Interpretable machine learning models based on multi-dimensional fusion data for predicting positive surgical margins in robot-assisted radical prostatectomy: a retrospective study.

ABSTRACT:

Objective

This study aimed to develop and validate interpretable machine learning (ML) models based on multi-dimensional fusion data for predicting positive surgical margins (PSM) in robot-assisted radical prostatectomy (RARP).

Methods

Patients who underwent RARP at our institution between January 2016 and July 2025 were enrolled. Demographic, clinical, biopsy pathology data, and MRI-derived anatomical features (measured using ITK-SNAP on axial, sagittal, and coronal planes) were collected. Feature selection was performed using intraobserver and interobserver correlation coefficients (ICCs), low-variance filtering, univariable logistic regression, Spearman's correlation analysis, the least absolute shrinkage and selection operator (LASSO) algorithm, and the Boruta algorithm.

SUBMITTER: Liu Z 

PROVIDER: S-EPMC12531042 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature