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Abstract #3188

Deep Learning Based Automated Diagnosis of Epilepsy in Patients with WHO II-IV Grade Cerebral Gliomas from Multiparametric MRI

Hongxi Yang1, Ankang Gao2, Yida Wang1, Xu Yan3, Jingliang Cheng2, Jie Bai2, and Guang Yang1
1Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China, 2Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China, 3MR Scientific Marketing, Siemens Healthineers, Shanghai, China


We had retrospectively enrolled 371 glioma patients in this study to develop an automated scheme to predict epilepsy in patients with WHO II-IV grade cerebral gliomas from multi-parametric MRI (mp-MRI). Gliomas tumor was segmented by a segmentation model trained with nnU-Net. Then a classification model based on ResNet-18 using segmented tumor region as anatomical attention was used to predict epilepsy from mp-MRI images. In the independent test cohort, the segmentation model achieved a mean dice of 0.899, while the classification model achieved an AUC of 0.890, better than the baseline ResNet-18 model with a test AUC of 0.783.

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