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

Publication/Presentation Date

6-1-2019

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.

Volume

17

Issue

7

First Page

1228

Last Page

1238

ISSN

1542-7714

Disciplines

Business Administration, Management, and Operations | Health and Medical Administration | Management Sciences and Quantitative Methods

PubMedID

30326302

Department(s)

Administration and Leadership

Document Type

Article

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