BMJ oncology最新文献

{"title":"Clinical prediction models using machine learning in oncology: challenges and recommendations.","authors":"Gary S Collins, Mae Chester-Jones, Stephen Gerry, Jie Ma, Joao Matos, Jyoti Sehjal, Biruk Tsegaye, Paula Dhiman","doi":"10.1136/bmjonc-2025-000914","DOIUrl":"https://doi.org/10.1136/bmjonc-2025-000914","url":null,"abstract":"<p><p>Clinical prediction models are widely developed in the field of oncology, providing individualised risk estimates to aid diagnosis and prognosis. Machine learning methods are increasingly being used to develop prediction models, yet many suffer from methodological flaws limiting clinical implementation. This review outlines key considerations for developing robust, equitable prediction models in cancer care. Critical steps include systematic review of existing models, protocol development, registration, end-user engagement, sample size calculations and ensuring data representativeness across target populations. Technical challenges encompass handling missing data, addressing fairness across demographic groups and managing complex data structures, including censored observations, competing risks or clustering effects. Comprehensive internal and external evaluation requires assessment of both statistical performance (discrimination and calibration) and clinical utility. Implementation barriers include limited stakeholder engagement, insufficient clinical utility evidence, a lack of consideration of workflow integration and the absence of post-deployment monitoring plans. Despite significant potential for personalising cancer care, most prediction models remain unimplemented due to these methodological and translational challenges. Addressing these considerations from study design through post implementation monitoring is essential for developing trustworthy tools that bridge the gap between model development and clinical practice in oncology.</p>","PeriodicalId":72436,"journal":{"name":"BMJ oncology","volume":"4 1","pages":"e000914"},"PeriodicalIF":0.0,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12506039/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145260033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding prostate cancer care: recent progress and future direction.","authors":"Kristoffer Thomas Stewart, Ananya Choudhury","doi":"10.1136/bmjonc-2025-000985","DOIUrl":"https://doi.org/10.1136/bmjonc-2025-000985","url":null,"abstract":"","PeriodicalId":72436,"journal":{"name":"BMJ oncology","volume":"4 1","pages":"e000985"},"PeriodicalIF":0.0,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12506135/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145259985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adjuvant chemotherapy in locoregionally advanced nasopharyngeal carcinoma: where do we stand?","authors":"Pui Lam Yip, Shing Fung Lee, Melvin Chua","doi":"10.1136/bmjonc-2025-000894","DOIUrl":"10.1136/bmjonc-2025-000894","url":null,"abstract":"","PeriodicalId":72436,"journal":{"name":"BMJ oncology","volume":"4 1","pages":"e000894"},"PeriodicalIF":0.0,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12406820/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145002025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inequities in lung cancer treatment: lessons from international variation in chemotherapy and radiotherapy use.","authors":"Emma L O'Dowd, Matthew Ej Callister","doi":"10.1136/bmjonc-2025-000877","DOIUrl":"10.1136/bmjonc-2025-000877","url":null,"abstract":"","PeriodicalId":72436,"journal":{"name":"BMJ oncology","volume":"4 1","pages":"e000877"},"PeriodicalIF":0.0,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12406937/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145002080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inferior control arms in oncology trials in LMICs: contextualised or compromised?","authors":"Laure-Anne Teuwen, Bishal Gyawali","doi":"10.1136/bmjonc-2025-000931","DOIUrl":"10.1136/bmjonc-2025-000931","url":null,"abstract":"","PeriodicalId":72436,"journal":{"name":"BMJ oncology","volume":"4 1","pages":"e000931"},"PeriodicalIF":0.0,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12406892/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145002086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Five years after CONSORT-AI, not much has changed: a call to action for artificial intelligence research in oncology.","authors":"Jethro C C Kwong, David-Dan Nguyen, Adree Khondker, Tiange Li, Girish S Kulkarni","doi":"10.1136/bmjonc-2025-000891","DOIUrl":"10.1136/bmjonc-2025-000891","url":null,"abstract":"","PeriodicalId":72436,"journal":{"name":"BMJ oncology","volume":"4 1","pages":"e000891"},"PeriodicalIF":0.0,"publicationDate":"2025-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12414233/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145024915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Concordance with CONSORT-AI guidelines in reporting of randomised controlled trials investigating artificial intelligence in oncology: a systematic review.","authors":"David Chen, Kristen Arnold, Ronesh Sukhdeo, John Farag Alla, Srinivas Raman","doi":"10.1136/bmjonc-2025-000733","DOIUrl":"10.1136/bmjonc-2025-000733","url":null,"abstract":"<p><strong>Background: </strong>The advent of artificial intelligence (AI) tools in oncology to support clinical decision-making, reduce physician workload and automate workflow inefficiencies yields both great promise and caution. To generate high-quality evidence on the safety and efficacy of AI interventions, randomised controlled trials (RCTs) remain the gold standard. However, the completeness and quality of reporting among AI trials in oncology remains unknown.</p><p><strong>Objective: </strong>This systematic review investigates the reporting concordance of RCTs for AI interventions in oncology using the CONSORT (Consolidated Standards of Reporting Trials) 2010 and CONSORT-AI 2020 extension guideline and comprehensively summarises the state of AI RCTs in oncology.</p><p><strong>Methods and analysis: </strong>We queried OVID MEDLINE and Embase on 22 October 2024 using AI, cancer and RCT search terms. Studies were included if they reported on an AI intervention in an RCT including participants with cancer.</p><p><strong>Results: </strong>This study included 57 RCTs of AI interventions in oncology that were primarily focused on screening (54%) or diagnosis (19%) and intended for clinician use (88%). Among all 57 RCTs, median concordance with CONSORT 2010 and CONSORT-AI 2020 was 82%. Compared with trials published before the release of CONSORT-AI (n=8), trials published after the release of CONSORT-AI (n=49) had lower median overall CONSORT (82% vs 92%) and CONSORT 2010 (81% vs 92%) concordance but similar CONSORT-AI median concordance (93% vs 93%). Guideline items related to study methodology necessary for reproducibility using the AI intervention, such as input data inclusion and exclusion, algorithm version, low quality data handling, assessment of performance error and data accessibility, were consistently under-reported. When stratifying included trials by their overall risk of bias, trials at serious risk of bias (57%) were less concordant to CONSORT guidelines compared with trials at moderate (71%) or low (84%) risk of bias.</p><p><strong>Conclusion: </strong>Although the majority of CONSORT and CONSORT-AI items were well-reported, critical gaps related to reporting of methodology, reproducibility and harms persist. Addressing these gaps through consideration of trial design to mitigate risks of bias coupled with standardised reporting is one step towards responsible adoption of AI to improve patient outcomes in oncology.</p>","PeriodicalId":72436,"journal":{"name":"BMJ oncology","volume":"4 1","pages":"e000733"},"PeriodicalIF":0.0,"publicationDate":"2025-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12414185/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145024852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Novel radiopharmaceuticals for molecular imaging of renal cell carcinoma.","authors":"Jasmin Weindler, Muhammad Ali, Cristian Udovicich, Michael S Hofman, Shankar Siva","doi":"10.1136/bmjonc-2024-000645","DOIUrl":"10.1136/bmjonc-2024-000645","url":null,"abstract":"<p><p>Conventional diagnostic strategies for imaging patients with renal cell carcinoma (RCC) have predominantly relied on ultrasonography, CT and MRI. However, a paradigm shift is underway with the emergence of several new radiotracers for molecular imaging. Carbonic anhydrase IX (CA-IX) imaging and sestamibi imaging can assist with identifying malignant renal tumours, whereas fluorodeoxyglucose, prostate-specific membrane antigen and CA-IX tracers can give guidance for diagnosis and staging of RCC. These tracers can assist in enabling better decision-making by minimising overtreatment of renal masses with biopsy, better selecting patients for curative-intented management and optimising treatment for patients with oligo-metastatic disease, among other emerging indications. However, none of them is yet recommended as a standard clinical diagnostic procedure. In this review, we investigate the latest developments in molecular imaging for detecting and staging RCC, aiming to advance precision diagnosis and improve patient outcomes.</p>","PeriodicalId":72436,"journal":{"name":"BMJ oncology","volume":"4 1","pages":"e000645"},"PeriodicalIF":0.0,"publicationDate":"2025-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12359567/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144884412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tumour-infiltrating lymphocyte therapy landscape: prospects and challenges.","authors":"Ruqin Chen, Jeffrey Johnson, Ali Rezazadeh, Arkadiusz Z Dudek","doi":"10.1136/bmjonc-2024-000566","DOIUrl":"10.1136/bmjonc-2024-000566","url":null,"abstract":"<p><p>Tumour-infiltrating lymphocyte (TIL) therapy has emerged as a promising adoptive cell transfer strategy for solid tumours. The recent accelerated approval of lifileucel by the Food and Drug Administration marks a significant milestone in the clinical application of TIL therapy. This review comprehensively examines the historical development, biology, clinical efficacy, safety and limitations of TIL therapy. We explore advancements in TIL manufacturing, including novel culture techniques, genetic modifications and automation, to enhance scalability and effectiveness. Despite promising results, TIL therapy faces challenges such as high-dose interleukin-2 toxicity, complex manufacturing processes and immune evasion mechanisms. Emerging strategies, including checkpoint inhibitor combinations, engineered TIL constructs and metabolic reprogramming, aim to improve TIL therapeutic efficacy. This review provides insights into the evolving landscape of TIL therapy and its potential to enhance current cancer immunotherapy.</p>","PeriodicalId":72436,"journal":{"name":"BMJ oncology","volume":"4 1","pages":"e000566"},"PeriodicalIF":0.0,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12323532/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144790886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of control arms in oncology randomised trials in Brazil: a cross-sectional study.","authors":"Ana Elisa Boracini Sanches, Luiza Aleixo Barros Leite Fadul, Debora Aparecida Pires de Campos Zuccari, Beatriz de Menezes Dobbert, Lorena Forner, Júlia Belone Lopes, Daniel Vilarim Araujo","doi":"10.1136/bmjonc-2025-000808","DOIUrl":"10.1136/bmjonc-2025-000808","url":null,"abstract":"<p><strong>Objective: </strong>Low-middle-income countries (LMICs) have increased their participation in international oncology trials. However, considerable disparities in treatment standards across countries have raised ethical concerns regarding the use of control arms that may not align with the established standards of care in high-income countries. This trial aims to describe the control arms of randomised oncology trials recruiting in Brazil, an LMIC where the majority of patients receive care through the public health system, the Unified Health System (SUS), which provides limited access to cancer treatments.</p><p><strong>Methods and analysis: </strong>This cross-sectional study included randomised clinical trials recruiting in Brazil on 4 December 2023 (ClinicalTrials.gov). Abstracted data included sample size, sponsor, tumour site, study phase and control arm. Two independent investigators classified control arms as superior, equal or inferior based on National Comprehensive Cancer Network (NCCN), Brazilian private insurance and SUS standards. Data were summarised in means, medians and proportions. Fisher's exact test compared categories. A p<0.05 was considered statistically significant.</p><p><strong>Results: </strong>A total of 98 studies were included. The median intended sample size was 555 patients (54-6000). Most studies were phase 3 (84.7%) and pharma-sponsored (97%). Lung (29.6%) and breast (24.4%) were the most commonly studied tumour sites. Regarding treatment setting, 23 studies (23.5%) were (neo)adjuvant trials, 48 (49.0%) first-line and 27 (27.5%) second-line or later. Overall, 80 (81.7%), 82 (83.7%) and 58 studies (59.1%) employed control arms considered equivalent to the standards of NCCN, private insurance and SUS, respectively. 18 studies (18.3%) had a suboptimal control arm according to NCCN guidelines, while 16 studies (16.3%) according to Brazilian private insurance. No studies used control arms inferior to SUS standards. Of the 18 control arms inferior to NCCN, 3 were superior and 15 were equal to standard of care offered by SUS. No studies had their control arms superior to NCCN or private insurance; whereas, 40 (40.9%) were superior to SUS.</p><p><strong>Conclusion: </strong>A significant number of studies employed control arms inferior to NCCN guidelines; however, these were considered superior or equal to the standards offered by SUS. Such discrepancies may hinder the appropriate interpretation of study findings.</p>","PeriodicalId":72436,"journal":{"name":"BMJ oncology","volume":"4 1","pages":"e000808"},"PeriodicalIF":0.0,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12323517/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144790885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}