Automated Quantitative Analysis of Enhancing and Peritumoral Cerebral Blood Volume for Differentiating Glioblastoma From Central Nervous System Lymphoma

Objective: To quantitatively compare cerebral blood volume (CBV) in contrast-enhancing tumor areas and nonenhancing peritumoral fluid-attenuated inversion recovery (FLAIR) hyperintense regions between glioblastoma and central nervous system lymphoma (CNSL), and to assess the incremental diagnostic value of peritumoral CBV beyond enhancing tumor CBV. Methods: The study included 34 patients with histopathologically confirmed glioblastoma (n=22) or CNSL (n=12) who underwent pretreatment magnetic resonance imaging, including FLAIR, dynamic susceptibility contrast perfusion imaging, and contrast-enhanced T1-weighted imaging. Automated regions of interest (ROIs) were defined for enhancement areas (EAs) and nonenhancing peritumoral FLAIR abnormalities (PFAs). Quantitative indices included the median CBV within EAs (CBV EA ) and the 95th percentile CBV within PFAs (CBV PFA ). Intergroup differences were assessed, and diagnostic performance was evaluated using univariate and multivariate logistic regression models incorporating CBV EA and CBV PFA , receiver operating characteristic (ROC) analysis, and likelihood ratio testing (LRT). Results: Both CBV EA and CBV PFA were significantly higher in the glioblastoma group than in the CNSL group [CBV EA : 4.90 (4.14-5.58) vs. 2.84 (2.04-3.21) mL/100 g, P <0.0001, CBV PFA : 5.67 (3.75-8.08) vs. 4.10 (3.00-5.23) mL/100 g; P =0.029]. CBV EA showed the highest discriminatory performance in univariate analysis, whereas CBV PFA demonstrated a more modest association with tumor type. Although the AUC of the combined model was not significantly different from that of the CBV EA model alone [CBV EA : 0.913 (0.816-1.000) vs. combined model: 0.932 (0.848-1.000), P =0.602], the combined model showed a significant improvement in model fit according to LRT (χ 2 =4.1, P =0.043). Conclusions: Automated ROI-based analysis demonstrated significant differences in peritumoral CBV between glioblastoma and CNSL, and adding peritumoral CBV to the logistic regression model significantly improved overall model fit for differentiating between these entities beyond enhancing tumor CBV alone.