Hanyu Jiang1,2, Bin Song1, Yun Qin1, Yi Wei1, Kyle J. Lafata2,3, Meghana Konanur2, Matthew DF McInnes4,5, and Mustafa R. Bashir2,6,7
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
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
Developed based on hard data, rLI-RADS
demonstrated superior simplicity, sensitivity, and accuracy for HCC than v2018
LI-RADS without substantial loss of specificity; hence should be the preferred major
feature diagnostic system for HCC in at-risk patients.
Decision tree illustrating major feature combinations and HCC proportions(%HCC)/counts according to LI-RADS v2018 in the training (A) and testing (B) sets. Data were computed based on sum interpretations of the three readers while adjusted with a generalized estimating equation model. rLI-RADS diagnostic table based on rLR 3-5 category definitions derived from the decision tree (C).
LI-RADS, Liver Imaging Reporting and Data System; rLI-RADS, revised Liver Imaging Reporting and Data System.