Explainable Machine Learning Model for Predicting Postoperative Survival in Patients With Locally Advanced Gastric Cancer.

ABSTRACT:

Purpose

This study aims to develop and validate an explainable machine learning model for predicting postoperative survival in patients with locally advanced gastric cancer (LAGC), optimizing predictive accuracy while ensuring clinical applicability to facilitate personalized prognostication for patients.

Methods

The study utilized data from 8616 LAGC patients who underwent gastrectomy (2004-2015) in the Surveillance, Epidemiology, and End Results (SEER) database for model development and validation, with external validation performed using 235 postoperative LAGC cases (2016-2022) from Maoming People's Hospital (Maoming, China). Five predictive models-Cox proportional hazards model (CoxPH), random survival forest (RSF), extreme gradient boosting (XGBoost), gradient boosting

SUBMITTER: Gong Z 

PROVIDER: S-EPMC12638127 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature