Abstract

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Title:: Automated glioma grading based on 3D deep residual network and multimodal MRI

Author(s):: WANG Rui1; LIU Zhiqiang2; QI Chong1; MENG Lanxi1; LI Shaowu1; 1. Beijing Neurosurgical Institute/Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China 2. National Cancer Center/National Clinical Research Center for Cancer/Department of Radiotherapy, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China

Keywords:: Keywords: glioma automated grading 3D deep residual network multimodal MRI

Abstract:: Abstract: Objective To achieve automated glioma grading using 3D deep residual network and multimodal MRI. Methods The multimodal MRI data of 293 high grade gliomas (HGG) and 76 low grade gliomas (LGG) from the BraTS2020 public dataset were used to train and test the 3D deep residual network. After being preprocessed by 3D clipping, resampling and normalization, the multimodal MRI images and grade labeling were input into the network model for training (64%), validation (16%) and testing (20%). The glioma grading performance of the 3D deep residual network was evaluated in terms of accuracy (ACC), area under the receiver operating characteristic (ROC) curve (AUC). Results The ACC and AUC were 0.93 and 0.97 in the 59 cases from validation dataset (48 cases of HGG and 11 cases of LGG), and 0.89 and 0.93 in the 75 cases from testing dataset (62 cases of HGG and 13 cases of LGG). Conclusion The 3D deep residual network has a good performance in automated glioma grading on multi-modal MRI dataset, and it can provide significant reference for treatment option determination and prognosis prediction.