A tumoral and peritumoral CT-based Radiomics and Machine learning approach to predict the microsatellite instability of rectal carcinoma

Abstract

Purpose To predict the status of microsatellite instability (MSI) of rectal carcinoma (RC) using different machine learning algorithms based on tumoral and peritumoral radiomics combined with clinicopathological characteristics. Methods There were 487 RC patients enrolled in this retrospective study. The tumoral and peritumoral CT-based radiomic features were calculated after tumor segmentation. The radiomic features from two radiologists were compared by the method of inter-observer correlation coefficient (ICC). After methods of variance, correlation, and dimension reduction, six machine learning algorithms of logistic regression (LR), Bayes, support vector machine, random forest, k-nearest neighbor, and decision tree were conducted to develop models in predicting MSI status of RC. The relative standard deviation (RSD) was quantified. The radiomics and significant clinicopathological variables were integrated the radiomics-clinicopathological nomogram. The receiver operator curve (ROC) was made by Delong test and the area under curve (AUC) with 95% confidence interval (95%CI) was calculated to evaluate the performance of the model. Results The venous phase of CT examination was selected for further analysis because of the proportion of radiomic features with ICC greater than 0.75 was higher. The tumoral and peritumoral model by LR algorithm (M-LR) with minimal RSD showed good performance in predicting MSI status of RC with the AUCs of 0.817 and 0.726 in the training and validation set. The radiomic-clinicopathological nomogram conducted better in both the training and validation set with the AUCs of 0.843 and 0.737. Conclusion The radiomics-clinicopathological nomogram demonstrated the better predictive performance in evaluating the MSI status of RC.