CT texture analysis for the prediction of KRAS mutation status in colorectal cancer via a machine learning approach.

Purpose: This study aimed to investigate whether a machine learning-based computed tomography (CT) texture analysis could predict the mutation status of V-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) in colorectal cancer.

Method: This retrospective study comprised 40 patients with pathologically confirmed colorectal cancer who underwent KRAS mutation testing, contrast-enhancement CT, and F-fluorodeoxyglucose (FDG) positron emission tomography (PET) before treatment. Of the 40 patients, 20 had mutated KRAS genes, whereas 20 had wild-type KRAS genes. Fourteen CT texture parameters were extracted from portal venous phase CT images of primary tumors, and the maximum standard uptake values (SUV) on F-FDG PET images were recorded. Univariate logistic regression was used to develop predictive models for each CT texture parameter and SUV, and a machine learning method (multivariate support vector machine) was used to develop a comprehensive set of CT texture parameters. The area under the receiver operating characteristic (ROC) curve (AUC) of each model was calculated using five-fold cross validation. In addition, the performance of the machine learning method with the CT texture parameters was compared with that of SUV.

Results: In the univariate analyses, the AUC of each CT texture parameter ranged from 0.4 to 0.7, while the AUC of the SUV was 0.58. Comparatively, the multivariate support vector machine with comprehensive CT texture parameters yielded an AUC of 0.82, indicating a superior prediction performance when compared to the SUV.

Conclusions: A machine learning-based CT texture analysis was superior to the SUV for predicting the KRAS mutation status of a colorectal cancer.