Diagnostic performance of the machine learning models in the training and validation cohorts
NB
REGRESSION
After PCC and RFE with stratified tenfold cross-validation, 27 radiomic features were chosen in the training cohort (Figs. Boxplot of Selected features after RFE. Features were reduced to twenty-seven features during training of the machine learning models.Recursive feature elimination (RFE) with tenfold cross‐validation; number of features selected vs. cross‐validation score.The following features were chosen to develop the predictive models for BRAFRadiomics features selected after PCC and RFE analysis.In the training cohort, AUCs for KNN, LDA, LR, NB, SVM_L, and SVM_ RBF were 0.96 (95% confidence interval [CI]: 0.89–0.99), 1.00 (95% CI 0.96–1.00), 1.00 (95% CI 0.96–1.00), 0.96 (95% CI 0.89–0.99), 1.00 (95% CI 0.96–0.1.00), and 1.00 (95% CI 0.96–1.00), respectively (Table The mixed ROC curves of the six machine learning models in the training cohort. ROC: receiver operating characteristic; KNN: K-nearest neighbor; LDA: linear discriminant analysis; LR: logistic regression; NB: Naïve Bayes;SVM_L: support vector classifier with the linear kernel; SVM_RBF: support vector classifier with the radial basis function.In the validation cohort, AUCs for NB, KNN, LDA, LR, SVM_L, and SVM_RBF were 0.80 (95% confidence interval [CI]: 0.65–0.91), 0.87 (95% CI 0.73–0.95), 0.91(95% CI 0.79–0.98), 0.92 (95% CI 0.80–0.98), 0.93 (95% CI 0.80–0.98), and 0.98 (95% CI 0.88–1.00), respectively. The SVM_RBF model performed the best in the validation cohort, followed by the LR, SVM_L, LDA, KNN, and NB models, in that order (Fig. The mixed ROC curves of the six machine learning models in the validation cohort. ROC: receiver operating characteristic. KNN: K-nearest neighbor; LDA: linear discriminant analysis; LR: logistic regression; NB: Naïve Bayes; SVM_L: support vector classifier with the linear kernel; SVM_RBF: support vector classifier with the radial basis function.Furthermore, the DCA was used to evaluate the clinical utility of these models (Fig. Decision curve for predictive models based on machine learning models in the training cohort (Calibration curves of the machine learning (