Vesselin Z. Miloushev

Abstract Hyperpolarized (HP) MRI using [1-13C] pyruvate is a novel method that can characterize energy metabolism in the human brain and brain tumors. Here, we present the first dynamically acquired human brain HP 13C metabolic spectra and spatial metabolite maps in cases of both untreated and recurrent tumors. In vivo production of HP lactate from HP pyruvate by tumors was indicative of altered cancer metabolism, whereas production of HP lactate in the entire brain was likely due to baseline metabolism. We correlated our results with standard clinical brain MRI, MRI DCE perfusion, and in one case FDG PET/CT. Our results suggest that HP 13C pyruvate-to-lactate conversion may be a viable metabolic biomarker for assessing tumor response. Significance: Hyperpolarized pyruvate MRI enables metabolic imaging in the brain and can be a quantitative biomarker for active tumors. Graphical Abstract: http://cancerres.aacrjournals.org/content/canres/78/14/3755/F1.large.jpg. Cancer Res; 78(14); 3755–60. ©2018 AACR.

BACKGROUND AND PURPOSE: A limitation in postoperative monitoring of patients with glioblastoma is the lack of objective measures to quantify residual and recurrent disease. Automated computer-assisted volumetric analysis of contrast-enhancing tissue represents a potential tool to aid the radiologist in following these patients. In this study, we hypothesize that computer-assisted volumetry will show increased precision and speed over conventional 1D and 2D techniques in assessing residual and/or recurrent tumor. MATERIALS AND METHODS: This retrospective study included patients with native glioblastomas with MR imaging performed at 24-48 hours following resection and 2-4 months postoperatively. 1D and 2D measurements were performed by 2 neuroradiologists with Certificates of Added Qualification. Volumetry was performed by using manual segmentation and computer-assisted volumetry, which combines region-based active contours and a level set approach. Tumor response was assessed by using established 1D, 2D, and volumetric standards. Manual and computer-assisted volumetry segmentation times were compared. Interobserver correlation was determined among 1D, 2D, and volumetric techniques. RESULTS: Twenty-nine patients were analyzed. Discrepancy in disease status between 1D and 2D compared with computer-assisted volumetry was 10.3% (3/29) and 17.2% (5/29), respectively. The mean time for segmentation between manual and computer-assisted volumetry techniques was 9.7 minutes and <1 minute, respectively (P < .01). Interobserver correlation was highest for volumetric measurements (0.995; 95% CI, 0.990-0.997) compared with 1D (0.826; 95% CI, 0.695-0.904) and 2D (0.905; 95% CI, 0.828-0.948) measurements. CONCLUSIONS: Computer-assisted volumetry provides a reproducible and faster volumetric assessment of enhancing tumor burden, which has implications for monitoring disease progression and quantification of tumor burden in treatment trials.