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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