Non-Gaussian diffusion MR imaging of glioma: comparisons of multiple diffusion parameters and correlation with histologic grade and MIB-1 (Ki-67 labeling) index

This study was conducted to compare the association of Gaussian and non-Gaussian magnetic resonance imaging (MRI)-derived parameters with histologic grade and MIB-1 (Ki-67 labeling) index (MI) in brain glioma. Sixty-five patients with pathologically confirmed glioma, who underwent diffusion-weighted MRI with 2 b values (0, 1000 s/mm(2)) and 22 b values (a parts per thousand currency sign5000 s/mm(2)), respectively, were divided into three groups of grade II (n = 35), grade III (n = 8), and grade IV (n = 22). Comparisons by two groups were made for apparent diffusion coefficient (ADC), slow diffusion coefficient (Dslow), distributed diffusion coefficient (DDC), and heterogeneity index alpha. Analyses of receiver operating characteristic (ROC) curve were performed to maximize the area under the curve (AUC) for differentiating grade III + IV (high-grade glioma, HGG) from grade II (low-grade glioma, LGG) and grade IV (glioblastoma multiforme, GBM) from grade II + III (other grade glioma, OGG). Correlations with MI were analyzed for the MRI parameters. On tumor regions, the values of ADC, Dslow, DDC, and alpha were significantly higher in grade II [(1.37 +/- 0.29, 0.70 +/- 0.11, 1.39 +/- 0.34) (x10(-3) mm(2)/s) and 0.88 +/- 0.05, respectively] than in grade III [(0.99 +/- 0.13, 0.55 +/- 0.07, 1.04 +/- 0.20) (x10(-3) mm(2)/s) and 0.80 +/- 0.03, respectively] and grade IV [(1.03 +/- 0.14, 0.50 +/- 0.05, 1.02 +/- 0.16) (x10(-3) mm(2)/s) and 0.76 +/- 0.04, respectively] (all P < 0.001). The parameter alpha showed the highest AUCs of 0.950 and 0.922 in discriminating HGG from LGG and GBM from OGG, respectively. Significant correlations with histologic grade and MI were observed for the MRI parameters. The non-Gaussian MRI-derived parameters alpha and Dslow are superior to ADC in glioma grading, which are comparable with ADC as reliable biomarkers in noninvasively predicting the proliferation level of glioma malignancy.