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Open AccessArticle

Non-Invasive Prediction of IDH Mutation in Patients with Glioma WHO II/III/IV Based on F-18-FET PET-Guided In Vivo 1H-Magnetic Resonance Spectroscopy and Machine Learning

1
Department of Neurology and Wilhelm Sander-NeuroOncology Unit, Regensburg University Hospital, 93053 Regensburg, Germany
2
Institute of Functional Genomics, University of Regensburg, 93053 Regensburg, Germany
3
Department of Radiology and Division of Neuroradiology, Regensburg University Hospital, 93053 Regensburg, Germany
4
Department of Nuclear Medicine, Regensburg University Hospital, 93053 Regensburg, Germany
5
Department of Neurosurgery, Regensburg University Hospital, 93053 Regensburg, Germany
6
Department of Neuropathology, Regensburg University Hospital, 93053 Regensburg, Germany
7
Department of Neurology, Saint John of God Hospital Linz, 4021 Linz, Austria
*
Author to whom correspondence should be addressed.
Shared first authorship.
Shared last authorship.
Cancers 2020, 12(11), 3406; https://doi.org/10.3390/cancers12113406
Received: 14 October 2020 / Revised: 8 November 2020 / Accepted: 13 November 2020 / Published: 17 November 2020
(This article belongs to the Special Issue Perioperative Imaging and Mapping Methods in Glioma Patients)
Approximately 75–80% of according to the classification of world health organization (WHO) grade II and III gliomas are characterized by a mutation of the isocitrate dehydrogenase (IDH) enzymes, which are very important in glioma cell metabolism. Patients with IDH mutated glioma have a significantly better prognosis than patients with IDH wildtype status, typically seen in glioblastoma WHO grade IV. Here we used a prospective O-(2-18F-fluoroethyl)-L-tyrosine (18F-FET) positron emission tomography guided single-voxel 1H-magnetic resonance spectroscopy approach to predict the IDH status before surgery. Finally, 34 patients were included in this neuroimaging study, of whom eight had additionally tissue analysis. Using a machine learning technique, we predicted IDH status with an accuracy of 88.2%, a sensitivity of 95.5% and a specificity of 75.0%. It was newly recognized, that two metabolites (myo-inositol and glycine) have a particularly important role in the determination of the IDH status.
Isocitrate dehydrogenase (IDH)-1 mutation is an important prognostic factor and a potential therapeutic target in glioma. Immunohistological and molecular diagnosis of IDH mutation status is invasive. To avoid tumor biopsy, dedicated spectroscopic techniques have been proposed to detect D-2-hydroxyglutarate (2-HG), the main metabolite of IDH, directly in vivo. However, these methods are technically challenging and not broadly available. Therefore, we explored the use of machine learning for the non-invasive, inexpensive and fast diagnosis of IDH status in standard 1H-magnetic resonance spectroscopy (1H-MRS). To this end, 30 of 34 consecutive patients with known or suspected glioma WHO grade II-IV were subjected to metabolic positron emission tomography (PET) imaging with O-(2-18F-fluoroethyl)-L-tyrosine (18F-FET) for optimized voxel placement in 1H-MRS. Routine 1H-magnetic resonance (1H-MR) spectra of tumor and contralateral healthy brain regions were acquired on a 3 Tesla magnetic resonance (3T-MR) scanner, prior to surgical tumor resection and molecular analysis of IDH status. Since 2-HG spectral signals were too overlapped for reliable discrimination of IDH mutated (IDHmut) and IDH wild-type (IDHwt) glioma, we used a nested cross-validation approach, whereby we trained a linear support vector machine (SVM) on the complete spectral information of the 1H-MRS data to predict IDH status. Using this approach, we predicted IDH status with an accuracy of 88.2%, a sensitivity of 95.5% (95% CI, 77.2–99.9%) and a specificity of 75.0% (95% CI, 42.9–94.5%), respectively. The area under the curve (AUC) amounted to 0.83. Subsequent ex vivo 1H-nuclear magnetic resonance (1H-NMR) measurements performed on metabolite extracts of resected tumor material (eight specimens) revealed myo-inositol (M-ins) and glycine (Gly) to be the major discriminators of IDH status. We conclude that our approach allows a reliable, non-invasive, fast and cost-effective prediction of IDH status in a standard clinical setting. View Full-Text
Keywords: glioma; IDH mutation; 18F-FET; 1H-MRS; D-2-hydroxyglutarate; linear support vector machine; glycine; myo-inositol glioma; IDH mutation; 18F-FET; 1H-MRS; D-2-hydroxyglutarate; linear support vector machine; glycine; myo-inositol
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Bumes, E.; Wirtz, F.-P.; Fellner, C.; Grosse, J.; Hellwig, D.; Oefner, P.J.; Häckl, M.; Linker, R.; Proescholdt, M.; Schmidt, N.O.; Riemenschneider, M.J.; Samol, C.; Rosengarth, K.; Wendl, C.; Hau, P.; Gronwald, W.; Hutterer, M. Non-Invasive Prediction of IDH Mutation in Patients with Glioma WHO II/III/IV Based on F-18-FET PET-Guided In Vivo 1H-Magnetic Resonance Spectroscopy and Machine Learning. Cancers 2020, 12, 3406.

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