Development and Validation of Radiomics Signatures to Predict KRAS Mutation Status Based on Triphasic Enhanced Computed Tomography in Patients with Colorectal Cancer

Purpose: In this study, we used computed tomography (CT)-based radiomics signatures to predict the mutation status of KRAS in colorectal cancer (CRC) patients. Methods: : This study involved 447 patients who underwent KRAS mutation testing and preoperative triphasic enhanced CT. They were categorised into training (n = 313) and validation cohorts (n = 134) in the ratio 7:3. Radiomics features were extracted from CT imaging. The Boruta algorithm was used to retain the features closely associated with KRAS mutation. Multivariate logistic regression was used to develop radiomics, clinical, and combined clinical-radiomics models for KRAS mutation. The receiver operating characteristic curves were used to evaluate the predictive performance and clinical usefulness of each model. Results: : Fourteen radiomics features were retained as the as final signatures for predicting KRAS mutations. Delayed phase models showed superior predictive performance compared to arterial phases models or venous phase models. The clinical-radiomics fusion model showed excellent performance, with an AUC, sensitivity and specificity were 0.772, 0.792 and 0.646 in the training cohort, while 0.755, 0.724 and 0.684 in the validation cohort, respectively. Conclusions: : The clinical-radiomics fusion model can be used as a potential imaging marker for preoperative detection of KRAS mutation status.