Abstract
Objectives: Half of the 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 the 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 a presurgical target area to predict an 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 magnetic resonance imaging, positron emission tomography, computed tomography, and postsurgical magnetic resonance imaging were co-registered for surgical target volume of interest (VOI) segmentation; all VOIs were decomposed into nine fixed views, then were inputted into the deep residual network (DRN) that was pretrained on Tiny-ImageNet dataset to extract and transfer deep features. A multi-kernel support vector machine (MKSVM) was used 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, and specificity. Receiver operating characteristic curves were generated, and the optimal area under the receiver operating characteristic 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, the classification performance DRN–MKSVM model using data from multiple neuroimaging showed an accuracy of 91.5%, a sensitivity of 96.2%, a specificity of 85.5%, and AUCs of 0.95, which were significantly better than any other single-modal neuroimaging (all p ˂ 0.05). Conclusion: DRN–MKSVM, using multimodal compared with unimodal neuroimaging from the surgical target area, accurately predicted postsurgical outcomes. The preoperative individualized prediction of seizure outcomes in patients who have been judged eligible for epilepsy surgery could be conveniently facilitated. This may aid epileptologists in presurgical evaluation by providing a tool to explore various surgical options, offering complementary information to existing clinical techniques.
Highlights
Surgery for drug-resistant focal epilepsy has been shown to be superior to medical management (Engel, 2008; Ryvlin et al, 2014; Moshe et al, 2015; Devinsky et al, 2018)
In the analysis of this cohort, deep residual network (DRN)–multi-kernel support vector machine (MKSVM) with the attention mechanism demonstrated the highest prediction accuracy compared with all other methods of SZF and SZR classification
The leave-one-out cross-validation for the DRN–MKSVM procedure showed that the accuracy, sensitivity, and specificity were 91.49, 96.20, and 85.48%, respectively, which demonstrated that MKSVM was universally better than other methods, including singlekernel, with/without a mask and multi-kernel without a mask
Summary
Surgery for drug-resistant focal epilepsy has been shown to be superior to medical management (Engel, 2008; Ryvlin et al, 2014; Moshe et al, 2015; Devinsky et al, 2018). Multimodal neuroimaging has become an important and indispensable part of preoperative delineation of EZ or surgical target area in clinical practice (LoPinto-Khoury et al, 2012; Burneo et al, 2015; West et al, 2015; Devinsky et al, 2018; Tang et al, 2018; Yu et al, 2019). No validated approach has incorporated multimodal neuroimaging of presurgical target area to predict an individual’s post-surgery seizure outcome. The differences in multimodal neuroimaging (Barba et al, 2016; Chassoux et al, 2017; Gleichgerrcht et al, 2018), different location and size of surgical target brain regions, and the fusion among multimodal features made the prediction of surgical outcomes a nontrivial task (Andrews et al, 2019). Machine learningpowered techniques may be useful because such techniques could perceive obscure associations between multimodal preoperative results and postsurgical outcomes in epilepsy surgery candidates (Gleichgerrcht et al, 2018; Roy et al, 2019)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
