Leveraging survival analysis and machine learning for accurate prediction of breast cancer recurrence and metastasis.

ABSTRACT: Breast cancer, with its high incidence and mortality globally, necessitates early prediction of local and distant recurrence to improve treatment outcomes. This study develops and validates predictive models for breast cancer recurrence and metastasis using Recurrence-Free Survival Analysis and machine learning techniques. We merged datasets from the Molecular Taxonomy of Breast Cancer International Consortium, Memorial Sloan Kettering Cancer Center, Duke University, and the SEER program, creating a comprehensive dataset of 272, 252 rows and 23 columns. Our methodology utilized three predictive strategies: assessing recurrence risk, differentiating local from distant recurrences, and identifying potential metastatic sites. Key prognostic factors were identified through survival analysis. L

SUBMITTER: Noman SM 

PROVIDER: S-EPMC11779859 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature