Identification of<i>IDH</i>and<i>TERTp</i>mutation status using¹H‐MRS in 112 hemispheric diffuse gliomas

<jats:sec><jats:title>Background</jats:title><jats:p>There is a growing interest in noninvasively defining molecular subsets of hemispheric diffuse gliomas based on the isocitrate dehydrogenase (<jats:italic>IDH</jats:italic>) and telomerase reverse transcriptase gene promoter (<jats:italic>TERTp</jats:italic>) mutation status, which correspond to distinct tumor entities, and differ in demographics, natural history, treatment response, recurrence, and survival patterns.</jats:p></jats:sec><jats:sec><jats:title>Purpose</jats:title><jats:p>To investigate whether metabolite levels detected with short echo time (TE) proton MR spectroscopy (<jats:sup>1</jats:sup>H‐MRS) at 3T can be used for noninvasive molecular classification of<jats:italic>IDH</jats:italic>and<jats:italic>TERTp</jats:italic>mutation‐based subsets of gliomas.</jats:p></jats:sec><jats:sec><jats:title>Study Type</jats:title><jats:p>Retrospective.</jats:p></jats:sec><jats:sec><jats:title>Subjects</jats:title><jats:p>In all, 112 hemispheric diffuse gliomas (70 males/42 females, mean age: 42.1 ± 13.9 years).</jats:p></jats:sec><jats:sec><jats:title>Field Strength/Sequence</jats:title><jats:p>Short‐TE<jats:sup>1</jats:sup>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.</jats:p></jats:sec><jats:sec><jats:title>Assessment</jats:title><jats:p><jats:sup>1</jats:sup>H‐MRS data were quantified using LCModel software.<jats:italic>TERTp</jats:italic>and<jats:italic>IDH1</jats:italic>or<jats:italic>IDH2</jats:italic>(<jats:italic>IDH1/2</jats:italic>) mutations in the tissue were determined by either minisequencing or Sanger sequencing.</jats:p></jats:sec><jats:sec><jats:title>Statistical Tests</jats:title><jats:p>Metabolic differences between<jats:italic>IDH</jats:italic>mutant and<jats:italic>IDH</jats:italic>wildtype gliomas were assessed by a Mann–Whitney<jats:italic>U</jats:italic>‐test. A Kruskal–Wallis test followed by a Tukey–Kramer test was used to analyze metabolic differences between<jats:italic>IDH</jats:italic>and<jats:italic>TERTp</jats:italic>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<jats:italic>IDH</jats:italic>and<jats:italic>TERTp</jats:italic>mutational status of gliomas, and the accuracy, sensitivity, and specificity values were estimated.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>Short‐TE<jats:sup>1</jats:sup>H‐MRS classified the presence of an<jats:italic>IDH</jats:italic>mutation with 88.39% accuracy, 76.92% sensitivity, and 94.52% specificity, and a<jats:italic>TERTp</jats:italic>mutation within primary<jats:italic>IDH</jats:italic>wildtype gliomas with 92.59% accuracy, 83.33% sensitivity, and 95.24% specificity.</jats:p></jats:sec><jats:sec><jats:title>Data Conclusion</jats:title><jats:p>Short‐TE<jats:sup>1</jats:sup>H‐MRS could be used to identify molecular subsets of hemispheric diffuse gliomas corresponding to<jats:italic>IDH</jats:italic>and<jats:italic>TERTp</jats:italic>mutations.</jats:p><jats:p><jats:bold>Level of Evidence:</jats:bold>3</jats:p><jats:p><jats:bold>Technical Efficacy Stage:</jats:bold>2</jats:p><jats:p>J. Magn. Reson. Imaging 2020;51:1799–1809.</jats:p></jats:sec>