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- Title
Myo-inositol concentration in MR spectroscopy for differentiating high grade glioma from primary central nervous system lymphoma.
- Authors
Nagashima, Hiroaki; Sasayama, Takashi; Tanaka, Kazuhiro; Kyotani, Katsusuke; Sato, Naoko; Maeyama, Masahiro; Kohta, Masaaki; Sakata, Junichi; Yamamoto, Yusuke; Hosoda, Kohkichi; Itoh, Tomoo; Sasaki, Ryohei; Kohmura, Eiji
- Abstract
It is sometimes difficult to distinguish gliomas from other tumors on routine imaging. In this study, we assessed whether 3-T magnetic resonance spectroscopy (MRS) with LCModel software might be useful for discriminating glioma from other brain tumors, such as primary central nervous system lymphomas (PCNSLs) and metastatic tumors. A total of 104 cases of brain tumor (66 gliomas, 20 PCNSLs, 6 metastatic tumors, 12 other tumors) were preoperatively investigated with short echo time (35 ms) single-voxel 3-T MRS. LCModel software was used to evaluate differences in the absolute concentrations of choline, N-acetylaspartate, N-acetylaspartylglutamate, glutamate + glutamine, myo-inositol (mIns), and lipid. mIns levels were significantly increased in high-grade glioma (HGG) compared with PCNSL (p < 0.001). In multivariate logistic regression analysis, mIns was the best marker for differentiating HGG from PCNSL (p < 0.0001, odds ratio 1.9927, 95% confidence interval 1.3628-3.2637). Conventional MRS detection of mIns resulted in a high diagnostic accuracy (sensitivity, 64%; specificity, 90%; area under the receiver operator curve, 0.80) for HGG. The expression of inositol 3-phosphate synthase (ISYNA1) was significantly higher in gliomas than in PCNSLs (p < 0.05), suggesting that the increased level of mIns in glioma is due to high expression of ISYNA1, the rate-limiting enzyme in the mIns-producing pathway. In conclusion, noninvasive analysis of mIns using single-voxel MRS may be useful in distinguishing gliomas from other brain tumors, particularly PCNSLs.
- Subjects
LYMPHOMA diagnosis; GLIOMAS; NUCLEAR magnetic resonance spectroscopy; INOSITOL; VOXEL-based morphometry; DIAGNOSIS
- Publication
Journal of Neuro-Oncology, 2018, Vol 136, Issue 2, p317
- ISSN
0167-594X
- Publication type
Article
- DOI
10.1007/s11060-017-2655-x