Quantitative imaging biomarkers for risk stratification of patients with recurrent glioblastoma treated with bevacizumab.

BACKGROUND:Anti-angiogenic therapy with bevacizumab is the most widely used treatment option for recurrent glioblastoma, but therapeutic response varies substantially and effective biomarkers for patient selection are not available. To this end, we determine whether novel quantitative radiomic strategies on the basis of MRI have the potential to noninvasively stratify survival and progression in this patient population. METHODS:In an initial cohort of 126 patients, we identified a distinct set of features representative of the radiographic phenotype on baseline (pretreatment) MRI. These selected features were evaluated on a second cohort of 165 patients from the multicenter BRAIN trial with prospectively acquired clinical and imaging data. Features were evaluated in terms of prognostic value for overall survival (OS), progression-free survival (PFS), and progression within 3, 6, and 9 months using baseline imaging and first follow-up imaging at 6 weeks posttreatment initiation. RESULTS:Multivariable analysis of features derived at baseline imaging resulted in significant stratification of OS (hazard ratio [HR] = 2.5; log-rank P = 0.001) and PFS (HR = 4.5; log-rank P = 2.1 × 10-5) in validation data. These stratifications were stronger compared with clinical or volumetric covariates (permutation test false discovery rate [FDR] <0.05). Univariable analysis of a prognostic textural heterogeneity feature (information correlation) derived from postcontrast T1-weighted imaging revealed significantly higher scores for patients who progressed within 3 months (Wilcoxon test P = 8.8 × 10-8). Generally, features derived from postcontrast T1-weighted imaging yielded higher prognostic power compared with precontrast enhancing T2-weighted imaging. CONCLUSION:Radiomics provides prognostic value for survival and progression in patients with recurrent glioblastoma receiving bevacizumab treatment. These results could lead to the development of quantitative pretreatment biomarkers to predict benefit from bevacizumab using standard of care imaging.