PREDICT breast v4.0: An update to the PREDICT breast prognostic model

PREDICT breast v4.0: An update to the PREDICT breast prognostic model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report PREDICT breast v4.0: An update to the PREDICT breast prognostic model Paul D.P. Pharoah, Yi-Wen Hsiao, Gordon C. Wishart, Pei-Chen Peng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7482708/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Nov, 2025 Read the published version in BMC Research Notes → Version 1 posted 2 You are reading this latest preprint version Abstract Objective The PREDICT breast prognostic and treatment benefit model has undergone several revisions since its release. The latest version (v3.1) was developed using a data set of 35,474 cases diagnosed between 2000 and 2017 in a single region of England. PREDICT breast provides predicted outcomes at 5, 10 and 15 years, but most clinicians use the 10-year outcomes for decision making. The purpose of this study was to reparameterize the model using a larger data set from across the UK and to compare the performance of v4.0 with that of v3.1. Results There were 172,208 eligible cases randomly split 50:50 into model development and validation data sets. Cox proportional hazards models were derived for estrogen receptor negative and estrogen receptor positive cancer for breast cancer specific mortality with a third model for non-breast cancer mortality. In cases with at least five years follow-up and censored at ten years, the model was well-calibrated with a less than 5% difference between observed and predicted breast cancer deaths. Model discrimination was also good with AUCs in the validation data of 0.735 and 0.794 for ER negative and ER positive cases respectively. Calibration and discrimination were slightly improved compared to PREDICT breast v3.1. Full Text Additional Declarations Competing interest reported. Gordon Wishart and Paul Pharoah each receive a share of the fees received by Cambridge Enterprise for the licensing of PREDICT Breast to commercial partners. The other authors have no non-financial conflicts of interest to declare. Cite Share Download PDF Status: Published Journal Publication published 14 Nov, 2025 Read the published version in BMC Research Notes → Version 1 posted Submission checks completed at journal 15 Oct, 2025 First submitted to journal 14 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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Gordon Wishart and Paul Pharoah each receive a share of the fees received by Cambridge Enterprise for the licensing of PREDICT Breast to commercial partners. 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