Magnetic Resonance Imaging and Proton Magnetic Resonance Spectroscopy for Differentiating Between Enhanced Gliomas and Malignant Lymphomas

Shigeo Ohba, Kazuhiro Murayama, Masato Abe, Mitsuhiro Hasegawa, Yuichi Hirose

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Abstract

Background: Although the treatment strategies for malignant lymphomas and gliomas differ, it is usually difficult to preoperatively distinguish between them. Magnetic resonance spectroscopy (MRS) was recently reported to be useful for preoperative diagnoses; however, MRS data analysis using LCModel, which is a quantitative and objective method, was performed in only a few of the existing reports. Methods: The clinical characteristics, conventional magnetic resonance imaging findings, and MRS parameters using LCModel were evaluated to identify the factors that can help distinguish between malignant lymphomas and enhanced gliomas. Results: In total, 59 cases were evaluated, including 13 cases of malignant lymphoma, 1 case of pilocytic astrocytoma, 5 cases of grade Ⅱ glioma, 5 cases of grade Ⅲ glioma, and 35 cases of glioblastoma. There was no correlation between clinical characteristics (sex and age) and diagnosis. Neither T1- nor T2-weighted image was useful for differentiation between the 2 forms of tumors, but the apparent diffusion coefficient minimum value was useful for distinguishing malignant lymphomas from gliomas, with an area under the curve (AUC) value of 0.852. MRS analysis using LCModel revealed differences in glutamate (Glu), N-acetylaspartate (NAA) + N-acetylaspartylglutamate (NAAG), Glu + glutamine, and Lipid (Lip) 13a + Lip13b between malignant lymphomas and gliomas. The largest AUC was 0.904, which was obtained for the Glu level, followed by 0.883 and 0.866 for NAA + NAAG and Lip13a + Lip13b, respectively. Conclusions: Quantitative analysis of proton-MRS using LCModel is considered to be a valuable method for distinguishing between gliomas and malignant lymphomas.

Original languageEnglish
Pages (from-to)e779-e787
JournalWorld Neurosurgery
Volume127
DOIs
Publication statusPublished - 07-2019

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All Science Journal Classification (ASJC) codes

  • Surgery
  • Clinical Neurology

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