Evidence Supporting LI-RADS Major Features for CT- and MR Imaging-based Diagnosis of Hepatocellular Carcinoma: A Systematic Review.
Publication/Presentation Date
1-1-2018
Abstract
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.
Volume
286
Issue
1
First Page
29
Last Page
48
ISSN
1527-1315
Published In/Presented At
Tang, A., Bashir, M. R., Corwin, M. T., Cruite, I., Dietrich, C. F., Do, R. K. G., Ehman, E. C., Fowler, K. J., Hussain, H. K., Jha, R. C., Karam, A. R., Mamidipalli, A., Marks, R. M., Mitchell, D. G., Morgan, T. A., Ohliger, M. A., Shah, A., Vu, K. N., Sirlin, C. B., & LI-RADS Evidence Working Group (2018). Evidence Supporting LI-RADS Major Features for CT- and MR Imaging-based Diagnosis of Hepatocellular Carcinoma: A Systematic Review. Radiology, 286(1), 29–48. https://doi.org/10.1148/radiol.2017170554
Disciplines
Business Administration, Management, and Operations | Health and Medical Administration | Management Sciences and Quantitative Methods
PubMedID
29166245
Department(s)
Administration and Leadership
Document Type
Article