Glioblastoma: Vascular Habitats Detected at Preoperative Dynamic Susceptibility-weighted Contrast-enhanced Perfusion MR Imaging Predict Survival

Abstract

[EN] Purpose: To determine if preoperative vascular heterogeneity of glioblastoma is predictive of overall survival of patients undergoing standard-of-care treatment by using an unsupervised multiparametric perfusion-based habitat-discovery algorithm.

Materials and Methods: Preoperative magnetic resonance (MR) imaging including dynamic susceptibility-weighted contrast material-enhanced perfusion studies in 50 consecutive patients with glioblastoma were retrieved. Perfusion parameters of glioblastoma were analyzed and used to automatically draw four reproducible habitats that describe the tumor vascular heterogeneity: high-angiogenic and low-angiogenic regions of the enhancing tumor, potentially tumor-infiltrated peripheral edema, and vasogenic edema. Kaplan-Meier and Cox proportional hazard analyses were conducted to assess the prognostic potential of the hemodynamic tissue signature to predict patient survival.

Results: Cox regression analysis yielded a significant correlation between patients' survival and maximum relative cerebral blood volume (rCBV(max)) and maximum relative cerebral blood flow (rCBF(max)) in high-angiogenic and low-angiogenic habitats (P < .01, false discovery rate-corrected P < .05). Moreover, rCBF(max) in the potentially tumor-infiltrated peripheral edema habitat was also significantly correlated (P < .05, false discovery rate-corrected P < .05). Kaplan-Meier analysis demonstrated significant differences between the observed survival of populations divided according to the median of the rCBV(max) or rCBF(max) at the high-angiogenic and low-angiogenic habitats (log-rank test P < .05, false discovery rate-corrected P < .05), with an average survival increase of 230 days.

Conclusion: Preoperative perfusion heterogeneity contains relevant information about overall survival in patients who undergo standard-of-care treatment. The hemodynamic tissue signature method automatically describes this heterogeneity, providing a set of vascular habitats with high prognostic capabilities.