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Modifying LI-RADS on Gadoxetate Disodium-Enhanced MRI: A Secondary Analysis of a Prospective Observational Study.

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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 used for diagnosing hepatocellular carcinoma (HCC), however, with unsatisfactory sensitivity, complex ancillary features, and inadequate integration with gadoxetate disodium (EOB)-enhanced MRI.To modify LI-RADS (mLI-RADS) on EOB-MRI.Secondary analysis of a prospective observational study.Between July 2015 and September 2018, 224 consecutive high-risk patients (median age, 51 years; range, 26-83; 180 men; training/testing sets: 169/55 patients) with 742 (median size, 13 mm; interquartile range, 7-27; 498 HCCs) LR-3/4/5 observations.3.0 T T2 -weighted fast spin-echo, diffusion-weighted spin-echo based echo-planar, and 3D T1 -weighted gradient echo sequences.Three radiologists (with 5, 5, and 10 years of experience in liver MR imaging, respectively) blinded to the reference standard (histopathology or imaging follow-up) reviewed all MR images independently. In the training set, the optimal LI-RADS version 2018 (v2018) features selected by Random Forest analysis were used to develop mLI-RADS via decision tree analysis.In an independent testing set, diagnostic performances of mLI-RADS, LI-RADS v2018, and the Korean Liver Cancer Association (KLCA) guidelines were computed using a generalized estimating equation model and compared with McNemar's test. A two-tailed P < 0.05 was statistically significant.Five features (nonperipheral "washout," restricted diffusion, nonrim arterial phase hyperenhancement [APHE], mild-moderate T2 hyperintensity, and transitional phase hypointensity) constituted mLI-RADS, and mLR-5 was nonperipheral washout coupled with either nonrim APHE or restricted diffusion. In the testing set, mLI-RADS was significantly more sensitive (72%) and accurate (80%) than LI-RADS v2018 (sensitivity, 61%; accuracy 74%; both P < 0.001) and the KLCA guidelines (sensitivity, 64%; accuracy 74%; both P < 0.001), without sacrificing positive predictive value (mLI-RADS, 94%; LI-RADS v2018, 94%; KLCA guidelines, 92%).In high-risk patients, the EOB-MRI-based mLI-RADS was simpler and more sensitive for HCC than LI-RADS v2018 while maintaining high positive predictive value.2 TECHNICAL EFFICACY: Stage 2.© 2022 International Society for Magnetic Resonance in Medicine.

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大类 | 2 区医学

小类 | 2 区核医学

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大类 | 2 区医学

小类 | 2 区核医学

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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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