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 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.