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Abstract #2659

Machine Learning-Based Contrast Enhanced T2-FLAIR Radiomics method for predicting IDH1 Genotype of Diffuse Gliomas

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


The current study aims to build isocitrate dehydrogenase 1 (IDH1) genotype prediction models based on selected radiomics features derived from contrast-enhanced T2 fluid attenuated inversion recovery (CE-T2-FLAIR) in predicting IDH1 genotype of diffuse gliomas. Radiomics features from CE-T2-FLAIR images go a step further to enrich the content of MRI-based radiomics. It was concluded that machine learning-based radiomics of CE-T2-FLAIR could efficiently predict the IDH1 genotype of diffuse gliomas.

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