| Autor: |
Cornelius Cadrien, Sukrit Sharma, Philipp Lazen, Roxane Licandro, Julia Furtner, Alexandra Lipka, Eva Niess, Lukas Hingerl, Stanislav Motyka, Stephan Gruber, Bernhard Strasser, Barbara Kiesel, Mario Mischkulnig, Matthias Preusser, Thomas Roetzer-Pejrimovsky, Adelheid Wöhrer, Michael Weber, Christian Dorfer, Siegfried Trattnig, Karl Rössler, Wolfgang Bogner, Georg Widhalm, Gilbert Hangel |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Cancer Imaging, Vol 24, Iss 1, Pp 1-11 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1470-7330 |
| DOI: |
10.1186/s40644-024-00704-9 |
| Popis: |
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. 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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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