OBJECTIVE Fluorodeoxyglucose-positron emission tomography (FDG-PET) is an established, independent, strong predictor of surgical outcome in refractory epilepsy. In this study, we explored the added value of quantitative [18F]FDG-PET features combined with clinical variables, including electroencephalography (EEG), [18F]FDG-PET, and magnetic resonance imaging (MRI) qualitative interpretations, to predict long-term seizure recurrence (mean post-op follow-up of 5.85 ± 3.77 years). METHODS Machine learning predictive models of surgical outcome were created using a random forest classifier trained on quantitative features in 89 patients with drug-refractory temporal lobe epilepsy evaluated at the Hospital of the University of Pennsylvania epilepsy surgery program (2003-2016). Quantitative features were calculated from asymmetry features derived from image processing using Advanced Normalization Tools (ANTs). RESULTS The best-performing model used quantification and had an out-of-bag accuracy of 0.71 in identifying patients with seizure recurrence (Engel IB or worse) which outperformed that using qualitative clinical data by 10%. This model is shared through open-source software for research use. In addition, several asymmetry features in temporal and extratemporal regions that were significantly associated with seizure freedom are identified for future study. SIGNIFICANCE Complex quantitative [18F]FDG-PET imaging features can predict seizure recurrence in patients with refractory temporal lobe epilepsy. These initial retrospective results in a cohort with long-term follow-up suggest that using quantitative imaging features from regions in the epileptogenic network can inform the clinical decision-making process.

中文翻译：

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定量 [18]FDG PET 不对称特征预测难治性癫痫的长期癫痫发作复发

目的 氟脱氧葡萄糖-正电子发射断层扫描 (FDG-PET) 是一种已确立的、独立的、强有力的难治性癫痫手术结果的预测指标。在这项研究中，我们探索了定量 [18F]FDG-PET 特征与临床变量相结合的附加价值，包括脑电图 (EEG)、[18F]FDG-PET 和磁共振成像 (MRI) 定性解释，以预测长期长期癫痫复发（平均术后随访时间为 5.85 ± 3.77 年）。方法 使用随机森林分类器创建手术结果的机器学习预测模型，该分类器对宾夕法尼亚大学医院癫痫手术项目（2003-2016 年）评估的 89 名药物难治性颞叶癫痫患者的定量特征进行了训练。定量特征是根据使用高级归一化工具 (ANT) 的图像处理衍生的不对称特征计算的。结果 表现最好的模型使用量化，在识别癫痫复发患者（Engel IB 或更差）方面的袋外准确率为 0.71，比使用定性临床数据的模型高 10%。该模型通过开源软件共享以供研究使用。此外，确定了与癫痫无发作显着相关的颞叶和颞叶外区域的几个不对称特征，以供未来研究。意义 复杂的定量 [18F]FDG-PET 成像特征可以预测难治性颞叶癫痫患者的癫痫发作复发。