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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