チエルノフ　ミハイル    Department   School of Medicine(Tokyo Women's Medical University Adachi Medical Center), School of Medicine    Position   Assistant Professor
Article types	Original article
Language	English
Peer review	Peer reviewed
Title	Differentiation of High-Grade and Low-Grade Gliomas Using Pattern Analysis of Long-Echo Single-Voxel Proton Magnetic Resonance Spectroscopy ((1)H-MRS)
Journal	Formal name：Neuroradiol　J. ISSN code：01509861
Volume, Issue, Page	21(3),pp.338-349
Author and coauthor	CHERNOV Mikhail†, ONO Yuko, MURAGAKI Yoshihiro, KUBO Osami, NAKAMURA Ryoichi, ISEKI Hiroshi, HORI Tomokatsu, TAKAKURA Kintomo
Authorship	Lead author
Publication date	2008/06
Summary	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 distributionof 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.
DOI	10.1177/197140090802100308
Document No.	24256903