18F-FDG-PET patterns of surgical success and failure in mesial temporal lobe epilepsy. (14th March 2017) - Chassoux, Francine; Artiges, Eric; Semah, Franck; Laurent, Agathe; Landré, Elisabeth; Turak, Baris; Gervais, Philippe; Helal, Badia-Ourkia; Devaux, Bertrand
Abstract : Objective: To search for [ 18 F]-fluorodeoxyglucose (FDG)-PET patterns predictive of long-term prognosis in surgery for drug-resistant mesial temporal lobe epilepsy (MTLE) due to hippocampal sclerosis (HS). Methods: We analyzed metabolic data with [ 18 F]-FDG-PET in 97 patients with MTLE (53 female participants; age range 15–56 years) with unilateral HS (50 left) and compared the metabolic patterns, electroclinical features, and structural atrophy on MRI in patients with the best outcome after anteromesial temporal resection (Engel class IA, completely seizure-free) to those with a non-IA outcome, including suboptimal outcome and failure. Imaging processing was performed with statistical parametric mapping (SPM5). Results: With a mean follow-up of >6 years (range 2–14 years), 85% of patients achieved a class I outcome, including 45% in class IA. Class IA outcome was associated with a focal anteromesial temporal hypometabolism, whereas non-IA outcome correlated with extratemporal metabolic changes that differed according to the lateralization: ipsilateral mesial frontal and perisylvian hypometabolism in right HS and contralateral fronto-insular hypometabolism and posterior white matter hypermetabolism in left HS. Suboptimal outcome presented a metabolic pattern similar to the best outcome but with a larger involvement of extratemporal areas, including the contralateral side in left HS. Failure was characterized by a mild temporal involvement sparing the hippocampus and relatively high extratemporal hypometabolism on both sides. These findings were concordant with electroclinical features reflecting the organization of the epileptogenic zone but were independent of the structural abnormalities detected on MRI. Conclusions: [ 18 F]-FDG-PET patterns help refine the prognostic factors in MTLE and should be implemented in predictive models for epilepsy surgery.