BIMG-14. IDENTIFICATION OF IDH MUTATION STATUS USING PROTON MR SPECTROSCOPY AND MASS SPECTROMETRY: A STUDY OF 178 GLIOMAS
Author(s) -
Banu Sacli-Bilmez,
Cansu Akın-Levi,
Ayça Erşen Danyeli,
Cengiz Yakıcıer,
M. Necmettin Pamir,
Koray Özduman,
Alp Di̇nçer,
Özge Can,
Esin Öztürk-Işık
Publication year - 2021
Publication title -
neuro-oncology advances
Language(s) - English
Resource type - Journals
ISSN - 2632-2498
DOI - 10.1093/noajnl/vdab024.013
Subject(s) - metabolite , glutamine , chemistry , glioma , in vivo magnetic resonance spectroscopy , in vivo , creatine , choline , carnitine , biochemistry , medicine , amino acid , magnetic resonance imaging , biology , cancer research , radiology , microbiology and biotechnology
IDH mutation, a key factor in predicting glioma prognosis, alters the levels of some metabolites in brain, including 2-hydroxyglutarate (2HG), glutamine (Gln), and glutathione (GSH). While proton MR spectroscopy (1H-MRS) enables in-vivo detection of these metabolites, liquid chromatography-mass spectrometry (LC-MS/MS) is a sensitive in-vitro method to measure absolute metabolite concentrations. This study aims to examine the correlation of metabolic concentrations measured using 1H-MRS and LC-MS/MS in gliomas, and to detect IDH mutation with machine learning based on 1H-MRS and LC-MS/MS metabolic intensities. The patient cohort included 178 glioma patients (111M/67F, mean age:44.09±13.95 years, 100 IDH-mut, 78 IDH-wt). The patients were scanned pre-surgery by a 3T MR scanner with a 32-channel head coil. 1H-MRS was obtained from a manually placed region of interest with no necrosis, edema, and hemorrhage, using a Point Resolved Spectroscopy (PRESS) sequence (TR/TE=2000/30ms). LCModel software was used for quantification of eighteen metabolites of 1H-MRS data. Metabolite concentrations including creatine (Cr), choline (Cho), Gln, glutamate (Glu), gamma-aminobutyric acid (GABA), N-acetyl aspartate (NAA), myo-inositol (mIns), 2HG, and lactate (Lac) were also determined with LC-MS/MS for surgical specimen of the same patients. Spearman correlation coefficients were calculated between the metabolite concentrations measured with 1H-MRS and LC-MS/MS. Additionally, machine-learning algorithms were used to detect IDH mutation in gliomas based on metabolite concentrations obtained with 1H-MRS and LC-MS/MS. Consequently, there were statistically significant correlations between 1H-MRS and LC-MS/MS results for 2HG (p=0.036), Cr (p=0.009), mIns (p<0.001), Lac (p=0.007) and NAA (p=0.004). IDH mutation was detected with an accuracy of 92.42% (sensitivity=91.70%, specificity=93.46) and 82.94% (sensitivity=84.04, specificity=81.43) based on LC-MS/MS and 1H-MRS metabolic intensities, respectively. In conclusion, 1H-MRS and LC-MS/MS metabolic intensities were highly correlated and these techniques were successful in identifying IDH mutation in gliomas. This study has been supported by TUBITAK 1003 grant 216S432.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom