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

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.