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Abstract #2651

Machine Learning-Based Radiomics Predicting the IDH1 Genotype of Diffuse Gliomas

Qirui Zhao1, Zongfang Li1, Yi Lu1, Han Bao1, Zujun Hou2, Liuyang Chen3, Wei Xie1, Qing Wang1, Wei Zhao1, Tong-San Koh4, and Lisha Nie5
1Department of Radiology, The First Affiliated Hospital, Kunming Medical University, Kunming, China, 2Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China, 3Fisca Healthcare Ltd, Kunming, China, 4Department of Oncologic Imaging, National Cancer Center, Singapore, Singapore, 5GE Healthcare, MR Research, Beijing, China


The current study aims to evaluate the value of susceptibility weighted imaging (SWI) and contrast-enhanced T1-weighted imaging (CE-T1WI) radiomics features in predicting isocitrate dehydrogenase1 (IDH1) genotype of diffuse gliomas and build prediction models. It was concluded that SWI and CE-T1WI radiomics features can effectively predict the IDH1 genotype of diffuse gliomas, and CE-T1WI performed better. By combining SWI with CE-T1WI radiomics features, the prediction performance can be improved.

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