Differentiation between low-grade and high-grade glioma using combined diffusion tensor imaging metrics

Abstract

Objective: To ascertain whether diffusion tensor imaging (DTI) metrics including tensor shape measures such as planar and spherical isotropy coefficients (CP and CS) can be used to distinguish high-grade from low-grade gliomas.

Methods: Twenty-five patients with histologically proved brain gliomas (10 low-grade and 15 high-grade) were included in this study. Contrast-enhanced T1-weighted images, non-diffusion weighted b=0 (b0) images, fractional anisotropy (FA), apparent diffusion coefficient (ADC), CS and CP maps were co-registered and each lesion was divided into two regions of interest (ROI): enhancing and immediate peritumoral edema (edema adjacent to tumor). Univariate and multivariate logistic regression analyses were applied to determine the best classification model.

Results: There was a statistically significant difference in the multivariate logistic regression analysis. The best logistic regression model for classification combined three parameters (CS, FA and CP) from the immediate peritumoral part (p=0.02), resulting in 86% sensitivity, 80% specificity and area under the curve of 0.81.

Conclusion: Our study revealed that combined DTI metrics can function in effect as a non-invasive measure to distinguish between low-grade and high-grade gliomas.

Keywords: Diffusion tensor imaging; High-grade glioma; Low-grade glioma; Magnetic resonance imaging.