Abstract #2113

Data-driven modification of the LI-RADS major feature system: toward better sensitivity and simplicity

Hanyu Jiang^1,2, Bin Song¹, Yun Qin¹, Yi Wei¹, Kyle J. Lafata^2,3, Meghana Konanur², Matthew DF McInnes^4,5, and Mustafa R. Bashir^2,6,7

¹Radiology, West China Hospital, Sichuan University, Chengdu, China, ²Radiology, Duke University Medical Center, Durham, NC, United States, ³Radiation Oncology, Duke University School of Medicine, Durham, NC, United States, ⁴Radiology, University of Ottawa, Ottawa, ON, Canada, ⁵Epidemiology, University of Ottawa, Ottawa, ON, Canada, ⁶Center for Advanced Magnetic Resonance in Medicine, Duke University Medical Center, Durham, NC, United States, ⁷Division of Gastroenterology, Department of Medicine, Duke University Medical Center, Durham, NC, United States

We aimed to propose a revised LI-RADS (rLI-RADS) for hepatocellular carcinoma (HCC) based on the LI-RADS version 2018 (v2018) major feature system on gadoxetate disodium (EOB)-enhanced MRI. We retrieved 224 consecutive at-risk patients with 742 LR-3 to LR-5 hepatic observations from a prospectively-collected database. In the training set, LI-RADS v2018 major features evaluated by three independent radiologists were used to develop rLI-RADS according to the likelihood of HCC. Compared with LI-RADS v2018, rLI-RADS was completely data-driven, remarkably simpler, and demonstrated significantly optimized diagnostic sensitivity and accuracy while maintaining reasonable specificity for HCC.

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