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

Synopsis

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.

This abstract and the presentation materials are available to members only; a login is required.

Join Here