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Data-Driven Modification of the LI-RADS Major Feature System on Gadoxetate Disodium-Enhanced MRI: Toward Better Sensitivity and Simplicity.

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机构： [1]Department of Radiology, West China Hospital, Sichuan University, Chengdu, China. [2]Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA. [3]Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, China. [4]Departments of Radiology and Epidemiology, University of Ottawa, Ottawa, Ontario, Canada. [5]Clinical Epidemiology Program, The Ottawa Hospital Research Institute, Ottawa, Ontario, Canada. [6]Department of Radiation Oncology, Duke University School of Medicine, Durham, North Carolina, USA. [7]Department of Electrical and Computer Engineering, Pratt School of Engineering, Duke University, Durham, North Carolina, USA. [8]Center for Advanced Magnetic Resonance in Medicine, Duke University Medical Center, Durham, North Carolina, USA. [9]Division of Gastroenterology, Department of Medicine, Duke University Medical Center, Durham, North Carolina, USA.

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The Liver Imaging Reporting and Data System (LI-RADS) is widely accepted as a reliable diagnostic scheme for hepatocellular carcinoma (HCC) in at-risk patients. However, its application is hampered by substantial complexity and suboptimal diagnostic sensitivity.To propose data-driven modifications to the LI-RADS version 2018 (v2018) major feature system (rLI-RADS) on gadoxetate disodium (EOB)-enhanced magnetic resonance imaging (MRI) to improve sensitivity and simplicity while maintaining high positive predictive value (PPV) for detecting HCC.Retrospective.Two hundred and twenty-four consecutive at-risk patients (training dataset: 169, independent testing dataset: 55) with 742 LR-3 to LR-5 liver observations (HCC: N = 498 [67%]) were analyzed from a prospective observational registry collected between July 2015 and September 2018.3.0 T/T2-weighted fast spin-echo, diffusion-weighted spin-echo based echo-planar and three-dimensional (3D) T1-weighted gradient echo sequences.All images were evaluated by three independent abdominal radiologists who were blinded to all clinical, pathological, and follow-up information. Composite reference standards of either histopathology or imaging follow-up were used.In the training dataset, LI-RADS v2018 major features were used to develop rLI-RADS based on their associated PPV for HCC. In an independent testing set, diagnostic performances of LI-RADS v2018 and rLI-RADS were computed using a generalized estimating equation model and compared with McNemar's test. A P value <0.05 was considered statistically significant.The median (interquartile range) size of liver observations was 13 mm (7-27 mm). The diagnostic table for rLI-RADS encompassed 9 cells, as opposed to 16 cells for LI-RADS v2018. In the testing set, compared to LI-RADS v2018, rLI-RADS category 5 demonstrated a significantly superior sensitivity (76% vs. 61%) while maintaining comparably high PPV (92.5% vs. 94.1%, P = 0.126).Compared with LI-RADS v2018, rLI-RADS demonstrated improved simplicity and significantly superior diagnostic sensitivity for HCC in at-risk patients.3 TECHNICAL EFFICACY STAGE: 2.© 2021 International Society for Magnetic Resonance in Medicine.

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大类 | 2 区医学

小类 | 2 区核医学

最新[2023]版：

大类 | 2 区医学

小类 | 2 区核医学

第一作者：

第一作者机构： [1]Department of Radiology, West China Hospital, Sichuan University, Chengdu, China. [2]Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA.

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通讯机构： [2]Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA. [8]Center for Advanced Magnetic Resonance in Medicine, Duke University Medical Center, Durham, North Carolina, USA. [9]Division of Gastroenterology, Department of Medicine, Duke University Medical Center, Durham, North Carolina, USA.

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