Machine learning-based predictive model for immune checkpoint inhibitors response in gastrointestinal cancers.

ABSTRACT:

Introduction

Gastrointestinal (GI) cancers present significant clinical challenges characterized by dismal survival outcomes and suboptimal prognoses. Currently, only partial indicators are available to predict the response of immunotherapy. A critical gap remains in the development of models capable of accurately predicting response rates to immunotherapy regimens. In this study, we developed a machine-learning (ML) model based on factorial, molecular, demographic, and clinical data to predict the response rate.

Methods

This multicentre retrospective study analyzed the clinical data of 506 patients, comprising 352 cases collected from Zhongnan Hospital of Wuhan University and Hubei Cancer Hospital, along with 154 cases obtained from the publicly available dataset of Memoria

SUBMITTER: Lv Y 

PROVIDER: S-EPMC12575239 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature