Classifying adult-type diffuse glioma using 7T magnetic resonance spectroscopic imaging
Abstract
Abstract Objectives Established clinical MR imaging offers limited accuracy for the precise classification of adult-type diffuse glioma. The purpose of this study was to identify robust 7T MRSI metabolic markers that distinguish glioblastoma, IDH-wildtype (GBM); astrocytoma, IDH-mutant (AST) and oligodendroglioma, IDH-mutant, 1p/19q-codeleted (ODG), and to assess their predictive value alongside established radiological markers. Materials and methods We prospectively evaluated patients with suspected adult-type diffuse glioma. Final diagnoses were established according to the 2021 WHO classification. Routine 3T MRI scans included: T1w-CE; T2; FLAIR; and DWI-ADC. 7T MRSI was performed with a 3D-FID-CRT sequence and median values of metabolites in tumors were extracted from 16 metabolite maps. Group differences and predictive performance were assessed using non-parametric statistics and ROC-curve analysis. Results Fifty patients (15 GBM, 24 AST, 11 ODG) were included. On 7T MRSI, the metabolite ratios that discriminated between subtypes were: mI/tNAA; Glu/tCr; Gln/tCr; Glx/tCr; and mI/tCr for GBM vs AST (AUCs 0.78–0.86); eight ratios distinguished GBM from ODG (AUC < 0.93), comparable and slightly more robust than the clinical standard. tCho/tNAA and GSH/tNAA were the most predictive for AST vs. ODG (AUC < 0.90) with GSH/tNAA exceeding the predictive abilities of CE, the T2-FLAIR mismatch sign, and nADC. Conclusion 7T MRSI enables non-invasive metabolic profiling of gliomas and shows potential for the differentiation of GBM, AST, and ODG. Several metabolite ratios demonstrated discriminatory performance comparable to that of conventional MRI markers in this cohort. These findings provide preliminary evidence supporting the utility of 7T MRSI for glioma subtype characterization and suggest further validation in larger studies.
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Authors: Sara Huskic, Philipp Lazen, Thomas Roetzer-Pejrimovsky, Rebeka Rumbak, Ahmet Azgın, Lisa Koerner, Barbara Kiesel, Stanislav Motyka, Bernhard Strasser, Lukas Hingerl, Johannes Leitner, Juliane Hennenberg, Matthias Preusser, Anna Sophie Berghoff, Anita Kloss-Brandstätter, Karl Rössler, Günther Grabner, Wolfgang Bogner, Georg Widhalm, Gilbert Hangel
Institutions: Medical University of Vienna, Christian Doppler Laboratory for Thermoelectricity, FH Kärnten, Austrian Research Institute for Artificial Intelligence