Hybrid 11C-MET PET/MRI Combined With "Machine Learning" in Glioma Diagnosis According to the Revised Glioma WHO Classification 2016. (March 2019)

• Record Type: Journal Article
• Authors: Kebir, Sied; Weber, Manuel; Lazaridis, Lazaros; Deuschl, Cornelius; Schmidt, Teresa; Mönninghoff, Christoph; Keyvani, Kathy; Umutlu, Lale; Pierscianek, Daniela; Forsting, Michael; Sure, Ulrich; Stuschke, Martin; Kleinschnitz, Christoph; Scheffler, Björn; Colletti, Patrick M.; Rubello, Domenico; Rischpler, Christoph; Glas, Martin
• Is Part Of: Clinical nuclear medicine. Volume 44:Number 3(2019)
• Publication Date: 2019-03
• Subjects: glioma -- methionine -- PET/MRI -- machine learning; Nuclear medicine -- Periodicals; Radioisotope scanning -- Periodicals; Nuclear Medicine -- Periodicals; 616.07575
• DOI: 10.1097/RLU.0000000000002398
• Languages: English
• ISSNs: 0363-9762

Abstract

Abstract : 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 11 C-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.