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7 Tesla magnetic resonance spectroscopic imaging predicting IDH status and glioma grading.

Authors :
Cadrien C
Sharma S
Lazen P
Licandro R
Furtner J
Lipka A
Niess E
Hingerl L
Motyka S
Gruber S
Strasser B
Kiesel B
Mischkulnig M
Preusser M
Roetzer-Pejrimovsky T
Wöhrer A
Weber M
Dorfer C
Trattnig S
Rössler K
Bogner W
Widhalm G
Hangel G
Source :
Cancer imaging : the official publication of the International Cancer Imaging Society [Cancer Imaging] 2024 May 27; Vol. 24 (1), pp. 67. Date of Electronic Publication: 2024 May 27.
Publication Year :
2024

Abstract

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. In this study, we evaluated the potential of 3D 7 T-MRSI for the preoperative noninvasive classification of glioma grade and isocitrate dehydrogenase (IDH) status. We demonstrated that IDH mutation and glioma grade are detectable by ultra-high field (UHF) MRI. This technique might potentially optimize the perioperative management of glioma patients.<br />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.<br />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.<br />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.<br /> (© 2024. The Author(s).)

Details

Language :
English
ISSN :
1470-7330
Volume :
24
Issue :
1
Database :
MEDLINE
Journal :
Cancer imaging : the official publication of the International Cancer Imaging Society
Publication Type :
Academic Journal
Accession number :
38802883
Full Text :
https://doi.org/10.1186/s40644-024-00704-9