Radiomic features selection and model construction
REGRESSION
To reduce bias and avoid overfitting, we first removed features with infinite, null or constant values and features with extremely small variance. Then, we calculated the Pearson correlation coefficient between each pair of radiomic features. To eliminate the redundancy, we removed the one with larger mean absolute correlation in pairs with |r| > 0.9. Next, univariate logistic regression analysis was used to investigate the association between each radiomic feature and the patient’s KRAS status. We included the features with The preliminarily selected features were standardized by z score normalization. Then, least absolute shrinkage and selection operator (LASSO) regression was conducted to identify predictive features in the training cohort. Next, the selected key radiomic features were used to construct a radiomic prediction model with the support vector machine (SVM) classifier. Youden index was used to determine cut-off in the training cohort. Then, this cut-off was applied both in the training and testing cohort for risk stratification. The predictive value of the radiomic model and the cut-off was assessed in the training and testing cohort by receiver operating characteristic (ROC) curve analysis, sensitivity, specificity, positive predictive value, negative predictive value, Kappa, F1, balanced accuracy and corrected C-index. The radiomic score distribution was presented, while decision curve analysis (DCA) was conducted to evaluate its net benefits.