Development and validation of an interpretable machine learning model for predicting Gleason score upgrade in prostate cancer.

ABSTRACT:

Background

The high incidence of Gleason score upgrade (GSU) can lead urologists to underestimate tumor aggressiveness, resulting in suboptimal treatment decisions. This study aimed to develop an interpretable machine learning model to predict the risk of GSU in individuals with prostate cancer (PCa) based on readily available clinical parameters.

Methods

A retrospective analysis was conducted on patients who underwent radical prostatectomy (RP) at Shanghai General Hospital and West China Hospital. Data from Shanghai General Hospital were categorized into a training set (80%) and a test set (20%), while data from West China Hospital were used for external validation. Preoperative clinical and pathological data were collected. Nine machine learning models [including random fo

SUBMITTER: Li SF 

PROVIDER: S-EPMC12271951 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature