PT  - JOURNAL ARTICLE
AU  - Cai, Bingyang
AU  - Jiang, Shize
AU  - Huang, Hui
AU  - Li, Jiwei
AU  - Yuan, Siyu
AU  - Cui, Ya
AU  - Bao, Weiqi
AU  - Hu, Jie
AU  - Luo, Jie
AU  - Chen, Liang
TI  - Fusion of FDG and FMZ PET Reduces False-Positives in Predicting Epileptogenic Zone
AID  - 10.3174/ajnr.A8647
DP  - 2025 Jun 12
TA  - American Journal of Neuroradiology
4099  - http://www.ajnr.org/content/early/2025/06/12/ajnr.A8647.short
4100  - http://www.ajnr.org/content/early/2025/06/12/ajnr.A8647.full
AB  - BACKGROUND AND PURPOSE: Epilepsy, a globally prevalent neurologic disorder, necessitates precise identification of the epileptogenic zone (EZ) for effective surgical management. While the individual utilities of FDG-PET and flumazenil (FMZ)-PET have been demonstrated, their combined efficacy in localizing the epileptogenic zone remains underexplored. We aim to improve the noninvasive prediction of 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 patients with unilateral TLE (14 mesial TLE, 6 lateral TLE) and 2 control groups (n = 29 for FDG, n = 20 for FMZ). EZ of each patient was confirmed by postsurgical pathology and 1-year follow-up, while propagation zone (PZ) and noninvolved 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 by using arithmetic operations. Spearman correlation was used to investigate the associations between FDG and FMZ, while multiple linear regression analyses were 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 by using 10-fold cross-validation at ROI level, and leave-1-patient-out cross-validation at patient level.RESULTS: FDG SUVR significantly decreased in EZ and PZ compared with 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 versus NIZ and an AUC of 0.79 [0.77–0.81] for EZ versus PZ, eliminating 100% false-positives in 50% of patients, and ≥80% FPs in 90% of patients at patient level.CONCLUSIONS: Combined FDG and FMZ offer a promising avenue for noninvasive localization of the epileptogenic zone in TLE, potentially refining surgical planning.AUCarea under the curveEIepileptogenicity indexEZepileptogenic zoneFCDfocal cortical dysplasiaFMZflumazenilFPfalse-positiveGABAAgamma-aminobutyric acid type AHPCHuman Connectome ProjectLASSOleast absolute shrinkage and selection operatorMADmean absolute deviationMNIMontreal Neurological InstituteNIZnot-involved zonePZpropagation zoneRMSroot mean squareROCreceiver operating characteristic curveSEEGstereo-electroencephalographySPMstatistical parametric mappingSUVRstandard uptake value ratioTLEtemporal lobe epilepsyTPtrue-positive