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Hybrid 11C-MET PET/MRI Combined With “Machine Learning” in Glioma Diagnosis According to the Revised Glioma WHO Classification 2016

Kebir, Sied, MD*†‡§∥; Weber, Manuel, MD§∥¶; Lazaridis, Lazaros, MD*§∥; Deuschl, Cornelius, MD**; Schmidt, Teresa, MD*§∥; Mönninghoff, Christoph, MD††; Keyvani, Kathy, MD‡‡; Umutlu, Lale, MD**; Pierscianek, Daniela, MD§∥‡‡; Forsting, Michael, MD**; Sure, Ulrich, MD§∥‡‡; Stuschke, Martin, MD§§; Kleinschnitz, Christoph, MD∥∥; Scheffler, Björn, MD†‡§∥; Colletti, Patrick M., MD¶¶; Rubello, Domenico, MD***; Rischpler, Christoph, MD§∥¶; Glas, Martin, MD*†‡§∥

doi: 10.1097/RLU.0000000000002398
Original Articles
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Purpose With the advent of the revised WHO classification from 2016, molecular features, including isocitrate dehydrogenase (IDH) mutation have become important in glioma subtyping. This pilot trial analyzed the potential for 11C-methionine (MET) PET/MRI in classifying glioma according to the revised WHO classification using a machine learning model.

Methods Patients with newly diagnosed WHO grade II–IV glioma underwent preoperative MET-PET/MRI imaging. Patients were retrospectively divided into four groups: IDH wild-type glioblastoma (GBM), IDH wild-type grade II/III glioma (GII/III-IDHwt), IDH mutant grade II/III glioma with codeletion of 1p19q (GII/III-IDHmut1p19qcod) or without 1p19q-codeletion (GII/III-IDHmut1p19qnc). Within each group, the maximum tumor-to-brain-ratio (TBRmax) of MET-uptake was calculated. To gain generalizable implications from our data, we made use of a machine learning algorithm based on a development and validation subcohort. A support vector machine model was fit to the development subcohort and evaluated on the validation subcohort. Receiver operating characteristic (ROC) analysis served as metric to assess model performance.

Results Of a total of 259 patients, 39 patients met the inclusion criteria. TBRmax was highest in the GBM cohort (TBRmax 3.83 ± 1.30) and significantly higher (P = 0.004) compared to GII/III-IDHmut1p19qnc group, where TBRmax was lowest (TBRmax 2.05 ± 0.94). ROC analysis showed poor AUC for glioma subtyping (AUC 0.62) and high AUC of 0.79 for predicting IDH status. In the GII/III-IDHmut1p19qcod group, TBR values were slightly higher than in the IDHmut1p19qnc group.

Conclusions MET-PET/MRI imaging in pre-operatively classifying glioma entities appears useful for the assessment of IDH status. However, a larger trial is needed prior to translation into the clinical routine.

From the *Division of Clinical Neurooncology, Department of Neurology, and

DKFZ-Division Translational Neurooncology at the West German Cancer Center (WTZ), University Hospital Essen, University Duisburg-Essen;

Translational Oncology German Cancer Consortium, Partner Site University Hospital Essen;

§West German Cancer Center (WTZ), University Hospital Essen, University Duisburg-Essen;

German Cancer Consortium, Partner Site University Hospital Essen;

Department of Nuclear Medicine,

**Institute of Diagnostic and Interventional Radiology and Neuroradiology, Departments of

††Neuropathology,

‡‡Neurosurgery,

§§Radiotherapy, and

∥∥Neurology, University Hospital Essen, University Duisburg-Essen, Essen, Germany;

¶¶Department of Radiology, University of Southern California, Los Angeles, CA; and

***Department of Nuclear Medicine, Radiology, NeuroRadiology, Clinical Pathology, S. Maria della Misericordia Hospital, Rovigo, Italy.

Received for publication September 25, 2018; revision accepted October 12, 2018.

S. K., M. W., C. R. and M. G. are shared authorship.

Conflicts of interest and sources of funding: none declared.

Ethics Approval and Consent to Participate.

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. For this retrospective study formal consent is not required.

Correspondence to: Martin Glas, MD, Division of Clinical Neurooncology, Department of Neurology, University Hospital Essen, University Duisburg-Essen, D-45147 Essen, Germany. E-mail: Martin.Glas@uk-essen.de; and Domenico Rubello, Department of Nuclear Medicine, Radiology, NeuroRadiology, Clinical Pathology, S. Maria della Misericordia Hospital, Via Tre Martiri; 35043, Rovigo, Italy. E-mail: domenico.rubello@aulss5.veneto.it.

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