Spatiotemporal Heterogeneity in Multiparametric Physiologic MRI Are Associated with Patient Outcomes in IDH-wildtype Glioblastoma

Spatiotemporal Heterogeneity in Multiparametric Physiologic MRI Are Associated with Patient Outcomes in IDH-wildtype Glioblastoma:

Purpose: Heterogeneity in glioblastomas is associated with poorer outcomes, and physiologic heterogeneity can be quantified with non-invasive imaging. We developed spatial habitats based on multiparametric physiologic magnetic resonance imaging (MRI) and evaluated associations between temporal changes in these habitats and progression-free survival (PFS) after concurrent chemoradiotherapy (CCRT) in patients with glioblastoma. Experimental Design: Ninety-seven patients with IDH-wild type glioblastoma were enrolled and two serial MRI examinations after CCRT were analyzed. Cerebral blood volumes and apparent diffusion coefficients were grouped using k-means clustering into three spatial habitats. . Associations between temporal changes in spatial habitats and PFS were investigated using Cox proportional hazard modeling. The performance of significant predictors for PFS and overall survival (OS) was measured using a discrete increase of habitat (habitat risk score) in a temporal validation set from a prospective registry (n = 53, ClinicalTrials.gov NCT02619890). The site of progression was matched with the spatiotemporal habitats. Results: Three spatial habitats of hypervascular cellular, hypovascular cellular, and nonviable tissue were identified. A short-term increase in the hypervascular cellular habitat (hazard ratio [HR] 40.0, P=.001) and hypovascular cellular habitat was significantly associated with shorter PFS (HR 3.78, P<.001) after CCRT. Combined with clinical predictors, the habitat risk score showed a C-index of 0.79 for PFS and 0.74 for OS and stratified patients with short-, intermediate-, and long-PFS (P=.016). An increase in the hypovascular cellular habitat predicted tumor-progression sites. Conclusions: Hypovascular cellular habitats derived from multiparametric physiologic MRIs may be useful predictors of clinical outcomes in patients with post-treatment glioblastoma.