Machine learning-based radiomics approach assessing preoperative non-contrast CT for microsatellite instability prediction in colon cancer.

ABSTRACT:

Objectives

To assess the feasibility of non-contrast CT-based radiomics model for predicting microsatellite instability (MSI) status in colon cancer.

Methods

Leveraging non-contrast abdominal CT imaging data from 57 retrospectively enrolled patients with balanced class distribution (training cohort: n = 38, 19 non-MSI-H and 19 MSI-H; test cohort: n = 19, 9 non-MSI-H and 10 MSI-H), we implemented a voxel volume-based tumor feature selection method. Feature selection integrated four feature selection filters-correlation analysis, univariate logistic regression, least absolute shrinkage and selection operator (LASSO), and recursive feature elimination (RFE). We comparatively evaluated multiple classifiers using cross-validation combined with accuracy for choosing the best class

SUBMITTER: Ren D 

PROVIDER: S-EPMC12515810 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature