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Abstract #0424

IDH1 genotype prediction in lower-grade gliomas: a machine learning study with VASARI and ADC radiomics

Shiteng Suo1, Mengqiu Cao1, Xiaoqing Wang1, Wei Yang2, Jianrong Xu1, and Yan Zhou1
1Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China, 2Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China

Preoperative noninvasive prediction of IDH mutation status is crucial for prognosis and therapeutic decision making. In this study, we evaluated the qualitative and quantitative MRI features, namely, Visually Accessible Rembrandt Images (VASARI) features and apparent diffusion coefficient radiomics features in identifying IDH1 mutation status in lower-grade gliomas (WHO grade II-III). Results by machine learning methods showed that the combination achieved a better prediction performance. Our model may have the potential to serve as an alternative to the conventional workflow for the noninvasive identification of the molecular profiles.

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