Machine Learning Approaches for Survival Prediction in Bladder Cancer: A Single-Center Analysis of Clinical and Inflammatory Markers.

This study investigated the application of machine learning algorithms for survival prediction in bladder cancer patients undergoing cystectomy. We analyzed retrospective data from 370 patients, developing predictive models for disease-free survival (DFS), overall survival (OS), and cause of death. Multiple machine learning approaches were employed, including linear regression, random forests, gradient boosting, support vector machines, and neural networks. The models achieved mean absolute errors of 22-23 months for survival predictions and 66.67% accuracy in cause-of-death classification. Clinical T-stage emerged as the strongest predictor, while the Systemic Immune-Inflammation Index (SII) demonstrated a consistent negative correlation with survival outcomes. An unexpected positive correlation between age and survival was observed, possibly reflecting selection bias in surgical candidates. The studys findings suggest that machine learning approaches, despite current limitations, offer promising tools for risk stratification in clinical trial design and patient allocation, though further refinement is needed for individual prognostication.