Fig. 2

Workflow of this study. (I) VOI segmentation: Segmentation in DWI and T1C. (II) Habitats generation: SLIC to generate 100 super-pixels, and K-means methods for clustering habitats. (III) Features extraction: K nearest neighbor approach to address missing data. Radiomics features including shape, histogram and texture features. (IV) Features selection: Statistical evaluation, Pearson’s correlation and LASSO analysis for selecting the most important features. (V) Model evaluation: AUC and 95%CI for evaluating the performance, DCA for assessing the clinical net benefit, and calibration curve to evaluate the goodness of fit of the optimal model. (VI) Model interpretation was given by SHAP. VOI, volume of interest, DWI, diffusion-weighted imaging, T1C, contrast-enhanced T1-weighted sequence, SLIC, Simple Linear Iterative Clustering, LASSO, least absolute shrinkage and selection operator, AUC, area under the receiver operating characteristic curve, DCA, decision curve analysis, SHAP, shapley additive explanations analysis