Histogram Analysis versus Region of Interest Analysis of Dynamic Susceptibility Contrast Perfusion MR Imaging Data in the Grading of Cerebral Gliomas

Abstract

BACKGROUND AND PURPOSE: Histogram analysis can be applied to dynamic susceptibility contrast (DSC) perfusion MR imaging datasets and can be as effective as traditional region-of-interest (ROI) measurements of relative cerebral blood volume (rCBV), an operator-dependent method. We compare the routine ROI method with histogram analysis in the grading of glial neoplasms.

MATERIALS AND METHODS: Ninety-two patients underwent conventional and DSC MR imaging. Routine rCBV (rCBV_max) measurements were obtained from ROIs of the maximal abnormality within the glioma. Histogram analysis rCBV_T was performed with an ROI drawn around the maximal tumor diameter. Spearman rank correlations measured associations among glioma grade, rCBV_max, and histogram measures. Mann-Whitney tests compared grade with respect to rCBV and histogram measures. Logistic regression and McNemar test compared the utility of rCBV_max and histogram measures for detecting high grade gliomas.

RESULTS: Routine rCBV_max analysis showed significant correlation with grade (r = 0.734, P < .001). Histogram rCBV_T metrics showed significant correlation with grade (P < .008); the 3 highest were rCBV_T SD, SD₅₀, and mean₂₅ (r = 0.718, 0.684, and 0.683, respectively). Grade could be predicted by rCBV_max (P < .001) as well as rCBV_T (P < .008). Three rCBVT histogram measures (SD, SD₂₅, and SD₅₀) detected high-grade glioma with significantly higher specificity than rCBV_max when the diagnostic tests were constrained to have at least 95% sensitivity.

CONCLUSION: rCBV_T histogram analysis is as effective as rCBV_max analysis in the correlation with glioma grade. Inexperienced operators may obtain perfusion metrics using histogram analyses that are comparable with those obtained by experienced operators using ROI analysis.