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Abstract #3748

Explainable and Quantitative LI-RADS Automated Grading System for Hepatocellular Carcinoma based on Dynamic Contrast-Enhanced MRI

Xueqin Xia1, Li Yang2,3, Ruofan Sheng2,3, Rencheng Zheng4, Weibo Chen5, Chengyan Wang1, Mengsu Zeng2,3,6, and He Wang1,4
1Human Phenome Institute, Fudan University, Shanghai, China, 2Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China, 3Shanghai Institute of Medical Imaging, Shanghai, China, 4Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China, 5Philips Healthcare, Shanghai, China, 6Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China


The judgment of the three major features of LI-RADS by radiologists is subjective and time-consuming. We proposed an explainable and quantitative algorithm based on DCE MRI to recognize the three major features and then get LI-RADS grades together with tumor diameter. The AUC is 0.96, 0.92, 0.70 in the validation set and 0.98, 0.90, 0.76 in the testing set for arterial phase hyper-enhancement (APHE), washout, and capsule. The overall accuracy of LI-RADS grades is 0.68 and 0.71 for the validation and testing set. The developed automatic LI-RADS grading system can provide explainable results for HCC diagnosis with high efficiency.

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