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

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 classifier. Results A multilayer perceptron-based classification model was developed, achieving average multifold accuracy of 0.871 in cross-validation on the training cohort. In the test cohort, the model achieved an AUC of 0.944 (95% CI 0.841–1.000) with accuracy of 0.842, while maintaining sensitivity of 0.889 and specificity of 0.800, demonstrating excellent and comparable performance to previous contrast-enhanced CT-based radiomics models. Conclusion We validated the feasibility of non-contrast CT for MSI prediction in colon cancer with radiomics analysis, highlighting its potential as a flexible and cost-effective preliminary screening tool. This approach, which does not require supplementary medical examination, may enhance clinical decision-making by providing a valuable tool for identifying MSI-H molecular subtypes in colon cancer patients.