Development and validation of interpretable machine learning models to predict distant metastasis and prognosis of muscle-invasive bladder cancer patients.

ABSTRACT: Muscle-Invasive Bladder Cancer (MIBC) is a more aggressive disease than non-muscle-invasive bladder cancer (NMIBC), with greater chances of metastasis. We sought to develop machine learning (ML) models to predict metastasis and prognosis in MIBC patients. Clinical data of MIBC cases from 2000 to 2020 were sourced from the Surveillance, Epidemiology, and End Results (SEER) database. Clinical variables used to predict DM were identified through univariate and multivariate logistic regression, and Recursive Feature Elimination (RFE). Thirteen ML models predicting DM were evaluated based on AUC, PRAUC, accuracy, sensitivity, specificity, precision, cross-entropy, Brier score, balanced accuracy, and F-beta score. SHapley Additive exPlanations (SHAP) framework helped interpret the best model. Ad

SUBMITTER: Deng Q 

PROVIDER: S-EPMC11973202 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature