Fusion of FDG and FMZ PET Reduces False Positive in Predicting Epileptogenic Zone

Bingyang Cai; Shize Jiang; Hui Huang; Jiwei Li; Siyu Yuan; Ya Cui; Weiqi Bao; Jie Hu; Jie Luo; Liang Chen

doi:10.3174/ajnr.A8647

Fusion of FDG and FMZ PET Reduces False Positive in Predicting Epileptogenic Zone

AJNR Am J Neuroradiol. 2025 Jan 10:ajnr.A8647. doi: 10.3174/ajnr.A8647. Online ahead of print.

Authors

Bingyang Cai¹, Shize Jiang¹, Hui Huang¹, Jiwei Li¹, Siyu Yuan¹, Ya Cui¹, Weiqi Bao¹, Jie Hu¹, Jie Luo¹, Liang Chen¹

Affiliation

¹ From the School of Biomedical Engineering (B.C., H.H., J.L., S.Y., Y.C., J.L.), Shanghai Jiao Tong University, Shanghai, China; Department of Neurosurgery (S.J., J.H., L.C.), and PET Center (W.B.), Huashan Hospital, Fudan University, Shanghai, China.

PMID: 39794135
DOI: 10.3174/ajnr.A8647

Abstract

Background and purpose: Epilepsy, a globally prevalent neurological disorder, necessitates precise identification of the epileptogenic zone (EZ) for effective surgical management. While the individual utilities of FDG PET and FMZ PET have been demonstrated, their combined efficacy in localizing the epileptogenic zone remains underexplored. We aim to improve the non-invasive prediction of epileptogenic zone (EZ) in temporal lobe epilepsy (TLE) by combining FDG PET and FMZ PET with statistical feature extraction and machine learning.

Materials and methods: This study included 20 drug-resistant unilateral TLE patients (14 mesial TLE, 6 lateral TLE), and two control groups (N=29 for FDG, N=20 for FMZ). EZ of each patient was confirmed by post-surgical pathology, and one-year follow-up, while propagation zone (PZ) and non-involved zone (NIZ) were derived from the epileptogenicity index based on presurgical stereo-encephalography (SEEG) monitoring. Whole brain PET scans were obtained with dual tracers [¹⁸F]FDG and [¹⁸F]FMZ on separate days, from which standard uptake value ratio (SUVR) was calculated by global mean scaling. Low-order statistical parameters of SUVRs and t-maps derived against control groups were extracted. Additionally, fused FDG and FMZ features were created using arithmetic operations. Spearman correlation was used to investigate the associations between FDG and FMZ, while multiple linear regression analysis was used to explore the interaction effects of imaging features in predicting epileptogenicity. Crafted imaging features were used to train logistic regression models to predict EZ, whose performance was evaluated using 10-fold cross-validation at ROI-level, and leave-one-patient-out cross-validation at patient-level.

Results: FDG SUVR significantly decreased in EZ and PZ compared to NIZ, while FMZ SUVR in EZ significantly differed from PZ. Interaction effects were found between FDG and FMZ in their prediction of epileptogenicity. Fusion of FDG and FMZ provided the best prediction model with an area under the curve (AUC) of 0.86 [0.84-0.87] for EZ vs. NIZ and an AUC of 0.79 [0.77-0.81] for EZ vs. PZ, eliminating 100% false positives in 50% of patients, and ≥80% FPs in 90% patients at patient level.

Conclusions: Combined FDG and FMZ offer a promising avenue for non-invasive localization of the epileptogenic zone in TLE, potentially refining surgical planning.

Abbreviations: AUC = Area under the curve; EI = Epileptogenicity index; EZ = Epileptogenic zone; FMZ = Flumazenil; GABAA = Gammaaminobutyric acid type A; NIZ = Not-involved zone; PZ = Propagation zone; SEEG = Stereo-electroencephalography; SUVR = Standard uptake value ratio; TLE = Temporal lobe epilepsy.