Identification of IDH and TERTp mutation status using 1 H-MRS in 112 hemispheric diffuse gliomas

Abstract

Background: There is a growing interest in noninvasively defining molecular subsets of hemispheric diffuse gliomas based on the isocitrate dehydrogenase (IDH) and telomerase reverse transcriptase gene promoter (TERTp) mutation status, which correspond to distinct tumor entities, and differ in demographics, natural history, treatment response, recurrence, and survival patterns.

Purpose: To investigate whether metabolite levels detected with short echo time (TE) proton MR spectroscopy (¹ H-MRS) at 3T can be used for noninvasive molecular classification of IDH and TERTp mutation-based subsets of gliomas.

Study type: Retrospective.

Subjects: In all, 112 hemispheric diffuse gliomas (70 males/42 females, mean age: 42.1 ± 13.9 years).

Field strength/sequence: Short-TE ¹ H-MRS (repetition time (TR) = 2000 msec, TE = 30 msec, number of signal averages = 192) and routine clinical brain tumor MR protocols were acquired at 3T.

Assessment: ¹ H-MRS data were quantified using LCModel software. TERTp and IDH1 or IDH2 (IDH1/2) mutations in the tissue were determined by either minisequencing or Sanger sequencing.

Statistical tests: Metabolic differences between IDH mutant and IDH wildtype gliomas were assessed by a Mann-Whitney U-test. A Kruskal-Wallis test followed by a Tukey-Kramer test was used to analyze metabolic differences between IDH and TERTp mutational molecular subsets of gliomas. A Spearman rank correlation coefficient was used to assess the correlations of metabolite intensities with the Ki-67 index. Furthermore, machine learning was employed to classify the IDH and TERTp mutational status of gliomas, and the accuracy, sensitivity, and specificity values were estimated.

Results: Short-TE ¹ H-MRS classified the presence of an IDH mutation with 88.39% accuracy, 76.92% sensitivity, and 94.52% specificity, and a TERTp mutation within primary IDH wildtype gliomas with 92.59% accuracy, 83.33% sensitivity, and 95.24% specificity.

Data conclusion: Short-TE ¹ H-MRS could be used to identify molecular subsets of hemispheric diffuse gliomas corresponding to IDH and TERTp mutations.

Level of evidence: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:1799-1809.

Keywords: 1H-MRS; IDH; TERTp; glioma; machine learning.