An interpretable CT-based machine learning model for predicting recurrence risk in stage II colorectal cancer.

ABSTRACT:

Objectives

This study aimed to develop an interpretable 3-year disease-free survival risk prediction tool to stratify patients with stage II colorectal cancer (CRC) by integrating CT images and clinicopathological factors.

Methods

A total of 769 patients with pathologically confirmed stage II CRC and disease-free survival (DFS) follow-up information were recruited from three medical centers and divided into training (n = 442), test (n = 190), and validation cohorts (n = 137). CT-based tumor radiomics features were extracted, selected, and used to calculate a Radscore. A combined model was developed using artificial neural network (ANN) algorithm, by integrating the Radscore with significant clinicoradiological factors to classify patients into high- and low-risk groups. Mode

SUBMITTER: Wu Z 

PROVIDER: S-EPMC12314294 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature