Abstract #3284

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

Xiaoyuan Hou^1,2, Hui Zhang^1,2, Yan Tan³, Zhenchao Tang^1,2, Hui Zhang³, and Jie Tian^1,2

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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