Yuankai Zhu1,2,3,4, Jianhua Feng5, Shuang Wu1,2,3,4, Haifeng Hou1,2,3,4, Jianfeng Ji1,2,3,4, Kai Zhang1,2,3,4, Qing Chen1,2,3,4, Lin Chen1,2,3,4, Haiying Cheng5, Liuyan Gao5, Zexin Chen6, Hong Zhang1,2,3,4, Mei Tian7,2,3,4. 1. Department of Nuclear Medicine, The Second Hospital of Zhejiang University School of Medicine, Hangzhou, China. 2. Zhejiang University Medical PET Center, Hangzhou, China. 3. Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, China. 4. Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, China. 5. Department of Pediatrics, The Second Hospital of Zhejiang University School of Medicine, Hangzhou, China; and. 6. Department of Clinical Epidemiology & Biostatistics, The Second Hospital of Zhejiang University School of Medicine, Hangzhou, China. 7. Department of Nuclear Medicine, The Second Hospital of Zhejiang University School of Medicine, Hangzhou, China meitian@zju.edu.cn.
Abstract
PET with 18F-FDG has been used for presurgical localization of epileptogenic foci; however, in nonsurgical patients, the correlation between cerebral glucose metabolism and clinical severity has not been fully understood. The aim of this study was to evaluate the glucose metabolic profile using 18F-FDG PET/CT imaging in patients with epilepsy. Methods: One hundred pediatric epilepsy patients who underwent 18F-FDG PET/CT, MRI, and electroencephalography examinations were included. Fifteen age-matched controls were also included. 18F-FDG PET images were analyzed by visual assessment combined with statistical parametric mapping (SPM) analysis. The absolute asymmetry index (|AI|) was calculated in patients with regional abnormal glucose metabolism. Results: Visual assessment combined with SPM analysis of 18F-FDG PET images detected more patients with abnormal glucose metabolism than visual assessment only. The |AI| significantly positively correlated with seizure frequency (P < 0.01) but negatively correlated with the time since last seizure (P < 0.01) in patients with abnormal glucose metabolism. The only significant contributing variable to the |AI| was the time since last seizure, in patients both with hypometabolism (P = 0.001) and with hypermetabolism (P = 0.005). For patients with either hypometabolism (P < 0.01) or hypermetabolism (P = 0.209), higher |AI| values were found in those with drug resistance than with seizure remission. In the post-1-y follow-up PET studies, a significant change of |AI| (%) was found in patients with clinical improvement compared with those with persistence or progression (P < 0.01). Conclusion: 18F-FDG PET imaging with visual assessment combined with SPM analysis could provide cerebral glucose metabolic profiles in nonsurgical epilepsy patients. |AI| might be used for evaluation of clinical severity and progress in these patients. Patients with a prolonged period of seizure freedom may have more subtle (or no) metabolic abnormalities on PET. The clinical value of PET might be enhanced by timing the scan closer to clinical seizures.
PET with 18F-FDG has been used for presurgical localization of epileptogenic foci; however, in nonsurgical patients, the correlation between cerebral glucose metabolism and clinical severity has not been fully understood. The aim of this study was to evaluate the glucose metabolic profile using 18F-FDG PET/CT imaging in patients with epilepsy. Methods: One hundred pediatric epilepsypatients who underwent 18F-FDG PET/CT, MRI, and electroencephalography examinations were included. Fifteen age-matched controls were also included. 18F-FDG PET images were analyzed by visual assessment combined with statistical parametric mapping (SPM) analysis. The absolute asymmetry index (|AI|) was calculated in patients with regional abnormal glucose metabolism. Results: Visual assessment combined with SPM analysis of 18F-FDG PET images detected more patients with abnormal glucose metabolism than visual assessment only. The |AI| significantly positively correlated with seizure frequency (P < 0.01) but negatively correlated with the time since last seizure (P < 0.01) in patients with abnormal glucose metabolism. The only significant contributing variable to the |AI| was the time since last seizure, in patients both with hypometabolism (P = 0.001) and with hypermetabolism (P = 0.005). For patients with either hypometabolism (P < 0.01) or hypermetabolism (P = 0.209), higher |AI| values were found in those with drug resistance than with seizure remission. In the post-1-y follow-up PET studies, a significant change of |AI| (%) was found in patients with clinical improvement compared with those with persistence or progression (P < 0.01). Conclusion:18F-FDG PET imaging with visual assessment combined with SPM analysis could provide cerebral glucose metabolic profiles in nonsurgical epilepsypatients. |AI| might be used for evaluation of clinical severity and progress in these patients. Patients with a prolonged period of seizure freedom may have more subtle (or no) metabolic abnormalities on PET. The clinical value of PET might be enhanced by timing the scan closer to clinical seizures.