Comparing Perfusion Metrics Obtained from a Single Compartment Versus Pharmacokinetic Modeling Methods Using Dynamic Susceptibility Contrast-Enhanced Perfusion MR Imaging with Glioma Grade

Abstract

BACKGROUND AND PURPOSE: Numerous different parameters measured by perfusion MR imaging can be used for characterizing gliomas. Parameters derived from 3 different analyses were correlated with histopathologically confirmed grade in gliomas to determine which parameters best predict tumor grade.

METHODS: Seventy-four patients with gliomas underwent dynamic susceptibility contrast-enhanced MR imaging (DSC MR imaging). Data were analyzed by 3 different algorithms. Analysis 1 estimated relative cerebral blood volume (rCBV) by using a single compartment model. Analysis 2 estimated fractional plasma volume (V_p) and vascular transfer constant (K^trans) by using a 2-compartment pharmacokinetic model. Analysis 3 estimated absolute cerebral blood flow (CBF), cerebral blood volume (CBV), and mean transit time (MTT) by using a single compartment model and an automated arterial input function. The Mann-Whitney U test was used make pairwise comparisons. Binary logistic regression was used to assess whether rCBV, V_p, K^trans, CBV, CBF, and MTT can discriminate high- from low-grade tumors.

RESULTS: rCBV was the best discriminator of tumor grade ype, followed by CBF, CBV, and K^trans. Spearman rank correlation factors were the following: rCBV = 0.812 (P < .0001), CBF = 0.677 (P < .0001), CBV = 0.604 (P < .0001), K^trans = 0.457 (P < .0001), V_p = 0.301 (P =.009), and MTT = 0.089 (P = .448). rCBV was the best single predictor, and K^trans with rCBV was the best set of predictors of high-grade glioma.

CONCLUSION: rCBV, CBF, CBV K^trans, and V_p measurements correlated well with histopathologic grade. rCBV was the best predictor of glioma grade, and the combination of rCBV with K^trans was the best set of metrics to predict glioma grade.