Introduction to the Liver Imaging Reporting and Data System for Hepatocellular Carcinoma.

Abstract

The Liver Imaging Reporting And Data System (LI-RADS) was created with the support of the American College of Radiology (ACR) to standardize the acquisition, interpretation, reporting, and data collection for imaging examinations in patients at risk for hepatocellular carcinoma (HCC). A comprehensive and rigorous system developed by radiologists, hepatologists, pathologists, and surgeons, LI-RADS addresses a wide range of imaging contexts. Currently, 4 algorithms are available publicly on the ACR website: ultrasound for HCC surveillance, computed tomography and magnetic resonance imaging for HCC diagnosis and tumor staging, contrast-enhanced ultrasound for HCC diagnosis, and computed tomography/magnetic resonance imaging for treatment response assessment. Each algorithm is supported by a decision tree, categorization table, lexicon, atlas, technical requirements, and reporting and management guidance. Category codes reflecting the relative probability of HCC and malignancy are assigned to imaging-detected liver observations, with emerging evidence suggesting that LI-RADS accurately stratifies HCC and malignancy probabilities. LI-RADS is an evolving system and has been updated and refined iteratively since 2011 based on scientific evidence, expert opinion, and user feedback, with input from the American Association for the Study of Liver Diseases and the Organ Procurement Transplantation Network/United Network for Organ Sharing. Concurrent with its most recent update, LI-RADS was integrated into the American Association for the Study of Liver Diseases HCC guidance released in 2018. We anticipate continued refinement of LI-RADS and progressive adoption by radiologists worldwide, with the eventual goal of culminating in a single unified system for international use.

• Authors: An Tang, Amit G Singal, Donald G Mitchell, Elizabeth M Hecht, Kathryn J Fowler, Laura Kulik, Neehar D Parikh, Yuko Kono, Claude B Sirlin
• Journal: Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association (Clin Gastroenterol Hepatol) Vol. 17 Issue 7 Pg. 1228-1238 (06 2019) ISSN: 1542-7714 [Electronic] United States
• PMID: 30326302 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't, Review)
• Copyright: Copyright © 2019 AGA Institute. Published by Elsevier Inc. All rights reserved.

Topics

• Algorithms
• Carcinoma, Hepatocellular (diagnosis)
• Humans
• Liver (diagnostic imaging)
• Liver Neoplasms (diagnosis)
• Magnetic Resonance Imaging (statistics & numerical data)
• Radiology Information Systems (organization & administration)
• Tomography, X-Ray Computed (statistics & numerical data)
• Ultrasonography (statistics & numerical data)