[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126345-en":3,"doc-seo-126345-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126345,962085564381,"Clementine","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Clinical prediction models using machine learning in oncology - challenges and recommendations","Clinical prediction models are widely developed in oncology to provide individualized risk estimates that support diagnosis and prognosis. Machine learning is increasingly used, but many models contain methodological weaknesses that restrict clinical uptake. This review summarizes essential considerations for building robust, equitable models, including systematic model review, protocol development, registration, stakeholder engagement, sample size planning, and data representativeness. It also details technical hurdles (missing data, fairness, complex structures, censored outcomes, competing risks, clustering), and emphasizes internal/external evaluation of discrimination, calibration, and clinical utility, plus post-deployment monitoring to bridge development and practice.","University of Birmingham  \nClinical prediction models using machine learning in oncology  \nCollins, Gary S; Chester-Jones, Mae; Gerry, Stephen; Ma, Jie; Sehjal, Jyoti; Matos, Joao; Tsegaye, Biruk; Dhiman, Paula  \nDOI:  \n10.1136/bmjonc-2025-000914  \nLicense:  \nCreative Commons: Attribution (CC BY)  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nCitation for published version (Harvard):  \nCollins, GS, Chester-Jones, M, Gerry, S, Ma, J, Sehjal, J, Matos, J, Tsegaye, B & Dhiman, P 2025, 'Clinical prediction models using machine learning in oncology: challenges and recommendations', BMJ Oncology, vol. 4, no. 1, e000914 . [https://doi.org/10.1136/bmjonc-2025-000914](https://doi.org/10.1136/bmjonc-2025-000914)  \nLink to publication on Research at Birmingham portal  \nGeneral rights  \nUnless a licence is specified above, all rights (including copyright and moral rights) in this document are retained by the authors and/or the copyright holders. The express permission of the copyright holder must be obtained for any use of this material other than for purposes permitted by law.  \n•Users may freely distribute the URL that is used to identify this publication.  \n•Users may download and/or print one copy of the publication from the University of Birmingham research portal for the purpose of private study or non-commercial research.  \n•User may use extracts from the document in line with the concept of ‘fair dealing’ under the Copyright, Designs and Patents Act 1988 (?)  \n•Users may not further distribute the material nor use it for the purposes of commercial gain.  \nWhere a licence is displayed above, please note the terms and conditions of the licence govern your use of this document.  \nWhen citing, please reference the published version.  \nTake down policy  \nWhile the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive.  \nIf you believe that this is the case for this document, [please contact UBIRA@lists.bham.ac.uk](please contact UBIRA@lists.bham.ac.uk) providing details and we will remove access to the work immediately and investigate.  \nDownload date: 04. Aug. 2026  \nOpen access Review  \nClinical prediction models using machine learning in oncology: challenges and recommendations  \nGary S Collins  ,1 Mae Chester-Jones,2 Stephen Gerry,2 Jie Ma,2 Joao Matos,2 Jyoti Sehjal,2 Biruk Tsegaye,2 Paula Dhiman2  \nTo cite: Collins GS, ChesterJones M, Gerry S, et al. Clinical prediction models using machine learning in oncology: challenges and recommendations. BMJ Oncology 2025;4:e000914 . doi:10.1136/ bmjonc-2025-000914  \nReceived 20 June 2025  \nAccepted 22 September 2025  \n© Author(s) (or their employer(s)) 2025. Re-use permitted under CC BY. Published by BMJ Group. 1Department of Applied Health Sciences, University of Birmingham, Birmingham, UK 2University of Oxford, Oxford, UK  \nCorrespondence to  \nProfessor Gary S Collins; [g.s.collins@bham.ac.uk](g.s.collins@bham.ac.uk)  \nABSTRACT  \nClinical 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, enduser 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 bot","cbCaiqro8nGBIcUr","https://ap.wps.com/l/cbCaiqro8nGBIcUr","pdf",373059,7,1,10,"English","en",105,"# Abstract\n# Introduction\n## Diagnostic vs prognostic models\n## Multivariable prediction benefits\n# Review scope and key considerations\n## Development and governance\n## Technical challenges\n## Evaluation and clinical utility\n# Implementation and translational barriers\n## Stakeholder engagement and workflow\n## Evidence of clinical utility and monitoring","[{\"question\":\"What is the main purpose of clinical prediction models in oncology?\",\"answer\":\"They provide individualized risk estimates to support diagnosis and prognosis and help guide clinical decision-making across the cancer care pathway.\"},{\"question\":\"Which factors can limit clinical implementation of machine learning prediction models?\",\"answer\":\"Common limitations include methodological flaws, insufficient evidence of clinical utility, weak workflow integration consideration, limited stakeholder engagement, and lack of monitoring plans after deployment.\"},{\"question\":\"How should robust machine learning prediction models be evaluated?\",\"answer\":\"Evaluation should include both statistical performance (discrimination and calibration) and clinical utility, using comprehensive internal and external assessment while ensuring equity across demographic groups.\"}]","Clinical prediction models using machine learning in oncology - 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