{"paper_id":"311b3fba-aefa-48ed-a372-25cfbec8d610","body_text":"Leveraging Survival Analysis and Machine Learning for Accurate Prediction of Breast Cancer Recurrence and Metastasis | 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 Article Leveraging Survival Analysis and Machine Learning for Accurate Prediction of Breast Cancer Recurrence and Metastasis Shahd M. Noman, Youssef M. Fadel, Nada A. Attia, Mayar Tarek, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5059228/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Breast cancer, with its high incidence and mortality globally, necessitates early prediction of local and distant recurrence toimprove treatment outcomes. This study develops and validates predictive models for breast cancer recurrence and metastasisusing Recurrence-Free Survival Analysis (RFS) and machine learning techniques. We merged datasets from the MolecularTaxonomy of Breast Cancer International Consortium (METABRIC), Memorial Sloan Kettering Cancer Center (MSK), DukeUniversity, and the SEER program, creating a comprehensive dataset of 190,789 rows and 23 columns. Our methodologyutilized three predictive strategies: assessing recurrence risk, differentiating local from distant recurrences, and identifyingpotential metastatic sites. Key prognostic factors were identified through survival analysis. LightGBM, XGBoost, and RandomForest models were employed and validated against data from the Baheya Foundation. The models demonstrated strongperformance; the survival analysis achieved a C-index of 0.837. The LightGBM model reached an AUC of 92% in predictingrecurrences, while XGBoost and Random Forest models distinguished recurrence types with up to 86% accuracy and predictedspecific metastatic sites effectively. This study highlights the significant potential of machine learning in advancing breastcancer management and sets a new benchmark for predictive analytics. Future research will integrate genetic data to furtherenhance these models. breast cancer recurrence prediction machine learning metastasis survival analysis Full Text Additional Declarations No competing interests reported. Supplementary Files are not available with this version Cite Share Download PDF Status: Published Journal Publication published 29 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 17 Dec, 2024 Reviews received at journal 16 Nov, 2024 Reviewers agreed at journal 07 Nov, 2024 Reviews received at journal 13 Oct, 2024 Reviewers agreed at journal 03 Oct, 2024 Reviewers agreed at journal 03 Oct, 2024 Reviewers invited by journal 03 Oct, 2024 Editor assigned by journal 03 Oct, 2024 Editor invited by journal 23 Sep, 2024 Submission checks completed at journal 21 Sep, 2024 First submitted to journal 09 Sep, 2024 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. 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