 keratopathy (31.6%) was comparable with historical rates despite the less frequent dosing. Other adverse events, including hematologic and gastrointestinal toxicities, were manageable and consistent with known profiles of KRd or belamaf. The combination demonstrated deep and durable responses, including in patients at high-risk and with lenalidomide maintenance dose-refractory disease. These results support the feasibility of KRd-b with extended-interval belamaf and warrant further evaluation in phase 2 trials to confirm its clinical benefit in RRMM. This trial is registered at www.clinicaltrials.gov as NCT04822337.
13 May 2026
Read appraisal →Journal of chemical information and modeling
mDADGAN: Predicting miRNA-Drug Response Associations Using lncRNAs and a Diffusion-Based Generative Adversarial Network
MicroRNAs (miRNAs) are key regulators of anticancer drug response, including sensitivity and resistance. Accurate prediction of miRNA-drug response associations is important for understanding response mechanisms and guiding targeted therapy. Existing prediction methods are difficult to fully capture complex high-order biological interactions and are sensitive to data sparsity and class imbalance. In this paper, we present an miRNA-drug response association prediction method called mDADGAN by constructing a diffusion-based generative adversarial network. First, we curated a large-scale and biologically informative data set from four public data resources by aggregating functional associations to facilitate model training. Second, we built a miRNA-lncRNA-drug interaction network by integrating lncRNA into the miRNA-drug network to address data sparsity and employed heterogeneous graph convolution to extract latent features from the interactions among miRNAs, lncRNAs, and drugs. Third, we applied diffusion-based perturbation to denoise and enhance the robustness of feature representations. Finally, we performed cost-sensitive learning to optimize the objective function in an adversarial training process to address class imbalance. The experimental results on four data sets demonstrated that, compared with the existing six methods, mDADGAN achieved higher values for AUC, AUPR, ACC, Recall, and F1-score. The case study demonstrates that integrating lncRNA information effectively enhances miRNA-drug response association prediction. mDADGAN can not only accurately identify known associations in the data sets but also predict previously unreported candidate associations supported by published evidence. Overall, our method mDADGAN has the ability to identify potential miRNA-drug response associations and can provide guidance for future biological experiments to enhance experimental efficiency and reduce experimental costs.
13 May 2026
Read appraisal →Journal of clinical oncology : official journal of the American Society of Clinical Oncology
Phase I Study of Mosperafenib, a Novel Paradox Breaker B-Raf Proto-Oncogene Serine/Threonine Kinase (BRAF) Inhibitor, in Patients With BRAF V600-Mutant Solid Tumors
PURPOSE: BRAF V600 mutations are tumor-agnostic oncogenic drivers whose targeted inhibition is challenged by therapeutic resistance. Mosperafenib is a novel paradox breaker and brain-penetrant BRAF inhibitor (BRAFi), tested in this study for safety, maximum tolerated dose (MTD), pharmacokinetics (PK), and preliminary clinical activity. PATIENTS AND METHODS: This phase Ia/b study was conducted in patients with advanced solid tumors harboring a BRAF V600 mutation (ISRCTN13713551). The modified continuous reassessment method guided dose escalation of mosperafenib, which was given orally up to 3,600 mg once daily in 28-day cycles. The primary objective was to estimate the MTD and/or recommended phase II dose(s). RESULTS: Eighty patients (60% BRAFi-exposed)-63 with colorectal cancer (CRC), 13 with melanoma, and 4 with other solid tumors-received ≥1 dose of mosperafenib as a single agent for a median of 3.7 months (range, 0.2-28.6). Two dose-limiting grade 3 toxicities of rash and rash maculopapular were reported. MTD was not reached. Grade 3 to 4 treatment-related adverse events (TRAEs) occurred in 13 patients (16.3%); no grade 5 TRAEs events were reported. Two patients (2.5%) discontinued study treatment due to TRAEs. There were no reports of palmar-plantar erythrodysesthesia or keratoacanthoma. Linear and time-independent PK was demonstrated across the tested dose range, achieving exposure levels with sustained PK-derived pERK inhibition ≥90%. An exposure-response relationship was observed. Overall response rate was 24.2%, including two complete responses and 14 partial responses. Median progression-free survival was 6.4 months in patients with CRC (200 mg once daily-1200 mg three times a day) and 3.5 months in patients with melanoma (200 mg once daily-800 mg twice a day). CONCLUSION: Mosperafenib demonstrated a favorable safety profile, sustained target inhibition, and early signs of clinically meaningful single-agent activity in BRAFi-naïve and BRAFi-exposed patients.
13 May 2026
Read appraisal →Neurosurgical review
Effectiveness and safety of proton therapy in intracranial meningioma treatment: a systematic review and meta-analysis
Meningiomas are the most common primary intracranial tumors, often treated surgically. However, complete resection is frequently limited by proximity to critical structures, necessitating adjuvant or definitive radiotherapy. Proton therapy offers dosimetric advantages over photon-based radiotherapy, particularly in sparing adjacent normal tissues. This study aims to systematically evaluate the effectiveness and safety of proton therapy for intracranial meningiomas across tumor grades and clinical scenarios. A systematic review and meta-analysis was conducted according to PRISMA 2020 guidelines using PubMed, EMBASE, Scopus, Web of Science, and Cochrane from inception to November 10, 2025. Studies were eligible if they reported clinical outcomes of proton therapy in ≥ 10 adult meningioma patients. Data extraction and risk-of-bias assessment were performed independently by two reviewers. Pooled complication rates and survival outcomes were calculated using random-effects models. Nineteen studies involving 1,431 patients were included. WHO Grade I tumors comprised 70.6% of cases; Grades II/III made up 25.2% and 4.2%, respectively. The most common proton dose regimens ranged from 13 to 70.2 Gy (RBE). The pooled complication rate was 16% (95% CI 5-27; p < 0.001; I² = 98.5%). Nine studies reported a statistically significant 5-year overall-survival proportion of 91% (95% CI 88-94; p < 0.001; I² = 49.3%). Radiologic local control averaged 71% (95% CI 50-86; I² = 88.2%). Proton therapy provides effective tumor control with acceptable toxicity, especially for low-grade or anatomically complex meningiomas. It is a valuable option for select patients, though further prospective studies are needed to optimize dosing and assess long-term outcomes.
13 May 2026
Read appraisal →The oncologist
Characterization and clinical management of adverse events following treatment with repotrectinib: a TRIDENT-1 analysis
BACKGROUND: Repotrectinib, a next-generation ROS1/TRK tyrosine kinase inhibitor, is approved for ROS1 fusion-positive non-small cell lung cancer and NTRK fusion-positive solid tumors. Its side effects and safety management strategies require further characterization. PATIENTS AND METHODS: The safety profile of repotrectinib (treatment-emergent/related adverse events [TEAEs/TRAEs]) was established in patients who initiated treatment at the recommended dose (160 mg daily [QD] for 14 days, then 160 mg twice daily [BID]) across all cohorts of the global, multicenter phase 1/2 TRIDENT-1 study. AE management strategies were outlined. RESULTS: In 472 patients, the most common TRAEs (dizziness [58%] and dysgeusia [50%]) were likely TRK inhibition-related. Median relative dose intensity was 90%; 14% (n = 66/472) of patients did not increase their initial QD dose to BID (mostly due to CNS AEs). Rates of dizziness (median onset, 7 days) were similar in patients with/without baseline brain metastases. Dose modifications downgraded severity or resolved dizziness in 78% of patients; 58% of patients had pharmacologic intervention without dose modification. Dizziness was downgraded/resolved in 62% (n = 120/195) of patients who did not receive dose modification or pharmacologic intervention. Treatment-related cognitive impairment and weight gain occurred in 19% and 12% of patients, respectively. Treatment-emergent withdrawal pain occurred in 14% of patients (median resolution time, 2.1 weeks). Dose interruption and reduction from TRAEs occurred in 39% and 38% of patients, respectively; 10% reported later re-escalation back to 160 mg BID. CONCLUSION: Many repotrectinib AEs, including neurological AEs secondary to TRK inhibition, were mitigated with appropriate management, including dose modification and/or pharmacologic intervention.
12 May 2026
Read appraisal →Current oncology reports
Physicians' Knowledge and Experiences of Counseling on Complementary, Alternative, and Integrative Medicine in Cancer Care: a Qualitative Systematic Review
PURPOSE OF REVIEW: The increasing use of complementary, alternative, and integrative medicine (CAIM) among patients with cancer raises questions about physicians' knowledge and counseling practices in this area. This qualitative systematic review used a narrative synthesis approach to explore physicians' understanding and experiences with CAIM counseling in cancer care. RECENT FINDINGS: A systematic search of MEDLINE, EMBASE, and AMED, supplemented by hand-searching reference lists, identified 35 eligible studies. Analysis revealed four key themes: limited knowledge and training on CAIM; concerns about CAIM safety and efficacy; recognition of CAIM's role in holistic cancer care; and challenges in patient-physician communication regarding CAIM. Physicians acknowledge the importance of CAIM in person-centered cancer care but express concerns about its safety and effectiveness, underscoring the need for improved CAIM education and training. Future research should include perspectives from medical trainees and other healthcare providers to enhance counseling practices and support integrative cancer care.
12 May 2026
Read appraisal →Journal of clinical oncology : official journal of the American Society of Clinical Oncology
Radiographic Progression With and Without Prostate-Specific Antigen Rise in Patients With Advanced Prostate Cancer Treated With Enzalutamide
PURPOSE: In patients who received enzalutamide, we characterized the development of radiographic progression (rPD) without prostate-specific antigen (PSA) rise or progression in metastatic hormone-sensitive prostate cancer (mHSPC) and nonmetastatic castration-resistant prostate cancer (nmCRPC). METHODS: We conducted a post hoc analysis in two phase III trials (N = 2,551; ARCHES [ClinicalTrials.gov identifier: NCT02677896]; PROSPER [ClinicalTrials.gov identifier: NCT02003924]) for co-occurrence of rPD ± Prostate Cancer Working Group 2/3-defined PSA progression, and rPD ± any PSA rise from baseline/nadir by treatment. Kaplan-Meier methods were used for overall survival (OS) outcomes. RESULTS: In ARCHES, 3.5% and 8.5% of 574 patients with mHSPC treated with enzalutamide plus androgen-deprivation therapy (ADT) had no PSA rise or PSA progression at rPD, respectively. Of 79 patients with rPD who received enzalutamide plus ADT, 25.3% had no PSA rise and 62.0% did not meet PSA progression criteria compared with 7.4% and 38.3% of 188 patients with rPD treated with ADT alone, respectively. In PROSPER, 4.4% and 10.3% of 933 patients with nmCRPC treated with enzalutamide plus ADT had no PSA rise or PSA progression, respectively, at rPD. However, of 187 patients with rPD who received enzalutamide plus ADT, 21.9% had no PSA rise and 51.3% did not meet PSA progression criteria compared with 3.6% and 18.8% of 224 patients treated with placebo/ADT, respectively. Liver metastases were >5-fold higher in enzalutamide-treated patients with rPD events versus control, although sample size was small. Compared with no rPD, enzalutamide-treated patients with rPD ± PSA rise or progression had worse OS. CONCLUSION: Given the frequent discordance and poor prognosis of imaging-based progression in the absence of PSA changes during enzalutamide treatment in mHSPC and nmCRPC, periodic surveillance using imaging is recommended.
12 May 2026
Read appraisal →Japanese journal of clinical oncology
Impact of intensity-modulated radiotherapy on survival and pulmonary toxicities for localized esophageal squamous cell carcinoma
OBJECTIVE: Definitive chemoradiotherapy (CRT) is a treatment strategy for localized esophageal squamous cell carcinoma (ESCC). Herein, we aimed to evaluate clinical outcomes of definitive CRT for ESCC. METHODS: We reviewed 127 patients who received definitive CRT for localized ESCC at our institution between January 2004 and December 2022. All patients received elective nodal irradiation with concurrent chemotherapy, primarily comprising cisplatin and 5-fluorouracil, during radiotherapy. Seventeen patients (13%) received intensity-modulated radiation therapy (IMRT) only, and 16 patients (13%) were treated with IMRT as a boost. The median total dose was 61.4 Gy. Approximately 80% of the patients had clinical stage III or higher disease. We analyzed overall survival (OS), progression-free survival (PFS), locoregional recurrence rate (LRR), prognostic factors, and adverse events. RESULTS: The median follow-up period was 13 months for all patients and 22 months for survivors. The 2-year OS, PFS, and LRR were 40.4%, 27.1%, and 30.8%, respectively. The overall complete response rate was 40.2%. On multivariate analysis, clinical stage 0-III (non-T4) (P < .001) and the use of IMRT (P = .034) were significantly associated with better OS. Pulmonary toxicity was significantly lower in the IMRT group (P = .049). CONCLUSIONS: IMRT for localized ESCC may improve prognosis and reduce pulmonary toxicity.
12 May 2026
Read appraisal →Journal of neuro-oncology
cGAS-STING agonists in preclinical glioblastoma animal models: a systematic review of tumor microenvironment modulation and survival outcomes
BACKGROUND/PURPOSE: Glioblastoma (GBM) is characterized by an immunosuppressive tumor microenvironment and poor responsiveness to immune checkpoint blockade, driving interest in cGAS-STING pathway activation to stimulate antitumor immunity. We performed a systematic review of preclinical in vivo studies evaluating STING agonists in glioblastoma, with emphasis on immune microenvironment effects and survival outcomes. METHODS: A systematic review of PubMed, Cochrane, and Embase was conducted through February 2025 following PRISMA guidelines. Studies were included if they evaluated STING agonists in live animal models of GBM and reported survival outcomes or tumor microenvironment changes. Two reviewers independently screened studies and extracted data. Risk of bias was assessed qualitatively based on study design and reporting characteristics. Given heterogeneity in models, treatments, and outcomes, results were synthesized descriptively. RESULTS: Fourteen studies met the inclusion criteria, spanning synthetic and natural cyclic dinucleotides and non-cyclic STING agonists. Across diverse delivery methods, including intracranial injection, hydrogels, and nanoparticle systems, STING agonists were associated with increased CD8 + T-cell and NK cell infiltration and repolarization of tumor-associated macrophages toward pro-inflammatory phenotypes. Several studies reported prolonged survival, including long-term tumor clearance in select models. Combination therapies with immune checkpoint inhibitors or radiotherapy showed synergistic effects in some studies. CONCLUSIONS: STING agonists can enhance anti-tumor immune responses and prolong survival in preclinical GBM models by promoting cytotoxic lymphocyte recruitment and remodeling the tumor-associated myeloid landscape. However, translation remains challenging due to limitations of current models, the limited clinical traction STING modulation has thus far achieved in other cancer types, incomplete understanding of cell-type-specific STING activation in the brain, and the possibility that sustained STING signaling may drive maladaptive inflammation rather than durable antitumor immunity.
12 May 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
A systematic review of the assessment model for palliative care needs in cancer patients
BACKGROUND: Timely palliative care can reduce the disease burden and improve quality of life in patients with cancer. Although several studies have developed assessment models for palliative care needs in cancer patients, the quality and clinical applicability of these models remain unclear. OBJECTIVE: To systematically review existing assessment models for palliative care needs in patients with cancer, with a focus on their characteristics, predictors, risk of bias, and applicability. METHODS: A systematic search was conducted in PubMed, Cochrane Library, Embase, Web of Science, CINAHL, Scopus, China National Knowledge Infrastructure (CNKI) through September 12, 2025. Data extraction and evaluation were rigorously performed by two researchers based on the Prediction Model Risk of Bias Assessment Tool (PROBAST). RESULTS: A total of 5714 articles were identified, and eight studies were included, which covered 24 models for assessing palliative care needs. The sample size of the included studies ranged from 179 to 54,628, with areas under the curve ranging from 0.724 to 0.998. The models in all the included studies encompassed four categories of predictive factors: general demographic data, symptom/functional assessments, laboratory indicators, and treatment status. Five studies were rated as having a high risk of bias, primarily due to high risks associated with participants and conclusions, with generally low applicability. CONCLUSION: Existing models demonstrate potential for identifying patients with cancer who have increased palliative care needs using routinely collected clinical data. Commonly included predictors were symptom burden, functional status, laboratory parameters, treatment-related factors, and demographic characteristics. However, the overall body of evidence is constrained by a substantial risk of bias, particularly arising from inappropriate data sources, limited sample sizes, suboptimal handling of continuous variables, insufficient reporting of missing data, and the lack of robust internal or external validation. In addition, many models adopted mortality-based surrogate outcomes rather than clinically meaningful indicators of palliative care needs. Therefore, the currently available models should be interpreted with caution, and further high-quality model development and external validation are required before they can support broader routine clinical implementation. Future research should prioritize clinically actionable outcomes and incorporate patient-, caregiver-, and family-level factors to improve the relevance of these models for referral decisions and care planning.
12 May 2026
Read appraisal →The oncologist
Characterization and clinical management of adverse events following treatment with repotrectinib: a TRIDENT-1 analysis
BACKGROUND: Repotrectinib, a next-generation ROS1/TRK tyrosine kinase inhibitor, is approved for ROS1 fusion-positive non-small cell lung cancer and NTRK fusion-positive solid tumors. Its side effects and safety management strategies require further characterization. PATIENTS AND METHODS: The safety profile of repotrectinib (treatment-emergent/related adverse events [TEAEs/TRAEs]) was established in patients who initiated treatment at the recommended dose (160 mg daily [QD] for 14 days, then 160 mg twice daily [BID]) across all cohorts of the global, multicenter phase 1/2 TRIDENT-1 study. AE management strategies were outlined. RESULTS: In 472 patients, the most common TRAEs (dizziness [58%] and dysgeusia [50%]) were likely TRK inhibition-related. Median relative dose intensity was 90%; 14% (n = 66/472) of patients did not increase their initial QD dose to BID (mostly due to CNS AEs). Rates of dizziness (median onset, 7 days) were similar in patients with/without baseline brain metastases. Dose modifications downgraded severity or resolved dizziness in 78% of patients; 58% of patients had pharmacologic intervention without dose modification. Dizziness was downgraded/resolved in 62% (n = 120/195) of patients who did not receive dose modification or pharmacologic intervention. Treatment-related cognitive impairment and weight gain occurred in 19% and 12% of patients, respectively. Treatment-emergent withdrawal pain occurred in 14% of patients (median resolution time, 2.1 weeks). Dose interruption and reduction from TRAEs occurred in 39% and 38% of patients, respectively; 10% reported later re-escalation back to 160 mg BID. CONCLUSION: Many repotrectinib AEs, including neurological AEs secondary to TRK inhibition, were mitigated with appropriate management, including dose modification and/or pharmacologic intervention.
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 →PloS one
DeepDRP: Dose-response predictions of drug pairs using deep learning based on data-driven feature representation and dose-response curve characteristics
Combination therapies have become a cornerstone of modern medicine, offering improved treatment outcomes and reduced side effects compared to monotherapies. However, the efficacy and safety of drug combinations depend heavily on the specific doses of each component, making the optimization of dosing regimens a crucial yet challenging task. Despite the importance of dose optimization, few computational methods address this challenge. Here, we propose DeepDRP, a novel approach that integrates two complementary models: the global model, which is trained using all available combinations, and the local model, which utilizes only samples with similar information to queries. The DeepDRP architecture comprises three main modules: the first module has three embedding networks to extract features from drugs, doses, and cell lines, and then predicts synergy using the fused knowledge. The second module constructs a graph using the input query and the set of similar retrieved training samples fed into a semi-supervised graph convolutional network to predict the synergy value. Finally, these two models are aggregated to have the final predicted value. To evaluate the proposed approach, it is applied to the NCI-ALMANAC dataset and O'Neil dataset. The obtained results denote that the proposed method achieves superior performance with respect to the other approaches.
11 May 2026
Read appraisal →PloS one
An exploratory dosimetric and treatment-time analysis of tangent-arc and continuous semi-arc VMAT in deep inspiration breath-hold radiotherapy for stage I left-sided breast cancer
BACKGROUND: The use of the deep inspiration breath-hold (DIBH) technique reduces cardiac and lung radiation exposure during left breast cancer radiotherapy. However, the optimal beam delivery technique and the effects of patient adaptation during DIBH remain incompletely understood. OBJECTIVE: In this study, the dosimetric differences between continuous semi-arc and tangent-arc plans in stage I left-sided breast cancer patients using DIBH were compared, and the treatment session duration was descriptively analyzed to characterize treatment-time trends during routine DIBH delivery. METHODS: Twenty patients treated at our hospital from 01/05/2022-31/05/2023 were retrospectively selected from the institutional database for exploratory dosimetric analysis. Two radiotherapy plans were created on the basis of each patient's computed tomography (CT) images. Dosimetric parameters for the planning target volume (PTV) and organs at risk (OARs), and beam-on and total treatment times, were compared. RESULTS: The conformity index (CI) for the PTV was significantly better with the continuous semi-arc plan (P < 0.05), whereas the other PTV parameters did not significantly differ between the plans (P > 0.05). The doses and beam-on time for all OARs (except the left ventricle) were significantly lower for the tangent-arc plan (P < 0.05). Treatment time tended to stabilize across fractions, with a significant difference between the 15th and 16th sessions (P < 0.05). CONCLUSION: With the tangent-arc plan, the beam-on time and radiation exposure to OARs were observed to be lower, while adequate PTV coverage was maintained in patients with stage I left-sided breast cancer using DIBH. Treatment times tended to stabilize with increasing treatment fractions. This observation suggests gradual patient adaptation during routine DIBH rather than a predefined training effect. Given the exploratory nature of these findings and the limited sample size from a single institution, these findings should be interpreted with caution and warrant further investigation in larger, multicenter studies.
11 May 2026
Read appraisal →Analytical chemistry
FiLM-Enhanced Biologically Informed Neural Networks for Multiclass Omics Analysis and Biomarker Discovery
High-throughput proteomics enables detailed molecular phenotyping but poses challenges for predictive modeling and interpretation due to high dimensionality, sparsity, and nonlinear interactions. Biologically informed neural networks (BINNs) address these challenges by embedding pathway knowledge into network architectures, providing interpretable models of complex omics data. We present BINN-FiLM, which extends conventional BINNs by integrating feature-wise linear modulation, enabling task-specific scaling and shifting of pathway-level activations while preserving a fixed Reactome-based hierarchy. Unlike BINNs that share all pathway parameters across classes, BINN-FiLM captures class-dependent biological rewiring, making it particularly effective for multiclass classification tasks. Multiclass problems are reformulated as multitask binary learning, allowing pathway activations to adapt to disease-specific contexts. We evaluated BINN-FiLM on three multiclass proteomic data sets, and BINN-FiLM consistently outperformed conventional BINNs and standard machine learning models. FiLM modulation parameters revealed task-specific pathway activity, and SHAP-based interpretation identified key proteins and pathways driving disease discrimination.
11 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 →Journal for immunotherapy of cancer
Phase II trial of combination radiation, hormone, and immunotherapy in grade group 5 prostate cancer
BACKGROUND: Grade group 5 (GG5) prostate cancer (PCa) carries a less favorable prognosis after standard-of-care (SOC) therapy, necessitating novel therapeutic approaches. High-dose rate brachytherapy (HDRBT) and androgen deprivation therapy (ADT) may modulate immune response in GG5 PCa, particularly in tumors with increased immune content. This study evaluated whether the addition of nivolumab to SOC was associated with improved disease control in patients with high-volume GG5 PCa, including those with oligometastatic disease. METHODS: In this non-randomized phase II trial, 31 patients with localized or oligometastatic GG5 PCa and >30% positive biopsy cores were evaluated between September 2018 and April 2021. Patients received four doses of nivolumab (240 mg every 2 weeks) beginning 4 weeks prior to HDRBT, alongside ADT, HDRBT, and external beam radiation. The primary endpoint was to evaluate whether the 2-year freedom from biochemical recurrence (FFBR) rate would exceed a prespecified historical control rate of 75%. RESULTS: Among the 31 patients, the median follow-up was 38.8 months (IQR 31.0-46.5 months). The addition of nivolumab to SOC RT with ADT was associated with a 2-year FFBR rate of 90.3% (95% CI 74.3% to 98.0%) (median FFBR not reached), exceeding the prespecified historical control rate of 75% (one-sided p value from binomial test=0.024). Definitive and probable nivolumab-related toxicity included 6.3% acute grade 2 and 6.3% acute grade 3 adverse events (AEs), with no grade 4+ AEs observed. A higher Decipher immunosuppression score at diagnosis correlated with early pathologic response (p=0.005) and was independently associated with time to metastatic failure (p=0.044). CONCLUSIONS: Nivolumab combined with SOC was associated with encouraging FFBR in this high-risk GG5 PCa population and may represent a promising therapeutic intensification strategy. The Decipher immunosuppression score may serve as a predictive biomarker for response. These findings warrant further investigation in randomized trials.
11 May 2026
Read appraisal →European journal of medicinal chemistry
AI-driven identification of a selective dual function inhibitor blocking HK2 activity and HK2-VDAC1 interaction displaying enhanced anticancer efficacy under hypoxia
Selective inhibition of hexokinase 2 (HK2) represents a promising therapeutic strategy due to the pivotal role of HK2 in the Warburg effect, enhancement of glycolysis and anti-apoptosis via HK2-Voltage-Dependent Anion Channel 1 (VDAC1) protein-protein interaction. Moreover, HK2 initiates glycolysis to generate lactate, hence this central enzyme can be pharmacologically targeted to enhance therapy outcomes. Currently, no HK2 inhibitors (HK2is) exist in the clinic. Herein, we employed GCVec, an artificial intelligence (AI)-based compound-protein interaction (CPI) prediction tool, along with molecular docking, to identify the HK2i, 106. This compound exhibited an IC50 of 0.79 ± 0.07 μM and a consistent Kd of 0.41 ± 0.03 μM against HK2 enzyme. It also apparently blocked HK2-VDAC1 interaction as indicated by the disrupted colocalization of HK2-GFP and VDAC1-mCherry. Furthermore, 106 demonstrated enhanced anticancer efficacy under hypoxia in tumor cells with elevated HIF-1α/HK2 and VDAC1 levels. Compound 106 selectively targeted SW480 colorectal cancer cells with high HK2 expression, achieving a growth inhibition IC50 value of 5.00 ± 0.94 μM. Consistently, knockout of HK2 in these tumor cells significantly rescued the IC50 values and eliminated the glycolytic inhibition induced by 106. We further showed that 106 reduced lactate and ATP levels and induced markers of apoptosis, including increased p-AMPK/AMPK ratio and increased Bax levels, as well as decreased Bcl2 levels. Collectively, our findings highlight the potential of GCVec in identifying 106, a first in class dual-function HK2i which emerges as a promising lead compound for further development into a possible anticancer therapeutic agent.
11 May 2026
Read appraisal →Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Raman spectroscopy with machine-learning classification for the prediction of stereotactic radiotherapy induced treatment toxicity in high-risk localised prostate cancer
Radiotherapy can lead to late-onset toxicity, to varying extents between individuals due to differences in radiosensitivity. Predicting which patients are most at risk is key to augmenting the therapeutic window. However, the underlying biological mechanisms remain poorly understood, and current experimental methods often lack clinical applicability. This study employs Raman spectroscopy to analyse biochemical profiles in peripheral lymphocytes and plasma, aiming to monitor radiotherapeutic response and predict intrinsic radiosensitivity in high-risk localised prostate cancer patients treated with stereotactic radiotherapy. Partial-least squares discriminant analysis classification of Raman spectra at baseline (n = 20) from post-hormone therapy (n = 19), mid-treatment (pre-4th fraction; n = 21) and 3-months after treatment (n = 18) returned mean area under the curve values ranging from 0.88 to 0.93. Ensemble classifiers applied to imbalanced late toxicity datasets (grade 0-1, n = 16; grade 2+, n = 4) yielded mean F1 scores of 0.74 (random forest, lymphocytes) and 0.69 (AdaBoost, plasma); metrics based on best performing model for minority-class. Classical least squares lymphocyte and plasma toxicity models identified major concentration differences in amino acids, proteins, lipids, DNA and related biomolecules (p < 0.05). These findings demonstrate the potential of Raman spectroscopy as a minimally invasive, objective tool for classifying blood-based biochemical profiles across radiotherapy treatment time points and distinguishing patients with late grade 0-1 and grade 2+ toxicity.
10 May 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Telemedicine adoption, pandemic-related fear, and treatment adherence in cancer care during COVID-19: a prospective cohort study
PURPOSE: To evaluate the impact of telemedicine adoption and pandemic-related fear on treatment adherence, healthcare utilization, and supportive care processes among patients undergoing cancer therapy during the COVID-19 pandemic. METHODS: In this prospective cohort study, we followed 149 cancer patients over two years to evaluate telemedicine utilization, treatment modifications, and COVID-19-related fear. We employed validated psychometric tools, including the COVID-19 Fear Scale. RESULTS: Telemedicine adoption (24.8% of patients) was associated with fewer hospital visits (7.3 ± 3.2 vs 9.4 ± 4.1, p = 0.004) and reduced emergency room utilization (16.2% vs 46.4%, p < 0.001). Treatment modifications occurred in 55.9% of patients, predicted by COVID-19 infection (OR = 2.8, 95% CI:1.7-4.6) and neutropenia (OR = 2.1, 95% CI:1.3-3.4). High fear scores were linked to increased mortality (8.1% vs 2.7%, p = 0.032), disease progression (37.8% vs 21.4%, p = 0.018), lower treatment adherence (75.7% vs 87.5%, p = 0.041), and higher hospitalization rates (51.3% vs 24.1%, p < 0.001). CONCLUSION: This study demonstrates that telemedicine can reduce acute care utilization while treatment modifications and high pandemic-related fear significantly worsen cancer outcomes. These findings highlight the need for oncology teams to proactively integrate remote care strategies and targeted psychological support to maintain treatment adherence and mitigate adverse outcomes during health crises.
10 May 2026
Read appraisal →European journal of cancer (Oxford, England : 1990)
Survival modelling of relapse-free survival and competing-risk analysis in patients with high-risk localized prostate cancer treated in GETUG-12.
BACKGROUND: Androgen-deprivation therapy (ADT) plus radiotherapy is a standard of care for men with high-risk localized prostate cancer (PC). Adding docetaxel in this setting demonstrated better relapse-free survival (RFS) but not survival. Few data are available on relapse patterns. Our aim was to investigate whether different patterns of relapses exist in men with high-risk localized PC. PATIENTS AND METHODS: Prospective data from the GETUG12 phase 3 randomized controlled trial comparing ADT alone vs ADT + docetaxel and estramustine (DE) was examined. RFS (main endpoint) was analysed using parametric survival models and second event-free survival (SEFS) defined from biochemical progression (BP) was analysed using a competing-risk approach. RESULTS: Overall, 413 patients were randomized from 2002 to 2006, 206 treated with ADT and 207 with ADT+DE, in addition to local treatment. First analysis showed that the piecewise-exponential model with two time-intervals ([0-3[; ≥3 years) was the model that best characterized RFS with no time-dependent effect of risk factors and treatment. Second analyses limited to patients with a BP showed that the risk of a second event was significantly lower in patients with a longer time-interval from randomization to BP (hazard ratio (HR≥ 3 versus < 3 years): 0.49 [95% CI 0.30-0.79]). In competing-risk analysis, a significant and protective effect of this time-interval was observed for metastases (sub-HR≥ 3 versus < 3 years: 0.41 [0.23-0.73] accounting for salvage treatment. CONCLUSION: Time-interval from randomization to BP is a major prognostic factor for developing local or distant recurrences for patients with high-risk localized PC.
10 May 2026
Read appraisal →European journal of cancer (Oxford, England : 1990)
MuTriM: A multiscale deep learning model integrating longitudinal radiomics and pathomic features for predicting recurrence and adjuvant radiation benefit in breast cancer.
PURPOSE: We present MuTriM, a multimodal deep learning model integrating DCE-MRI and whole-slide pathology to predict survival and radiation benefit in breast cancer. PATIENTS AND METHODS: MuTriM employs an attention-based cross-modal and cross-temporal fusion framework that concurrently integrates DCE-MRI radiomic features with pathomic features of cellular morphology on WSI. MuTriM was trained on the Fudan University Shanghai Cancer Center (FUSCC) cohort (N = 335) and externally tested on the TCGA cohort (N = 126) using matched DCE-MRI and WSI. RESULTS: MuTriM was prognostic of recurrence-free survival (RFS) in a subtype-agnostic setting (HR = 5.26, 95% CI, 1.69-16.4, p = 0.004; C-index = 0.75), outperforming unimodal models based on DCE-MRI alone (C-index = 0.65) or WSI alone (C-index = 0.70). MuTriM was also prognostic among HER2 + (HR = 6.82, 95% CI, 1.08-43.2, p = 0.04) and ER+ populations (HR = 4.61, 95% CI, 1.25-17.1, p = 0.02). A high MuTriM score was associated with improved RFS (HR = 0.15, 95% CI 0.03-0.87; P = 0.03), whereas a low MuTriM score showed no significant benefit (HR = 4.06, 95% CI 0.05-31; P = 0.53) (interaction P = 0.04). Transcriptomic analyses revealed that high-risk tumors showed suppressed cytotoxic T-cell activity and enrichment of extracellular matrix remodeling, neutrophil degranulation, and neuronal signaling, consistent with immune evasion. Upregulation of NRXN1, GABRA3, KCNK3, and KLK12 was linked to worse survival, whereas SYNGR3 and CD207 were associated with favorable outcomes. CONCLUSION: By unifying radiology, pathology, and transcriptomics, MuTriM provides an interpretable multimodal framework for breast cancer prognosis and treatment guidance.
10 May 2026
Read appraisal →Signal transduction and targeted therapy
Total neoadjuvant chemotherapy combined with PD‑1 blockade and IL‑2 in MSS/pMMR locally advanced rectal cancer: short-term results of a prospective, single-arm phase II study
Neoadjuvant therapy has become a cornerstone in the management of locally advanced rectal cancer (LARC). In this single-arm, open-label phase II study, we evaluated the efficacy and safety of total neoadjuvant chemotherapy (TNT) using a CapOX regimen combined with a programmed cell death protein 1 (PD‑1) antibody (sintilimab) and interleukin‑2 (IL‑2) in patients with microsatellite stable (MSS)/defining proficient mismatch repair (pMMR) LARC. A total of 33 patients, aged 18-75 years, with rectal tumors located within 12 cm from the anal verge and staged as cT3/4N_anyM0 or cT_anyN⁺M0, were enrolled. Patients received a regimen consisting of oxaliplatin, sintilimab, capecitabine, and IL‑2 administered in a three‑week cycle, with response evaluations performed after every two cycles. Following six cycles of treatment, 33 patients underwent radical surgery, achieving a 100% R0 resection rate. The pathological complete response (pCR) rate was 42.4% (95% CI: 25.68-59.16%) while the remaining 19 patients (57.6%, 95% CI: 41.07-74.09%) were assessed as having a partial response. The tumor regression grades (TRG) were, TRG1: 3 cases (9.1%, 95% CI: 1.90-25.97%), TRG2: 14 cases (42.4%, 95% CI: 26.27-60.38%), and TRG3: 2 cases (6.1%, 95% CI: 0.73-20.37%), respectively. Surgical safety reported as no cases of grade B/C anastomotic leakage or bowel obstruction. Adverse events (AEs) were manageable, with a 21.2% incidence of grade 3 events and no grade IV/V events or treatment‑related deaths. With a median follow‑up of 25.5 weeks, no recurrences were observed. Analysis of blood and tissue samples from patients with different treatment outcomes revealed significant activation of CD8+ T cells, NK cells, and M1 macrophage subsets in the tumor microenvironment of patients achieving pCR. These compelling data demonstrate promising efficacy with a favorable safety profile in MSS/pMMR LARC. These findings warrant studies to validate this regimen as a novel treatment paradigm for rectal cancer. ClinicalTrials.gov registration: NCT06108596.
10 May 2026
Read appraisal →European journal of cancer (Oxford, England : 1990)
Stereotactic body radiotherapy versus hepatic resection for recurrent small hepatocellular carcinoma: A post-hoc analysis with propensity score adjustment from two randomized trials
BACKGROUND: Stereotactic body radiotherapy (SBRT) has been proven effective for treating small hepatocellular carcinoma (sHCC); however, the comparative efficacy of SBRT and surgical resection remains unknown. This analysis aims to compare the outcomes of SBRT and resection in treating recurrent sHCC. METHODS: Two randomized controlled trials enrolled recurrent sHCC undergoing resection (NCT01833286) or SBRT (NCT04047173). Patients with single recurrent HCC ≤ 5 cm were included in this study. The primary endpoint was local progression-free survival (LPFS). RESULTS: A total of 158 patients were included, with 83 and 75 patients assigned to the SBRT and resection cohorts, respectively. The cumulative 3-year LPFS rates were 84.3% (95% CI, 76.1-92.5) with SBRT and 76.8% (95% CI, 67.2-86.4) with resection, respectively (HR, 0.70 [95% CI, 0.35-1.40]; P = 0.311). The median progression-free survival time of the SBRT and resection group was 37.6 (95% CI, 26.0-49.2) and 26.3 (95% CI, 18.8-33.8) months, respectively, with no significant differences (HR, 0.80 [95% CI, 0.53-1.21]; P = 0.287). The 3-year overall survival rates were 90.3% (95% CI, 83.4-97.2) in the SBRT group, and 81.1% (95% CI, 76.5-90.1) in the resection group (HR, 0.53 [95% CI, 0.23-1.20]; P = 0.118). The comparison results remained consistent after adjusting for confounding variables. The incidences of adverse events were similar between the two groups (P = 0.695). CONCLUSIONS: SBRT provides satisfactory tumor control and long-term outcomes, comparable to those of resection. SBRT could be a potential alternative to resection in recurrent sHCC.
10 May 2026
Read appraisal →European journal of cancer (Oxford, England : 1990)
Sex differences in efficacy and toxicity of first-line treatment of metastatic colorectal cancer: An analysis of 18,041 patients in the ARCAD CRC database.
BACKGROUND: Greater toxicity of adjuvant chemotherapy in females has been reported previously, but the impact of patients' sex on chemotherapy (CT)+/-targeted therapies for non-resectable, metastatic colorectal cancer (mCRC) has remained unclear. We compared efficacy and toxicity of first-line chemotherapy, with or without targeted therapies, between male and female mCRC patients. METHODS: Twenty-seven randomized trials from the ARCAD CRC database were included. Primary endpoint was overall survival (OS); secondary endpoints were progression-free survival (PFS), overall response rate, toxicity (according to NCI-CTCAE). Analyses were performed within three treatment groups: A) CT alone, B) CT+bevacizumab, and C) CT+epidermal growth factor receptor inhibitor. OS and PFS were estimated using Kaplan-Meier method. Associations with sex were assessed using study-stratified Cox models adjusted for age, performance status, body mass index, and CT backbone. RESULTS: Baseline data from 18,041 patients were analysed; among 15,271 included in outcome analyses, no sex differences in OS or PFS were observed. Adjusted OS hazard ratios (95% confidence interval) for females versus (vs.) males were: A: 1.01 (0.95-1.08, P = .69), B: 0.96 (0.89-1.04, P = .36), C: 0.97 (0.87-1.10, P = .67). Females experienced higher toxicity, including grade 1-4 and 3-4 nausea (71.8% vs. 60.5% and 7.5% vs. 4.8%), vomiting (47.7% vs. 35.5% and 6.6% vs. 4.4%), and neutropenia (52.4% vs. 44.4% and 29.2% vs. 18.8%) (P < .001). Conversely, rash (grade 1-4 and 3-4) was more frequent in males (29.6% vs. 24.3% and 5.1% vs. 3.7%, P < .001). CONCLUSIONS: Despite higher chemotherapy-related toxicity in females, treatment efficacy was similar between sexes. Rash was more common in males, highlighting that patients' sex modulates toxicity profiles in both directions. Strategies to reduce adverse events in female patients are warranted.
10 May 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Prevalence of antibody drug conjugated-induced nausea and vomiting (ADCINV) in patients with cancer
INTRODUCTION: Antibody-drug conjugates (ADC) have emerged as an important part of systemic treatment across disease sites. To date, there is no robust pooled prevalence estimate of nausea and vomiting induced by ADCs. Establishing such estimates is essential to determine if ADC-induced nausea and vomiting (ADCINV) represents a clinically significant problem warranting further research into antiemetic prophylaxis. Our aim is to report the prevalence of reported ADCINV across literature. METHODS: A systematic search of Medline, Embase, Cochrane CENTRAL and Web of Science was conducted from database inception until September 24, 2025. Articles were included if they reported nausea and/or vomiting due to ADCs used in cancer treatment, in the abstract. Pooled prevalence was reported. Subgroup analysis was conducted by ADC. Meta-regression was conducted by age and sex. Quality assessment was conducted. Type I error was set at 0.05. RESULTS: A total of 209 studies with 15,493 patients were included. Thirty-nine percent (95%CI, 36-42%) of patients experience any nausea, and 26% (95%CI, 23-29%) experience any vomiting. Younger patients are more likely to experience nausea; each 10-year increase in age is associated with a 12% decrease in nausea rates. Higher emetogenic ADCs include trastuzumab deruxtecan, sacituzumab govitecan, brentuximabvedotin and patritumab deruxtecan. Lower emetogenic agents include disitimab vedotin, telisotuzumab vedotin and rovalpituzumab tesirine. DISCUSSION: This is the first study to report prevalence of ADCINV across ADCs. It is a prevalent adverse effect akin to chemotherapy-induced nausea and vomiting that should be viewed as clinically relevant and further investigated to help develop optimal strategies related to antiemetics.
10 May 2026
Read appraisal →Neural networks : the official journal of the International Neural Network Society
MGML: A plug-and-play meta-guided multi-modal learning framework for incomplete multimodal brain tumor segmentation
Leveraging multimodal information from Magnetic Resonance Imaging (MRI) plays a vital role in lesion segmentation, especially for brain tumors. However, in clinical practice, multimodal MRI data are often incomplete, making it challenging to fully utilize the available information. Therefore, maximizing the utilization of this incomplete multimodal information presents a crucial research challenge. We present a novel meta-guided multi-modal learning (MGML) framework that comprises two components: meta-parameterized adaptive modality fusion and consistency regularization module. The meta-parameterized adaptive modality fusion (Meta-AMF) enables the model to effectively integrate information from multiple modalities under varying input conditions. By generating adaptive soft-label supervision signals based on the available modalities, Meta-AMF explicitly promotes more coherent multimodal fusion. In addition, the consistency regularization module enhances segmentation performance and implicitly reinforces the robustness and generalization of the overall framework. Notably, our approach does not alter the original model architecture and can be conveniently integrated into the training pipeline for end-to-end model optimization. We conducted extensive experiments on the public BraTS2020 and BraTS2023 datasets. Compared to multiple state-of-the-art methods from previous years, our method achieved superior performance. On BraTS2020, for the average Dice scores across fifteen missing modality combinations, building upon the baseline, our method obtained scores of 87.55, 79.36, and 62.67 for the whole tumor (WT), the tumor core (TC), and the enhancing tumor (ET), respectively. We have made our source code publicly available at https://github.com/worldlikerr/MGML.
9 May 2026
Read appraisal →Cell
Deep-learning-based de novo discovery and design of therapeutics that reverse disease-associated transcriptional phenotypes
Identifying drugs that reverse disease-associated transcriptomic features has been widely explored for drug repurposing, but its potential for de novo drug discovery remains underexplored. Here, we present gene expression profile predictor on chemical structures (GPS), a deep-learning-based drug discovery platform, guided by transcriptomic features, that screens large compound libraries and optimizes lead molecules. We first develop a model that captures transcriptomic perturbation signatures solely from chemical structures and deploy it to library compounds. We refine scoring methods and employ a tree-search method for optimization. By incorporating structure-gene-activity relationships, we uncover drug mechanisms from transcriptomic data. We evaluate GPS across multiple diseases and conduct extensive validation in two cases. In hepatocellular carcinoma, we discover two unique compound series with favorable cellular selectivity and in vivo efficacy. In idiopathic pulmonary fibrosis, we identify one repurposing candidate and one novel anti-fibrotic compound by reversing gene expression of multiple distinct cell types derived from single-cell transcriptomics.
5 May 2026
Read appraisal →Cancer medicine
Predicting Prognosis for Gastric Cancer Patients Receiving Neoadjuvant Treatment With Body Composition-Based Deep Learning
BACKGROUND: This study sought to develop an innovative body composition (BC)-based deep learning (DL) model to precisely evaluate survival in gastric cancer (GC) patients undergoing neoadjuvant treatment (NT). MATERIALS AND METHODS: This retrospective study included GC patients undergoing NT from two centers. CT images both pre-NT and post-NT were preprocessed, focusing on the automatic segmentation of subcutaneous fat, visceral fat, and skeletal muscle regions using TotalSegmentator. Delta Radiomics features were extracted using Pyradiomics. After feature fusion and selection, the optimal model is Naive Bayes (Rad model). A hybrid DL model was developed by combining ResNet18 and Transformer networks for feature extraction. The Clinic_Rad_DL model was constructed by combining clinical features, radiomic signatures, and DL signatures. The ExtraTree classifier was used for the Clinic_Rad_DL model, while a separate Cox regression model was developed for survival analysis using the same features. RESULTS: A total of 356 patients (mean age, 59 ± 10 years; 264 males [74.2%]) were enrolled and divided into training, validation, and test sets in a 7:2:1 ratio. The DL model outperformed the Rad model. The Clinic_Rad_DL model outperformed both Rad model and DL model, with AUC of 0.915, 0.890, and 0.890 in training, validation, and test sets, respectively. The Cox proportional hazards model showed C-index of 0.806, 0.803, and 0.819, effectively stratifying patients into high- and low-risk groups with significant survival differences. CONCLUSION: The study developed and validated a BC-based DL model to predict survival in GC patients undergoing NT, offering potential for personalized treatment strategies in clinical practice.
5 May 2026
Read appraisal →European journal of nuclear medicine and molecular imaging
Machine learning models classifiers enable a strong prediction of radioembolization-induced liver disease, and define a new bilirubin threshold for selection of patients.
PURPOSE: Selective Internal Radiotherapy (SIRT) is an established treatment option for hepatocellular carcinoma (HCC). However, a major complication is radioembolization-induced liver disease (REILD). METHODS: This retrospective study, analyzed patients treated with SIRT for HCC to identify clinical factors associated with REILD and to predict treatment response. Machine learning (ML) methods were applied to two distinct cohorts to determine predictors of toxicity and response. RESULTS: Among 138 patients analyzed for REILD, ML identified bilirubin as a key predictor. A refined threshold of 26.5 µmol/L (1.55 mg/dL) was associated with toxicity risk. In the response cohort (136 patients), predictive performance was limited, nevertheless tumor dose appeared as the most frequent feature selected by the models. CONCLUSIONS: Bilirubin was confirmed as a critical factor for REILD prediction with the identification of a new threshold that may improve patient risk stratification. While tumor dose appears as the main predictor of treatment outcome, robust response models require additional features.
5 May 2026
Read appraisal →Annals of surgical oncology
Computed Tomography-Pathology Deep Learning Model for the Precise Prediction of Recurrence in Pathological Stage IA Lung Adenocarcinoma
BACKGROUND: The postoperative prognosis of pathological stage IA lung adenocarcinoma (LUAD) exhibits significant heterogeneity. While the tumor node metastasis (TNM) staging system offers limited recurrence prediction capability, this study aims to develop a precise deep learning model for prognostic stratification. PATIENTS AND METHODS: We retrospectively analyzed a consecutive cohort of patients with pathological stage IA LUAD who underwent surgical resection at Zhongshan Hospital Fudan University. Our novel ResNet 3D-Pathology Fusion (Res3D-PF) model-integrating a three-dimensional ResNet backbone with an image-pathology fusion module-was developed to predict recurrence-free survival (RFS) using preoperative computed tomography (CT) images and International Association for the Study of Lung Cancer (IASLC) grading. Model performance was evaluated through receiver operating characteristic curve analysis, with independent RFS predictors identified via multivariable Cox regression. RESULTS: Among 551 patients with stage IA LUAD (median age 61 years; 339 women) divided into training (n = 368) and validation (n = 183) sets, the CT-pathology fusion model achieved superior predictive performance. In the validation cohort, Res3D-PF significantly outperformed the 8th T-stage (AUC 0.837 vs. 0.660, p = 0.001) and IASLC grade (AUC 0.837 vs. 0.684, p = 0.015) for 5-year RFS prediction. Multivariable analysis confirmed Res3D-PF as an independent prognostic factor (HR 15.772, 95% CI 3.384-73.508; p < 0.001). Model-stratified high-risk patients demonstrated significantly reduced 5-year RFS (73.1% vs. low-risk 98.5%, p < 0.001) and shorter median RFS (74.4 vs. 96.2 months, p < 0.001). CONCLUSIONS: We developed and validated a CT-pathology deep learning model that outperforms conventional TNM staging and IASLC grading for predicting postoperative recurrence in stage IA LUAD. This approach enables individualized risk stratification to guide precision treatment strategies.
4 May 2026
Read appraisal →Radiology. Artificial intelligence
Radiopathomic Graph Deep Learning for Multiscale Spatial-Contextual Modeling of Intratumoral Heterogeneity to Predict Breast Cancer Response to Neoadjuvant Therapy
Purpose To develop an explainable radiopathomic graph deep learning (RPGDL) system for multiscale spatial-contextual modeling of intratumoral heterogeneity and evaluate its performance for the prediction of pathologic complete response (pCR) to neoadjuvant therapy in breast cancer. Materials and Methods The RPGDL system was developed from dual-center retrospective analysis of patients with biopsy-proven invasive breast cancer (May 2018-August 2024). For each tumor, individual radiomic and pathomic graphs were generated from pretherapeutic MRI and hematoxylin-eosin-stained biopsy slide images, respectively. These graphs were then processed by three distinct graph neural networks (GNNs): radiomic, pathomic, and radiopathomic. GNN performance was assessed with the area under the receiver operating characteristic curve (AUC), net reclassification index (NRI), and integrated discrimination improvement (IDI). A multifaceted approach was used to explain the GNNs' predictions. Results The training set included 582 (mean age, 52 years ± 9 [SD]) patients and the external test set 468 (50 years ± 10) patients from centers 1 and 2, respectively. The radiomic GNN achieved AUCs of 0.89 (95% CI: 0.85, 0.93) in the training set and 0.84 (95% CI: 0.80, 0.89) in the external test set; the pathomic GNN achieved AUCs of 0.87 (95% CI: 0.83, 0.91) in the training set and 0.83 (95% CI: 0.78, 0.88) in the external test set, with no significant difference between them (P > .05). The radiopathomic GNN outperformed both single-modality GNNs (training set: AUC, 0.95 [95% CI: 0.92, 0.98]; external test set: AUC, 0.91 [95% CI: 0.87, 0.94]; P < .05; NRI and IDI confirmed). Pathomic graphs dominated probability increases for pCR predictions, while radiomic graphs drove probability decreases for non-pCR predictions. Multifaceted analyses verified GNNs' explainability. Conclusion The developed RPGDL system enabled multiscale spatial-contextual intratumoral heterogeneity modeling for high-performance, explainable prediction of pCR to neoadjuvant therapy in breast cancer. Keywords: Dynamic Contrast-enhanced MRI, Breast, Tumor Response, Radiology-Pathology Integration, Prognosis, Principal Component Analysis, Perception, Supervised Learning, Reconstruction Algorithm Supplemental material is available for this article. © RSNA, 2026.
4 May 2026
Read appraisal →European radiology
Predicting molecular types of adult-type diffuse gliomas based on MRI reports with large language models
OBJECTIVES: To evaluate the performance of large language models (LLMs) in predicting molecular types of adult-type diffuse gliomas according to the 2021 WHO classification using MRI radiology reports. MATERIALS AND METHODS: This retrospective study included 2169 patients diagnosed with adult-type diffuse gliomas (294 oligodendrogliomas, 295 IDH-mutant astrocytomas, and 1580 IDH-wildtype glioblastomas) between July 2005 and March 2024 from four hospitals in Asia and Europe. Seven proprietary and open-source LLMs were assessed: GPT-4o-mini, GPT-4.1-mini, Llama 3.1 8B, Llama 3.1 70B, Qwen2.5 7B, Deepseek-r1 8B, and Mistal 7B. The performance of LLMs in classifying molecular types was compared based on the provision of relevant knowledge of glioma imaging findings (knowledge-based vs. naïve prompt). The impact of radiologists' subspecialization in neuro-oncology, report quality, and reporting language on LLMs' performance was also evaluated. RESULTS: LLMs achieved significantly higher (naïve vs. knowledge-based; GPT-4o-mini, 77.0% vs. 79.1%, p < 0.001; Qwen2.5 7B, 75.9% vs. 79.5%, p < 0.001; Deepseek-r1 8B, 66.0% vs. 73.2%, p < 0.001) or comparable accuracy (GPT-4.1-mini, 78.7% vs. 78.6%; Llama 3.1 70B, 78.0% vs. 78.1%; Mistral 7B, 58.4% vs. 57.4%) using knowledge-based prompt compared to naïve prompt, except for Llama 3.1 8B (65.4% vs. 44.6%, p < 0.001). Differences in accuracy were more pronounced in smaller-sized LLMs. Additionally, the accuracy was significantly higher with reports by neuro-oncology specialists and high-quality reports in all LLMs (p < 0.001). CONCLUSIONS: LLMs may provide preoperative information on the tumor types of adult-type diffuse gliomas from MRI reports by providing relevant knowledge in the prompt. Informative and descriptive reports could further enhance LLMs' performance. KEY POINTS: Question Our study aimed to evaluate large language models' (LLMs) ability to efficiently predict molecular types of adult-type diffuse gliomas according to the 2021 WHO classification. Findings Larger models generally showed better accuracy and were less sensitive to domain-specific knowledge. Their performance improved when using high-quality, longer reports or reports by neuro-oncology specialists. Clinical relevance These findings highlight the potential role of LLMs in predicting glioma molecular types, underscoring the importance of informative and descriptive reports in enhancing their performance.
4 May 2026
Read appraisal →European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
Machine learning identifies a 10-gene signature predicting hepatocellular carcinoma recurrence and immune-metabolic reprogramming
BACKGROUND: Hepatocellular carcinoma (HCC) remains a major cause of cancer-related mortality, with frequent recurrence after curative treatment. Conventional clinicopathological prognostic systems fail to capture the molecular heterogeneity underlying recurrence, highlighting the need for biologically informed biomarkers. METHODS: We developed a transcriptome-based prognostic model for HCC recurrence using a machine learning-guided feature selection strategy designed to reduce survival-time bias. LASSO-based gene selection was integrated with multivariable Cox regression, and model performance was assessed through stratified cross-validation and independent external validation. RESULTS: Analysis of the TCGA-LIHC cohort (n = 344) identified a stable 38-gene recurrence-associated signature (AUC: 0.715-0.847), which was distilled into a reproducible 10-gene classifier combined with tumor stage. This integrated model effectively stratified patients into high- and low-risk groups (log-rank P < 0.0001) and was independently validated in the HCCDB25 cohort (n = 158; P = 0.031). High-risk tumors exhibited an immune-excluded phenotype with reduced cytotoxic immune infiltration. Gene set enrichment analyses revealed progressive activation of proliferative signaling, metabolic dysregulation, and immune evasion pathways. CONCLUSIONS: The integrated 10-gene-plus-stage classifier is a robust and generalizable predictor of HCC recurrence, providing mechanistic insights into immune-metabolic reprogramming and highlighting potential implications for risk-informed patient stratification in future studies.
4 May 2026
Read appraisal →BMJ supportive & palliative care
Hand-foot syndrome prevention: pharmacological interventions-systematic review and network meta-analysis
INTRODUCTION: Hand-foot syndrome (HFS) is a dermatological side effect of chemotherapies such as capecitabine and pegylated liposomal doxorubicin. Over recent years, numerous randomised controlled trials (RCTs) have investigated various pharmacological strategies to prevent HFS. Although urea cream and celecoxib have shown promising results, the efficacy of topical diclofenac remains unclear. To provide a comprehensive comparison of pharmacological interventions for HFS prevention in cancer patients undergoing chemotherapy, we conducted a network meta-analysis (NMA). METHODS: We systematically searched PubMed, Cochrane and Scopus for relevant RCTs published up to November 2024. The primary outcome was the incidence of moderate to severe HFS (grade 2-3), while secondary outcomes included the occurrence of all-grade HFS (grade≥1) and chemotherapy modifications. Risk ratios (RR) with 95% CIs were estimated for all outcomes using NMA. RESULTS: Thirteen RCTs comprising 1983 patients, most of whom received capecitabine (n=1849) were included, evaluating five pharmacological interventions. Both topical diclofenac (RR 0.26, 95% CI 0.10 to 0.66) and celecoxib (RR 0.46, 95% CI 0.26 to 0.84) significantly reduced the risk of moderate to severe HFS compared with placebo. Notably, topical diclofenac was the only intervention that significantly reduced the incidence of all-grade HFS and the need for chemotherapy modifications. CONCLUSION: Our NMA demonstrates that topical diclofenac provides the most robust protective effect among available pharmacological interventions for HFS prevention and is also associated with fewer chemotherapy modifications. These findings support its consideration as a preferred preventative strategy for patients at risk of HFS during chemotherapy. PROSPERO REGISTRATION NUMBER: CRD42025643635.
3 May 2026
Read appraisal →BMJ supportive & palliative care
Memantine efficacy in chemotherapy-induced peripheral neuropathy: double-blind placebo-controlled randomised clinical trial
BACKGROUND AND AIMS: Chemotherapy-induced peripheral neuropathy (CIPN) is a detrimental side effect of chemotherapeutic agents. If left untreated, CIPN can persist for several years following the chemotherapy. CIPN management may enhance the quality of life of the affected patients. This study evaluated the efficacy of memantine, an antagonist of N-methyl-D-aspartate, in CIPN management. METHODS: This double-blinded randomised clinical trial was performed on 176 individuals with CIPN. Patients were randomised to either the memantine or placebo groups (88 in each). The memantine group received a single daily dose of 5 mg for the initial 3 days. Subsequently, the dosage was increased to 20 mg per day, with 5 mg increments every 3 days. The placebo group received tablets of equal shape and size. All individuals received medications for 8 weeks. The McGill pain questionnaire and tuning fork test were used to evaluate the severity of neuropathic pain in patients, both at baseline and after the intervention ended. RESULTS: Based on tuning fork test results, significant improvement in neuropathy was observed among patients who received memantine compared with those who received placebo (p<0.001 vs 0.665). McGill pain questionnaire score revealed a significant difference between memantine and placebo groups (p<0.001 for both groups). This difference was also significant after performing intention-to-treat analysis. Moreover, a higher proportion of patients in the memantine group achieved minimal clinically important difference. CONCLUSION: The use of memantine mitigated the CIPN intensity. Thus, it appears that memantine is a promising option for the management of patients experiencing CIPN.
3 May 2026
Read appraisal →American Society of Clinical Oncology educational book. American Society of Clinical Oncology. Annual Meeting
Harnessing Artificial Intelligence for the Management of Patients With GI Cancers
Artificial intelligence (AI) has the potential of reshaping GI oncology by enabling more nuanced interpretation of complex clinical, imaging, and molecular data, while supporting more timely and patient-centered decisions. This article synthesizes perspectives across the GI cancer continuum, beginning with a framework for context-aware AI that emphasizes metadata, multimodal integration, and lifecycle quality and safety as foundations for trustworthy tools that clarify, rather than conceal, uncertainty. Next, AI in endoscopy is highlighted as an example in clinic practice, focusing on computer-aided detection and diagnosis systems that not only increase adenoma detection rates but also raise questions about surveillance burden, real-world effectiveness, and the balance between skill enhancement and potential deskilling of endoscopists. Another section explores how AI can help GI oncologists design, prioritize, and implement highly innovative clinical trials-particularly multi-omic and imaging-driven approaches-while envisioning a future in which far more patients participate in trials that align with their goals and values. The final section reviews emerging AI-enabled clinical trial matching pipelines, including large language model-based retrieval and prescreening tools that operate on real-world electronic health record and protocol data, and discusses challenges related to bias, privacy, explainability, and workflow integration. Together, these contributions argue that the greatest impact of AI in GI oncology will come from deliberately aligning technical capabilities with highly relevant patient-centered clinical questions, ethical governance, and implementation strategies that expand access to trials and improve outcomes for patients with GI malignancies.
2 May 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Conducting a randomized controlled clinical trial on palliative care in patients with glioblastoma - what are the challenges?
PURPOSE: Patients with glioblastoma represent a highly vulnerable cohort as they often experience rapid health deterioration with severe symptom burden including neurological, (neuro)psychological, and psychiatric symptoms. The aim of this sub-analysis of the "Early Palliative Care for Patients with Glioblastoma" (EPCOG) trial was to investigate the specific challenges of conducting a multicenter, randomized, controlled, clinical trial in glioblastoma patients testing a specialized palliative care (PC) intervention. METHODS: We analyzed screening protocols and protocol deviations with respect to number and reasons for non-participation, skipped/delayed visits and attrition using descriptive statistics and content analysis of free-text comments. RESULTS: In total, 41.5% of 556 screened patients were enrolled. Main reasons for non-participation were lack of interest (25.7%) and low functional status (11.5%). Attrition due to death (57.6%) was higher than due to illness (5.2%) or other reasons (21.2%). Main reasons for visit deviations were structural issues (in > 50% of neurosurgical visits), health status, and patient request. Protocol deviations showed that specialized PC intervention visits were least frequently skipped (4.5%) compared to study-specific outcome assessment (10.1%) and neurosurgical (43.3%) visits. Further, only 11.0% of the specialized PC intervention visits were delayed compared to 22.3% of the outcome assessment and 56.4% of the neurosurgical visits. CONCLUSION: In this clinical trial involving glioblastoma patients, a high level of motivation among the study participants could be reached, as reflected by low protocol deviations during the specialized PC intervention and study-specific outcome assessment visits. Reasons for this might be a close guidance as well as a patient and caregiver-oriented communication, e.g., by a personal contact of the PC team in the intervention group, personal outcome assessment visits at patients' whereabouts, or the inclusion of a study nurse at each site. Considering the high vulnerability of glioblastoma patients is crucial when designing and conducting clinical trials.
2 May 2026
Read appraisal →Signal transduction and targeted therapy
Abexinostat, a histone deacetylases inhibitor, for patients with relapsed or refractory follicular lymphoma: a multi-center, single-arm phase 2 study
This phase 2, single-arm, multi-center study (NCT03934567) evaluates the efficacy and safety of abexinostat, a histone deacetylase inhibitor, in patients with relapsed or refractory (r/r) follicular lymphoma (FL). Eligible participants had previously received a minimum of two systemic treatment lines, such as cytotoxic agents and/or anti-CD20 monoclonal antibodies. Participants received abexinostat 80 mg oral twice daily on a schedule of seven days on and seven days off, within 28-day cycles, continuing until unacceptable toxicity or disease progression occurred. The primary endpoint was objective response rate (ORR), evaluated by an independent review committee (IRC). Secondary endpoints comprised duration of response (DoR), disease control rate (DCR), progression-free survival (PFS), overall survival (OS), and safety. Between May 2, 2019, and November 19, 2023, 90 patients were enrolled. As of October 08, 2024, with a median follow-up of 30.55 months (95% confidence interval [CI] 23.72-33.64), 17.8% (16/90) of patients were still on study treatment. The ORR was 69.5% (57/82, 95% CI 58.4-79.2), with a complete response rate of 14.6% (12/82). The DCR was 91.5% (75/82, 95% CI 83.2-96.5). Tumor size reduced in 89% (73/82) of patients. The median DoR was 13.96 months (95% CI 9.20-not reached [NR]) and the median PFS was 13.80 months (95% CI 9.69-30.26). The median OS was 47.18 months (95% CI 45.70-NR). The most common treatment-emergent adverse events were thrombocytopenia (77/90, 85.6%), neutropenia (53/90, 58.9%), and leukopenia (47/90, 52.2%). The results of this study demonstrated that abexinostat had promising efficacy and manageable safety profile, supporting abexinostat as a new treatment option for the third-line or later-line treatment of r/r FL.
2 May 2026
Read appraisal →Clinical & experimental metastasis
All-site radiotherapy for metastatic ewing's sarcoma: a short-term analysis of feasibility, response, and safety.
BACKGROUND: Metastatic Ewing sarcoma (EWS) has a poor prognosis. While metastasis-directed therapy benefits oligometastatic disease, the role of comprehensive all-site radiotherapy in widespread disease remains underexplored. This study aimed to preliminarily assess its short-term efficacy and safety. METHODS: This retrospective analysis included 21 consecutive metastatic EWS patients treated with helical tomotherapy (Aug 2024-Jun 2025). All known metastases and unresected primary tumors received radiotherapy (45-55 Gy in 20 fractions for most sites; 12-45 Gy for lung/pleural metastases). Concurrent systemic therapies included chemotherapy, tyrosine kinase inhibitors (TKIs), immune checkpoint inhibitors (ICIs), or combinations. Primary endpoints were local objective response rate (ORR, RECIST 1.1) at 2 months post-radiotherapy and acute toxicity (CTCAE 5.0). Systemic therapy subgroup analyses were exploratory. RESULTS: Median follow-up was 6 months (range 2-11). Among 77 target lesions, ORR was 61.0% (CR 48.1%), and disease control rate (DCR) was 97.4%. Soft tissue lesions had a significantly higher CR rate than bone metastases (78.3% vs 3.2%, p < 0.001), potentially influenced by RECIST 1.1 limitations and treatment heterogeneity. Median progression-free survival (PFS) was 6.0 months (95% CI: 2.30-9.70), and median overall survival (OS) was 8.0 months (95% CI: 6.69-9.31). Combination systemic therapy was associated with improved PFS (HR 3.94, 95%CI 1.32-11.78; p = 0.014), with chemotherapy-based regimens yielding the best PFS (8.5 months). TKI-containing regimens were linked to shorter PFS (4.0 vs 8.5 months, p = 0.048) due to selection bias. Acute toxicity was manageable, with grade 3 thrombocytopenia (19.0%) and pneumonitis (4.7%) resolving with treatment. CONCLUSION: All-site radiotherapy achieves promising short-term local control with manageable toxicity in metastatic EWS, demonstrating feasibility and a lesion site-specific response warranting further investigation. Systemic therapy, particularly chemotherapy, correlates with improved PFS. Given high systemic progression rates, long-term outcomes may benefit from integration with more effective systemic therapies, supporting future prospective trials.
2 May 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Cardiac adverse events associated with axitinib: A real-world pharmacovigilance study based on the Japanese adverse drug event report database
OBJECTIVE: To evaluate the disproportionality, times to onset, and outcomes of cardiac adverse events (AEs) associated with axitinib, using data from the Japanese Adverse Drug Event Report (JADER) database. METHODS: We analyzed data for the period from April 2004 to December 2024. Those on cardiac AEs were extracted and the disproportionality of axitinib-associated AEs was assessed by calculating reporting odds ratios (RORs). RESULTS: Of the 2,988,681 reports with sex and age information, we identified 4788 reports of AEs associated with axitinib, including 508 cardiac AEs. Signals were detected for 16 cardiac AEs after adjustment for sex and age. Fatal outcomes were reported for five of these events with particularly high rates observed for cardiac failure, myocarditis, and immune-mediated myocarditis. Median times to onset indicated that cardiac AEs associated with axitinib occurred 14 to 420 days after administration. Weibull distributions showed that seven AEs (blood pressure increased, myocardial infarction, acute myocardial infarction, blood pressure systolic increased, oedema peripheral, oedema, and generalised oedema) occurred constantly throughout the exposure period (random failure type), and stress cardiomyopathy and immune-mediated myocarditis developed in a dose-dependent manner (wear-out failure-type). CONCLUSIONS: We focused on cardiac AEs associated with axitinib as post-marketing AEs. Some with fatal outcomes may occur not only early in treatment but also later in the course. Continuous monitoring is essential to ensure timely detection and management of these potentially serious AEs in clinical practice.
28 Apr 2026
Read appraisal →Journal of ethnopharmacology
A multicenter, placebo-controlled clinical trial and preliminary experimental study exploring the efficacy of modified Banxia Xiexin Decoction in the treatment of advanced colorectal cancer
ETHNOPHARMACOLOGICAL RELEVANCE: Recent studies in China have increasingly focused on the evidence-based evaluation of malignant tumors, including colorectal cancer (CRC), leveraging the unique properties of herbal medicine. This has led to notable progress in the development of novel therapies. Clinical observations indicate that the modified Banxia Xiexin Decoction (mBXD) exhibits significant anti-cancer effects. However, well-designed clinical trials and foundational research in this field remain insufficient. AIMS OF THE STUDY: This study aims to assess the clinical efficacy and safety of mBXD in the treatment of advanced CRC, alongside preliminary in vivo and in vitro verification of its anti-CRC effects. METHODS: A clinical trial was conducted from October 2020 to October 2024 across 12 hospitals in Southwest China. Eligible participants were randomly assigned to either the mBXD group or the placebo group. Both groups received standard chemotherapy combined with targeted therapy; the mBXD group received mBXD granules (Ready-to-use), while the placebo group received a placebo. Treatment lasted for six months (six cycles). The primary endpoint was progression-free survival (PFS), while secondary endpoints included overall survival (OS), objective response rate (ORR), disease control rate (DCR), and serum tumor markers. Efficacy was assessed every two cycles, with follow-ups conducted every three months after treatment completion until disease progression or death. This study is registered with the Chinese Clinical Trial (No. ChiCTR2100041643). Basic research included the establishment of a subcutaneous CT26 colon cancer model and the use of the HCT116 cell line, with mBXD interventions to monitor tumor growth and evaluate the viability, invasion, and migration of HCT116 cells. RESULTS: A total of 129 participants were enrolled. Compared to the placebo group, the mBXD group showed a 1.9-month and 2.8-month extension in PFS and OS, respectively, with a 17 % reduction in the risk of disease progression and a 15 % reduction in the risk of death (P = 0.341, 0.465). After two, four, and six cycles, the ORR and DCR in the mBXD group were 3.7-12.6 % and 1.2-18.5 % higher than those in the placebo group, respectively, with only the DCR after six cycles reaching statistical significance (P = 0.037). Moreover, serum levels of CEA and CA724 were significantly reduced in the mBXD group after four and six cycles (P ≤ 0.05). No severe adverse events were reported during the trial. Additionally, mBXD significantly inhibited subcutaneous tumor growth after 14 days of treatment (P < 0.05), and its drug-containing serum notably suppressed the viability, migration, and invasion of HCT116 cells. CONCLUSIONS: mBXD demonstrates clinical efficacy in prolonging PFS and OS, stabilizing tumor lesions, and reducing serum tumor marker levels in advanced CRC patients. The treatment is both safe and well-tolerated. Furthermore, mBXD significantly inhibits the growth of subcutaneous CT26 colon cancer and suppresses the viability, invasion, and migration of HCT116 cells.
28 Apr 2026
Read appraisal →Journal for immunotherapy of cancer
Phase 2 trial of pTVG-HP ± pTVG-AR DNA vaccines and pembrolizumab in patients with metastatic, castration-resistant prostate cancer (mCRPC)
BACKGROUND: We previously reported a trial using pembrolizumab with a DNA vaccine encoding prostatic acid phosphatase (pTVG-HP) in patients with metastatic castration-resistant prostate cancer (mCRPC). Favorable clinical outcomes were associated with T-cell activation, suggesting immune response to vaccination was critical. In the current trial, we evaluated whether immunization with two vaccines, to broaden the T-cell response to other cancer-associated antigens, would elicit greater anti-tumor efficacy. MATERIALS AND METHODS: 60 patients with mCRPC were treated with vaccine on days 1 and 8 of a 3 week cycle, with pembrolizumab given on day 1 of each cycle. Patients in Arm A (n=30) received pTVG-HP as the vaccine, and patients in Arm B (n=30) received pTVG-AR (encoding the ligand-binding domain of the androgen receptor) alternating with pTVG-HP every two cycles. After eight cycles (6 months), patients continued to receive pembrolizumab alone, with the option to receive further booster immunizations at prostate-specific antigen (PSA) progression. The primary objective was 6-month progression-free survival (PFS). Secondary objectives included safety, objective response rate, PSA response rate, overall survival (OS), and immunological evaluations. RESULTS: Overall median time to progression was 24 weeks (24 weeks Arm A, 24 weeks Arm B, p=0.80). The month 6 PFS rate was 51% in Arm A and 45% in Arm B. Median OS was 2.8 years (2.5 years Arm A, 3.7 years Arm B, p=0.16). 20% of patients in each arm experienced a PSA decline of >50%. 20 patients (n=8 Arm A, n=12 Arm B) had measurable disease, and 6/20 (30%) experienced a partial response. Eight patients received booster immunizations after 6 months, 3 of whom subsequently experienced a PSA decline. Three patients in Arm B experienced creatine kinase elevation with or without increased transaminases, which were not observed in Arm A. T cell immune responses to both antigens were detected in patients treated in Arm B. CONCLUSIONS: Treatment with DNA vaccines and pembrolizumab demonstrated anti-tumor activity in terms of PSA declines and objective responses. The addition of pTVG-AR did not significantly increase measures of clinical efficacy; however, it was associated with a slight increase in toxicity to tissues that also express AR. TRIAL REGISTRATION NUMBER: NCT04090528.
27 Apr 2026
Read appraisal →American Society of Clinical Oncology educational book. American Society of Clinical Oncology. Annual Meeting
Tech That Scales: A Practical Framework for Artificial Intelligence-Enabled Cancer Care in Low- and Middle-Income Countries and Underserved US Counties
Cancer outcomes remain starkly unequal: 5-year survival rates for common malignancies in low- and middle-income countries (LMICs) often lag 20-40 percentage points behind high-income benchmarks, and similar disparities persist between well-resourced metropolitan centers and rural or safety-net settings within high-income countries. The gap is driven less by the absence of effective interventions than by workforce shortages, fragmented referral pathways, limited infrastructure, and loss to follow-up after abnormal screens. Technologies that scale must therefore function as deployable workflows integrating staffing, logistics, quality assurance (QA), governance, and monitoring not merely as stand-alone algorithms or devices.1 This review synthesizes evidence across four complementary technology families that address these constraints across the continuum of care: artificial intelligence (AI)-supported screening and triage as the population entry-point layer; Project ECHO telementoring as the workforce-capacity layer; electronic patient-reported outcomes and remote symptom monitoring as the longitudinal continuity layer; and AI-powered clinical trial prescreening hubs as the access-to-innovation layer. The technologies do not carry equal evidentiary weight, and they should not be deployed identically in every setting. Our aim is to show how oncology leaders can sequence them pragmatically inside a common operating logic while adapting to local infrastructure and governance. We organize the framework in patient journey order entry into care through screening, workforce support through telementoring, continuity through remote monitoring, and access to innovation through trial prescreening, and draw examples from both LMIC programs and underserved US settings. We therefore present a practical implementation playbook, including a 90-day launch checklist, staffing models, QA frameworks, equity and bias monitoring metrics, and a program outcomes dashboard designed for oncology leaders seeking to move beyond pilots toward durable, monitored deployment.
27 Apr 2026
Read appraisal →Journal of medicinal chemistry
Machine Learning Approach to Anticancer Activity Prediction of Transition-Metal Complexes Based on a Large-Scale Experimental Database
In this work, we developed a straightforward data-driven approach to predict the cytotoxicity of metal complexes based entirely on their (metal + ligands) composition. To this end, we have manually curated MetalCytoToxDB─a comprehensive experimental database comprising 26,500 IC50 values for 7050 metal complexes against 754 cell lines from 1921 articles. Based on these, machine learning models were created to accurately assign the cytotoxicity class within the ruthenium and iridium subsets. Moreover, external validation of the best-performing model on the unseen data was carried out. The possibility of multimetal predictions was explored, enabling assessment of cytotoxicity among the complexes of metals, for which experimental data are relatively scarce. The interpretability and limitations of the developed models are discussed. Finally, a pipeline for the effective high-throughput computational screening of ruthenium complexes is proposed. The MetalCytoToxDB is available online for AI-assisted exploration at https://biometaldb.streamlit.app/.
25 Apr 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Predictive biomarkers of placebo response in patients with cancer: an exploratory analysis of the ABCD randomized clinical trial
PURPOSE: Placebo effect is commonly observed in symptom management randomized clinical trials. However, there are currently no established predictive biomarkers of placebo response. We examined the patterns of association between placebo response and baseline plasma cytokine levels for 10 symptoms in patients with cancer. METHODS: This is an exploratory analysis of the double-blind, randomized ABCD trial, which compared high-dose dexamethasone to placebo for dyspnea treatment in patients with cancer (clinicaltrials.gov; NCT03367156). The primary outcome was change in dyspnea intensity. Four plasma cytokines (TNF, IL-6, IL-8, and IL-10) and 10 Edmonton Symptom Assessment System symptoms were measured at baseline, day 7, and day 14. Generalized additive models and exploratory analyses were used to examine the patterns of association between baseline cytokine levels and symptom responses on day 7 and day 14 in placebo and dexamethasone groups. RESULTS: This analysis included data from 45 patients. In the placebo group, we observed a significant negative association (p < 0.05) between ≥ 1 of the measured cytokines' baseline levels across multiple symptoms on day 7 (5/10 symptoms) and day 14 (3/10 symptoms), including appetite, drowsiness, nausea, and pain. Exploratory analysis revealed a negative association in the placebo group in 33/40 cytokine-symptom response combinations for day 7 (p < 0.001) and 29/40 combinations for day 14 (p = 0.006). CONCLUSION: Our preliminary findings suggest patients with high baseline cytokine levels may be less likely to report placebo response for multiple symptoms. Upon further confirmation, these findings may have implications for future clinical trial design. In this exploratory analysis, we observed that patients with cancer were less likely to report placebo response for multiple symptoms if they had higher levels of baseline cytokines. These preliminary findings have important potential implications for placebo-controlled clinical trials, including patient selection, design of interventional controls, and trial interpretation. Additionally, they invite further research on possible mechanistic links between inflammation and placebo effect.
25 Apr 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Implementing an electronic health record workflow to identify parents with cancer and implications for supportive oncology care
PURPOSE: Parents with cancer face unique psychosocial challenges as they navigate treatment while caring for dependent children, yet parenthood status is rarely captured in oncology workflows. This initiative evaluated the feasibility and outcomes of a new electronic health record (EHR)-based workflow to systematically identify parents with cancer and then connect them to family-focused assessment and supportive care. METHODS: A two-phase quality improvement initiative was conducted at an NCI-designated comprehensive cancer center. Phase 1 included process mapping, analysis of tumor registry and distress screening data, and development of an EHR screening item assessing parenting status. Phase 2 implemented the item ("Patient is a parent/caregiver with child/ren < 22 years of age?") into oncology workflows, linking identified parents to a new supportive care program, Family Circle, which includes psychosocial assessment, navigation, and family-centered support. Training for oncology clinicians included in-service education. Analyses examined prevalence estimates and associations between parenting, distress, anxiety, and depression and reports of quality improvement metrics. RESULTS: Among N = 2,172 screened patients, 20.6% identified as parents; the age-adjusted prevalence of parenting with children under 21 was 16.9%, consistent with national estimates. Parents reported significantly higher distress (odds ratio [OR] = 1.35, 95% confidence interval [CI] = 1.11-1.64, p = 0.003) and anxiety (OR = 1.28, 95% CI = 1.06-1.52, p = 0.009) than matched non-parents. Parents with younger children exhibited greater anxiety than those with older children (F = 4.32, df = 2, 393, p = .01). During implementation, N = 431 parents were identified in 6 months (79.4% through the EHR item). CONCLUSION: Embedding parenting status screening into the EHR is feasible, accurate, and critical for linking parents with cancer to supportive care. Wider implementation of such workflows may enhance psychosocial oncology practice and improve cancer parenting outcomes.
24 Apr 2026
Read appraisal →Cancer immunology, immunotherapy : CII
Envafolimab combined with lenvatinib and albumin-bound paclitaxel in previously treated advanced gastric/gastroesophageal junction (G/GEJ) adenocarcinoma: a prospective, phase II, multi-cohort trial
BACKGROUND: This single-center, open-label, exploratory trial aimed to investigate the efficacy and safety of envafolimab combined with lenvatinib and chemotherapy in previous treated advanced G/GEJ adenocarcinoma. METHODS: Eligible patients with HER2-negative, microsatellite stable (MSS) advanced G/GEJ adenocarcinoma who had progressed after first-line treatment were enrolled in this phase II trial. Patients without previously receiving PD-1/PD-L1 inhibitors were enrolled in Group A, and those who progressed on the first-line PD-1 inhibitors were included in Group B. Patients in both groups received envafolimab (200 mg, subcutaneous injection (sc), days 1 and 15, Q4W) combined with lenvatinib and albumin-bound paclitaxel (100 mg/m2, IV, days 1, 8 and 15, Q4W, up to 6 cycles) until disease progression, unacceptable toxicity, or refusal of continuation. The primary endpoint was objective response rate (ORR). RESULTS: A total of 30 patients were included for safety and efficacy analysis. As of data cutoff (Sep 14, 2024), the median follow-up was 17.0 months (IQR: 8.0-18.8) in Group A. The ORR was 60.0%, and the DCR was 100.0%. The mPFS was 8.2 months (95% CI 6.1-10.4) with mOS of 14.8 months (95% CI 7.4-22.2). With a median follow-up of 9.0 months (IQR: 6.1-16.2) in Group B, the ORR was 46.7%, and the DCR was 100.0%. The mPFS was 5.9 months (95% CI 3.8-8.1), and the mOS was 11.5 months (95% CI 3.1-19.9). The overall incidence of adverse events (AEs) of any grade was 100%. While Group A showed numerically better outcomes than Group B, there was no statistically significant difference in PFS (P = 0.629) or OS (P = 0.873) between the two groups. CONCLUSIONS: Second-line envafolimab-based combination therapy demonstrated promising effects in previously treated advanced GC, particularly in those who have not previously received ICIs.
23 Apr 2026
Read appraisal →JAMA
Tumor Debulking in Combination With Chemotherapy in Multiorgan Metastatic Colorectal Cancer: The ORCHESTRA Randomized Clinical Trial
IMPORTANCE: Local therapy, including surgery, radiation, and ablation, is increasingly used in patients with multiorgan metastatic colorectal cancer (mCRC). However, prospective evidence for a survival benefit of tumor debulking is lacking. OBJECTIVE: To investigate whether tumor debulking added to palliative chemotherapy improves survival of patients with multiorgan mCRC. DESIGN, SETTING, AND PARTICIPANTS: This investigator-initiated, open-label, multicenter, randomized clinical trial enrolled patients with multiorgan mCRC between May 2013 and May 2023. The last date of follow-up was April 4, 2024. Patients were enrolled in 27 hospitals in the Netherlands and 1 in the UK. Adult patients with multiorgan mCRC were considered eligible if more than 80% tumor debulking was deemed feasible by resection, radiotherapy, and/or thermal ablation prior to starting first-line palliative chemotherapy. INTERVENTIONS: After achieving objective tumor response or stable disease after 3 cycles of capecitabine and oxaliplatin or 4 cycles of 5-fluorouracil and oxaliplatin with or without bevacizumab, patients were randomized 1:1 to receive chemotherapy alone (standard care group) or tumor debulking followed by chemotherapy. MAIN OUTCOMES AND MEASURES: The primary end point was overall survival. Secondary end points included progression-free survival and serious adverse events. These outcomes were analyzed in the intention-to-treat population, applicable from randomization. A prespecified interim analysis performed after the initial 100 participants were enrolled revealed that the trial was both safe and feasible to proceed. RESULTS: A total of 382 of 454 enrolled patients were randomized: 192 in the chemotherapy alone group (133 [69%] male) and 190 in the chemotherapy plus tumor debulking group (127 [67%] male). The median age was 64 years in both groups. After a median follow-up of 32.3 months, median overall survival in the chemotherapy alone group was 27.5 months vs 30.0 months in the chemotherapy plus tumor debulking group (adjusted hazard ratio, 0.88 [95% CI, 0.70-1.10]; P = .26). Median progression-free survival in the chemotherapy alone group was 10.4 months vs 10.5 months in the chemotherapy plus tumor debulking group (adjusted hazard ratio, 0.83 [95% CI, 0.67-1.02]; P = .08). More patients in the chemotherapy plus tumor debulking vs chemotherapy alone group had any serious adverse events (101 [53%] vs 74 [39%]; P = .006). CONCLUSIONS AND RELEVANCE: Tumor debulking in addition to first-line palliative systemic treatment failed to improve overall survival compared with systemic treatment alone for patients with multiorgan mCRC and should not be considered standard care. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT01792934.
23 Apr 2026
Read appraisal →JMIR human factors
Perceptions of Health Care Professionals on the Integration and Use of AI in Clinical Cancer Care: Interview Study
BACKGROUND: Artificial intelligence (AI) is increasingly recognized for its potential to transform cancer care. However, much of the existing evidence of its efficacy comes from controlled settings. There remains a need to complement this knowledge with insights into how AI tools are perceived and used in real-world clinical settings, as well as how their use impacts clinical practice. OBJECTIVE: This study aimed to explore key factors influencing clinicians' acceptance of AI tools and examine how AI adoption and use impact clinical workflows in cancer care. METHODS: We used purposive sampling for recruiting oncology-related health care professionals and collected data using web-based semistructured interviews to gather their perceptions. Data were thematically analyzed and interpreted through the lenses of sociotechnical systems theory and the Unified Theory of Acceptance and Use of Technology. RESULTS: Participants largely accept and perceive AI tools as beneficial to clinical practice. Unified Theory of Acceptance and Use of Technology constructs were reflected in our data as determinants of intention to adopt AI tools. Trust appears as an influential factor in shaping attitudes toward AI tools. Acceptance is found to both precede AI tool use and to grow following successful integration. The use of AI tools is perceived to yield operational benefits, such as reduced workload and time savings, and clinical benefits, such as increased diagnostic reliability and reduced patient recall. Minimal disruption to clinical workflows following integration of AI tools was reported for some cancer screening applications and organ-at-risk segmentation, whereas greater disruption was anticipated for 3D cancer screening. Although accountability and lack of explainability are highlighted in literature as barriers to AI adoption, participants do not view these as significant obstacles in image-based diagnostic contexts. Additionally, negative impacts, such as overreliance on AI and reduced critical review of AI results, arise in association with the use of AI tools. CONCLUSIONS: Participants perceive AI tools to deliver benefits to clinical cancer care. However, their adoption relies on their alignment with clinical needs and seamless integration into clinical workflows. To encourage clinician acceptance, the identified concerns must be addressed. Future work should focus on training programs, co-design with clinicians, and exploration of mitigation strategies for emerging adverse effects, such as automation bias and potential skill erosion.
22 Apr 2026
Read appraisal →Journal of clinical oncology : official journal of the American Society of Clinical Oncology
Nivolumab ± Ipilimumab in Patients With Pretreated Advanced Neuroendocrine Carcinoma: The GCO-001 NIPINEC Randomized Phase II Trial
PURPOSE: There is no standard second-line therapy for gastroenteropancreatic (GEP) and lung large-cell neuroendocrine carcinoma (NEC) after the failure of platinum-based chemotherapy. This study aimed to investigate the efficacy of nivolumab ± ipilimumab. METHODS: The GCO-001-NIPINEC (ClinicalTrials.gov identifier: NCT03591731) trial was a noncomparative, open-label, phase II trial. The main inclusion criteria were age ≥18 years, performance status (PS) ≤2, advanced large- and small-cell GEP-NEC and large-cell lung NEC, and second- or third-line treatment for NECs refractory to platinum-based chemotherapy. Patients were randomly assigned (1:1) and stratified by age and PS to receive nivolumab (3 mg/kg/once every 2 weeks) ± ipilimumab (1 mg/kg/once every 6 weeks) for 2 years or until progression or unacceptable toxicity. The primary end point was objective response rate (ORR) at 8 weeks, assessed by investigators. RESULTS: A total of 185 patients (91 in the nivolumab arm and 94 in the nivolumab-ipilimumab arm) were enrolled between December 2018 and March 2021; 169 were analyzed (median age of 64.5 years, 71% male, 91% PS 0-1). The main primary tumor locations were lungs (50%), colorectal (15%), gastroesophageal (14%), and pancreatic (13%) regions. The ORR at 8 weeks was 7.2% (95% CI, 2.7 to 15.1]) in the nivolumab arm and 14.0% (95% CI, 7.4 to 23.1) in the nivolumab-ipilimumab arm. The best ORR was 9.6% and 20.9%, respectively, whereas the median progression-free and overall survival were approximately 2 months and 6 months in both arms. One treatment-related death occurred, in the nivolumab arm. The grade 3-4 adverse events (≥5%) were asthenia (13%), gamma-glutamyl transferase increase (10%), alkaline phosphatase increase (9%), dyspnea (7%), and anemia (6%) in the nivolumab-ipilimumab arm. CONCLUSION: Nivolumab-ipilimumab could be a second-/third-line treatment option for patients with NECs. However, given the limited magnitude of benefit, studies are warranted to evaluate its use earlier and/or associated with chemotherapy.
21 Apr 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
Read appraisal →Journal of clinical oncology : official journal of the American Society of Clinical Oncology
Pembrolizumab With or Without Lenvatinib as First-Line Therapy for Recurrent or Metastatic Head and Neck Squamous Cell Carcinoma: Phase III LEAP-010 Study
PURPOSE: The PD-1 inhibitor pembrolizumab is approved as first-line treatment for recurrent/metastatic head and neck squamous cell carcinoma (R/M HNSCC). In LEAP-010 (ClinicalTrials.gov identifier: NCT04199104), the multikinase inhibitor lenvatinib plus pembrolizumab was evaluated as first-line therapy in participants with PD-L1 combined positive score (CPS) ≥1 R/M HNSCC. METHODS: In this phase III, randomized, placebo-controlled, double-blind study, participants 18 years and older with PD-L1 CPS ≥1 R/M HNSCC deemed incurable by local therapy were randomly assigned 1:1 to lenvatinib 20 mg plus pembrolizumab 200 mg IV once every 3 weeks for ≤35 cycles or placebo orally once daily plus pembrolizumab 200 mg IV once every 3 weeks for ≤35 cycles. Primary end points were objective response rate (ORR), progression-free survival (PFS), and overall survival (OS). Per the prespecified analysis plan, ORR and PFS were reported from the first interim analysis (IA1; data cutoff: July 6, 2022), and OS from IA2 (data cutoff: May 30, 2023). RESULTS: Five hundred eleven participants were randomly assigned to lenvatinib plus pembrolizumab (n = 256) or placebo plus pembrolizumab (n = 255). The median time from random assignment to data cutoff was 11.5 months for IA1 and 21.3 months for IA2. At IA1, the median PFS was 6.2 months for lenvatinib plus pembrolizumab versus 2.8 months for placebo plus pembrolizumab (hazard ratio [HR], 0.64 [95% CI, 0.50 to 0.81]; P = .0001040); the ORR was 46.1% versus 25.4%, respectively (difference = 20.2% [95% CI, 10.5 to 29.6]; P = .0000251). At IA2, the median OS was 15.0 months for lenvatinib plus pembrolizumab versus 17.9 months for placebo plus pembrolizumab (HR,1.15 [95% CI, 0.91 to 1.45]; P = .882). At IA2, 170 (66.9%) participants receiving lenvatinib plus pembrolizumab had grade 3-4 all-cause adverse events compared with 97 (38.3%) participants on placebo plus pembrolizumab. CONCLUSION: In participants with PD-L1 CPS ≥1 R/M HNSCC, first-line lenvatinib plus pembrolizumab significantly improved ORR and PFS, but not OS, compared with placebo plus pembrolizumab. The safety profile was consistent with published data.
21 Apr 2026
Read appraisal →Journal of clinical oncology : official journal of the American Society of Clinical Oncology
CALYPSO: Final Results of Savolitinib and Durvalumab Combination in Metastatic Papillary Renal Cancer
PURPOSE: The CALYPSO study demonstrated activity of savolitinib and durvalumab in MET-driven papillary renal cancer (PRC). We report final efficacy outcomes and exploratory circulating tumor DNA (ctDNA) biomarker analysis. METHODS: This single-arm phase II study evaluated savolitinib and durvalumab in treatment-naïve or pretreated PRC. End points included overall response rate (ORR), progression-free survival (PFS), and overall survival (OS). FoundationOne CDx assessed DNA alterations and MET/PD-L1 or MET/tumor mutational burden (TMB) copositivity. ctDNA collected at baseline and on treatment was correlated with outcomes. RESULTS: At 41-months median follow-up, ORR was 34% (95% CI, 20.0 to 51.0) in the intention-to-treat (ITT) population (N = 41) and 53% (95% CI, 28.0 to 77.0) in MET-driven patients (n = 17). Median PFS was 6.5 (95% CI, 2.7 to 12.0) versus 13.9 months (95% CI, 2.9 to 23.8), and OS was 18.3 (95% CI, 7.3 to 30.7) versus 27.4 months (95% CI, 9.3 to 37.4), in the ITT population and the MET-driven population, respectively. PD-L1 (66% positive) and TMB (median 2.5 mut/Mb) status did not correlate with response. Baseline ctDNA positivity (10/21) correlated with shorter OS (median 7.3 v 33.3 months), while ctDNA clearance and mean variant allele frequency reduction correlated with improved OS (median 31.3 v 7.2 and 31.3 v 15.5 months, respectively). CONCLUSION: Savolitinib plus durvalumab shows OS in MET-driven PRC, supporting the ongoing SAMETA RIII trial (ClinicalTrials.gov identifier: NCT05043090). ctDNA may be a useful predictive biomarker.
21 Apr 2026
Read appraisal →Journal of clinical oncology : official journal of the American Society of Clinical Oncology
Efficacy and Safety of Ultra-Low-Dose Immunotherapy in Relapsed Refractory Solid Tumors: Phase III Superiority Randomized Trial (DELII)
PURPOSE: Immune checkpoint inhibitors (ICIs) achieve sufficient receptor occupancy at much lower than standard approved doses. We hypothesized that ultra-low-dose nivolumab would retain clinical efficacy. PATIENTS AND METHODS: In this phase III randomized superiority trial, patients with advanced solid tumors (Eastern Cooperative Oncology Group 0-1) and progression on ≥1 prior line of systemic therapy were randomly assigned 1:1 to ultra-low-dose nivolumab (20 mg intravenously once every 2 weeks) or standard chemotherapy (docetaxel or paclitaxel, as per tumor type). Treatment continued until progression or intolerable toxicity. The primary end point was overall survival (OS). RESULTS: From June 2020 to February 2024, we enrolled 500 patients: 250 per arm; 52% had head and neck and 36% lung cancers. The median number of prior lines of therapy was 1 (range, 1-8); 29% had received ≥2 prior lines. Median OS was significantly longer with ultra-low-dose nivolumab: 5.88 months (95% CI, 4.99 to 7.13) versus 4.70 months (95% CI, 3.91 to 5.65; hazard ratio [HR], 0.80 [95% CI, 0.66 to 0.97]; P = .022). One-year OS was 27.3% versus 16.9%. Median progression-free survival was similar: 2.04 months (95% CI, 2.00 to 2.10) with ultra-low-dose nivolumab and 2.09 months (95% CI, 2.04 to 2.17) with chemotherapy (HR, 1.03 [95% CI, 0.86 to 1.23]; P = .77). Grade ≥3 treatment-related adverse events were less frequent with ultra-low-dose nivolumab (42.5% v 60.8%; P < .001). Quality of life (QoL) was significantly better with ultra-low-dose nivolumab. CONCLUSION: Ultra-low-dose nivolumab significantly improves OS versus chemotherapy in pretreated solid tumors, with fewer severe toxicities and better QoL. These findings support re-evaluation of ICI dosing strategies and may enhance global access.
21 Apr 2026
Read appraisal →Journal of clinical oncology : official journal of the American Society of Clinical Oncology
VIKTORIA-1 Trial of Gedatolisib Plus Fulvestrant With or Without Palbociclib in Hormone Receptor-Positive/HER2-/PIK3CA Wild-Type Advanced Breast Cancer
PURPOSE: Gedatolisib potently targets all four class I PI3K isoforms and mTORC1 and mTORC2 to comprehensively block the PI3K/AKT/mTOR pathway and has shown compelling activity in early clinical trials with palbociclib and fulvestrant. METHODS: This phase III randomized trial (VIKTORIA-1; ClinicalTrials.gov identifier: NCT05501886) evaluated the efficacy of gedatolisib-based therapy, comparing gedatolisib, palbociclib, and fulvestrant (gedatolisib triplet) and gedatolisib plus fulvestrant (gedatolisib doublet) with fulvestrant monotherapy in patients with hormone receptor-positive, human epidermal growth factor receptor 2-negative (HER2-), PIK3CA wild-type (WT) advanced breast cancer. Eligible patients had disease progression during or after CDK4/6 inhibitor and aromatase inhibitor treatment. Comparison of progression-free survival as assessed by blinded independent central review for gedatolisib triplet versus fulvestrant and gedatolisib doublet versus fulvestrant was the primary objective. RESULTS: A total of 392 patients were randomly assigned 1:1:1. The median study follow-up was 10.1 months. The median progression-free survival was 9.3 months in the gedatolisib-triplet group, 2.0 months in the fulvestrant group (hazard ratio [HR] for progression or death, 0.24 [95% CI, 0.17 to 0.35]; P < .001), and 7.4 months in the gedatolisib-doublet group (HR, 0.33 [95% CI, 0.24 to 0.48]; P < .001 v fulvestrant). Grade ≥3 treatment-related adverse events (TRAEs) reported in the gedatolisib-triplet and gedatolisib-doublet groups, respectively, included neutropenia (62.3%, 0.8%), stomatitis (19.2%, 12.3%), rash (4.6%, 5.4%), hyperglycemia (2.3%, 2.3%), and diarrhea (1.5%, 0.8%). Study treatment discontinuation because of TRAEs was reported in 2.3% (triplet) and 3.1% (doublet) of patients. CONCLUSION: The addition of gedatolisib to fulvestrant, with or without palbociclib, significantly reduced the risk of disease progression or death in patients with hormone receptor-positive/HER2-, PIK3CA WT advanced breast cancer.
21 Apr 2026
Read appraisal →Journal of health organization and management
Implementing telemedicine for cancer care in European healthcare organizations: lessons from the eCAN Joint Action
PURPOSE: Telemedicine represents a promising innovation to complement conventional cancer care in Europe to improve treatment quality and patient outcomes. Funded between September 2022 and December 2024, the Joint Action "Strengthening eHealth including telemedicine and remote monitoring for health care systems for CANcer prevention and care" (eCAN JA) aimed to bring the benefits of telemedicine to cancer patients across the European Union (EU) Member States, with 35 partner organizations from 16 countries and explored its feasibility through multicenter clinical trials. This study sheds light on key factors to implement telemedicine services for cancer care in European healthcare organizations (HCOs) and offers a forward-looking perspective by conducting a foresight exercise as part of the Sustainability Work Package (WP4) of the eCAN JA. DESIGN/METHODOLOGY/APPROACH: Foresight is an umbrella term for innovative strategic planning, policy formulation and solution design methods that empower decision-makers. For our multi-country foresight study, we followed three sequential steps to gain qualitative and quantitative findings on the facilitators of telemedicine services for cancer patients in clinical settings, comprised of (1) literature review; (2) surveys to HCOs in different EU Member States and (3) a foresight workshop with survey respondents. FINDINGS: Telemedicine implementation in HCOs requires equipping healthcare professionals with the necessary skills, ensuring system compatibility and addressing resource constraints for hybrid care models for success. Future policies in the EU should focus on establishing telemedicine training for healthcare professionals and support interoperability as well as user-friendliness of telemedicine services in HCOs. Policies should also address clinical workload challenges, enable harmonized telemedicine protocols across Europe, provide incentives for implementation and invest in digital infrastructure in HCOs for a sustainable telemedicine adoption. ORIGINALITY/VALUE: To the best of our knowledge, this is the first study to conduct a foresight analysis on the feasibility of telemedicine for cancer care across different HCOs in EU Member States.
20 Apr 2026
Read appraisal →Platelets
Neural network reveals platelet age from fluorescence microscopy images
Platelets are small, anucleate cells with a primary physiological role in vascular damage repair (hemostasis) and initiation of thrombus formation in response to vascular injury. Platelets circulate approximately 7-10 days, slowly undergoing age-related changes in molecular composition, morphology, activation capacity, function, and surface receptor density. As older platelets are associated with poor clinical outcome, no in vitro tests are available to predict platelet age, or to determine the fitness of platelet transfusion products. In this study, we developed a convolutional neural network model that could determine platelets' chronological age from confocal microscopic images. The model was trained using platelets stored in platelet-rich plasma up to 8 hours and using routine platelet concentrates up to 10 days. The model predicted chronological age of stored platelets with >97% accuracy. To test our model in vivo, we analyzed a cohort of patients with acute myeloid leukemia, experiencing thrombocytopenia due to chemotherapy. Our model could reliably distinguish in vivo between samples with younger and older platelets during the course of treatment. This study demonstrates the ability to predict platelets' chronological age both in vitro during storage and in vivo, which may impact clinical transfusion medicine and the diagnosis and treatment of patients with platelet disorders. What is the context?Platelets circulate approximately 7–10 days, slowly undergoing age-related changes in molecular composition, morphology, activation capacity, function, and surface receptor density. During storage, proinflammatory changes occur in platelets over time and platelet age may be associated with poorer clinical outcomes. Therefore, understanding platelet aging and developing novel tools to interrogate platelet age will be useful. However, no platelet tests are currently available that can predict platelet age accurately based on platelet characteristics without keeping record of the (storage) time.What is new?We developed a convolutional neural network model that could determine platelets’ chronological age from confocal microscopic images. The model was trained using platelets stored in platelet-rich plasma up to 8 hours and using routine platelet concentrates up to 10 days. The model predicted chronological age of stored platelets with very high accuracy. Furthermore, our model was in vivo tested in a cohort of patients with acute myeloid leukemia, experiencing thrombocytopenia due to chemotherapy. Our model could reliably distinguish between in vivo samples with younger and older platelets during the course of treatment.What is the impact?Our study demonstrates the utility of a convolutional neural network model using confocal images of platelets to determine storage time-associated platelet phenotype in vitro as well as physiologic platelets’ chronological age in vivo. This model may be useful in several clinical settings, including clinical transfusion medicine, testing the quality of stored platelet concentrates, and diagnosing and treatment of patients with platelet disorders, thrombocytopenia, or thrombosis.
18 Apr 2026
Read appraisal →Journal of neurointerventional surgery
Safety and efficacy of selective and superselective intra-arterial cerebral infusion with blood-brain barrier disruption for glioma: a systematic review and meta-analysis
BACKGROUND: Selective and superselective intra-arterial cerebral infusion (SIACI/SSIACI) delivers chemotherapy directly to tumor-supplying arteries, achieving high local drug levels with reduced systemic toxicity. Often combined with blood-brain barrier disruption (BBBd) to enhance penetration, these methods have been widely studied, yet a comprehensive evidence synthesis is lacking. This study systematically assesses the safety and efficacy of intra-arterial chemotherapy with BBBd for glioma treatment. METHODS: We searched PubMed, Embase, and Web of Science for studies on SIACI with BBBd in glioma patients. Safety outcomes included rates of cases with complications, procedure-related complications (major/minor), stroke, intracranial and intratumoral hemorrhage, and mortality. Efficacy was based on tumor response (complete, partial, stable disease, progression). A subanalysis of SSIACI-only cases was also conducted. RESULTS: Nine studies with 230 glioma patients were included. The pooled rate of cases with complications was 27.1% (95% CI 19.8% to 35.7%), with procedure-related complications occurring in 15.4% (95% CI 6.1% to 24.6%). Major and minor complication rates were 4.3% (95% CI 0.9% to 7.7%) and 9.7% (95% CI 1.0% to 18.7%), respectively. Stroke, intracranial hemorrhage, and intratumoral hemorrhage occurred in 3.1% (95% CI 0% to 6.1%), 0.5% (95% CI 0% to 3.2%), and 0.04% (95% CI 0% to 3%), respectively, with no procedure-related deaths reported. Pooled response rates were 10.4% complete (95% CI 0% to 21.5%), 24.2% partial (95% CI 14.3% to 34.1%), 38.2% stable disease (95% CI 21.5% to 54.9%), and 39.3% progression (95% CI 14.8% to 63.8%). Subanalysis of superselective infusions yielded comparable outcomes to the overall cohort. CONCLUSION: The findings indicate that SIACI/SSIACI chemotherapy following BBBd is a feasible and safe approach for glioma treatment, demonstrating a favorable procedural risk profile and preliminary signs of efficacy. Further studies are warranted to validate these results and refine procedural protocols.
18 Apr 2026
Read appraisal →Breast cancer research and treatment
Oral selective estrogen receptor degraders in hormone receptor-positive, HER2-negative advanced breast cancer: a systematic review and meta-analysis
PURPOSE: The development of endocrine resistance is frequent in hormone receptor-positive, HER2-negative advanced breast cancer (HR + /HER2-ABC), particularly after CDK4/6 inhibitor exposure. Next-generation oral selective estrogen receptor degraders (SERDs) have been developed to improve estrogen receptor blockade; however, randomized trials have yielded heterogeneous results with uncertain clinical benefit. METHODS: A PRISMA 2020 compliant systematic review and meta-analysis with PROSPERO registration was conducted. PubMed, Embase, and Cochrane CENTRAL were searched through October 2025 for phase II-III randomized trials comparing oral SERDs with standard endocrine therapy (ET) in HR + /HER2-ABC after prior ET. The primary endpoint was progression-free survival (PFS); secondary endpoints included overall survival (OS), objective response rate (ORR), and treatment-related adverse events (TRAEs). Treatment effects were pooled using random effects models with prespecified subgroup analyses by ESR1 mutation status and key clinical characteristics. RESULTS: Six randomized trials, including 2808 patients, were analyzed. Oral SERDs improved PFS versus standard ET in the overall population (hazard ratio [HR] 0.79; 95% confidence interval [CI] 0.70 to 0.89). ORR was higher with oral SERDs (odds ratio [OR] 1.67; 95% CI 1.23 to 2.28), corresponding to absolute response rates of approximately 21% versus 14%. An OS improvement was observed (HR 0.72; 95% CI 0.57 to 0.90), although follow-up was limited. Benefit was concentrated in ESR1-mutated tumors (PFS, HR 0.57; 95% CI 0.48 to 0.67) with no significant PFS advantage in ESR1 wild-type disease. Gastrointestinal adverse events were more frequent with oral SERDs compared with the control ET. CONCLUSIONS: Pooled randomized evidence supports a clinically meaningful benefit of oral SERDs over standard ET after endocrine progression in HR + /HER2-ABC, with the strongest and most consistent efficacy observed in ESR1-mutated disease.
18 Apr 2026
Read appraisal →Clinical cancer research : an official journal of the American Association for Cancer Research
Recurrent Resistance Mutations to Lirafugratinib Inform Treatment Sequencing in FGFR2-Driven Tumors
PURPOSE: The use of reversible fibroblast growth factor receptor 2 (FGFR) inhibitors leads to the emergence of "undruggable" FGFR2 kinase domain mutations, hampering sequential treatment strategies. Lirafugratinib and futibatinib are irreversible FGFR inhibitors with the most promising clinical activity against FGFR2-driven tumors. EXPERIMENTAL DESIGN: We characterized resistance to lirafugratinib with circulating tumor DNA, tissue whole-exome sequencing, and bulk RNA sequencing in 30 patients with FGFR2-driven cancers, treated in the phase I/II ReFocus trial (NCT04526106) and enrolled in the UNLOCK program at Gustave Roussy. RESULTS: Among the 30 patients included, 18 (60%) had intrahepatic cholangiocarcinoma and 12 (40%) had other tumor types. Twenty-two patients (73%) were FGFR inhibitor-naïve. Among those experiencing primary resistance to lirafugratinib, we identified potential resistance mechanisms in five of six pretreatment samples. Patients with acquired lirafugratinib resistance manifested an unprecedented emergence of FGFR2 mutations in the M538 and/or L618 residues of the kinase domain, documented in 11 of 16 cases (69%). Compared with futibatinib resistance, FGFR2 molecular brake (N550) and gatekeeper (V565) mutations were rare. Leveraging the spectrum of FGFR2 kinase domain mutations at resistance to lirafugratinib and futibatinib, respectively, we identified the complementarity of the two irreversible inhibitors. On the basis of viability assays in FGFR2::BICC1-dependent Ba/F3 models and in vivo studies on patient-derived xenografts, we propose treatment sequences with the two agents. After lirafugratinib progression, three patients received futibatinib and experienced prolonged disease response. CONCLUSIONS: The complementary activity of lirafugratinib and futibatinib against FGFR2 kinase domain mutations supports their sequential use when precise resistance mutations are detected in patients.
17 Apr 2026
Read appraisal →Journal of medical Internet research
Artificial Intelligence for Identifying Patient-Reported Outcome and Experience Measures in Oncology: Retrospective Cross-Sectional Study Using ClinicalTrials.gov
BACKGROUND: Traditional health care systems have evolved to increasingly recognize patients' perspectives as key to improving the quality of care, especially in oncology. Hence, patient-reported outcome measures (PROMs) and patient-reported experience measures (PREMs) can enhance patient-health care provider communication while facilitating individualized care. This tailored approach improves patient outcomes and highlights the need for research methods to account for variability in patient experiences across diverse sociodemographic and clinical backgrounds while proposing artificial intelligence (AI) to automate and accelerate the identification of PROMs and PREMs. OBJECTIVE: This study aimed to estimate the proportion of clinical studies including PROMs or PREMs, using either a traditional expert-based identification method or an AI-enriched approach. METHODS: In a retrospective cross-sectional study using the ClinicalTrial.gov database, we focused on oncology studies between 2012 and 2022. Two methods were used to identify PROMs and PREMs: (1) a traditional expert-based method, where an algorithm identified PROMs and PREMs from a list of 346 oncology-specific PROMs and PREMs (extracted from the PROQOLID database, Mapi Research Trust) and/or 11 PROM- and PREM-specific terms, and (2) an AI-enriched method using a bidirectional encoder representations from transformers model, trained on 2399 outcomes labeled by experts. To evaluate algorithm performance, results were compared with expert decisions. Studies were classified by PROM/PREM use, analyzed using logistic regression to identify drivers, and a named entity recognition model identified frequently used measures. RESULTS: A total of 24,491 studies were included. According to the traditional expert-based algorithm, 7549 (31%) studies used at least one PROM or PREM, as compared to 8029 (33%) studies identified by the AI-enriched algorithm, increasing from 2012 to 2022 (P<.001). With 90% (95% CI 88%-92%) accuracy, the AI-enriched algorithm outperformed (P<.001) the expert-based algorithm (84%, 95% CI 82%-86%) in identifying PROMs and PREMs. Breast and digestive cancers accounted for nearly 50% of all oncology studies using PROMs and PREMs, with the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-Core 30 being the most frequently used. As expected, trials in phases 2 to 4 more frequently included PROMs and PREMs than preclinical or early phase 1 studies (odds ratio [OR] 1.8, 95% CI 1.1-2.8 for phase 2; OR 3.6, 95% CI 2.3-5.8 for phase 3; and OR 2.6, 95% CI 1.6-4.4 for phase 4). In observational studies, cross-sectional and prospective studies incorporated more PROMs and PREMs than retrospective studies (OR 4.6, 95% CI 3.3-6.4 and OR 3.2, 95% CI 2.5-4.1, respectively). CONCLUSIONS: Our study shows that an AI-enriched algorithm outperforms traditional expert methods in identifying PROMs and PREMs in oncology. Combining expert-labeled data with AI enables scalable, automated trial monitoring, supports efficient research, informs stakeholders, enhances patient-centered decisions, and can be extended to other diseases and databases.
17 Apr 2026
Read appraisal →Signal transduction and targeted therapy
HDAC inhibitor tucidinostat and metronomic capecitabine plus endocrine therapy for patients with HR-positive HER2-negative advanced breast cancer after CDK4/6 inhibitors treatment: clinical findings and exploratory circulating tumor cell and ctDNA biomarker analyses of a multicenter, phase 2 study (SYSUCC-020 trial)
Treatment options for patients with hormone receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2)-negative advanced breast cancer (ABC) that progresses after CDK4/6 inhibitor (CDK4/6i) continue to evolve, and no single regimen has been established as the preferred standard of care. We conducted a phase 2 trial (SYSUCC-020, NCT05411380) to investigate the efficacy and safety of a novel combination of tucidinostat and metronomic capecitabine (mCAP) with endocrine therapy (ET) in patients following progression on CDK4/6i. This trial adopted a Simon two-stage design: eligible patients received tucidinostat plus metronomic capecitabine together with either an aromatase inhibitor (Cohort 1) or fulvestrant (Cohort 2), selected according to prior ET. Seventy-two female patients were screened and sixty-six patients were included in the efficacy and safety analyses. The median follow-up was 13.88 months (Interquartile range, 8.67-20.67) and the objective response rate was 25.8%, with 17 partial responses. The median progression-free survival (PFS) was 5.39 months (95% CI, 4.07-7.69), namely 6.93 months (95% CI, 4.76-12.22) in Cohort 1 and 3.98 months (95% CI, 2.79-7.59) in Cohort 2. Most patients (93.9%) experienced at least one adverse event. In addition, exploratory analyses of biomarkers indicated that the baseline TP53 mutation status (Wild type vs. mutated, 7.64 months vs. 3.55 months) and circulating tumor cell (CTC) status (CTC-negative vs. CTC-positive, 7.59 months vs. 3.78 months) were associated with the median PFS. Our study demonstrated that tucidinostat combined with mCAP and ET is efficacious and well-tolerated in patients with HR-positive HER2-negative ABC previously treated with CDK4/6i.
17 Apr 2026
Read appraisal →Cancer research
Mutant and Wild-Type RAS Cross-talk and Stoichiometric Deficiencies Are Determinants of Sensitivity to Targeted Therapies in KRASG12R Pancreatic Ductal Adenocarcinoma
UNLABELLED: Therapies targeting the RAF-MEK-ERK pathway are generally considered to have limited efficacy in KRAS-mutant cancers. However, specific KRAS mutants exhibit distinct behaviors. Notably, KRASG12R pancreatic ductal adenocarcinoma (PDAC) tumors have shown sensitivity to MEK inhibitors (MEKi) in combination with autophagy inhibitors, but a better understanding of the underlying mechanisms is needed to optimize this treatment strategy. Using a systems-level approach, we uncovered a mechanistic explanation for this phenomenon. Due to distinct biophysical properties, KRASG12R had an impaired ability to activate wild-type HRAS and NRAS (WT-RAS) compared with other KRAS mutants, such as KRASG12D. This reduced activation stemmed from the weaker interaction between KRASG12R and guanine exchange factors (SOS), as well as the tumor suppressor neurofibromin (NF1), crucial in regulating WT-RAS activity. The impaired ability to activate WT-RAS led to weaker holistic MAPK signaling in KRASG12R-driven tumors, which conferred increased sensitivity to MEKi. To substantiate the preclinical findings, the utility of MEKi in combination with the autophagy inhibitor hydroxychloroquine was analyzed in patients with KRASG12R-mutated metastatic PDAC. Five of the eight (62.5%) patients treated in first- or second-line settings had a progression-free survival exceeding 6 months. Three patients had impressive disease control: two had stable disease of 11 and 22.7 months, and one achieved a partial response with an 83% decrease in tumor size that lasted for 8.9 months. Overall, this work highlights how systems-based approaches in precision medicine can uncover mechanistic insights to guide the identification of patients with PDAC most likely to benefit from tailored therapeutic strategies. SIGNIFICANCE: The unique sensitivity of KRASG12R-mutant cancers to MEK inhibitors offers a critical advancement in understanding MAPK signaling and paving the way for precision-targeted therapies in previously untreatable contexts. See related commentary by Tiriac and Engle, p. 1817 See related article by Burge et al., p. 1854 See related article by Burge et al., p. 1868.
17 Apr 2026
Read appraisal →Clinical cancer research : an official journal of the American Association for Cancer Research
Camonsertib, an ATRi, in Combination with Low-Dose Gemcitabine in Solid Tumors with DNA Damage Response Aberrations: Preclinical and Phase Ib Results.
PURPOSE: The utility of combination treatment with gemcitabine and camonsertib, an ataxia telangiectasia and Rad3-related kinase inhibitor, in mediating tumor cell death was assessed in preclinical models, prompting clinical investigation. The phase Ib TRESR study (NCT04497116) aimed to evaluate the safety, tolerability, and preliminary efficacy of the combination in patients with advanced solid tumors harboring DNA damage repair (DDR) gene alterations. PATIENTS AND METHODS: Cell lines and tumor xenografts were tested across a range of dose levels and schedules. Patients (N = 76) harboring tumors with DDR gene alterations received camonsertib (80-120 mg) and de-escalating gemcitabine (1,000-100 mg/m2) in 21- or 28-day cycles on an intermittent dosing regimen. Safety, tolerability, and preliminary efficacy were assessed to identify an optimal dosing regimen. RESULTS: In preclinical models, low-dose camonsertib (1/3 maximum tolerated dose) and gemcitabine led to tumor regression and was well tolerated with minimal body weight loss observed. In patients, synergistic toxicities were observed, primarily myelosuppression, resulting in gemcitabine de-escalation. The introduction of a 1 week on/1 week off schedule in combination with low-dose gemcitabine allowed for spontaneous neutrophil recovery, fewer dose modifications, and improved tolerability. Tumor responses were primarily observed in patients with gynecologic cancers, with tumor control maintained for greater than 1 year in some patients. CONCLUSIONS: Camonsertib and low-dose gemcitabine demonstrated preliminary clinical activity, but due to challenging tolerability, further evaluation is warranted to identify the optimal dosing regimen and subset of patients who may benefit most from this combination.
17 Apr 2026
Read appraisal →Clinical cancer research : an official journal of the American Association for Cancer Research
Neoadjuvant Nivolumab with or without Ipilimumab for Cisplatin-Ineligible Patients with Muscle-Invasive Bladder Cancer
PURPOSE: Many patients with muscle-invasive bladder cancer (MIBC) are ineligible for cisplatin-based therapy. We conducted a phase II trial of neoadjuvant nivolumab ± ipilimumab for cisplatin-ineligible patients. PATIENTS AND METHODS: Patients with MIBC were enrolled in two consecutive cohorts: (i) nivolumab alone and (ii) ipilimumab/nivolumab. A third cohort with alternative dosing was planned. The primary endpoint was eligibility for cystectomy ≤60 days after last treatment. Correlative analyses were performed, with the PURE-01 trial used as an independent dataset. RESULTS: Fifteen patients enrolled onto each cohort. In cohorts 1 and 2, 12 of 15 and eight of 15 were eligible for cystectomy within 60 days, respectively. Due to cohort 2's failure to meet the primary endpoint, cohort 3 was not initiated. With nivolumab alone, four patients achieved <ypT2ypN0 (26%), with two pathologic complete responses (pCR; 13%). With ipilimumab/nivolumab, three achieved <ypT2ypN0 (20%), with one pCR (7%). One patient after nivolumab and two after ipilimumab/nivolumab had durable clinical complete responses (CR) without cystectomy. Twelve-month recurrence-free survival (RFS) was 79% with nivolumab [95% confidence interval (CI), 61-100] and 61% with ipilimumab/nivolumab (95% CI, 39-95). Sequencing analyses suggest that NCOR1 alterations may be associated with improved clinical outcomes. Gene expression profiling indicated a potential association between tumor-infiltrating immune cells and longer RFS (log-rank P = 0.18); this was also observed in PURE-01 (P = 0.022). CONCLUSIONS: Among cisplatin-ineligible patients with MIBC, nivolumab alone was well tolerated. Ipilimumab/nivolumab caused toxicity that delayed cystectomy. Cases of progression before cystectomy indicated insufficient efficacy of pure neoadjuvant immunotherapy for unselected patients. Despite low response rates, some patients experienced sustained clinical CR without cystectomy.
16 Apr 2026
Read appraisal →Cancer immunology, immunotherapy : CII
Hematological toxicities and thrombosis risk associated with immune checkpoint inhibitors: a pharmacovigilance study from 2014 to 2024
BACKGROUND: Immune checkpoint inhibitors (ICIs) have significantly improved clinical outcomes in a wide range of cancers but may also induce hematological toxicities and thrombosis risk. This study comprehensively evaluated hematological and thrombotic adverse events (AEs) associated with ICIs in real-world settings using the FDA Adverse Event Reporting System (FAERS) database, aiming to characterize their clinical features. METHODS: Data were extracted from the FAERS database (Q1 2014 to Q4 2024) for disproportionality analysis. AEs were classified using the Medical Dictionary for Regulatory Activities (MedDRA). Associations between ICIs and hematological/thrombotic AEs were assessed via the reporting odds ratio (ROR) and Bayesian Confidence Propagation Neural Network (BCPNN) methods. Clinical characteristics of affected patients were analyzed, and time-to-onset across ICI regimens was further evaluated. RESULTS: We identified 14,753 ICI-associated hematological toxicities and thrombotic events. Among reports with available sex information, a higher reporting frequency was observed in men (50.90%) than in women (40.11%). The most frequent AEs were anemia (2963 cases, 16.62%), thrombocytopenia (1961, 11.00%), febrile neutropenia (1935, 10.85%), neutropenia (1392, 7.81%), myelosuppression (979, 5.49%), pancytopenia (739, 4.14%), lymphadenopathy (475, 2.66%), disseminated intravascular coagulation (DIC; 468, 2.62%), leukopenia (468, 2.62%), and deep vein thrombosis (DVT; 213, 1.19%). The median time-to-onset for ICI monotherapy-associated AEs was 28 days (IQR 11-79), with 78.22% occurring within 3 months. Notably, DIC and DVT had the highest mortality proportion among the top 10 AEs. CONCLUSION: Hematological and thrombotic AEs associated with ICI therapy show notable reporting frequencies in FAERS. Combination regimens demonstrated stronger reporting signals for certain events. These AEs frequently showed early reporting and notable mortality proportions, highlighting the importance of clinical vigilance. Further studies are needed to confirm these findings.
16 Apr 2026
Read appraisal →Clinical cancer research : an official journal of the American Association for Cancer Research
Becotatug Vedotin in Patients with Recurrent or Metastatic Squamous Cell Carcinoma of the Head and Neck: A Multicenter, Phase IIa Trial
PURPOSE: This multicenter, phase IIa trial (NCT04868162) investigated the efficacy and safety of becotatug vedotin, an anti-epidermal growth factor receptor (EGFR) antibody-drug conjugate, in patients with recurrent or metastatic squamous cell carcinoma of the head and neck (R/M SCCHN) who have limited therapeutic options. PATIENTS AND METHODS: Patients with R/M SCCHN who progressed after platinum-based chemotherapy and/or programmed cell death 1 (PD-1)/programmed death ligand 1 (PD-L1) inhibitors, including those with multiple lines of prior therapy, were administered intravenous becotatug vedotin at either 2 or 2.3 mg/kg every 3 weeks. The primary endpoint was the objective response rate (ORR). RESULTS: Sixty-seven patients were enrolled (35 received 2 mg/kg and 32 received 2.3 mg/kg). The ORR was 20.9% [14/67, 95% confidence interval (CI), 11.9-32.6], with a median duration of response (DoR) of 10.9 months (95% CI, 2.6-15.1). The median progression-free survival (PFS) was 2.9 months (95% CI, 1.8-3.9), and the median overall survival (OS) was 6.7 months (95% CI, 5-8.9). Treatment-related adverse events (TRAE) were reported in 91% (61/67) patients, most commonly being rash (26.9%), pruritus (25.4%), constipation (23.9%), and anemia (20.9%). CONCLUSIONS: Becotatug vedotin demonstrated promising antitumor activity with a manageable safety profile in previously heavily treated R/M SCCHN, particularly at the recommended dose of 2.3 mg/kg, among patients who had failed platinum-based chemotherapy and PD-1/PD-L1 inhibitors (≤2 prior lines of therapy).
16 Apr 2026
Read appraisal →Neurosurgery
Congress of Neurological Surgeons Systematic Review and Evidence-Based Guidelines for the Treatment of Adults With WHO Grade II Diffuse Glioma: Update
BACKGROUND: The management of World Health Organization (WHO) grade II diffuse glioma is an important facet of all physicians involved in neuro-oncology. OBJECTIVE: This is an update of the evidence-based guidelines for management of WHO grade II diffuse gliomas published by the Congress of Neurological Surgeons and American Association of Neurological Surgeons in 2015. METHODS: The medical literature from January 1, 2013, through January 31, 2020, was searched to determine if information was available to update, modify, or create new recommendations related to imaging, surgical approaches, neuropathology and molecular markers, radiotherapy, chemotherapy, and management of tumor recurrence. RESULTS: The writing group used the information from the updated literature search to formulate recommendations based on this evidence and not simply built on biased consensus or expert opinion. CONCLUSION: This series of guideline documents provides an update of the information and recommendations provided in the 2015 version. It sets a benchmark as to the published information we have to support the management of this difficult disease. It also provides clues to key investigations that are necessary to move us toward effective control of WHO grade II diffuse gliomas.
16 Apr 2026
Read appraisal →Clinical cancer research : an official journal of the American Association for Cancer Research
A Multicenter Phase II Randomized Controlled Trial Comparing 177Lu-Dotatate/Capecitabine Combination Treatment with 177Lu-Dotatate Monotherapy in Patients with Neuroendocrine Tumors
PURPOSE: Peptide receptor radionuclide therapy (PRRT) with [177Lu]Lu-[DOTA0,Tyr3]octreotate (177Lu-Dotatate) is an effective and safe treatment for patients with metastatic gastroenteropancreatic neuroendocrine tumors (GEP NET). Whereas 177Lu-Dotatate prolongs progression-free survival (PFS) and preserves quality of life (QoL), objective response rates (ORR) remain limited. Capecitabine, as a radiosensitizer, could increase efficacy without increasing 177Lu-Dotatate activity. This phase II randomized controlled trial investigated the additional cytotoxic or radiosensitizing effect of capecitabine in combination with 177Lu-Dotatate. PATIENTS AND METHODS: Patients with advanced somatostatin receptor-positive GEP NET or bronchopulmonary NET were scheduled to receive four cycles of 7.4 GBq 177Lu-Dotatate and capecitabine or 177Lu-Dotatate alone. Capecitabine (1,650 mg/m2/day) was administered for 2 weeks from the start of each PRRT cycle. Primary endpoints were ORR, PFS, and median overall survival (OS). Secondary endpoints included biochemical response, adverse events, and QoL. RESULTS: A total of 111 of the 200 predefined patients were randomized for 177Lu-Dotatate/capecitabine (n = 50) or 177Lu-Dotatate (n = 61). According to the intention-to-treat analysis, the ORR was 32% for the 177Lu-Dotatate/capecitabine group versus 46% for the 177Lu-Dotatate group (P = 0.348). Treatment with 177Lu-Dotatate/capecitabine did not prolong median PFS (45.7 vs. 31.7 months, P = 0.629) or median OS (75.8 vs. 61.4 months, P = 0.530). Quality-adjusted life years (QALY) were lower in the 177Lu-Dotatate/capecitabine group (1.30 ± 0.48 vs. 1.47 ± 0.48, P = 0.014). CONCLUSIONS: In this prematurely closed phase II study, 177Lu-Dotatate/capecitabine did not improve ORR or prolong PFS and OS in patients with advanced NET compared with 177Lu-Dotatate alone and was associated with reduced QALY.
16 Apr 2026
Read appraisal →Computers in biology and medicine
Deep learning with limited data: a transfer learning approach for transcriptomic survival prediction
BACKGROUND: Deep neural networks (DNNs) are promising for analyzing high-dimensional transcriptomic data in cancer research but are limited by data scarcity and heterogeneity. Transfer learning (TL), which leverages large datasets to improve performance on smaller ones, has been widely applied in classification and clustering, yet its use in survival prediction remains underexplored. METHODS: We evaluated transfer learning for disease-free survival prediction using RNA-seq data from 7509 patients across 27 tumor types from The Cancer Genome Atlas. A pan-cancer DNN was optimized via random search across 750 configurations. Tumor-specific baseline models were trained independently, while transfer learning models were built with a leave-one-out pre-training strategy followed by fine-tuning on the target tumor. Performance was assessed with the concordance index. Biological relevance was evaluated through a LIME-like interpretability approach and gene-set enrichment analysis. RESULTS: Transfer learning outperformed tumor-specific baselines in 24 of 27 tumor types, with the greatest improvements in smaller cohorts. Fine-tuning consistently enhanced predictions by adapting pan-cancer features to tumor-specific data. Enrichment analysis confirmed that the models captured both general oncogenic mechanisms and tumor-specific pathways. CONCLUSIONS: Transfer learning improves survival prediction from transcriptomic data, especially in small cohorts, by exploiting molecular patterns shared across cancers. These findings support transfer learning as a robust strategy for survival modeling and a step toward foundation models in molecular oncology.
16 Apr 2026
Read appraisal →Cancer cell
HS-20093, a B7-H3-targeted antibody-drug conjugate in lung cancer: Results from the ARTEMIS-001 phase 1a/b trial
This phase 1a/b study (NCT05276609) evaluated the safety, pharmacokinetics, and efficacy of B7-H3-targeted antibody-drug conjugate HS-20093 (GSK5764227) in 306 patients with previously treated advanced solid tumors. In phase 1a, 12.0 mg/kg was established as the maximum tolerated dose. Among 236 lung cancer patients who received 8.0 or 10.0 mg/kg HS-20093, the most frequent grade ≥3 treatment-related adverse events (AEs) included decreased neutrophil (25.5% vs. 50.5%) and white blood cell counts (19.7% vs. 42.4%), and anemia (16.8% vs. 34.3%), respectively. Treatment-related interstitial lung disease and AEs leading to death occurred in 3.4% and 3.8% of patients, respectively. Among response-evaluable patients, the confirmed objective response rate was 52.3% (95% CI: 39.5, 64.9) for extensive-stage SCLC (ES-SCLC, N = 65) and 22.4% (95% CI: 16.0, 29.8) for non-small cell lung cancer (NSCLC, N = 152) patients, with comparable rates between 8.0 mg/kg and 10.0 mg/kg dose cohorts across both groups. These results support further development of HS-20093, with 8.0 mg/kg selected for phase 3 trials.
16 Apr 2026
Read appraisal →Signal transduction and targeted therapy
STRATEGIC-1: multiple-line, randomized, open-label GERCOR-PRODIGE-39 phase III trial in unresectable RAS/BRAF wild-type metastatic colorectal cancer
Managing unresectable metastatic colorectal cancer (mCRC) requires a comprehensive strategy. While chemotherapy, anti-angiogenic, and anti-epidermal growth factor receptor (EGFR) agents are available, strategy trials are needed to optimize their use and sequencing. The STRATEGIC-1 phase III trial (NCT01910610) was designed to determine the optimal treatment sequence in patients with untreated, unresectable wild-type RAS/BRAFV600E mCRC. Patients were randomized (1:1) to FOLFIRI-cetuximab then mFOLFOX6-bevacizumab (arm A) or OPTIMOX-bevacizumab then FOLFIRI-bevacizumab followed by EGFR monoclonal antibody +/- irinotecan (arm B). The primary endpoint was the duration of disease control (DDC). Secondary endpoints were overall survival (OS), time to failure of strategy (TFS), progression-free survival (PFS), overall response rate (ORR), salvage surgery rate, safety, and health-related quality of life (HRQoL). Overall, 263 patients (arm A:131, arm B:132) were randomized. After 68.4 months of median follow-up (95% CI, 76.5-98.0), the median DDC was 22.8 months (95% CI, 20.4-28.8) in arm A and 23.5 months (95% CI, 17.9-26.3) in arm B (HR = 1.01, 95% CI, 0.76-1.34; log-rank P = 0.945). The median OS was 40.4 months (95% CI, 32.4-51.1) in arm A and 34.4 months (95% CI, 27.5-42.2) in arm B (HR = 1.30, 95% CI, 0.99-1.72). The ORR was higher in arm A (82.4% versus 65.4%) in the first-line group but not in the second-line group (20.7% versus 16.4%). Adverse events were consistent with the well-known safety profiles. STRATEGIC-1 did not meet its primary endpoint and was inconclusive in identifying the optimal treatment strategy in wild-type RAS/BRAFV600E mCRC.
16 Apr 2026
Read appraisal →International immunopharmacology
CXCL9 as a key feature for deep learning-based immune subtyping and prediction of immune checkpoint blockade response in triple-negative breast cancer
BACKGROUND: Immune checkpoint blockade (ICB) therapy has provided a promising treatment option for patients with triple-negative breast cancer (TNBC). However, existing efficacy prediction strategies based on programmed death-ligand 1 (PD-L1) expression and Tumor Mutational Burden (TMB) have significant limitations, leading to inaccurate patient selection. METHODS: This study integrated multi-omics data obtained from GEO, TCGA, and GTEx datasets. Based on ComBat correction results, two deep learning-driven unsupervised clustering methods were constructed to identify potential immune subtypes. These identified TNBC subtypes were subjected to comprehensive bioinformatics analyses to investigate their differences in overall survival, immune cell infiltration, and functional enrichment analysis, which clarified the biological rationale of the proposed classification. Subsequently, differential expression analysis (DEA) and multiple machine learning algorithms were applied to identify subtype-specific features, with the expression and differentiation status of the key feature in immune cells further validated using single-cell sequencing (scRNA-seq). Finally, co-expression networks at multiple cellular levels were constructed to predict the co-expressed regulatory targets of the key feature, and the hypothesized regulatory mechanism was preliminarily validated in in vitro experiments (qRT-PCR, western blotting, immunofluorescence, and ELISA). RESULTS: After 5-fold cross-validation, the AE-K-means clustering method classified TNBC into three distinct latent subtypes. This algorithm exhibited superior classification efficacy compared with the other models (including NMF, ConsensusClusterPlus and VAE-GMM), as demonstrated by markedly elevated Silhouette Coefficients (training: 0.585 ± 0.030; test: 0.698 ± 0.103; validation: 0.611) and substantially reduced Davies-Bouldin Index values (training: 0.623 ± 0.032; test: 0.385 ± 0.218; validation: 0.558) at K = 3. These three identified TNBC subtypes displayed significant differences in overall survival and exhibited pronounced immune heterogeneity. Specifically, the K2 subtype was characterized by high PD-L1 expression and abundant M1 macrophage infiltration, while the K3 subtype displayed an opposing, immunosuppressive microenvironment. Feature selection results from multiple machine learning models were intersected to identify key features including CXCL9, and an RF model constructed based on these intersected genes showed high efficacy, particularly in distinguishing the K2 and K3 subtypes (AUC = 0.941). Further scRNA-seq analysis revealed that CXCL9 was specifically highly expressed in myeloid cells, and the proportion of CXCL9+ macrophages differed significantly between responders and non-responders. Given the strong positive correlation between CXCL9 and IDO1 expression, we subsequently treated macrophages with IDO1 inhibitors and observed a marked upregulation in both the expression and secretion of CXCL9, suggesting that IDO1 overexpression in macrophages may represent a potential mechanism underlying CXCL9 exhaustion in the tumor microenvironment. CONCLUSIONS: Our study developed a novel immune classification system for TNBC, identifying CXCL9 as a key biomarker positively correlated with ICB response and regulated by IDO1. These findings provide a basis for precision immunotherapy in TNBC and support the exploration of IDO1 inhibitors in combination with ICB.
15 Apr 2026
Read appraisal →Journal of affective disorders
Association of social isolation and loneliness with cancer prognosis: an analysis of the UK Biobank
BACKGROUND: Evidence is scarce for the association between social isolation and loneliness and prognosis of any cancer or other common cancer types. METHODS: To address this gap, we performed a cohort study from 2006 to 2022 using data from the UK Biobank (UKB). Social isolation and loneliness were identified based on the questionnaire of the UKB. Cases of death were obtained from the death registry, where both primary and contributory causes were recorded using ICD-10 codes. The Cox proportional hazard models were used to estimate hazard ratios (HRs) and associated 95% confidence intervals (CIs) of death in relation to social isolation and loneliness among patients with cancer. RESULTS: Among cancer patients studied, 6206 (9.6%) were socially isolated and 3130 (4.8%) reported loneliness. Over a median follow-up of 6.7 years, there were 2378 deaths (38.3%) among exposed patients and 16,566 deaths (29.1%) among unexposed patients. Social isolation was associated with poorer cancer prognosis overall (HR 1.21, 95% CI 1.16-1.26, P < 0.001), while loneliness correlated with a similar risk (HR 1.18, 95% CI 1.11-1.25, P < 0.001). Social isolation specifically impacted survival in colon, melanoma, breast, prostate, kidney, and bladder cancers, as well as myeloproliferative neoplasms. Loneliness was linked to poorer outcomes in breast, prostate, and bladder cancers. CONCLUSIONS: Our findings suggested that social support for these vulnerable patients might have the potential to improve prognosis.
15 Apr 2026
Read appraisal →Cancer
A phase 1 study of ASTX727 plus talazoparib in patients with triple-negative or hormone resistant/HER2-negative metastatic breast cancer
BACKGROUND: Poly(adenosine diphosphate ribose) polymerase (PARP) is recruited to DNA damage sites along with epigenetic factors such as DNA methyltransferase 1 (DNMT1). Inhibitors of DNMT modulate reactive oxygen species (ROS)-cyclic adenosine monophosphate (cAMP)/Protein Kinase A signaling and induce a "BRCAness phenotype" that further sensitizes cells to PARPi. In preclinical studies, combined DNMTi + PARPi therapy was effective in both triple-negative (TNBC) and hormone resistant (HRBC) models with intact BRCA. METHODS: The authors conducted a phase 1 study combining the oral DNMTi ASTX727 with the PARPi talazoparib in patients with previously treated TNBC or HRBC. Patients with deleterious mutations of BRCA were excluded. A classical 3+3 design guided dose escalation/de-escalation, and 28 days constituted each cycle. Serial peripheral blood mononuclear cells (PBMCs) were analyzed for changes in methylation using the Infinium Methylation EPIC BeadChip and LINE1 sequencing. RESULTS: Thirty-four evaluable patients were enrolled and treated in eight dose cohorts. Myelosuppression was common with grade >3 neutropenia in 42% and grade 3 anemia and thrombocytopenia in 13%. Dose-limiting toxicity was limited to neutropenia. Efficacy was assessed in 29 patients. There were no objective responses, six patients had stable disease persisting for >4 months in three patients. LINE1 demethylation ranged from ∼2%-10% and immune-specific CpGs (methylation in immune cells) changed 1%-5% at day 15. Methylation changes were not dose-dependent. CONCLUSIONS: ASTX727 plus talazoparib produces significant myelosuppression without other adverse events. Modest methylation changes in PBMCs were detected. There were no objective responses, but some heavily pretreated patients had stable disease for >4 months despite the attenuated doses.
15 Apr 2026
Read appraisal →International immunopharmacology
Machine learning derived proliferating T cell-related signature: a novel biomarker for prognosis and treatment efficacy in clear cell renal cell carcinoma
BACKGROUND: Clear cell renal cell carcinoma (ccRCC) exhibits profound molecular heterogeneity, and reliable biomarkers for predicting clinical outcomes are urgently needed. Proliferating T cells (Tprolif) are central to immune system activation but their related signatures in predicting prognosis and therapeutic effect of ccRCC patients remains unexplored. METHODS: We developed a Tprolif-related RCC score (TRRS) using an integrative machine learning framework. Transcriptomic data from the CheckMate025 trial formed the discovery cohort. The model was validated across multiple independent cohorts, including IMmotion151, JAVELIN Renal 101, TCGA-KIRC, and West China Hospital (WCH) cohort from our center. Multi-omics analyses, including spatial and single-cell transcriptomics, were employed to investigate the associated biology and identify key mediators. RESULTS: The final TRRS model, built from 7 genes, demonstrated robust performance in stratifying patients for overall survival (OS) and progression-free survival (PFS) in training and all validation sets. TRRS was a powerful predictor of improved outcomes not only for immune checkpoint inhibitor (ICI) monotherapy but also for ICI-based combination therapy and targeted therapies. Biologically, a high TRRS was associated with aggressive tumor hallmarks, a distinct metabolic profile favoring aerobic glycolysis and glutamine metabolism, and an immunosuppressive tumor microenvironment despite high immune cell infiltration based on WCH cohort transcriptome expression profile. Through multi-omics screening, we identified CST3 as a key target, with spatial and single-cell analyses confirming its role in promoting tumor malignancy and enhancing cell-cell communication among microenvironment components. CONCLUSION: The TRRS is a novel, validated biomarker that effectively predicts prognosis and therapeutic responses in advanced ccRCC. It reflects critical biological features of tumor aggressiveness and immune evasion, with CST3 emerging as a potential central mediator and therapeutic target.
15 Apr 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Mepitel Film versus StrataXRT for the prevention of radiation dermatitis in post-mastectomy radiotherapy: a systematic review and meta-analysis of randomized controlled trials
BACKGROUND: Radiation dermatitis (RD) affects up to 90% of patients receiving post-mastectomy radiotherapy, with moist desquamation causing significant morbidity. Barrier films and dressings such as Mepitel Film and StrataXRT have been shown to be effective compared to moisturizers, but their relative effectiveness remains debated. This systematic review and meta-analysis compares these interventions for preventing RD. METHODS: Following PRISMA guidelines, we systematically searched MEDLINE, Embase, and Cochrane Library through March 14, 2026. An additional grey literature search was performed in clinicaltrials.gov and Google Scholar. Eligible studies were RCTs directly comparing StrataXRT and Mepitel Film in breast cancer patients receiving adjuvant radiation therapy. Two reviewers independently screened studies, extracted data, and assessed risk of bias using RoB 2.0 and certainty of evidence with the GRADE appraoch. Meta-analysis used random-effects models to pool risk ratios (RR) for moist desquamation, and incidence of grade 2 and 3 RD, with I2 statistics quantifying heterogeneity. RESULTS: From 33 screened records, two intra-patient, non-inferiority RCTs (n = 120) met inclusion criteria. Both studies randomized medial/lateral chest wall halves to each intervention in post-mastectomy patients. Pooled analysis revealed no significant difference in moist desquamation incidence between StrataXRT (21.7%) and Mepitel Film (20.0%) (RR 1.03; 95% CI 0.52-2.04; P = 0.93), with low heterogeneity (I2 = 35%). Secondary outcomes showed Mepitel Film significantly reduced grade 2 or 3 RD (RR 1.34, 95% CI 1.10-1.64, P = 0.004). However, Mepitel Film trended toward higher incidence of itchiness (10.8% vs. 5.0%, P = 0.11). Patient preference data were conflicting, with one trial favoring StrataXRT for ease of use and the other showing no clear preference. CONCLUSION: While StrataXRT and Mepitel Film demonstrated similar efficacy for preventing moist desquamation, Mepitel Film offered superior protection against grade 2 to 3 RD. However, this conclusion should be interpreted with caution given the underlying methodological limitations of the included studies, including small and potentially underpowered sample sizes, and variations in radiation fractionation schedules and RT techniques.
15 Apr 2026
Read appraisal →Blood advances
Zamtocabtagene autoleucel in relapsed/refractory B-NHL: 5-year follow-up of a CD20/19 tandem CAR T-cell phase 1 trial
Emerging long-term data indicate relapse rates of >50% after CD19-redirected chimeric antigen receptor (CAR) T-cell therapy in relapsed or refractory (R/R) B-cell non-Hodgkin lymphoma (B-NHL). To reduce selective pressure on the CD19 antigen, we conducted a first-in-human phase 1 clinical trial of zamtocabtagene autoleucel (zamto-cel), a noncryopreserved tandem CD20-CD19-directed CAR T-cell therapy. Two predefined dose levels (dose level 1 [DL1], 1 × 106 and DL2, 2.5 × 106 CAR+ T cells per kg bodyweight) were applied. The primary end point (EP) was the maximum tolerated dose (MTD). Secondary EPs included adverse events, best overall response (BOR), and biomarker assessments. A total of 12 patients, 6 patients per DL, were treated. No dose-limiting toxicity and no cytokine release syndrome or immune effector cell-associated neurotoxicity syndrome of grade ≥3 were observed. Thus, MTD was not reached. The BOR by investigator assessment was 75%, with 5 of 12 patients (42%) achieving complete remission (CR) until month 12 with no relapse in clinical evaluation up to 5 years after infusion. CR was associated with a higher mean maximum observed concentration of zamto-cel and detection of zamto-cel beyond month 6. Additional product characterization revealed increased expression of CD27 and CD127 along with increased expansion of CAR+ central memory T cells in patients with CR, facilitating persistence and improved outcomes in R/R B-NHL treated with zamto-cel. Based on the promising risk-to-benefit ratio, evaluation of zamto-cel at DL2 is ongoing in pivotal phase 2 clinical trials for patients with R/R aggressive B-NHL. This trial was registered at www.ClinicalTrials.gov as NCT03870945.
15 Apr 2026
Read appraisal →JMIR cancer
Leveraging Innovative Electronic Health Record Data to Characterize Social Determinants of Health Among Survivors of Cancer in Persistent Poverty Areas: Cross-Sectional Study
BACKGROUND: Residents in persistent poverty areas experience higher cancer mortality due to social determinants of health that negatively affect multiple factors, including health behaviors. OBJECTIVE: This study aimed to characterize demographic, clinical, and social determinant of health factors among survivors of cancer in persistent poverty areas using electronic health record (EHR) data-including an embedded social risk screener and natural language processing (NLP) of social work notes-to inform community-engaged adaptation of lifestyle interventions. METHODS: EHR data from a large multispecialty group practice were extracted for patients with cancer residing in zip codes inclusive of persistent poverty areas targeted for a health behavior intervention and receiving care between January 2018 and November 2023. Self-reported social determinant of health data were obtained using the Protocol for Responding to and Assessing Patients' Assets, Risks, and Experiences (PRAPARE) and through NLP of social histories from a social work visit. RESULTS: We identified 2672 unique patients with cancer, of whom 578 (21.6%) had PRAPARE data and 1597 (59.8%) had social history data available for analysis. The most common cancers among survivors (n=1420, 53.1% female; n=1299, 48.6% Black individuals; mean age 65.2, SD 13.7 years) included breast (n=536, 20.1%), prostate (n=400, 15%), and lymphoid or hematopoietic (n=323, 12.1%) cancer. Among survivors in persistent poverty areas (n=509, 19%; all with a high Social Vulnerability Index), 34.6% (176/509) were single, 55.4% (282/509) had Medicare coverage (with only 73/509, 14.3% having private insurance), 36.5% (186/509) had obesity, 63.9% (325/509) had hypertension, and 31.2% (159/509) had diabetes. Of survivors in persistent poverty areas with PRAPARE data, 15.8% (19/120) lacked transportation, 4.2% (5/120) lived with housing insecurity, and 6.7% (8/120) felt unsafe where they lived. CONCLUSIONS: Innovative EHR and NLP approaches identified several socioeconomic and safety-related challenges along with opportunities for health behavior interventions to leverage Medicare coverage and target multiple comorbidities when adapting interventions for survivors of cancer living in persistent poverty areas.
14 Apr 2026
Read appraisal →Signal transduction and targeted therapy
Deutenzalutamide, a novel androgen receptor inhibitor, after progression on docetaxel and abiraterone in metastatic castration-resistant prostate cancer: results from the randomized phase III HC-1119-04 trial
Metastatic castration-resistant prostate cancer (mCRPC) after treatment with docetaxel and androgen receptor signaling inhibitors (ARSIs) has limited treatment options. Although enzalutamide has shown activity after abiraterone and docetaxel, robust evidence from randomized phase III trials is lacking. Deutenzalutamide, a novel derivative with slower metabolism and improved pharmacokinetics, may offer enhanced safety and efficacy. This phase III, double-blind trial conducted at 36 centers in China enrolled patients whose disease progressed on or who were intolerant to abiraterone and docetaxel, or who were ineligible for docetaxel. Patients were randomized (2:1) to receive deutenzalutamide 80 mg once daily or placebo until progression or unacceptable toxicity; the primary endpoint was radiographic progression-free survival (rPFS). Of 417 patients (276 deutenzalutamide; 141 placebo), all had previously received abiraterone, and 68% had also received docetaxel. Deutenzalutamide significantly improved rPFS (HR, 0.58; P = 0.0001), reducing the risk of progression by 42%. Although the initial OS analysis was not significant (HR, 0.95), sensitivity analyses adjusting for subsequent therapies showed significant OS benefits (HR, 0.65-0.73). Treatment-related grade 3 or higher adverse events occurred in 22.3% of patients treated with deutenzalutamide, compared with 15.0% with placebo. The most common treatment-related adverse event was anemia, reported at any grade in 21.2% versus 17.9%, with grade 3/4 anemia in 6.6% versus 2.9%, respectively. Notably, no seizures or falls were reported. In summary, deutenzalutamide significantly prolonged rPFS and, after adjustment, showed a potential OS benefit with a favorable safety profile, supporting its promise as a new treatment option for mCRPC. Clinical trial registration: NCT03851640.
13 Apr 2026
Read appraisal →Medicine
Glioma biomarker analysis and drug targeting prediction based on ferroptosis-related genes
Glioma represents a significant challenge in neuro-oncology due to its highly invasive nature and poor prognosis. Recent studies indicate that ferroptosis, a form of regulated cell death, plays a crucial role in the pathogenesis of glioma. Modulating ferroptosis may offer a novel therapeutic approach for glioma treatment. This study aims to identify ferroptosis-related biomarkers and patterns of immune infiltration in glioma using bioinformatics techniques, and to predict potential therapeutic drugs targeting these biomarkers. Gene expression profiles (GSE16011, GSE50161) were retrieved from the GEO database, while ferroptosis-related genes were sourced from the FerrDb database. Intersection genes were identified using the CytoHubba algorithm and a consensus clustering method. Immune infiltration was assessed using the CIBERSORT algorithm. Functional enrichment analysis and gene set enrichment analysis were conducted with Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases. Drug targeting predictions were made based on identified hub genes. We identified 10 hub genes: TP53, RRM2, EZH2, CDKN1A, MYCN, KIF20A, BLM, GLS2, HMOX1, and GOT1. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed that ferroptosis is primarily associated with apoptosis, reactive oxygen metabolism, the p53 signaling pathway, and immune regulation. Immune infiltration analysis showed significant differences in the expression of plasma cells, CD8 T cells, follicular helper T cells, activated NK cells, and macrophages between groups. qRT-PCR validation in glioma cell lines confirmed the differential expression of key ferroptosis-related genes, supporting the robustness of our bioinformatic findings. Furthermore, molecular docking demonstrates excellent binding properties between the drug components and key targets, and these key targets were further validated by PCR analysis in vitro. In summary, this study highlights the potential of ferroptosis-related biomarkers in glioma and suggests novel drug targets, providing a foundation for future therapeutic strategies.
12 Apr 2026
Read appraisal →Proceedings of the National Academy of Sciences of the United States of America
Resistance to neoadjuvant talazoparib in triple-negative breast cancer by BRN2-induced ATR/STAT3 pathways or SHLD2 subclone expansion
Intrinsic and acquired resistance to poly(ADP-ribose) polymerase (PARP) inhibitors (PARPi) remains a major barrier in treating homologous recombination (HR) repair-deficient tumors, including those with germline or somatic BRCA1/2 mutations. Although PARPi are FDA approved for adjuvant treatment of locally advanced or metastatic breast cancer in patients with germline BRCA1/2 mutations, emerging data support their use as monotherapy in the neoadjuvant setting. Promising safety profiles of newer-generation PARPi further support this potential. However, resistance mechanisms specific to the neoadjuvant setting are poorly understood. To address this gap, we leveraged resources from a phase II neoadjuvant clinical trial (NCT03499353), analyzing tumors from patients with germline BRCA1/2 mutant breast tumors before and after six months of talazoparib monotherapy. Whole-transcriptome analyses were performed on these samples. Additionally, we established orthotopic patient-derived xenograft models from a subset of the patient tumors and conducted whole-exome and whole-transcriptome analysis. This integrative approach revealed both known and previously unknown PARPi resistance mechanisms. In one case, overexpression of BRN2, encoding a transcription factor that plays a critical role in neurogenesis, led to activation of ATR/RAD51 and STAT3 pathways, restoring HR repair. BRN2-driven resistance could be reversed with ATR and STAT3 inhibitors, resensitizing cells to talazoparib. In another, an HR repair proficient tumor subclone lacking Shieldin 2 expression expanded during treatment and accounted for intrinsic resistance. Our findings highlight the need to determine intrinsic and anticipate acquired resistance pathways in treatment-naïve tumors and support combining PARPi with targeted agents to improve outcomes in the neoadjuvant setting.
12 Apr 2026
Read appraisal →Vaccine
The safety and efficacy of in situ vaccine FOLactis for advanced hepatic malignancies.
BACKGROUND: Recent years have witnessed the emergence of a novel research direction in the field of tumor vaccines. An in situ vaccine known as FOLactis, comprised of genetically engineered Lactococcus lactis expressing a fusion protein of Fms-like tyrosine kinase 3 ligand (Flt3L) and co-stimulator OX40 ligand (OX40L), was developed by the Oncology Centre of Nanjing Drum Tower Hospital. The present study was designed to evaluate the efficacy and safety of combination therapy based on intratumoral injection of the in situ vaccine FOLactis and intravenous PD-1 monoclonal antibody in combination with immunoradiotherapy or low-dose chemotherapy in the treatment of advanced hepatic malignancies. METHODS: In this exploratory clinical study, patients with advanced hepatic malignancies were enrolled and randomly assigned to Group A and Group B, corresponding to different treatment regimens. The treatment regimen in Group A consisted of the in situ vaccine FOLactis combined with a PD-1 monoclonal antibody and immunoradiotherapy (5Gy/fraction×3fractions). In contrast, the regimen in Group B included the in situ vaccine FOLactis, a PD-1 monoclonal antibody, and low-dose chemotherapy (cisplatin plus vincristine). All treatments were given in 21-day cycles. The primary observation indicator of the study was the objective response rate (ORR) as defined by RECIST v1.1. Secondary observation indicators included the disease control rate (DCR) and the incidence of adverse events. RESULTS: A total of 18 eligible patients were included in the study. In Group A, the ORR and DCR for targeted lesions were 33.3%(95%CI:12.1-64.6%) and 77.8%(95%CI:45.3-96.1%), while the ORR and DCR for non-targeted lesions were 22.2%(95%CI:3.9-54.7%) and 66.7%(95%CI:35.4-87.9%), respectively. Targeted and non-targeted lesions in Group B did not reduce significantly, and their respective DCRs were 66.7%(95%CI:35.4-87.9%) and 22.2%(95%CI:3.9-54.7%). Patients in Group A experienced no significant adverse events. Patients in Group B experienced grade 1-2 hyperthermia as a side effect. CONCLUSIONS: The combination therapy based on the in situ vaccine FOLactis and intravenous PD-1 monoclonal antibody showed a certain degree of anti-tumor effect, and was safe and well tolerated in patients with advanced hepatic malignancies. TRIAL REGISTRATION: The Ethics Committee of Nanjing Drum Tower Hospital approved this study (approval number 2022-101-01). The associated trial is registered at ChiCTR.gov.cn (ChiCTR2200060660).
12 Apr 2026
Read appraisal →Cancer immunology, immunotherapy : CII
A phase I, single-arm, open-label, dose-escalation, multicenter study of SAR445419, an off-the-shelf, ex vivo expanded allogeneic natural killer cell product, in participants with relapsed or refractory acute myeloid leukemia
BACKGROUND: SAR445419 is an investigational, off-the-shelf, allogeneic NK cell therapy derived from donor peripheral blood mononuclear cells and expanded ex vivo using PM21 particles. METHODS: This multicenter, Phase 1, dose-escalation study (NCT05712278) evaluated optimal dose(s), safety, and tolerability of SAR445419 in adults (aged ≥ 18 years) with relapsed/refractory acute myeloid leukemia (R/R AML). Following lymphodepleting chemotherapy (fludarabine 30 mg/m2/day and cytarabine 2 g/m2/day for 5 days), participants received six intravenous SAR445419 (1 × 109 and 3 × 109 NK cells/dose) doses, starting with the lower dose. The primary endpoint was the incidence of dose-limiting toxicities (DLTs), and key secondary endpoints included adverse events (AEs), hematological recovery, hematopoietic stem cell transplantation, and response rate. RESULTS: Of the 12 planned participants, 7 patients were enrolled, of whom 6 received SAR445419 before sponsor decided early study termination for reasons unrelated to safety or efficacy. No DLTs were observed. All participants experienced treatment-emergent adverse events (TEAEs); six had grade ≥ 3 TEAEs; and four had serious adverse events (SAEs). Two SAR445419-related SAEs (infusion-related reactions: both grade 2) were managed with supportive care. All participants experienced grade 3 anemia and grade 4 thrombocytopenia and neutropenia. Five deaths were reported, all due to disease progression, none related to SAR445419. No clinical responses were observed. CONCLUSIONS: This study demonstrated the overall safety of off-the-shelf ex vivo expanded allogeneic NK cells in patients with R/R AML. The successful manufacturing and distribution support the feasibility of donor-derived off-the-shelf NK cells in a clinical setting. Further investigations are required to improve the clinical efficiency of NK cells.
12 Apr 2026
Read appraisal →Journal for immunotherapy of cancer
Anti-TIM-3 antibody TQB2618 in combination with penpulimab in relapsed or refractory classic Hodgkin lymphoma previously treated with PD-1/PD-L1 therapy: a multicenter, open-label, single-arm, phase Ib clinical trial
BACKGROUND: Immune checkpoint inhibitors achieve high response rates in relapsed or refractory (r/r) classical Hodgkin lymphoma (cHL), but few treatment options are available for patients who experience failure after PD-1/PD-L1 blockade. T cell immunoglobulin and mucin-domain containing-3, a key mediator of immune escape from PD-1/PD-L1 inhibition, is targeted by TQB2618, a humanized IgG4 monoclonal antibody. This phase Ib study aimed to evaluate the safety and efficacy of TQB2618 in combination with the anti-PD-1 antibody penpulimab in patients with r/r cHL. METHODS: This multicenter phase 1b study was conducted from June 2022 to September 2024 at 12 sites in China (NCT05400876). The study included a dose-escalation phase in which patients with r/r lymphoma received TQB2618 (600 or 1200 mg, every 3 weeks, Q3W) plus penpulimab (200 mg, Q3W) to evaluate dose-limiting toxicities and determine the recommended phase II dose (RP2D). The subsequent dose-expansion phase enrolled patients with r/r cHL previously treated with PD-1/PD-L1 inhibitors to assess the objective response rate (ORR). RESULTS: 10 patients with r/r lymphoma were enrolled in the dose-escalation phase, and TQB2618 (600 mg, Q3W) plus penpulimab (200 mg, Q3W) was selected as the RP2D. In the dose-expansion phase, 18 additional patients with r/r cHL previously treated with PD-1/PD-L1 inhibitors were enrolled. Among the total 21 r/r cHL patients, the median age was 32 years (range, 22-65), and 12 (57%) were male. The ORR was 52%, including 1 complete response and 10 partial responses. Treatment-related adverse events (TRAEs) occurred in 18 patients (86%), with grade ≥3 TRAEs in 5 (24%). The most common TRAEs (≥20%) were platelet count decreased (24%), anemia (24%), and aspartate aminotransferase increased (24%). As of the data cut-off in December 2024, the median follow-up was 14.1 months, the median duration of response and the median overall survival had not yet been reached. CONCLUSIONS: TQB2618 in combination with penpulimab was well tolerated and demonstrated promising efficacy in patients with cHL who had failed prior anti-PD-1/PD-L1 therapy, supporting its potential as a therapeutic option for this difficult-to-treat population. TRIAL REGISTRATION NUMBER: NCT05400876.
12 Apr 2026
Read appraisal →PloS one
Predicted meta-omics: A potential solution to multi-omics data scarcity in microbiome studies
Imbalances in the gut microbiome have been linked to conditions such as inflammatory bowel disease, diabetes, and cancer. While metagenomics and amplicon sequencing are commonly used to study the microbiome, they do not capture all layers of microbial functions. Other meta-omics data can provide more insights, but these are more costly and laborious to procure. The growing availability of paired meta-omics data offers an opportunity to develop machine learning models that can infer connections between metagenomics data and other forms of meta-omics data, enabling the prediction of these other forms of meta-omics data from metagenomics. We evaluated several machine learning models for predicting meta-omics features from various meta-omics inputs. Simpler architectures such as elastic net regression and random forests generated reliable predictions of transcript and metabolite abundances, with correlations of up to 0.77 and 0.74, respectively, but predicting protein profiles was more challenging. We also identified a core set of well-predicted features for each meta-omics output type, and showed that multi-output regression neural networks performed similarly when trained using fewer output features. Lastly, our experiments demonstrated that predicted features can be used for the downstream task of inflammatory bowel disease classification, with performance comparable to that of experimental data.
11 Apr 2026
Read appraisal →PLoS medicine
Spatial omics and AI for clinically actionable cancer biomarkers
Integrating spatial omics with artificial intelligence is likely to advance biomarker research and diagnostics, with the potential to pair mechanistic insight into spatial target biology. By developing scalable, reproducible quantification in routine pathology, it can help bridge discovery, validation, and real-world clinical implementation.
11 Apr 2026
Read appraisal →Journal of clinical oncology : official journal of the American Society of Clinical Oncology
Development of a Classifier for Metabolic Subtypes of Nasopharyngeal Carcinoma to Guide Personalized Immunotherapy Strategies: Biomarker Analysis of the Phase III CONTINUUM and DIPPER Trials
PURPOSE: Personalized immunotherapy strategies are urgently needed for patients with locoregionally advanced nasopharyngeal carcinoma (NPC). We aim to identify biomarkers predictive of immunotherapy benefits, using data from the phase III CONTINUUM (ClinicalTrials.gov identifier: NCT03700476) and DIPPER (ClinicalTrials.gov identifier: NCT03427827) randomized clinical trials. PATIENTS AND METHODS: Tumor samples from 407 patients in the CONTINUUM (discovery cohort) and DIPPER (validation cohort) trials were subjected to RNA sequencing. In the discovery cohort, metabolic gene-based consensus clustering was performed to determine subtypes. A machine learning-based classifier was subsequently developed in the discovery cohort and then applied to the validation cohort to assign metabolic subtypes. Gene set enrichment analyses were used to characterize the biological features of each metabolic subtype. The clinical end point was event-free survival (EFS). RESULTS: In the discovery cohort, three metabolic subtypes were identified with distinct tumor-intrinsic and immune features as well as differential EFS benefits from adding anti-PD-1 to chemoradiotherapy (CRT). Specifically, the MS1 subtype exhibited a significant improvement in 3-year EFS in the anti-PD-1 plus CRT arm compared with the CRT-alone arm (3-year EFS, 90.2% v 69.6%; hazard ratio, 0.27 [95% CI, 0.11 to 0.67]), whereas MS2 (3-year EFS, 94.1% v 93.8%) and MS3 subtypes (3-year EFS, 75.0% v 75.0%) derived no significant survival benefit. The subtype features were preserved in the validation cohort, with consistent prognostic and predictive value. A pooled analysis of both cohorts demonstrated the significant interaction between metabolic subtypes and the treatment effect (Pinteraction = 0.0074). CONCLUSION: In this biomarker study, we defined metabolic subtypes of NPC that predicted the EFS benefit from immunotherapy. This novel molecular classification provides a promising predictive biomarker for personalized treatment decision for patients with locoregionally advanced NPC.
11 Apr 2026
Read appraisal →Signal transduction and targeted therapy
Irinotecan with trifluridine/tipiracil and bevacizumab for second-line metastatic colorectal cancer: a phase II multicenter study
Patients diagnosed with metastatic colorectal cancer (mCRC) face a constrained therapeutic landscape following the failure of initial treatment. This multicenter, single-arm, phase II trial (NCT06202001) aimed to assess the efficacy and safety of a novel second-line regimen comprising trifluridine/tipiracil (TAS-102), irinotecan, and bevacizumab. Patients with mCRC resistant to prior fluoropyrimidine and oxaliplatin-based chemotherapy were enrolled. Based on a preceding phase I trial, patients received biweekly cycles of oral TAS-102 (30 mg/m² twice daily, days 1-5), intravenous irinotecan (150 mg/m²), and intravenous bevacizumab (5 mg/kg). The primary outcome measure was the objective response rate (ORR). From October 2023 to August 2024, 60 patients were enrolled. As of December 2024, the ORR was 18.3% (2 complete and 9 partial responses), and the disease control rate (DCR) was 83.3%. The median progression-free survival (PFS) was 6.6 months (95% CI, 4.39-8.81), and the median overall survival (OS) was 17.3 months (95% CI, 13.55-21.05). Subgroup analyses indicated that prior resection of the primary tumor was associated with significantly longer median OS (21.9 vs. 16.2 months; p = 0.048) and PFS (8.9 vs. 5.2 months; p = 0.004). The most frequently reported treatment-related adverse events (TRAEs) were nausea (100%), neutropenia (86.7%), and anemia (83.3%). The predominant grade 3/4 TRAEs included neutropenia (48.3%), febrile neutropenia (8.3%), and diarrhea (6.7%). In conclusion, the combination of irinotecan, TAS-102, and bevacizumab shows encouraging efficacy and a manageable safety profile as a second-line therapy for mCRC, meriting further investigation.
11 Apr 2026
Read appraisal →Journal for immunotherapy of cancer
Simlukafusp alfa (FAP-IL2v) plus atezolizumab with or without bevacizumab in unresectable, metastatic renal cell carcinoma: a randomized, open-label phase Ib study
BACKGROUND: Simlukafusp alfa (FAP-IL2v) was engineered to preferentially activate CD8+ T and natural killer (NK) cells in tumor microenvironments overexpressing fibroblast activation protein (FAP). Checkpoint inhibitors combined with antiangiogenic agents are standard therapy for metastatic renal cell carcinoma (mRCC), which overexpresses FAP. Here, we explored the efficacy, safety, and pharmacodynamic effects of FAP-IL2v in combination with atezolizumab with or without bevacizumab in patients with mRCC. METHODS: Patients with treatment-naïve or pretreated clear cell and/or sarcomatoid mRCC were eligible. Dose escalation explored FAP-IL2v every 2 weeks (Q2W) with atezolizumab Q2W (doublet, arm A), and with atezolizumab and bevacizumab Q2W (triplet, arm B) in patients treated with up to one prior systemic therapy. Dose extension explored in untreated patients the recommended FAP-IL2v dose administered Q2W (doublet, arm A; and triplet, arm B) or 3-weekly (doublet, arm C; and triplet, arm D). Primary objectives were the recommended FAP-IL2v dose and antitumor activity. Secondary objectives included safety, pharmacodynamics, and exploratory biomarkers in peripheral blood and paired biopsies. RESULTS: By the data cut-off date (31 August, 2021), 66 patients were enrolled. The median duration of treatment was 11.0 months. Objective response rates (ORRs) were 25% for the doublet and 47% for the triplet, and median progression-free survival was 6.3 and 18.3 months, respectively. Safety profiles were consistent with the individual drugs, including expected interleukin-2 (IL-2) class-specific adverse events (AEs). Two deaths were recorded caused by AEs related to study treatment (acute kidney injury, n=1; pancytopenia, n=1). Expansion and activation of NK and T cells, but not regulatory T cells, was observed in peripheral blood, leading to increased tumor infiltration and inflammation in paired biopsies. The addition of bevacizumab led to a reduced angiogenesis signature score and reduced vessel density. CONCLUSION: The combination of FAP-IL2v plus atezolizumab with or without bevacizumab was consistent with the known safety profile of the individual drugs. The maximum tolerated dose was not reached, with a recommended FAP-IL2v dose of 10 mg. ORR was higher among patients receiving the triplet therapy compared with the doublet. Pharmacodynamic results were consistent with the mechanism of action of IL-2, supporting further research in this field.
11 Apr 2026
Read appraisal →Journal for immunotherapy of cancer
Deep learning-driven recognition of panoramic tumor microenvironment features in H&E sections and its application
The tumor microenvironment (TME), composed of tumor cells together with stromal cells, immune cells, vascular networks, and other components, constitutes a complex ecosystem that plays a decisive role in tumor initiation, progression, metastasis and therapeutic response. Traditional pathological diagnosis mainly relies on pathologists manually examining H&E-stained tissue sections under the microscope, a method that not only suffers from substantial interobserver variability but also has relatively low analytical efficiency. With the rapid development of computational pathology, the integration of whole-slide imaging technology and deep learning algorithms has provided powerful tools for characterizing tumor microenvironment. These techniques enable automated characterization of cellular, spatial and molecular heterogeneity within the tumor microenvironment, providing integrated insights that advance precision diagnostics and improve prediction of therapeutic response and patient outcomes. Based on a comprehensive review of existing research, this paper highlights recent advances in deep learning-driven recognition of panoramic TME features from H&E slides and their clinical applications and further discusses both the translational potential and current limitations of this technology in oncology research and clinical applications.
11 Apr 2026
Read appraisal →PloS one
Development and evaluation of a multimodal feature-based predictive model for radiotherapy-induced oral mucositis in nasopharyngeal carcinoma
BACKGROUND: Accurate prediction of radiation-induced oral mucositis is crucial for personalized treatment in head and neck cancer. However, developing robust predictive models utilizing high-dimensional multimodal data (CT imaging, dose distribution, and clinical features) remains challenging, particularly in cohorts with limited sample sizes. OBJECTIVE: This study aimed to rigorously evaluate and compare the multi-class predictive performance of traditional machine learning algorithms and deep learning architectures under a small-cohort setting. METHODS: Multimodal data from 108 patients were collected. A comprehensive evaluation framework incorporating nine traditional machine learning algorithms and two deep learning models (a dimensionality-reduced 1D-CNN and a multimodal 3D-CNN) was established. To ensure robust evaluation, a stratified 5-fold cross-validation was employed. Model performance was comprehensively quantified using mean ± standard deviation (SD) across multiple metrics, including the Area Under the Curve (AUC), accuracy, and Matthews Correlation Coefficient (MCC). RESULTS: Inter-rater reliability for RIOM grading was excellent (Cohen's kappa = 0.82, 95% CI: 0.73-0.91). Among traditional machine learning approaches, the Extra Trees (ET) algorithm achieved the highest discriminative capacity (AUC: 0.956 ± 0.046), while Logistic Regression (LR) demonstrated optimal overall accuracy (0.832 ± 0.155) and stability. Regarding deep learning, the lightweight 1D-CNN utilizing fused low-dimensional features exhibited highly competitive and robust performance (AUC: 0.900 ± 0.072; Accuracy: 0.732 ± 0.140). In stark contrast, the high-dimensional multimodal 3D-CNN suffered from severe overfitting and mode collapse phenomenon, yielding significantly inferior results (AUC: 0.568 ± 0.090; MCC: -0.025 ± 0.031). CONCLUSIONS: For small-cohort radiomics and dosimetric analyses, ensemble learning models (e.g., ET) and appropriately regularized linear models (e.g., LR) remain highly effective. While deep learning holds promise, high-dimensional architectures like 3D-CNNs are highly susceptible to mode collapse without massive datasets. Instead, employing feature dimensionality reduction combined with lightweight networks (1D-CNN) is a vastly superior strategy to extract reliable predictive patterns from limited clinical data.
10 Apr 2026
Read appraisal →JMIR cancer
Empathic AI for Patient-Centered Cancer Care: A Scoping Review of Patient Navigation, Support, and Clinical Practice
BACKGROUND: Artificial intelligence (AI) is rapidly reshaping oncology, offering advancements in clinical care and patient support. A growing area of interest is the integration of empathic AI: systems integrating clinical precision with emotional intelligence to support medical decision-making and the emotional and psychosocial well-being of patients and caregivers. OBJECTIVE: This review aimed to explore the role of empathic AI in cancer care, with a focus on its applications in patient education, clinician support, and emotional care. It also evaluated the ethical, cultural, and implementation challenges associated with its integration into clinical practice in oncology. METHODS: A systematic search of literature was conducted in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. Peer-reviewed papers published between January 2018 and January 2025 were identified through a search of PubMed, Scopus, and IEEE Xplore, and citation tracking. Eligible studies focused on applications of empathic AI in oncology. A total of 44 studies were included and analyzed thematically across 3 core clinical domains: tailored communication and education, diagnostics and care plan optimization, and emotional and psychosocial support. RESULTS: Empathic AI demonstrates the potential to improve cancer care by enhancing patient education, clinical decision-making, and emotional support. Common applications include personalized education for patients and providers, support for diagnostic and therapeutic decisions, and tools designed to recognize and respond to patient distress. Several studies noted improved patient engagement and reduced clinician burden. However, concerns were identified regarding overreliance on AI systems, cultural insensitivity, and patient privacy. CONCLUSIONS: Empathic AI represents a promising advancement in patient-centered oncology, integrating emotional intelligence into clinical care. Its successful implementation will require careful attention to ethical, cultural, and clinical considerations to ensure health equity, trust, and safety in AI-assisted cancer care.
10 Apr 2026
Read appraisal →Journal of clinical oncology : official journal of the American Society of Clinical Oncology
Neoadjuvant Chemotherapy With CAPOX Versus Chemoradiation for Locally Advanced Rectal Cancer With Uninvolved Mesorectal Fascia (CONVERT): Final Results of a Phase III Trial
PURPOSE: The neoadjuvant chemoradiotherapy (nCRT) might accentuate surgical complications and toxicity in the treatment of locally advanced rectal cancer (LARC) while neoadjuvant chemotherapy (nCT) alone shows promise as an alternative treatment. However, which patients deserve most from the nCT need further clarify. This trial aimed to assess the non-inferiority of nCT with capecitabine plus oxaliplatin (CAPOX) versus nCRT with capecitabine in LARC with uninvolved mesorectal fascia (MRF). METHODS: Patients with LARC within 12 cm from the anal verge and uninvolved MRF were randomly assigned to receive 4 cycles of CAPOX chemotherapy alone (nCT group) or CRT with concurrent Capecitabine (nCRT group). The primary end point is 3-year locoregional recurrence-free survival (LRRFS). Secondary end points, such as 3-year disease-free survival (DFS), 3-year overall survival (OS), and adverse events (AEs), were also reported. RESULTS: A total of 663 patients were enrolled and 589 patients received the allocated treatment (nCT, n = 300; nCRT, n = 289). LRRFS was analyzed with a median follow-up of 48 months. 3-year LRRFS was 97.4% (95% CI, 95.5 to 99.3) in the nCRT group and 96.3% (95% CI, 94.0 to 98.6) in the nCT group, resulting in a hazard ratio (HR) of 1.40 (95% CI, 0.53 to 3.68). The nCT and nCRT achieved similar 3-year DFS (89.2% v 87.9%; HR, 0.88 [95% CI, 0.54 to 1.44]) and 3-year OS (95.0% v 94.1%; HR, 0.86 [95% CI, 0.42 to 1.76]). The nCT group showed a lower incidence of grade 2 to 4 long-term AEs (16.0% v 26.3%, P = 0.002) and proctitis (33.6% v 41.7%, P = 0.049) compared with nCRT group. CONCLUSIONS: The non-inferiority of nCT was not confirmed with a very low incidence of local recurrence in both group. But nCT offers comparable DFS and OS while mitigating the burden of toxicity as compared to nCRT. These insights shed light on a potential paradigm shift in the treatment for LARC with uninvolved MRF.
10 Apr 2026
Read appraisal →Journal of clinical oncology : official journal of the American Society of Clinical Oncology
Transarterial Chemoembolization Combined With Camrelizumab and Rivoceranib for Unresectable Hepatocellular Carcinoma (CHANCE2005/CARES-005): A Randomized Phase II Trial
PURPOSE: Transarterial chemoembolization (TACE) alone has shown limited efficacy in improving survival among patients with unresectable hepatocellular carcinoma (HCC). This phase II trial compared TACE combined with camrelizumab (anti-PD-1 antibody) and rivoceranib (vascular endothelial growth factor receptor 2 inhibitor) versus TACE in unresectable HCC. METHODS: Patients with unresectable HCC (Barcelona Clinic Liver Cancer stage A to C without extrahepatic metastases) and Child-Pugh class A liver function were randomly assigned (1:1), stratified by macrovascular invasion, previous tyrosine kinase inhibitor treatment, and number of previous TACE procedures, to receive TACE combined with camrelizumab (200 mg once every 3 weeks) and rivoceranib (250 mg once daily; TACE-C-R) or TACE alone. The primary end point was progression-free survival (PFS) per composite criteria (progression per Response Evaluation Criteria in Cancer of the Liver version 5, transient deterioration to Child-Pugh class C, or TACE failure or refractoriness) in the intention-to-treat population. RESULTS: Between December 28, 2020, and October 29, 2023, 200 patients were randomly assigned (100 in each group). Median PFS per composite criteria was significantly longer with TACE-C-R than with TACE (10.8 months [95% CI, 8.8 to 13.7] v 3.2 months [95% CI, 2.4 to 4.2]; hazard ratio, 0.34 [95% CI, 0.24 to 0.50], P < .001). Grade ≥3 treatment-related adverse events occurred in 74.5% (70 of 94) of patients with TACE-C-R and 22.3% (23 of 103) of patients with TACE, with the most common being increased AST (29 [30.9%] and 13 [12.6%]) and increased ALT (23 [24.5%] and 14 [13.6%]). CONCLUSION: The addition of camrelizumab and rivoceranib to TACE showed statistically significant improvement in PFS for patients with unresectable HCC, with a manageable safety profile. Follow-up for further overall survival analysis is ongoing.
10 Apr 2026
Read appraisal →Blood
Brentuximab vedotin and nivolumab in combination with chemotherapy for nonbulky, early-stage classical Hodgkin lymphoma
Most patients with early-stage classical Hodgkin lymphoma (cHL) are treated with doxorubicin, bleomycin, vinblastine, and dacarbazine with or without radiation therapy, although studies are now evaluating the incorporation of novel agents paired with abbreviated chemotherapy. We present the efficacy and safety of AN+AD (brentuximab vedotin [BV] and nivolumab in combination with doxorubicin and dacarbazine) in patients with early-stage cHL. In this phase 2 study, patients with nonbulky (<10 cm) Ann Arbor stage I or II cHL received 4 cycles of AN+AD. The primary end point was complete response (CR) rate at end of treatment (EOT) by investigator. At the time of this analysis, 154 patients received ≥1 dose of AN+AD. The objective response rate at EOT was 96% (95% confidence interval [CI], 91.7-98.6), and the CR rate was 92% (95% CI, 86.0-95.4). In the favorable (n = 56) and unfavorable (n = 97) subgroups, CR rates were 95% (95% CI, 85.1-98.9) and 91% (95% CI, 83.1-95.7), respectively. The proportion of patients with duration of CR of at least 2 years was 96% (95% CI, 90.9-98.4). At a median follow-up of 27.9 months, the estimated 2-year progression-free survival rate was 97% (95% CI, 92.0-98.8). Any-grade and grade ≥3 treatment-emergent adverse events occurred in 99% and 44% of patients, respectively; no events of febrile neutropenia were reported. Any-grade treatment-emergent immune-mediated adverse events occurred in 22% of patients. One disease-related death was reported after the safety reporting period. Results from this study support the use of BV and nivolumab in combination with limited chemotherapy for patients with nonbulky, early-stage cHL. This trial was registered at www.clinicaltrials.gov as NCT03646123.
10 Apr 2026
Read appraisal →Blood
A phase 3 study of intensive chemotherapy with or without dasatinib in core-binding factor acute myeloid leukemia
Core-binding factor acute myeloid leukemia (CBF-AML) is associated with KIT mutations and deregulated expression of KIT. We report results from the randomized, open-label, phase 3 trial of intensive chemotherapy with or without the multikinase inhibitor dasatinib in adult patients with CBF-AML. Patients received "3+7" induction therapy, followed by 4 cycles of high-dose cytarabine; in the investigational arm, patients received dasatinib 100 mg daily on days 8 to 21 in induction, and on days 6 to 28 in consolidation cycles, followed by 12-month single-agent dasatinib 100 mg daily. Primary end point was event-free survival (EFS). Secondary end points included overall survival, relapse-free survival, and cumulative incidence of relapse. A total of 202 patients were randomly assigned to the standard arm (n = 102) and to the dasatinib arm (n = 100). Median age was 49 years (range, 18-77); 94 patients had t(8;21), 108 had inv(16)/t(16;16); and 58 (28.7%) patients had a KIT comutation. There was no statistically significant difference in EFS (hazard ratio, 0.92; 95% confidence interval, 0.63-1.33; P = .66) or secondary end points between treatment arms. There was also no significant difference in EFS in subgroup analyses according to age, CBF-AML type, and KIT mutation status. The incidence of serious adverse events was higher in the investigational arm (64%) than in the standard arm (36%). In patients with CBF-AML, the addition of dasatinib to intensive chemotherapy failed to improve survival outcomes. The addition of dasatinib was associated with an increase in toxicity. This trial was registered at www.ClinicalTrials.gov as NCT02013648.
10 Apr 2026
Read appraisal →Biochemical and biophysical research communications
Interpretable prognostic modeling of glioblastoma using cross-cohort transcriptome integration and machine learning approaches
Glioblastoma (GBM) is an aggressive brain tumor with highly variable patient outcomes due to pronounced molecular heterogeneity. Prognosis remains dismal (median survival ∼15 months) and current prognostic models often function as "black boxes," lacking interpretability and limiting clinical utility. There is an urgent need for interpretable prognostic tools to better stratify GBM patients. This study performed cross-cohort, cross-platform transcriptome data integration (TCGA RNA-seq and GEO microarrays) and incorporated inferred immunogenomic features to capture GBM's complexity. An automated machine learning (AutoML) pipeline tested over 100 algorithmic combinations to build an optimal survival prediction model. The final model is a 22-gene signature, and SHAP (SHapley Additive exPlanations) analysis was applied to explain each gene's contribution to risk. Key genes identified by the model (e.g. UBE2W, EID1, HS2ST1) were validated by qRT-PCR and Western blot, confirming their dysregulated expression in GBM cell lines. The 22-gene model achieved a concordance index of ∼0.72 and was validated on independent cohorts (TCGA training and GEO validation), demonstrating robust performance. It effectively stratified patients into high- and low-risk groups with significant survival differences. High-risk tumors were associated with an immune-cell-enriched yet immune-evasive microenvironment, showing greater infiltration of immunosuppressive cells and higher TIDE scores (indicating immune escape). In contrast, low-risk patients had a more favorable immune profile, and their tumors were predicted to be more sensitive to multiple chemotherapeutic agents. This interpretable transcriptome-based integrative prognostic model can serve as a valuable tool for GBM risk stratification and may guide therapeutic decision-making by highlighting potential targets. Not only does it improve outcome prediction, but it also identifies novel prognostic biomarkers, holding promise for personalized treatment and clinical translation in glioblastoma.
10 Apr 2026
Read appraisal →PloS one
Deep learning for adaptive chemotherapy: A DDPG-based approach to optimizing tumor-immune dynamics
In this article, we propose a deep reinforcement learning based chemotherapy regulation framework to realize personalized and dynamic optimization of cancer treatment. We use a nonlinear dynamic system to model the dynamic evolution of the tumor microenvironment including tumor cell, normal cell and immune cell interactions, with drug concentration serving as the control input variable. The Deep Deterministic Policy Gradient (DDPG) algorithm makes agents can study optimal dosing strategy in a continuous space of movement to inhibit tumor growth effectively and minimize damage to normal tissues. To make the strategy more stable, Gauss noise is added to the model to simulate physiological oscillations and uncertainties in the treatment reaction. Experimental results show that it can control the growth of tumor in various initial scenarios and the accumulation of the drug concentration with high flexibility and safety. Our technique provides a feasible technical way for precision, low toxicity adaptive chemotherapy.
9 Apr 2026
Read appraisal →The New England journal of medicine
Setidegrasib in Advanced Non-Small-Cell Lung Cancer and Pancreatic Cancer
BACKGROUND: The KRAS p.G12D variant occurs in 5% of patients with non-small-cell lung cancer (NSCLC) and is the most common substitution variant in pancreatic ductal adenocarcinoma, occurring in 40% of patients, but no targeted therapies directed against this variant are currently approved for clinical use. Setidegrasib (ASP3082) is a first-in-class KRAS G12D-targeted protein degrader. METHODS: We conducted this phase 1 study to evaluate the safety, pharmacokinetics, pharmacodynamics, and antitumor activity of setidegrasib in patients with previously treated advanced solid tumors harboring KRAS p.G12D variants. The primary objectives were to evaluate the safety profile, as indicated by dose-limiting toxic effects and adverse events (the primary end points), and to determine the phase 2 dose. Setidegrasib was administered intravenously once weekly at doses of 10 to 800 mg. RESULTS: Overall, 203 patients were enrolled. Among the 76 patients who received setidegrasib at a dose of 600 mg, which was ultimately selected as the phase 2 dose, adverse events occurred during treatment in all the patients, with events of grade 3 or higher in 42%. Treatment-related adverse events occurred in 93% of the patients; the most common were transient infusion-related reactions (in 80%) and nausea (in 30%). Adverse events led to discontinuation in 2 patients. Among the 45 patients with NSCLC who received the 600-mg dose, 36% (95% confidence interval [CI], 22 to 51) had a partial response, the median progression-free survival was 8.3 months (95% CI, 4.1 to could not be estimated), and the estimated 12-month overall survival was 59% (95% CI, 40 to 74). Among the 21 patients with metastatic pancreatic ductal adenocarcinoma who received the 600-mg dose as second- or third-line treatment (of whom 67% received setidegrasib as third-line treatment), 24% (95% CI, 8 to 47) had a response, the median progression-free survival was 3.0 months (95% CI, 1.4 to 6.9), and the median overall survival was 10.3 months (95% CI, 4.2 to 13.0). CONCLUSIONS: Setidegrasib was associated with antitumor activity and a low incidence of treatment discontinuation due to adverse events in patients with previously treated advanced KRAS p.G12D-mutated NSCLC or pancreatic ductal adenocarcinoma. (Funded by Astellas Pharma; ClinicalTrials.gov number, NCT05382559.).
9 Apr 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Transitioning to adolescent and young adult or adult cancer survivorship care: A systematic review of contemporary guidelines and trials
BACKGROUND: Health service transitions of child, adolescent, and young adult cancer survivors between paediatric and adult cancer care services are pervasively fragmented, resulting in poor coordination, reduced satisfaction, and adverse outcomes for cancer survivors and significant others (e.g., caregivers and family members). Numerous trials and clinical guidelines have been developed to support care transitions, but their convergence, divergence, and methodological quality remain unclear. To advance clinical practice and quality research about care transitions, a synthesis of trials and guidelines on child, adolescent, and young adult care transition across age-appropriate cancer services is required to inform future research, practice, guideline, and policy development. METHODS: A systematic review (CRD420251029796) was conducted. Studies were identified from searches of six bibliographic databases, four trial registers, and grey literature. Inclusion criteria focused on clinical guidelines or trials published between January 2020 and April 2025 that addressed age-related transitional cancer care. RESULTS: A total of 3706 records were identified. After screening, 18 records met the inclusion criteria: 10 clinical guidelines, 4 completed trials, and 4 ongoing trials with registrations. While general recommendations between guidelines were consistent, they diverged in detail relating to their local contexts. Only two ongoing trials, and no completed studies, included independent control groups. CONCLUSION: Completed trials were limited and highly variable in approach, components, and outcome measures, suggesting regionally specific guidelines enhance relevance, applicability, and transition practices. Future research should develop core outcome sets for care transition trials to facilitate comparison of outcomes and should develop robust study designs, including control groups.
9 Apr 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Real-world incidence of nausea and vomiting induced by antineoplastic agents: insights and future directions from the Japanese adverse drug event report database
PURPOSE: Antineoplastic treatment commonly leads to nausea and vomiting (NV), which considerably affects the quality of life and adherence to therapy of patients. This study aimed to analyze the incidence of NV induced by antineoplastic agents using the Japanese Adverse Drug Event Report (JADER) database and to explore novel antiemetic therapies for oral antineoplastic agents. METHODS: Data from April 2004 to March 2023 were extracted from the JADER database. The analysis considered guideline-recommended antiemetics and involved the temporal evaluation of cases with NV. The onset patterns of NV were analyzed for each oral antineoplastic agent. The reported odds ratio was used to identify adjunctive medications that showed inverse associations with these symptoms. RESULTS: A large number of NV cases were reported for platinum compounds and irinotecan. Among the oral agents, a notable incidence of NV was induced by antimetabolites and tyrosine kinase inhibitors. A decreasing trend in the use of single-agent antiemetic therapy was observed from 2010. Distinct NV patterns were identified. Silodosin and meropenem emerged as potential adjunct therapies to suppress NV induced by oral agents. CONCLUSION: Although there is a trend toward improvement in NV management with increased adherence to antiemetic therapies, a substantial gap remains between guidelines and actual practice. The need for tailored management strategies based on the onset patterns of NV for specific agents has been emphasized. Investigating the mechanisms underlying the antiemetic effects of adjunctive therapies, such as silodosin and meropenem, may pave the way for new antiemetic options for oral antineoplastic agents.
9 Apr 2026
Read appraisal →Signal transduction and targeted therapy
Anti-PD-1 antibody penpulimab plus chemotherapy for recurrent or metastatic nasopharyngeal carcinoma: a randomized, double-blind phase 3 study
In this phase 3 trial, penpulimab combined with chemotherapy was assessed against a regimen of placebo plus chemotherapy for the first-line treatment of recurrent or metastatic nasopharyngeal carcinoma (R/M NPC). 291 patients were randomised and allocated in a 1:1 ratio in order to receive penpulimab (n = 144; 200 mg) or placebo (n = 147; 200 mg), plus chemotherapy (cisplatin/carboplatin and gemcitabine) every 3 weeks. Patients followed by maintenance therapy with penpulimab or placebo after 6 cycles. The primary endpoint of this study was progression-free survival (PFS) according to RECIST v1.1, and a significantly longer median PFS in the penpulimab arm versus the placebo arm (9.63 versus 7.00 months; hazard ratio 0.45, 95% CI: 0.33-0.62, P < 0.0001) was demonstrated in this prespecified interim analysis. The key secondary endpoint was the overall survival (OS). However, the OS data were still immature, and the median OS was not achieved (hazard ratio, 0.94; 95% CI: 0.63-1.40). The occurrence of treatment-related adverse events (grade ≥ 3) was 89.0% and 85.9% in two arms, with the most common being reduced the quantity of neutrophil (56.2% vs. 62.0%), reduced the quantity of white blood cell (54.1% vs. 54.9%), and anemia (45.2% vs. 38.7%). In the penpulimab arm, 6 patients (4.1%) experienced immune-related adverse events (grade ≥ 3). Adding penpulimab to chemotherapy led to a notable enhancement in PFS for the first-line R/M NPC treatment, alongside a safety profile that was both manageable and tolerable. ClinicalTrials.gov identifier NCT04974398.
8 Apr 2026
Read appraisal →JAMA
Treatment for Brain Metastases With Stereotactic Radiation vs Hippocampal-Avoidance Whole Brain Radiation: A Randomized Clinical Trial
IMPORTANCE: Brain metastases are common in patients with cancer, and radiation is often used for management. Among patients with more than 4 brain metastases, the effects of stereotactic radiation targeting only individual tumors, compared with whole brain radiation with hippocampal avoidance, which radiates both tumors and normal brain, remain unknown. OBJECTIVE: To determine whether stereotactic radiation improves symptom severity and interference with daily functioning, compared with whole brain radiation with hippocampal avoidance. DESIGN, SETTING, AND PARTICIPANTS: Phase 3, open-label, randomized clinical trial conducted at 4 United States-based centers. Eligible patients had 5 to 20 brain metastases and no prior brain-directed radiation. Enrollment occurred between April 11, 2017, and May 17, 2024 (final follow-up, March 18, 2025). INTERVENTION: Stereotactic radiation, compared with whole brain radiation with hippocampal avoidance. MAIN OUTCOMES AND MEASURES: Mean weighted patient-reported symptom severity and interference score change over 6 months postbaseline relative to baseline using the MD Anderson Symptom Inventory-Brain Tumor instrument (scale, 0-10; score change range, -10 to 10; -10 = best). A clinically meaningful Δ was defined as 0.98. RESULTS: Of 196 randomized patients (mean age, 61 years; 129 [66%] female; 176 [90%] White; median number of brain metastases, 14 [IQR, 11-18]; 49 [25%] with prior neurosurgical resection), 83 (42%) completed the 6-month assessment. For the primary outcome, between baseline and postbaseline assessments through the 6-month follow-up, stereotactic radiation changed the weighted composite MD Anderson Symptom Inventory-Brain Tumor score from 2.69 to 2.37 (mean change, -0.32) and hippocampal-avoidance whole brain radiation changed the score from 2.29 to 3.03 (mean change, 0.74) (mean difference, -1.06 [95% CI, -1.54 to -0.58]; P < .001). Related grade 3-5 adverse events occurred in 12 patients (12%) in the stereotactic radiation group and 13 patients (13%) in the hippocampal-avoidance whole brain radiation group; grade 1-3 fatigue was most frequent (27 [28%] vs 43 [44%], respectively). CONCLUSIONS AND RELEVANCE: In patients with 5 to 20 brain metastases, these findings support stereotactic radiation over hippocampal-avoidance whole brain radiation to improve symptoms and interference with daily functioning, key components of quality of life. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT03075072.
8 Apr 2026
Read appraisal →Journal of computer-aided molecular design
Identification of potential inhibitors of 3‑mercaptopyruvate sulfurtransferase with a deep-learning based screening of natural products
3-Mercaptopyruvate sulfurtransferase (3-MST), a key enzyme in sulfur metabolism, has recently gained attention as a potential anticancer target. However, reported 3-MST inhibitors remain limited, motivating the exploration of new scaffolds such as natural products. In this study, a library of 3744 natural products was virtually screened against human 3-MST using DiffDock (diffusion-model-based docking) followed by AutoDock Vina docking. Top-ranking candidates were further analyzed via molecular dynamics simulations and Molecular Mechanics Poisson-Boltzmann Surface Area binding free energy calculations. Methylophiopogonanone A (4), Daphnoretin (5), and L-asarinin (9) exhibited stable binding with favorable energetics, displaying binding free energies comparable to the reference ligand 7NC301. Binding mode analyses revealed that Methylophiopogonanone A primarily engaged in hydrophobic interactions, whereas Daphnoretin and L-asarinin formed extensive polar contacts, accompanied by higher desolvation penalties. In vitro cytotoxicity assays showed that Methylophiopogonanone A and L-asarinin reduced HCT116 cell viability by 40.3% and 26.3% at 25 µM, which is consistent with their inhibitory of 3-MST with IC50 = 5.83 ± 0.69 µM and IC50 = 19.11 ± 3.37 µM, respectively. These results suggested that the natural products identified in this study represent promising scaffolds for further optimization as potential 3-MST inhibitors. This work provides an AI-guided natural-product screening workflow for 3-MST and delivers prioritized inhibitor scaffolds for subsequent optimization and experimental validation.
6 Apr 2026
Read appraisal →Medicine
Adverse events associated with the use of nivolumab in the middle-aged and older population (>45 years): A pharmacovigilance analysis based on the FAERS database.
The clinical use of nivolumab has significantly improved the prognosis for survival in patients with a wide range of advanced malignancies. Given the increased incidence of tumors, there is a need to gain insight into the true extent of adverse events (AEs) associated with the nivolumab drug in the middle-aged and elderly population. This pharmacovigilance study was based on an analysis of reports from the US Food and Drug Administration's adverse event reporting system for the period January 1, 2014 to December 31, 2024, and compared AEs of the drug nivolumab in the middle-aged and elderly populations using proportional reporting ratio, reporting odds ratio, BCPN, and multi-item gamma Poisson shrinker methods. The analyses showed the presence of AEs in a variety of systems, mainly involving several systems such as endocrine disorders, nervous system disorders, skin and subcutaneous tissue disorders. The immune-related AEs listed in the package insert were validated by us and their clinical significance warrants careful consideration to further guide clinical dosing, adjust therapeutic decision-making, and develop age-adapted dosing algorithms for population pharmacokinetic development in order to minimize potential risks to patients. With the exception of reports of an unspecified nature, a greater proportion of the sample was male (65.7%) than female (32.5%), indicating significant variations. The highest percentage of reports was observed in patients aged between 65 and 85 years and in patients with a body mass index ranging from 50 to 100 kg/m2. The primary indication was non-small cell lung cancer. The top 3 AE signals reported with nivolumab were malignant neoplasm progression, death, and pyrexia, with the majority of AE cases occurring within the first month following nivolumab initiation.
5 Apr 2026
Read appraisal →Cancer causes & control : CCC
Comparing endocrine therapy adherence across EHR and claims data by race and ethnicity, language, and insurance.
PURPOSE: We compared adherence measurements across electronic health records (EHRs) and claims data for 79 HR + breast cancer survivors, considering their race, ethnicity, language, and insurance status. METHODS: Adherence was evaluated using the medication possession ratio (MPR) and proportion of days covered (PDC). The correlation between EHR and claims data was assessed with Spearman correlation coefficients, while Kaplan-Meier curves were employed to visualize and analyze the time to the first instance of non-adherence and the time to discontinuation. RESULTS: A total of 75 breast cancer patients receiving care at Massachusetts General Hospital (MGH) were identified. While there was a positive correlation, EHR data tended to overestimate adherence based on medication prescriptions, while claims data captured adherence through prescription fills. Kaplan-Meier analyses demonstrated differences in the time to first non-adherence between EHR and claims data sources, with observed variations based on language and insurance status. CONCLUSION: Our findings suggest that EHR and claims data offer complementary insights into adherence measurement, and their integration could improve adherence assessment across diverse populations.
5 Apr 2026
Read appraisal →Medicine
Post-marketing signal detection of fruquintinib-associated adverse events using the FAERS database
Fruquintinib, a targeted therapeutic agent approved in 2018 for the treatment of metastatic colorectal cancer, is gradually being applied in an expanding range of clinical settings. Given the complexities associated with real-world dosing, a comprehensive evaluation of its safety profile is essential for optimizing clinical decision-making. We conducted a retrospective pharmacovigilance study utilizing a spontaneous reporting system. Reports related to Fruquintinib were extracted through a standardized data cleaning process and cross-validated using 3 complementary signal detection algorithms: proportional reporting ratio, Bayesian confidence propagation neural network, and reporting odds ratio (ROR). This approach was employed to systematically assess the drug's safety and identify potential adverse event signals. A total of 92 statistically significant safety signals were identified from 1188 independent cases included in the analysis, corresponding to 1836 adverse event reports. The signal distribution exhibited organ system specificity, with gastrointestinal reactions being the most prevalent category. These reactions primarily manifested as typical drug-related toxicities, including diarrhea and nausea. Other notable complications included neurological, respiratory, dermatological, renal, and cardiovascular events. Important aes not mentioned in the instructions, such as bone marrow suppression (ROR = 11.17), peripheral neuropathy (ROR = 4.24) and dehydration (ROR = 3.98), were also discovered. This study systematically characterizes the post-marketing safety profile of Fruquintinib through a multi-algorithm synergistic analysis, confirming that its safety profile aligns with the clinically expected outcomes based on its mechanism of action. Future research should focus on analyzing drug interaction mechanisms and exploring biomarkers to develop a precise risk-benefit assessment model.
5 Apr 2026
Read appraisal →Medicine
Effectiveness of mindfulness-based stress reduction in managing fatigue, anxiety, and depression among non-small cell lung cancer patients receiving surgery and adjuvant chemotherapy: A randomized controlled trial
BACKGROUND: Non-small cell lung cancer (NSCLC) patients undergoing surgery and adjuvant chemotherapy often experience debilitating physical and psychological symptoms, including cancer-related fatigue, anxiety, and depression. Mindfulness-based stress reduction (MBSR) has emerged as a complementary intervention to alleviate these burdens and enhance quality of life, but evidence in NSCLC populations remains limited. METHODS: This randomized controlled trial enrolled 80 NSCLC patients, evenly divided into an MBSR group and a Control group. The MBSR group underwent an 8-week mindfulness program, while the Control group received standard care. Outcomes, including fatigue (Piper Fatigue Scale), anxiety (Self-Rating Anxiety Scale), depression (Self-Rating Depression Scale), self-efficacy (General Self-Efficacy Scale), and mindfulness awareness (Mindful Attention Awareness Scale), were assessed at 4 time points: baseline (before surgery), at the fourth week (before chemotherapy), and at 1 and 3 months post-intervention. Statistical analyses included repeated-measures ANOVA and t tests to evaluate group differences. RESULTS: The MBSR group demonstrated significant improvements across all outcomes compared to the Control group. Fatigue levels in the MBSR group peaked at the fourth week but returned close to baseline levels by 3 months post-intervention, while the Control group maintained elevated fatigue scores (P < .05). Anxiety (Self-Rating Anxiety Scale) and depression (Self-Rating Depression Scale) scores significantly decreased in the MBSR group by the fourth week and continued to improve at subsequent time points (P < .01). Self-efficacy (General Self-Efficacy Scale) and mindfulness awareness (Mindful Attention Awareness Scale) showed consistent improvements in the MBSR group, with significant differences evident at 1 and 3 months post-intervention (P < .01). CONCLUSIONS: Mindfulness-based stress reduction effectively reduced fatigue, anxiety, and depression while enhancing self-efficacy and mindfulness awareness in NSCLC patients undergoing surgery and chemotherapy. These findings support the integration of mindfulness-based interventions into routine care to improve patient well-being during and after treatment.
5 Apr 2026
Read appraisal →Blood
Belantamab mafodotin, lenalidomide, and dexamethasone for intermediate-fit and frail patients with newly diagnosed myeloma.
The phase 1/2 BelaRd (belantamab mafodotin [belamaf], lenalidomide, and dexamethasone) study evaluated the efficacy and safety of belamaf combined with lenalidomide and dexamethasone in unfit and frail transplant-ineligible patients with newly diagnosed multiple myeloma (NDMM). Part 1 (n = 36) established a recommended belamaf phase 2 dose (RP2D) of 1.9 mg/kg every 8 weeks (median follow-up, 39.3 months). In part 2, 30 patients were randomized 1:1 in group A (n = 15), where belamaf dosing was guided by ophthalmologist-assessed ocular adverse events (OAEs), whereas in group B (n = 15), belamaf dosing was based on hematologist-led vision-related anamnestic (VRA) tool and ophthalmologist-assessed grade ≥3 OAEs. Among the RP2D patients (n = 42), overall response rate was 97.6%, median progression-free survival (PFS)/overall survival have not been reached yet, and the 18-month PFS and time to progression rates were 83.0% and 97.2%, respectively. Ocular toxicities were similar between assessments by hematologists and ophthalmologists, and no ophthalmologist withholding of a hematologist-led dosing occurred. Less than 1% of patients stopped driving/reading because of OAEs. Median time to belamaf reinfusion was 13 weeks. Overall, BelaRd is an effective regimen for transplant-ineligible patients with NDMM and warrants a phase 3 study in this setting. OAEs' impact on quality of life appears limited, and implementation of the hematologist-led VRA tool may eventually reduce the necessity for ophthalmologist assessments. This trial was registered at www.clinicaltrials.gov as #NCT04808037.
5 Apr 2026
Read appraisal →Medicine
Neuroendocrine neoplasms diagnosed in the setting of inflammatory bowel disease: A systematic review and proposed management algorithm
BACKGROUND: Patients with inflammatory bowel disease (IBD) are at higher risk of developing malignancies, including neuroendocrine neoplasms, but the specifics of that are not well known. We reviewed the existing literature to map the evidence surrounding this phenomenon. METHODS: Following PRISMA 2020 guidelines, we searched PubMed from inception to September 2024 for patients with gastrointestinal neuroendocrine neoplasms (NENs) and either Crohn disease (CD) or ulcerative colitis (UC). Eligible studies included case reports, case series, and larger retrospective IBD cohorts. We extracted demographic and clinical data including tumor grade, location, metastatic status, and mode of detection. Comparative analyses between UC and CD were performed using Fisher exact test, chi-square test, and independent t-tests. RESULTS: We identified 67 case studies with data available for a total of 108 patients (61.1% male), ranging from 14 to 87 years of age. The median duration from IBD diagnosis to NEN detection was 13 years. Fifty-five (50.9%) patients had CD and 53 (49.1%) had UC. UC patients were older at NEN diagnosis (mean 49 vs 40 years, P = .004) and had a higher metastatic rate (39.6% vs 18.2%, P = .019). Most NENs were low-grade (69.4%), while 20.4% were high-grade and 8.3% were mixed. The most common tumor locations were the rectum (29.6%) and appendix (27.8%), with rectal and colonic NENs more prevalent in UC and small bowel or appendiceal NENs more common in CD. At diagnosis, 71.3% of patients had localized disease. For high-grade NENs, 86.4% of cases were metastatic, compared to 9.3% for low-grade tumors. Only 10.2% of NENs were detected via endoscopic surveillance. CONCLUSIONS: NENs in IBD show distinct patterns by IBD type, with significant differences in tumor location, grade, and metastatic rates between UC and CD. These hypothesis-generating observations suggest heightened vigilance after approximately 10 years of IBD or around age 44, focusing on rectal lesions in UC and small bowel/appendiceal lesions in CD. Validation in prospective, population-based studies is needed before these findings can inform clinical practice.
4 Apr 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Preventive effects of dexamethasone oral solution on radiation-induced mucositis in patients with head and neck cancer: a randomized, triple-blind, parallel-group trial
PURPOSE: Radiation-induced oral mucositis is a frequent and debilitating complication of curative radiotherapy for head and neck cancer. This randomized, triple-blind, placebo-controlled, parallel-group trial evaluated whether a topical dexamethasone oral solution (0.5 mg/10 mL) used as a prophylactic mouthwash reduces the incidence and severity of radiation-induced oral mucositis. METHODS: Fifty-four patients with non-metastatic head and neck squamous cell carcinoma scheduled to receive ≥ 60 Gy radiotherapy were randomized 1:1 to dexamethasone mouthwash or placebo and instructed to gargle 10 mL four times daily during radiotherapy. The primary endpoint was the incidence and maximum severity of oral mucositis occurring during radiotherapy, defined as the highest grade recorded using the WHO Oral Toxicity Scale and NCI-CTCAE criteria v. 5. Secondary endpoints included patient-reported pain (VAS), dysphagia, treatment discontinuation, and mucositis status at 2 months. Fifty patients completed the study (25 per arm); four patients discontinued (reasons reported in Results). RESULTS: The dexamethasone group had significantly lower mucositis severity at all time points by WHO and CTCAE scales (all p < 0.01), a delayed peak severity (week 5 vs week 3), lower pain scores from weeks 4-7 (median VAS 0-3 versus 4-6; p < 0.01), and lower dysphagia at 2 months (32% vs 72%; p = 0.01). No grade 4 mucositis occurred in the dexamethasone arm; two grade 4 events occurred in placebo. There were no statistically significant differences in treatment discontinuation. Topical dexamethasone was well tolerated; no signal of treatment-related systemic steroid toxicity or increased oral infections was observed. CONCLUSION: In this phase II study, prophylactic topical dexamethasone mouthwash reduced the severity and delayed the onset of radiation-induced oral mucositis and improved patient-reported pain. These findings are promising but require confirmation in larger multicenter trials with longer follow-up and formal patient-reported outcomes (e.g., EORTC QLQ-H&N35, OMAS) and comparison with guideline-recommended strategies such as photobiomodulation. TRIAL REGISTRATION NUMBER: Iranian Registry of Clinical Trials; trial registration number: IRCT20240525061893N1; date of registration: 21 July 2024.
4 Apr 2026
Read appraisal →Annals of oncology : official journal of the European Society for Medical Oncology
Next-generation multicenter studies: using artificial intelligence to automatically process unstructured health records of patients with lung cancer across multiple institutions
BACKGROUND: Manual abstraction of real-world data (RWD) from unstructured health records (HRs) remains resource intensive, error prone, and highly variable across institutions. Large language models (LLMs) offer a scalable alternative, but their performance in multicenter oncology settings is not fully validated. PATIENTS AND METHODS: We conducted a multicenter study within the French Large & Unified Cancer Cohort (LUCC) consortium to compare the accuracy of artificial intelligence (AI)-based data extraction against manual abstraction by clinical research professionals. A fine-tuned LLM was applied to de-identified unstructured HRs in PDF format to extract 31 variables from lung cancer patients across 10 centers. Ground truth was defined as concordant values across sources, with discrepant cases adjudicated by a blinded expert. The primary endpoint was the extraction error rates. Secondary endpoints included per-variable performance, interinstitutional variability, F1-score for multiple-choice variables, added value of hybrid AI-human workflows, and survival analyses. RESULTS: Among 10 327 patients with AI-based extraction, 311 were included in the test cohort. Across 8708 datapoints for 28 variables with only one correct answer, the LLM achieved a 7.0% error rate, outperforming manual abstraction (14.2%, P <0.001). The F1-scores of three multiple-choice variables were superior (gene alterations 0.97 versus 0.86, comorbidities 0.86 versus 0.76, metastatic sites 0.71 versus 0.69). Interinstitutional variance was lower with AI (0.12% versus 0.39%). A hybrid approach with targeted human review of 30% of low-confidence AI outputs further decreased error rates to 4.4%. Survival analyses based on AI-extracted data closely matched ground truth, with similar median overall and progression-free survival. CONCLUSIONS: In a multicenter setting, our AI pipeline yielded lower error rates and greater consistency than manual abstraction. These findings support the feasibility of next-generation, AI-enabled multicenter studies to generate high-quality RWD at scale, with potential applicability in prospective clinical trials.
4 Apr 2026
Read appraisal →Japanese journal of radiology
Super-resolution deep learning reconstruction improves brain MRI quality and detection of metastases.
PURPOSE: Accurate identification of brain metastases is critical for determining prognosis and guiding treatment. Deep learning reconstruction (DLR) enhances MRI quality by reducing noise, while super-resolution DLR (SR-DLR) may further improve spatial resolution and lesion detectability. To evaluate SR-DLR versus conventional DLR in detecting and visualizing brain metastases on postcontrast T1-weighted brain MRI. MATERIALS AND METHODS: This retrospective study included 47 consecutive patients who underwent postcontrast 3D whole-brain T1-weighted MRI between July and December 2024. Images were reconstructed using both SR-DLR and DLR. Three independent readers evaluated metastatic lesion detection and rated image quality. Subjective assessments included lesion visibility, visibility of normal structures, sharpness, noise, and overall image quality. Objective metrics-full width at half maximum (FWHM), edge rise distance (ERD), edge rise slope (ERS), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR)-were also measured. Statistical tests included jackknife alternative free-response receiver operating characteristic (JAFROC) analysis, Wilcoxon signed-rank test, McNemar's test, and paired t-tests, with significance threshold of p < 0.050. RESULTS: A total of 117 brain metastases were detected in 47 patients (mean age, 59 years ± 18; 27 men). SR-DLR demonstrated significantly better lesion detection performance than DLR (mean figure of merit = 0.842 vs. 0.797; p = 0.042). Subjective image quality ratings favored SR-DLR for lesion and structure visibility, sharpness, noise, and overall quality in most cases. Objectively, SR-DLR yielded lower FWHM (1.2 mm vs. 1.9 mm; p < 0.001), higher ERS (791.3 mm- 1 vs. 645.3 mm- 1; p = 0.013) indicating enhanced sharpness as well as improved CNR (27.5 vs. 24.9; p < 0.001) compared to DLR. CONCLUSION: Compared to DLR, SR-DLR significantly enhances brain MRI quality and improves detection of metastatic lesions.
4 Apr 2026
Read appraisal →International journal of medical informatics
Automated extraction of fluoropyrimidine treatment and treatment-related toxicities from clinical notes using natural language processing.
OBJECTIVE: Fluoropyrimidines are widely prescribed for colorectal and breast cancers, but are associated with toxicities such as hand-foot syndrome and cardiotoxicity. Since toxicity documentation is often embedded in clinical notes, we aimed to develop and evaluate natural language processing (NLP) methods to extract treatment and toxicity information. MATERIALS AND METHODS: We constructed a gold-standard dataset of 236 clinical notes from 204,165 adult oncology patients. Domain experts annotated categories related to treatment regimens and toxicities. We developed rule-based, machine learning-based (Random Forest [RF], Support Vector Machine [SVM], Logistic Regression [LR]), deep learning-based (BERT, ClinicalBERT), and large language models (LLM)-based NLP approaches (zero-shot and error analysis prompting). A 5-fold cross validation were conducted to validate each model. RESULTS: Error analysis prompting achieved optimal precision, recall, and F1 scores for treatment (F1 = 1.000) and toxicities extraction (F1 = 0.965), whereas zero-shot perform moderately (treatment F1 = 0.889, toxicities extraction F1 = 0.854) Rule-based reached F1 = 1.000 for treatment and F1 = 0.904 for toxicities extraction. LR and SVM ranked second and fourth for toxicities extraction (LR F1 = 0.914, SVM F1 = 0.903). Deep learning and RF underperformed, with performance of BERT reached F1 = 0.792 for treatment and F1 = 0.837 for toxicities extraction.,ClinicalBERT reached F1 = 0.797 for treatment and F1 = 0.884 for toxicities extraction). RF reached F1 = 0.745 for treatment and F1 = 0.853 for toxicities extraction. DISCUSSION: LMM-based error analysis outperformed all others, followed by machine learning methods. Machine learning and deep learning methods were limited by small training data and showed limited generalizability, particularly for rare categories. CONCLUSION: LLM-based error analysis most effectively extracted fluoropyrimidine treatment and toxicity information from clinical notes, and has strong potential to support oncology research and pharmacovigilance.
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 →Neurological research
Machine learning-based preoperative prediction of IDH mutation status in adult gliomas using clinical features
BACKGROUND: As isocitrate dehydrogenase (IDH) mutation status represents a critical prognostic factor in adult gliomas, there is a demand for a straightforward but effective predictive model to facilitate rapid preoperative diagnosis. METHODS: A cohort of 418 adult glioma patients from our institution were retrospectively enrolled and randomly partitioned into training (70%) and internal validation (30%) sets. Among 9 machine learning algorithms evaluated, AdaBoost demonstrated superior predictive performance based on receiver operating characteristic (ROC) analysis and was subsequently selected as the optimal model. Through feature importance ranking derived from the model, the top 8 most contributory features were identified and incorporated into the final streamlined prediction model. The model's generalizability was further assessed using an independent external validation cohort comprising 206 adult glioma cases. RESULTS: The AdaBoost model demonstrated robust discriminative performance, achieving an area under the receiver operating characteristic curve (AUC-ROC) of 0.901 in the internal validation cohort and 0.849 in the independent external validation cohort. SHAP (SHapley Additive exPlanations) interpretation revealed age as the most influential predictive feature, contributing significantly to the model's decision-making process. CONCLUSIONS: This validated AdaBoost model offers a practical and interpretable solution for preoperative IDH status assessment, demonstrating both diagnostic reliability and biological plausibility through its feature importance profile.
4 Apr 2026
Read appraisal →The Lancet. Oncology
Deep learning on histopathological images to predict breast cancer recurrence risk and chemotherapy benefit: a multicentre, model development and validation study
BACKGROUND: Genomic assays such as Oncotype DX have transformed adjuvant treatment selection for hormone receptor-positive, HER2-negative, early breast cancer but remain inaccessible to many patients because of high cost and logistical barriers. We aimed to develop and validate an artificial intelligence (AI) model that estimates Oncotype DX 21-gene recurrence scores directly from routine histopathology slides and clinicopathological variables. METHODS: In this multicentre, model development and validation study, a multimodal deep-learning model was trained on digital whole-slide images and clinical features using a foundation model pre-trained on 171 189 histopathology slides for predicting Oncotype DX recurrence score. We included slides from patients with hormone receptor-positive, HER2-negative, invasive breast cancers and without scanning artifacts and with at least 100 tissue tiles (1·6 mm2). The model was fine-tuned and validated on the TAILORx randomised trial (8284 patients after quality control). Prognostic and predictive performance was assessed in the TAILORx-test set and externally validated in six independent cohorts (Carmel, Haemek, and Sheba medical centres [Israel], the University of Chicago Medical Center [USA], the Australian Breast Cancer Tissue Bank [Australia], and the Cancer Genome Atlas Breast Invasive Carcinoma project [USA]). FINDINGS: In the TAILORx-test set (n=2407), the AI model classified 1097 (45·6%) patients as low risk, 1021 (42·4%) as intermediate risk, and 289 (12·0%) as high risk. For identifying high genomic-risk disease (recurrence score ≥26), the area under the curve (AUC) was 0·898 (95% CI 0·879-0·913). AI-based risk stratification was prognostic for recurrence-free interval (hazard ratio 2·61 [95% CI 1·68-4·04]), distant recurrence-free interval (2·88 [1·73-4·79]), and disease-free survival (1·32 [0·92-1·89]). Chemotherapy benefit was evident in premenopausal patients classified by AI as being at high risk (0·63 [0·46-0·86]) but absent in postmenopausal patients classified by AI as being at low risk (0·94 [0·78-1·12]). 151 (31·3%) clinically high-risk postmenopausal women (by MINDACT criteria) were reclassified as low AI risk with no chemotherapy benefit. Analysis on external cohorts (5497 patients) showed that the model is transferable to new data with high generalisability (recurrence score ≥26 AUC ranging from 0·858 to 0·903). INTERPRETATION: These findings show that AI applied to routine histopathology can serve as a practical and scalable tool for guiding chemotherapy decisions in hormone receptor-positive, HER2-negative, early breast cancer. This approach has the potential to reduce unnecessary chemotherapy and broaden access to precision oncology, particularly in resource-limited settings where genomic testing remains unavailable or unaffordable. FUNDING: Israel Innovation Authority (Kamin), Zimin Institute for Artificial Intelligence Solutions in Healthcare, Israel Precision Medicine Partnership program, and Israel Cancer Research Fund.
4 Apr 2026
Read appraisal →Critical reviews in oncology/hematology
Artificial intelligence and multi-omics convergence in breast cancer: Revolutionizing diagnosis, prognostication, and precision oncology
Breast cancer (BC) is a highly heterogeneous malignancy and remains a major cause of cancer-related mortality among women worldwide. Advances in multi-omics profiling spanning genomics, transcriptomics, epigenomics, proteomics, and metabolomics have enabled finer subtype stratification and more comprehensive characterisation of tumour biology, thereby accelerating the discovery of diagnostic and prognostic biomarkers and actionable therapeutic targets. Nonetheless, translating multi-layer molecular signals into clinically robust decision support remains challenging because of the high dimensionality and heterogeneity of omics data, cross-cohort and cross-platform variability, and the fragmentation inherent to single-modality analyses. This review summarises how multi-omics studies have refined BC subtype definitions and advanced biomarker and target identification, and then synthesises recent progress in artificial intelligence, particularly deep learning, for integrating multi-omics with imaging, pathology, and clinical variables to improve diagnosis, risk stratification, prognosis prediction, and treatment response assessment. We critically examine representative multimodal integration frameworks and emerging deep learning architectures that learn both shared and modality-specific representations, which in many settings enable more accurate patient-level prediction than unimodal baselines. We further delineate key barriers to clinical translation, including cross-centre heterogeneity and inconsistent endpoint definitions, structural missingness of modalities in real-world workflows, inadequate cross-platform normalisation, limited interpretability and auditability, and a lack of prospective validation. Finally, we propose realistic next steps, including standardised and auditable preprocessing pipelines, missingness-aware fusion strategies, explainable and uncertainty-aware modelling, privacy-preserving multi-centre learning, and prospective, workflow-based evaluation. Collectively, these perspectives provide a roadmap for advancing multimodal AI-multi-omics integration toward reliable clinical deployment in BC management.
4 Apr 2026
Read appraisal →Nature reviews. Clinical oncology
MicroRNAs in oncology: a translational perspective in the era of AI
Over the past three decades, knowledge of microRNA (miRNA) biology has advanced from the initial discovery of their regulatory functions to the finding of abnormal activity in leukaemias, and then to a comprehensive understanding of the roles of miRNAs in both normal physiology and most diseases, with cancer being extensively studied. miRNA dysregulation contributes to tumorigenesis, with certain miRNAs acting as either tumour suppressors or oncogenic factors in a context-dependent manner. A subset of miRNAs have shown promise as tumour biomarkers and therapeutic targets in preclinical studies, with several miRNA-based diagnostic tools and treatments progressing to clinical trials. Artificial intelligence (AI) and machine learning techniques began to be introduced into cancer research and oncology a decade ago and are now on the verge of revolutionizing biomarker identification and clinical trials. In this Review, we highlight important roles of miRNAs in cancer biology and their potential as diagnostic tools and therapeutic targets. In particular, we discuss emerging challenges and opportunities presented by AI-driven data analysis and combinatorial strategies, and how advances in these areas have addressed previous doubts on the clinical translation of miRNA-based biomarkers and therapeutics.
4 Apr 2026
Read appraisal →Cancer Cell
Precision treatment with artificial intelligence assisted subtyping enhances therapeutic efficacy in HR+/HER2- breast cancer: The LINUXtrial.
We report the results of LINUX (NCT05594095), a multicenter, randomized, controlled phase II platform trial aiming to identify effective precision treatments for hormone receptor-positive/human epidermal growth factor receptor 2-negative metastatic breast cancer after resistance to cyclin-dependent kinase 4/6 inhibitor. A total of 105 patients were categorized into four similarity network fusion (SNF) subtypes by artificial intelligence-assisted classification and randomly assigned to receive subtyping-based precision therapy (N = 70) or treatment of physician's choice (N = 35). Results demonstrate superior primary endpoint of objective response rates in the subtyping-based groups compared to controls: 10% versus 0% for SNF1, 65% versus 30% for SNF2, 40% versus 30% for SNF3, and 70% versus 20% for SNF4. Grade 3-4 treatment-related adverse events occurred in 37% of both groups. These findings highlight the clinical benefits of subtyping-based precision therapies, particularly for SNF2 and SNF4 subtypes, warranting further validation in phase III trials.
1 Feb 2026
Read appraisal →Neuro Oncol
EANS-EANO guidelines on the extent of resection in gliomas
Intense efforts are employed to optimize neurosurgical resection of gliomas in different clinical scenarios. However, the level of evidence employed toward personalized treatment decisions is still moderate. Therefore, a joint task force was created by the European Association for Neurosurgical Societies (EANS) and the European Association of Neuro-Oncology (EANO) in order to provide interdisciplinary guidance on the extent of resection (EOR) in glioma surgery. This task force assessed the data on the EOR in gliomas, evaluated the level of evidence, and composed recommendations. The EOR is determined routinely by postoperative magnetic resonance imaging. Since EOR may be associated with survival and may have an impact on epilepsy, neurocognition, quality of life, neurological status, and also on planning of radiation and pharmacotherapy (evidence classes I-IV), recommendations of different levels can be made for the resection of newly diagnosed glioblastomas (level A), recurrent glioblastomas (level C), newly diagnosed (level B) and recurrent (good practice point) IDH mutant gliomas, ependymomas in children (level B) and adults (good practice point) as well as World Health Organization CNS grade 1 tumors (good practice point). We hereby report the outcome of this collaborative effort by the joint task force between EANS and EANO.
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 →J Clin Oncol
Foundation Model for Predicting Prognosis and Adjuvant Therapy Benefit From Digital Pathology in GI Cancers
PURPOSE: Artificial intelligence (AI) holds significant promise for improving cancer diagnosis and treatment. Here, we present a foundation AI model for prognosis prediction on the basis of standard hematoxylin and eosin-stained histopathology slides. METHODS: In this multinational cohort study, we developed AI models to predict prognosis from histopathology images of patients with GI cancers. First, we trained a foundation model using over 130 million patches from 104,876 whole-slide images on the basis of self-supervised learning. Second, we fine-tuned deep learning models for predicting survival outcomes and validated them across seven cohorts, including 1,619 patients with gastric and esophageal cancers and 2,594 patients with colorectal cancer. We further assessed the model for predicting survival benefit from adjuvant chemotherapy. RESULTS: The AI models predicted disease-free survival and disease-specific survival with a concordance index of 0.726-0.797 for gastric cancer and 0.714-0.757 for colorectal cancer in the validation cohorts. The models stratified patients into high-risk and low-risk groups, with 5-year survival rates of 49%-52% versus 76%-92% in gastric cancer and 43%-72% versus 81%-98% in colorectal cancer. In multivariable analysis, the AI risk scores remained an independent prognostic factor after adjusting for clinicopathologic variables. Compared with stage alone, an integrated model consisting of stage and image information improved prognosis prediction across all validation cohorts. Finally, adjuvant chemotherapy was associated with improved survival in the high-risk group but not in the low-risk group (treatment-model interaction P = .01 and .006) for stage II/III gastric and colorectal cancer, respectively. CONCLUSION: The pathology foundation model can accurately predict survival outcomes and complement clinicopathologic factors in GI cancers. Pending prospective validation, it may be used to improve risk stratification and inform personalized adjuvant therapy.
1 Nov 2025
Read appraisal →Blood Rev
Advances in the critical care management for patients with hematological malignancies
Hematological malignancies (HMs) are increasingly associated with life-threatening complications requiring intensive care unit (ICU) management. Recent advancements in therapies, diagnostics, and critical care protocols have improved outcomes for these patients, yet significant challenges persist. This manuscript explores the evolving landscape of critical care in hematology, emphasizing the unique complications, management strategies, and future directions in the field. Patients with HMs are particularly vulnerable to infections, sepsis, organ dysfunction, and treatment-related toxicities such as cytokine release syndrome (CRS), immune effector cell-associated neurotoxicity syndrome (ICANS), and coagulopathies. Innovations in the management of acute respiratory failure, septic shock, and invasive fungal infections have contributed to better survival rates, yet outcomes remain suboptimal for certain high-risk groups. Furthermore, new therapies, including CAR-T cells, bispecific antibodies, and immune checkpoint inhibitors, present both opportunities and challenges in the ICU setting due to their potential toxicities. Emerging trends emphasize the importance of early ICU admission, multidisciplinary collaboration, and precision medicine in improving patient care. The integration of biomarker-driven strategies, advanced diagnostics, and artificial intelligence holds promise for optimizing therapeutic interventions and enhancing antimicrobial stewardship. Additionally, patient-centered approaches, including time-limited trials and goal-oriented discussions, aim to balance aggressive care with quality-of-life considerations. This review underscores the need for continued research to address disparities in access to care, improve long-term outcomes, and develop standardized protocols for managing critically ill hematology patients. By advancing the integration of oncology and critical care, clinicians can better navigate the complexities of modern therapies and provide holistic, evidence-based care that aligns with patient values and priorities.
1 Nov 2025
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