Machine learning-based prediction model for postoperative complications in gastric and colorectal cancer: A prospective nationwide multi-center study.

ABSTRACT:

Objective

This study aimed to develop and validate a predictive model for postoperative complications in gastrointestinal cancer patients using a large multicenter database, based on machine learning algorithms.

Methods

We analyzed the clinicopathological data of 3,926 gastrointestinal cancer patients from the Prevalence of Abdominal Complications After GastroEnterological surgery (PACAGE) database, covering 20 medical centers from December 2018 to December 2020. The predictive performance was evaluated using receiver operating characteristic (ROC) curves and Brier Score.

Results

The patients were divided into gastric (2,271 cases) and colorectal cancer (1,655 cases) groups and further divided into training and external validation sets. The overall postoperative compl

SUBMITTER: Lu J 

PROVIDER: S-EPMC12444345 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature