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Abstract #3284

Automatic Prediction of MGMT and IDH Genotype for Gliomas from MR Images via Multi-task Deep Learning Network

Xiaoyuan Hou1,2, Hui Zhang1,2, Yan Tan3, Zhenchao Tang1,2, Hui Zhang3, and Jie Tian1,2
1Beijing Advanced Innovation Center for Big Data-Based Precision Medicine(BDBPM) ,Beihang University,100083, Beijing, China, 2Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences,100190, Beijing, China, 3Department of Radiology, First Clinical Medical College, Shanxi Medical University,030001, Taiyuan, China

In order to preoperatively predict the multiple genotype mutation for gliomas, we proposed an end-to-end multi-task deep learning model based on MR images analysis for simultaneously predicting IDH and MGMT mutation. Best-performed model was obtained by changing the number of sharing layers in the network, achieving accuracy of 79.78% for MGMT, 78.88% for IDH in the test dataset. Our results indicated that multi-task deep learning model provided a potential solution for simultaneously prediction of multiple genotype in gliomas.

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