Abstract
Treatment of GBM with anti-angiogenic agents (AA) is changing the paradigm of patterns of recurrence, with tumor often recurring in a non-enhancing, diffusely invasive phenotype. Despite this, delineation of voxels at risk for progression for radiotherapy (RT) is purely a geometric expansion of anatomic (T1,T2) MRI. We hypothesize that by integrating spectroscopic, diffusion- and perfusion-weighted parameters that can identify voxel-level subclinical disease at diagnosis with planned dosimetry, the likelihood of voxel progression at radiographic recurrence (RR) can be predicted. Twenty-four patients with GBM treated with 60 Gy RT, temozolamide, and an AA (enzastaurin (N = 11) or bevacizumab (N = 13)), with dosimetry and imaging at baseline and RR were analyzed. Pre-treatment diffusion (ADC,FA), perfusion (rCBV,%Recovery), and spectroscopy (choline-to-NAA index:CNI) of voxels within the lesion (3654 voxels, 274 cc) or normal brain (19664 voxels, 1475 cc) were calculated and a multinomial logistic regression model (MLR) integrating dosimetry and distance from tumor was generated to predict voxel outcome (stable, improved or progressed). 90% of voxels recurred in the non-enhancing lesion (23% >2 cm from the baseline lesion). Decreasing FA and distance, increasing ADC and CNI, and dose were highly significant in predicting voxel progression. This is consistent with subclinical involvement showing decreased structure, increased diffusion and metabolism, and proximity to the baseline lesion. For voxels within the baseline lesion that improved at RR, both univariate and multivariate analysis showed decreased significance for FA, ADC, and CNI, but increased significance for rCBV and %Recov. Dose remained highly significant. MLR models predicting progression correctly identified 84% of voxels [range 47-95] compared to 65% [29–87] when using 2 cm outside the lesion as a cutoff. MLR correctly identified 68% [13–95] of voxels within the lesion that improved. Integrating physiologic and metabolic MRI with dosimetry can identify voxels at risk for progression and improvement, allowing for risk-adapted voxel-level RT planning.
