7 Tesla magnetic resonance spectroscopic imaging predicting IDH status and glioma grading.
Journal
Cancer imaging : the official publication of the International Cancer Imaging Society
ISSN: 1470-7330
Titre abrégé: Cancer Imaging
Pays: England
ID NLM: 101172931
Informations de publication
Date de publication:
27 May 2024
27 May 2024
Historique:
received:
13
09
2023
accepted:
27
04
2024
medline:
28
5
2024
pubmed:
28
5
2024
entrez:
27
5
2024
Statut:
epublish
Résumé
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. 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. 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. 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.
Identifiants
pubmed: 38802883
doi: 10.1186/s40644-024-00704-9
pii: 10.1186/s40644-024-00704-9
doi:
Substances chimiques
Isocitrate Dehydrogenase
EC 1.1.1.41
Choline
N91BDP6H0X
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
67Subventions
Organisme : Austrian Science Fund
ID : projects KLI 646
Organisme : Austrian Science Fund
ID : KLI 1089
Organisme : Austrian Science Fund
ID : KLI 679
Informations de copyright
© 2024. The Author(s).
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