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

Synopsis

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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