Fig. 6. Workflow of image preprocessing, radiomics feature extraction, and machine learning.

(1) Preprocessing and segmentation: For the radiomic feature extraction, registration of T2 and FLAIR to T1 images and normalization of signal intensities was performed. The regions of interest were put on the bilateral putamen and caudate. (2) Feature extraction: Three different categories of radiomic features—shape feature, first-order features, and second-order features were obtained. (3) Radiomics model construction: SelectKBest feature selection method combined with ExtraTrees classifier were used to develop two predictive models—clinical and combined (clinical + radiomics) model. The models were developed in the training set, then validated in the test set. (4) Model interpretation: We performed SHAP analysis to understand the contributing role of each selected radiomic feature and obtained decision plot, summary dot plot, and force plot.