Abstract #3247

Importance of Clinical MRI Features in Predicting Epilepsy Drug Treatment Outcome for Pediatric Tuberous Sclerosis Complex

Jun Yang^1,2, Cailei Zhao³, Shi Su⁴, Zhanqi Hu⁵, Jianxiang Liao⁵, Dong Liang^1,2,4, and Haifeng Wang^2,4

¹Research Centre for Medical AI, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, ²University of Chinese Academy of Sciences, Beijing, China, ³Department of Radiology, Shenzhen Children’s Hospital, Shenzhen, China, ⁴Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, ⁵Department of Neurology, Shenzhen Children’s Hospital, Shenzhen, China

Predicting epilepsy drug treatment outcome is important for treating children with tuberous sclerosis complex (TSC). Here, the best performing model was selected to explore the contribution of the features, using permutation importance (PIMP). An approach similar to PIMP was used to compare the magnetic resonance imaging (MRI) and non-MRI features. The best model multilayer perceptron (MLP) with a hidden layer size of 60 and 30 features selected by F-test achieved the best performance. The results based on 103 children patients showed that some features were more important than others, and MRI features contributed more than non-MRI features in prediction.

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