Explainable machine learning model predicts response to adjuvant therapy after radical cystectomy in bladder cancer.

ABSTRACT:

Purpose

Radical cystectomy (RC) is the standard treatment for muscle-invasive and select high-risk non-muscle-invasive bladder cancer. Despite definitive surgery, recurrence and progression remain major clinical concerns. Adjuvant chemotherapy and immunotherapy may improve outcomes, but therapeutic response varies due to tumor heterogeneity. Robust predictive models are needed to guide individualized treatment strategies.

Methods

This study retrospectively analyzed bladder cancer patients undergoing RC. Data included tumor morphology (e.g., vascular and perineural invasion), demographic variables (e.g., age, sex), and molecular markers (e.g., PD-L1, HER2, GATA3). LASSO regression identified key features, followed by model development using nine machine learning algorithms, i

SUBMITTER: Hou J 

PROVIDER: S-EPMC12615217 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature