Advanced [18F]FDG and [11C]flumazenil PET analysis for individual outcome prediction after temporal lobe epilepsy surgery for hippocampal sclerosis

Purpose: We have previously shown that an imaging marker, increased periventricular [C-11]flumazenil ([C-11] FMZ) binding, is associated with failure to become seizure free (SF) after surgery for temporal lobe epilepsy (TLE) with hippocampal sclerosis (HS). Here, we investigated whether increased preoperative periventricular white matter (WM) signal can be detected on clinical [F-18]FDG-PET images. We then explored the potential of periventricular FDG WM increases, as well as whole-brain [C-11]FMZ and [F-18]FDG images analysed with random forest classifiers, for predicting surgery outcome. Methods: Sixteen patients with MRI-defined HS had preoperative [F-18]FDG and [C-11]FMZ-PET. Fifty controls had [F-18]FDG-PET (30), [C-11]FMZ-PET (41), or both (21). Periventricular WM signal was analysed using Statistical Parametric Mapping (SPMS), and whole-brain image classification was performed using random forests implemented in R (htip://wvvw.r-project.org). Surgery outcome was predicted at the group and individual levels. Results: At the group level, non-seizure free (NSF) versus SF patients had periventricular increases with both tracers. Against controls, NSF patients showed more prominent periventricular [IIC]FMZ and [F-18]FOG signal increases than SF patients. All differences were more marked for [C-11]FMZ. For individuals, periventricular WM signal increases were seen at optimized thresholds in 5/8 NSF patients for both tracers. For SF patients, 1/8 showed periventricular signal increases for [C-11]FMZ, and 4/8 for [F-18]FDG. Hence, [F-18]FDG had relatively poor sensitivity and specificity. Random forest classification accurately identified 7/8 SF and 7/8 NSF patients using [C-11]FMZ images, but only 4/8 SF and 6/8 NSF patients with [F-18]FDG. Conclusion: This study extends the association between periventricular WM increases and NSF outcome to clinical [F-18]FDG-PET, but only at the group level. Whole-brain random forest classification increases [C-11]FMZ-PET's performance for predicting surgery outcome. (C) 2014 The Authors. Published by Elsevier Inc.