Cadrien, Cornelius, Sharma, Sukrit, Lazen, Philipp, Licandro, Roxane, Furtner, Julia, Lipka, Alexandra, Niess, Eva, Hingerl, Lukas, Motyka, Stanislav, Gruber, Stephan, Strasser, Bernhard, Kiesel, Barbara, Mischkulnig, Mario, Preusser, Matthias, Roetzer-Pejrimovsky, Thomas, Wöhrer, Adelheid, Weber, Michael, Dorfer, Christian, Trattnig, Siegfried, Rössler, Karl, Bogner, Wolfgang, Widhalm, Georg, and Hangel, Gilbert
Introduction: With the application of high-resolution 3D 7 Tesla Magnetic Resonance Spectroscopy Imaging (MRSI) in high-grade gliomas, we previously identified intratumoral metabolic heterogeneities. Methods: We prospectively included 36 patients with WHO 2021 grade 2–4 gliomas (20 IDH mutated, 16 IDH wildtype). Our 7 T 3D MRSI sequence provided high-resolution metabolic maps (e.g., choline, creatine, glutamine, and glycine) of these patients’ brains. We employed multivariate random forest and support vector machine models to voxels within a tumor segmentation, for classification of glioma grade and IDH mutation status. Results: Random forest analysis yielded an area under the curve (AUC) of 0.86 for multivariate IDH classification based on metabolic ratios. We distinguished high- and low-grade tumors by total choline (tCho) / total N-acetyl-aspartate (tNAA) ratio difference, yielding an AUC of 0.99. Tumor categorization based on other measured metabolic ratios provided comparable accuracy. Conclusions: We successfully classified IDH mutation status and high- versus low-grade gliomas preoperatively based on 7 T MRSI and clinical tumor segmentation. With this approach, we demonstrated imaging based tumor marker predictions at least as accurate as comparable studies, highlighting the potential application of MRSI for pre-operative tumor classifications.