Abstract #1884

Prediction of IDH Mutation Status of Gliomas Using Pre-operative MR Images: a Fully Automatic Convolutional Neural Networks Based Approach.

Xiaohua Chen¹, Zhiqiang Chen², Zhuo Wang¹, Shaoru Zhang¹, Yunshu Zhou¹, Shili Liu¹, Ruodi Zhang¹, Yuhui Xiong³, and Aijun Wang⁴

¹Clinical medicine school of Ningxia Medical University, Yinchuan, China, ²Department of Radiology ,the First Hospital Affiliated to Hainan Medical College, Haikou, China, ³GE Healthcare MR Research, Beijing, China, ⁴Department of Radiology, General Hospital of Ningxia Medical University, Yinchuan, China

Synopsis

Keywords: Machine Learning/Artificial Intelligence, BrainUsing an automated method of convolutional neural networks (CNNs), we aimed to predict the IDH mutation status of gliomas from conventional preoperative MRI in this study. We conclude that the Markov Random Field-U-Net network can accurately segment the tumor region. Using a modified 34-layer Resnet network, we were subsequently able to predict IDH mutation status effectively. Consequently, the model has the potential to be utilized more broadly as a practical tool with reproducibility, this model has the potential to be a practical tool for the non-invasive characterization of gliomas to help the individualized treatment planning.

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