Isocitrate dehydrogenase (IDH) mutation status has emerged as an important prognostic marker in gliomas.Currently, reliable IDH mutation determination requires invasive surgical procedures. Various studies have demonstrated the efficacy of deep learning in classify IDH status using Magnetic resonance images（MRI）data.In this study we propose a multi-channel architecture of 3D convolutional neural networks (CNNs) based on deep learning to predict the IDH status.We utilize traditional structures imaging and various diffusivity metric maps derived from diffusion tensor imaging (DTI) as input to the network.The final model achieved the AUC value of 0.93.
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