Abstract
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.
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