Radiomic feature selection
The retrieved features were normalized using a standard scalar to reduce bias and overfitting in the study. The dataset was divided into training and validation cohorts. To make each characteristic substantially independent, the row spatial dimension of the feature matrix was reduced using the Pearson correlation coefficient (PCC). Every pair of features with a PCC of more than 0.80 was deemed redundant.After PCC, recursive feature elimination (RFE) for feature selection was applied to the whole dataset using the Scikit-learn python module