An explainable machine learning model for predicting bladder tumor aecurrence risk.

ABSTRACT:

Background

Bladder cancer is associated with considerable postoperative recurrence rates. Accurate risk prediction remains challenging in clinical practice.

Objective

To develop and validate an explainable machine learning model for predicting bladder tumor recurrence following surgical treatment.

Methods

This retrospective cohort study enrolled 504 patients with pathologically confirmed bladder tumors treated at the Department of Urology, Zhejiang Dinghai Hospital, from October 2018 to October 2024. Postoperative surveillance was conducted at 3, 6, 12, and 24 months to assess recurrence status. The dataset was randomly partitioned into training (n=352) and testing (n=152) sets prior to analysis. LASSO regression with lambda.1se criterion was performed exclusively on

SUBMITTER: Wu S 

PROVIDER: S-EPMC12896212 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature