Advancing precision risk stratification in adult diffuse gliomas through DSC-MRI-based habitat analysis

Diffuse gliomas exhibit substantial molecular and spatial heterogeneity. This study aimed to evaluate the ability of habitat-based radiomics models derived from dynamic susceptibility contrast MRI (DSC-MRI) and conventional MRI to identify aggressive diffuse glioma phenotypes associated with integrated histologic-molecular risk.

This retrospective study included 197 adult patients with histopathologically confirmed diffuse gliomas. Multiparametric MRI data were preprocessed and segmented into tumor and peritumoral edema. K-means clustering was used to identify imaging-defined habitats reflecting spatial hemodynamic heterogeneity. A total of 855 radiomic features were extracted from each habitat and reduced through a sequential selection process involving univariate statistical testing, correlation filtering, recursive feature elimination, and LASSO regression. Random forest classifiers were developed to predict high-risk molecular subtypes, including IDH wildtype and other aggressive genetic alterations, and validated in internal held-out testing cohort.

Habitat-based models significantly outperformed whole-region analyses (AUC 0.949 (0.858, 0.989) vs. 0.931(0.833, 0.980), p = 0.013). Crucially, models derived from hemodynamic features (CBF + MTT) demonstrated superior predictive accuracy compared to conventional anatomical sequences (T1C+T2FLAIR) in both tumor (AUC 0.944 (0.852, 0.987) vs. 0.895 (0.787, 0.960)) and edema habitats (AUC 0.932 (0.835, 0.981) vs. 0.819(0.698, 0.907)). The optimal model relied solely on hemodynamic features from combined habitats (AUC 0.949 (0.858, 0.989)). Multimodal fusion failed to improve performance, suggesting that hemodynamic parameters may provide the most discriminative imaging information.

DSC-MRI-based habitat analysis provides significant value over conventional imaging by resolving perfusion heterogeneity. These findings highlight that hemodynamic features serve as a promising tool for prediction of integrated histologic-molecular risk status of adult diffuse gliomas, potentially serving as a promising imaging biomarker for preoperative assessment.