Comparison of region-of-interest analysis with three different histogram analysis methods in the determination of perfusion metrics in patients with brain gliomas

PURPOSE: To compare routine ROI analysis and three different histogram analyses in the grading of glial neoplasms. The hypothesis is that histogram methods can provide a robust and objective technique for quantifying perfusion data in brain gliomas. Current region-of-interest (ROI)-based methods for the analysis of dynamic susceptibility contrast perfusion magnetic resonance imaging (DSC MRI) data are operator-dependent. MATERIALS AND METHODS: A total of 92 patients underwent conventional and DSC MRI. Multiple histogram metrics were obtained for cerebral blood flow (CBF), cerebral blood volume (CBV), and relative CBV (rCBV) maps using tumoral (T), peritumoral (P), and total tumoral (TT) analysis. Results were compared to histopathologic grades. Statistical analysis included Mann-Whitney (MW) tests, Spearman rank correlation coefficients, logistic regression, and McNemar tests. RESULTS: The maximum value of rCBV (rCBV(max)) showed highly significant correlation with glioma grade (r = 0.734, P <0.001). The strongest histogram correlations (P <0.0001) occurred with rCBV(T) SD (r = 0.718), rCBV(P) SD(25) (r = 0.724) and rCBV(TT) SD(50) (r = 0.685). Multiple rCBV(T), rCBV(P), and rCBV(TT) histogram metrics showed significant correlations. CBF and CBV histogram metrics were less strongly correlated with glioma grade than rCBV histogram metrics. CONCLUSION: Histogram analysis of perfusion MR provides prediction of glioma grade, with peritumoral metrics outperforming tumoral and total tumoral metrics. Further refinement may lead to automated methods for perfusion data analysis.