Copyright ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Hepatol. Oct 27, 2020; 12(10): 738-753
Published online Oct 27, 2020. doi: 10.4254/wjh.v12.i10.738
Hepatocellular carcinoma Liver Imaging Reporting and Data Systems treatment response assessment: Lessons learned and future directions
Anum Aslam, Richard Kinh Gian Do, Avinash Kambadakone, Bradley Spieler, Frank H Miller, Ahmed M Gabr, Resmi A Charalel, Charles Y Kim, David C Madoff, Mishal Mendiratta-Lala
Anum Aslam, Department of Radiology, University of Michigan, Ann Arbor, MI 48019, United States
Richard Kinh Gian Do, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
Avinash Kambadakone, Abdominal Imaging and Interventional Radiology, Harvard Medical School, Massachusetts General Hospital, Boston, MA 02114, United States
Bradley Spieler, Department of Radiology, Louisiana State University Health Sciences Center, New Orleans, LA 70112, United States
Frank H Miller, Department of Radiology, Northwestern University Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, United States
Ahmed M Gabr, Department of Interventional Radiology, OHSU and Tanta University, Egypt, Portland, OR 97239, United States
Resmi A Charalel, Department of Radiology, Weill Cornell Medicine, New York, NY 10065, United States
Charles Y Kim, Department of Radiology, Duke University Medical Center, Duke University, Durham, NC 27710, United States
David C Madoff, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, United States
Mishal Mendiratta-Lala, School of Medicine, 1500 East Medical Center Drive, University of Michigan, Ann Arbor, MI 48109, United States
Author contributions: Aslam A analyzed the data and wrote the manuscript; Do RKG, Kambadakone A, Spieler B, Miller FH, Gabr AM, Charalel RA, Charalel RA, Kim CY, and Madoff DC made critical revisions related to important intellectual content of the manuscript; Mendiratta-Lala M analyzed the literature, wrote the manuscript and made critical revisions related to important intellectual content of the manuscript; and all authors have read and approve the final manuscript.
Conflict-of-interest statement: Mendiratta-Lala M is funded from National Institute of Health (NIH) PO1 CA59827. Kim CY serves as a consultant for Medical advisory board Boston Scientific, Genentech; Consultant: Medtronic. Charalel RA is a consultant for SirTEX Medical and has received a GERRAF research grant. No other author has any disclosures or conflicts of interest.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See:
Corresponding author: Anum Aslam, MD, Assistant Professor, Department of Radiology, University of Michigan, 1500 E. Medical Center Dr, Ann Arbor, MI 48019, United States.
Received: June 28, 2020
Peer-review started: June 28, 2020
First decision: July 28, 2020
Revised: August 7, 2020
Accepted: September 17, 2020
Article in press: September 17, 2020
Published online: October 27, 2020
Core Tip

Core Tip: Liver Imaging Reporting and Data Systems (LI-RADS) Treatment Response Algorithm (TRA) provides a new framework to describe treatment response for each individually treated hepatocellular carcinoma (HCC). Emerging evidence for its use in clinical practice is promising for ablation and non-radiation arterial-based therapies (i.e., transarterial chemoembolization). However, LI-RADS TRA should be applied cautiously when assessing HCC treated with radiation-based therapies (i.e., transarterial radioembolization, stereotactic body radiotherapy), in which early post-treatment persistent arterial phase hyperenhancement is common, and expected, and can confound treatment response.