The Liver Imaging Reporting and Data System (LI-RADS) standardizes the interpretation, reporting, and data collection for imaging examinations in patients at risk for hepatocellular carcinoma (HCC). It assigns category codes reflecting relative probability of HCC to imaging-detected liver observations based on major and ancillary imaging features. LI-RADS also includes imaging features suggesting malignancy other than HCC. Supported and endorsed by the American College of Radiology (ACR), the system has been developed by a committee of radiologists, hepatologists, pathologists, surgeons, lexicon experts, and ACR staff, with input from the American Association for the Study of Liver Diseases and the Organ Procurement Transplantation Network/United Network for Organ Sharing. Development of LI-RADS has been based on literature review, expert opinion, rounds of testing and iteration, and feedback from users. This article summarizes and assesses the quality of evidence supporting each LI-RADS major feature for diagnosis of HCC, as well as of the LI-RADS imaging features suggesting malignancy other than HCC. Based on the evidence, recommendations are provided for or against their continued inclusion in LI-RADS.q RSNA, 2017 Online supplemental material is available for this article.An Tang REVIEW: LI-RADS Major Features for Hepatocellular Carcinoma DiagnosisTang et al selection of five major features was based on expert opinion, the literature review was performed to ensure that imaging-based diagnostic criteria were able to achieve near-100% specificity for the noninvasive diagnosis of HCC. This review focused on the evidence supporting the inclusion of imaging features and did not attempt to gather evidence on the composition of the LI-RADS diagnostic algorithm and probability of HCC for different combinations of criteria (other than the hallmark combination of APHE and washout appearance) in the LI-RADS diagnostic table.Each subgroup was charged with developing key research questions and then critically reviewing the literature to answer research questions thematically related to its assigned topic. Search StrategyThe PICO (patient population, intervention, comparison, and outcome) format frequently used in structured reviews does not lend itself well to studies of diagnostic performance. Rather than using PICO-style questions to guide the searches, therefore, the subgroups formulated free-form questions in advance with feedback from the other subgroups. A total of 10 questions were formulated under the framework and with the understanding that their answers would inform recommendations for removing or continuing to include the corresponding LI-RADS features. After the questions were formulated, each subgroup searched the PubMed develop a standardized Liver Imaging Reporting and Data System (LI-RADS) for interpretation, reporting, and data collection of imaging studies in patients at risk for developing HCC (1). The committee was composed mainly of diagnostic radiologists, but also hepatologists, surgeons, patho...
The Liver Imaging Reporting and Data System (LI-RADS) is an American College of Radiology (ACR)-endorsed diagnostic system of standardized terminology, interpretation, and reporting for imaging examinations of the liver in patients at high risk for hepatocellular carcinoma (HCC). LI-RADS assigns a category to observations in the liver indicating the likelihood of benignity or HCC. LI-RADS categories include LR-1: Definitely Benign, LR-2: Probably Benign, LR-3: Intermediate Probability for HCC, LR-4: Probably HCC, LR-5: Definite HCC, LR-5V: Definite HCC with Tumor in Vein, LR-Treated: Treated HCC, LR-M Probable Malignancy, not specific for HCC. This article reviews the types of nodules seen in the cirrhotic liver, examines core LI-RADS concepts and definitions, and utilizes the LI-RADS v2014 algorithm to categorize representative observations depicted at magnetic resonance imaging in a case-based approach.
Purpose To assess accuracy of magnitude-based magnetic resonance imaging (M-MRI) in children to estimate hepatic proton density fat fraction (PDFF) using two to six echoes, with magnetic resonance spectroscopy (MRS)-measured PDFF as a reference standard. Materials and Methods This was an IRB-approved, HIPAA-compliant, single-center, cross-sectional, retrospective analysis of data collected prospectively between 2008 and 2013 in children with known or suspected non-alcoholic fatty liver disease (NAFLD). Two hundred and eighty-six children (8 – 20 [mean 14.2 ± 2.5] yrs; 182 boys) underwent same-day MRS and M-MRI. Unenhanced two-dimensional axial spoiled gradient-recalled-echo images at six echo times were obtained at 3T after a single low-flip-angle (10°) excitation with ≥ 120-ms recovery time. Hepatic PDFF was estimated using the first two, three, four, five, and all six echoes. For each number of echoes, accuracy of M-MRI to estimate PDFF was assessed by linear regression with MRS-PDFF as reference standard. Accuracy metrics were regression intercept, slope, average bias, and R2. Results MRS-PDFF ranged from 0.2 – 40.4% (mean 13.1 ± 9.8%). Using three to six echoes, regression intercept, slope, and average bias were 0.46 – 0.96%, 0.99 – 1.01, and 0.57 – 0.89%, respectively. Using two echoes, these values were 2.98%, 0.97, and 2.72%, respectively. R2 ranged 0.98 – 0.99 for all methods. Conclusion Using three to six echoes, M-MRI has high accuracy for hepatic PDFF estimation in children.
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