Machine Learning-Derived Multimodal Neuroimaging of Presurgical Target Area to Predict Individual's Seizure Outcomes After Epilepsy Surgery - Tang, Yongxiang; Li, Weikai; Tao, Lue; Li, Jian; Long, Tingting; Li, Yulai; Chen, Dengming; Hu, Shuo (Frontiers in Cell and Developmental Biology, 2022)
Objectives: Half of patients who have tailored resection of the suspected epileptogenic zone for drug-resistant epilepsy have recurrent postoperative seizures. Although neuroimaging has become an indispensable part of delineating epileptogenic zone, no validated method uses neuroimaging of presurgical target area to predict an individual’s post-surgery seizure outcome. We aimed to develop and validate a machine learning-powered approach incorporating multimodal neuroimaging of presurgical target area to predict individual’s post-surgery seizure outcome in patients with drug-resistant focal epilepsy. Materials and Methods: One hundred and forty-one patients with drug-resistant focal epilepsy were classified either as having seizure-free (Engel class I) or seizure-recurrence (Engel class II through IV) at least 1 year after surgery. The presurgical MRI, PET, CT and postsurgical MRI were co-registered for surgical target volume of interest (VOI) segmentation, all VOIs were decomposed into nine fixed views, then were inputted the deep residual network (DRN) pretrained on Tiny-ImageNet dataset to extract and transfer deep features. Multi-kernel support vector machine (MKSVM) was employed to integrate multiple views of feature sets, and to predict seizure outcomes of the targeted VOIs. Leave-One-Out validation was applied to develop a model for verifying the prediction. In the end, performance using this approach was assessed by calculating accuracy, sensitivity, specificity. Receiver operating characteristic (ROC) curves was generated and the optimal area under the ROC curve (AUC) was calculated as a metric for classifying outcomes. Results: Application of DRN-MKSVM model based on presurgical target area neuroimaging demonstrated good performance in predicting seizure outcomes. The AUC ranged from 0.799 to 0.952. Importantly, Classification performance DRN-MKSVM model using data from multiple neuroimaging showed accuracy of 91.5%, sensitivity of 96.2%, specificity of 85.5% and AUCs of 0.95, which were significantly better than any other single modal neuroimaging (all p˂0.05). Conclusions: DRN-MKSVM using multimodal compared to unimodal neuroimaging from surgical target area accurately predicted postsurgical outcome. It could be conveniently facilitated the preoperative individualized prediction of seizure outcomes in patients who have been judged eligible for epilepsy surgery. This may aid epileptologists in presurgical evaluation by providing a tool to explore various surgical options, offering complementary information to existing clinical techniques.