Differentiation between low-grade and high-grade glioma using combined diffusion tensor imaging metrics
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Updated: May 6, 2026
Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
Published on: July 28, 2013
1Digital Medical Research Center, Fudan University, Shanghai, China; Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention, Shanghai, China.
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.
