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Abstract #2499

Convolutional neural network to predict IDH mutation status in glioma from 7T chemical exchange saturation transfer imaging

Yifan Yuan1, Yang Yu2, Jun Chang1, Ying-Hua Chu3, Yi-Cheng Hsu3, Mianxin Liu4, and Qi Yue1
1Department of neurosurgery, Huashan Hospital Fudan University, Shanghai, China, 2Department of radiology, Huashan Hospital Fudan University, Shanghai, China, 3MR Collaboration, Siemens Healthineers Ltd, Shanghai, China, 4School of Biomedical Engineering, ShanghaiTech University, shanghai, China

Synopsis

Keywords: Tumors, CEST & MTNoninvasive prediction of isocitrate dehydrogenase (IDH) mutation status in glioma guides surgical strategies and individualized management. We explored the capability of preoperatively identifying IDH status by combining a 2D convolutional neural network (CNN) and amide proton transfer chemical exchange saturation transfer (APT-CEST) imaging. Five-fold cross-validation suggested the APT-CEST with the tumor shape information predicts IDH status optimally. The novel CNN model designed for 7T APT-CEST offers improved discriminatory accuracy in predicting the IDH status of glioma, holding great potential for facilitating decision-making in clinical practice.

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