Radiomics feature selection and model construction
PTB
REGRESSION
To eliminate redundant features, 688 features that showed no significant difference between PNMA and PTB and 2 highly correlated features with ICC values less than 0.75 were excluded. After screening out the redundant features by LASSO and correlation analysis, the five most robust radiomics features (including flatness, cluster shade, minimum, median and skewness) were retained. The relationships of the five radiomics features were shown to be significantly low (Fig. Radiomics feature selectionThe machine learning models logistic regression (LR), support vector machine (SVM) and random forest (RF) were developed based on the five radiomics features. The model established by the LR method had the best performance, and the AUC values in the training group and test group were 0.840 and 0.960, respectively (Table Diagnostic performance of the prediction modelsBar charts of the Radscore for each patient in the training cohort (