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Abstract #3068

Enhanced isocitrate dehydrogenase mutation classification in lower grade gliomas with deep learning and CEST and MTC MRI

Zuo Wang1,2, Meiyappan Solaiyappan1, Qihong Rui3, Hye-Young Heo1, Zhibo Wen3, Gregory Hager2, Jinyuan Zhou1, and Shanshan Jiang1

1Department of Radiology, Johns Hopkins University´╝î School of Medicine, Baltimore, MD, United States, 2Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United States, 3Zhujiang Hospital,Southern Medical University, guangzhou, China

we assess the feasibility of using molecular MRI with deep learning to differentiate IDH mutation status in patients with lower grade gliomas. Two separate deep learning models were used to analyze routine MRI and molecular MRI, and then, a combined model was also devised. 18% and 11% higher AUCs were obtained by the combined system, with respect to the routine MRI subsystem and the molecular MRI subsystem, respectively. Molecular MRI with deep learning algorithm demonstrated a great potential to diagnose IDH mutation status, which could be implemented as a robust approach to enhance routine MRI classification performance.

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