Abstract #0894

Machine learning with amide proton transfer and magnetization transfer MRI for predicting IDH mutation status in diffuse gliomas

Shanshan Jiang^1,2, Hye-Young Heo¹, Qihong Rui², Hao Yu², Yu Wang³, Charles Eberhart⁴, Peter Van Zijl^1,5, Zhibo Wen², and Jinyuan Zhou¹

¹Department of Radiology, Johns Hopkins University, Baltimore, MD, United States, ²Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China, ³Department of Pathology, Zhujiang Hospital, Southern Medical University, Guangzhou, China, ⁴Department of Pathology, Johns Hopkins University, Baltimore, MD, United States, ⁵F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States

The current diagnostic criterion for IDH mutation status is based on lab tests via surgical tissue samples. APTw and MT imaging contrast mechanisms can detect low-concentration mobile proteins and semi-solid macromolecules, respectively. We implemented a support vector machine (SVM)-based method to predict IDH1/2 genotype in gliomas using APTw and MT MRI features. 105 WHO Grade II and III glioma patients with complete imaging and genetic data were enrolled. Within the supervised classification framework, our results suggest that the use of APTw and MT features enabled the SVM to reach the accurate diagnosis of the IDH genotype in gliomas.

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