Multiparametric radiomics-based machine learning predicts consensus molecular subtype 4 of colorectal cancer: a multi-center study

Motivation: The consensus molecular subtype (CMS) is a novel classification system that reflects the genetic characteristics of the tumor. CMS4 is associated with the worst prognosis. Goal(s): To investigate whether a radiomics-based machine learning approach could predict CMS4 status in CRC patients. Approach: The sequencing data was input into the CMS classification system to generate CMS subtype outcomes. Radiomics features were extracted from baseline T2WI and contrast-enhanced MRI. Machine learning algorithms were applied to explore the best-performing and most robust model. Results: The best performing model achieved AUCs of 0.855 and 0.759 in the test set and external validation set. Impact: The genetic phenotype of CMS4 colorectal cancer may be potentially associated with morphological features. Multiparametric radiomics-based machine learning shows promising potential in distinguishing CMS4 from other subtypes of CRC.