Abstract #0945

Prediction of MGMT Methylation 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, BrainThis study aims to propose a fully automatic approach based on convolutional neural networks (CNNs) to predict the O6-Methylguanine-DNA-methyltransferase (MGMT) methylation status of gliomas using conventional pre-operative MR images. It was shown that the Markov Random Field-U-Net network can accurately segment the tumor region, and the improved 34-layer Resnet network can predict the MGMT methylation status effectively. 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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