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Updated: Aug 1, 2026

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
Published on: July 28, 2013
Differentiation of High-Grade and Low-Grade Gliomas Using Pattern Analysis of Long-Echo Single-Voxel Proton Magnetic
M F Chernov1, Y Ono, Y Muragaki
1Departments of Neurosurgery and International Research and Educational Institute for Integrated Medical Sciences (IREIIMS); Tokyo Women's Medical University; Tokyo, Japan - m_chernov@yahoo.com.
Abstract:
The usefulness of proton magnetic resonance spectroscopy ((1)H-MRS) for glioma grading is not clear, particularly due to the absence of standard criteria for data analysis. Previously we had developed an original classification of the pathological (1)H-MRS spectra based on the identification of the predominant metabolite peak, N-acetylaspartate (NAA) for Type I, choline-containing compounds (Cho) for Type II, and mobile lipids (Lip) for Type III, and presence or absence of other metabolite peaks: lactate (Lac), Lip, or Cho. The present study evaluated the effectiveness of this classification in grading of previously non-treated gliomas. A total of 38 low-grade and 33 high-grade neoplasms were investigated. Four tumors had (1)H-MRS spectra Type I, and all of those were low-grade. Three tumors had (1)H-MRS spectra Type III, and all those were glioblastomas. Fifteen tumors with (1)H-MRS spectra Type II had a Lip/NAA ratio more than 1 (Type II C with moderate elevation of lipids), and 12 of those neoplasms were high-grade. The differences in distribution of high-grade and low-grade gliomas among another 49 gliomas with (1)H-MRS spectra Type II did not depend on the presence of Lac and/or Lip peaks, and in this subgroup NAA/Cho ratio was also evaluated. Inclusion of both characteristics (type of the (1)H-MRS spectrum and NAA/Cho ratio with defined cut-off level of 0.6) into the diagnostic algorithm yielded 72% diagnostic accuracy (95% confidence interval: 62%-82%) in discriminating high-grade and low-grade neoplasms. In conclusion, pattern analysis of the pathological (1)H-MRS spectra using the proposed classification along with evaluation of NAA/Cho ratio might be helpful for non-invasive glioma grading.

