Spatial Habitats Features Derived from Multiparametric MRI Predicts Prognosis in High Grade Glioma

Motivation: High-grade glioma (HGG) is a highly invasive neoplasm characterized by significant intra-tumoral spatial heterogeneity. However, the clinical relevance of the observed spatial and physical imaging characteristics remains unknown. Goal(s): To identify tumor subregions and quantify their image-based habitat characteristics associated with survival time. Approach: We retrospectively analyzed quantitative tumor habitat based on initial MRI scans in 2 groups (long-term and short-term survivals) of patients diagnosed with HGGs. Kmeans clustering, Univariate and multivariate logistic and survival analysis were used. Results: The features of the high MK and low FLAIR habitat was most effective for predicting survival groups (AUC 0.91, Sensitivity 0.844, Specificity 0.867). Impact: Tumor habitat is a novel method and It’s an earlier attempt to use habitats from diffusion and T1 based perfuison to predict the survival time of HGG. It has high prediction capabilities for prognosis.