Glioblastoma: A Method for Predicting Response to Antiangiogenic Chemotherapy by Using MR Perfusion Imaging-Pilot Study

Purpose: To derive a magnetic resonance (MR)-based imaging metric that reflects local perfusion changes resulting from the administration of angiogenic-inhibiting chemotherapy in patients with recurrent glioblastoma multiforme (GBM). Materials and Methods: In this retrospective Institutional Review Board-approved HIPAA-compliant study, 16 patients (12 men, four women; mean age, 51.8 years +/- 15.1 [standard deviation]) with recurrent GBM received bevacizumab every 3 weeks (15 mg per kilogram of body weight) as part of a clinical trial. Baseline MR images were acquired, and follow-up images were acquired every 6 weeks thereafter until tumor progression or death. Imaging included perfusion and T1-weighted contrast material-enhanced MR imaging. Perfusion images were analyzed both with and without correction for contrast material leakage. The volumes of interest were selected as enhancing voxels on T1-weighted contrast-enhanced MR images. Relative cerebral blood volume (rCBV) maps were created from analysis of MR perfusion images. The volumes of interest were used to calculate the following parameters: size, mean rCBV, mean leakage coefficient K-2, and hyperperfusion volume (HPV), which is the fraction of the tumor with an rCBV higher than a predetermined threshold. Percent change in each parameter from baseline to first follow-up was compared with time to progression (TTP) by using a Cox proportional hazards model with calculation of hazard ratios. Results: The most significant hazard ratio was seen with a D HPV cutoff of rCBV greater than 1.00 (hazard ratio, 1.077; 95% confidence interval: 1.026, 1.130; P = .002). The only significant ratios greater than one were those that resulted from perfusion calculated as mean rCBV and D HPV. The ratios were also higher after correction for leakage. Conclusion: This pilot study derived an imaging metric (HPV) that reflects local perfusion changes in GBMs. This metric was found to show a significantly improved correlation to TTP as compared with more commonly used metrics. (C) RSNA, 2010