PURPOSE

To explore the performance of various parameters obtained from monoexponential (Gaussian), biexponential and stretched exponential (non-Gaussian) models of Diffusion Weighted Magnetic Resonance Imaging in differentiating gliomas with correlation to histopathology and Ki-67 labeling index (LI).

MATERIALS AND METHODS

This Institute Review Board approved retrospective study included 51 pathologically proven glioma patients (WHO Grade I, n = 1; Grade II, n = 19, Grade III, n = 12; Grade IV, n = 19), and immunohistochemistry for Ki-67 LI was obtained. The conventional Magnetic Resonance (MR) images and Diffusion Weighted (DW) images with 19 non-zero b values (0-4500 s/mm) followed by contrast-enhanced MR images were obtained at 3T preoperatively. All images were processed with Advantage Workstation 4.5 (GE Medical Systems). Region of interest (ROI) in the solid part of the tumor was manually drawn along the border meticulously excluding areas of edema, cyst, hemorrhage, necrosis, and/or calcification, and the parameters: Apparent Diffusion Coefficient (ADC) of monoexponential; pure molecular diffusion coefficient (Dslow), pseudo-diffusion coefficient (Dfast), and perfusion fraction (f) of biexponential; Distributed Diffusion Coefficient (DDC), and heterogeneity index (α) of stretched exponential models were obtained. ROI of 50 mm in the contralateral normal appearing white matter (NAWM) was drawn for the internal control either on centrum semiovale or white matter of the frontal lobe. Analysis of reliability by Intra-class Correlation Coefficient (ICC); correlation with Ki-67 LI by Spearman's rank correlation; comparison between high grade glioma (HGG) and low grade glioma (LGG) by either Mann Whitney U test or Independent t-Test; comparison among Grade II, III and IV gliomas by one-way ANOVA with Bonferroni; and diagnostic performance by analysis of Area Under Receiver Operating Characteristic (ROC) Curve (AUC) were conducted.

RESULTS

Highly significant differences were found between HGG and LGG for all the parameters (P < 0.001 for all). In differentiating HGG from LGG, AUC values were 0.955 for Ki-67 LI; 0.926 for α; 0.903 for Dslow; 0.897 for f; 0.863 for DDC; 0.852 for ADC; 0.820 for Dfast (P < 0.001 for all). The parameters ADC, Dslow, Dfast, f, DDC, and α showed moderate to good negative correlation with Ki-67 LI (P < 0.001 for all). The ICCs of all the parameters were found greater than 0.75 (P < 0.05 for all) suggesting good reliability of measurements.

CONCLUSION

In comparison to ADC derived from monoexponential model, the parameters α and Dslow derived from stretched exponential, and biexponential models respectively can efficiently differentiate HGG from LGG with high diagnostic accuracy. Additionally, f and DDC derived from biexponential, and stretched exponential models respectively are also more useful in differentiating HGG from LGG in comparison to ADC.