Lee HW, Ahn SH. Prediction models of hepatocellular carcinoma development in chronic hepatitis B patients. World J Gastroenterol 2016; 22(37): 8314-8321 [PMID: 27729738 DOI: 10.3748/wjg.v22.i37.8314]
Corresponding Author of This Article
Sang Hoon Ahn, MD, PhD, Department of Internal Medicine, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, South Korea. ahnsh@yuhs.ac
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: http://creativecommons.org/licenses/by-nc/4.0/
World J Gastroenterol. Oct 7, 2016; 22(37): 8314-8321 Published online Oct 7, 2016. doi: 10.3748/wjg.v22.i37.8314
Prediction models of hepatocellular carcinoma development in chronic hepatitis B patients
Hye Won Lee, Sang Hoon Ahn
Hye Won Lee, Sang Hoon Ahn, Department of Internal Medicine, Yonsei University College of Medicine, Seoul 03722, South Korea
Sang Hoon Ahn, Institute of Gastroenterology, Yonsei University College of Medicine, Seoul 03722, South Korea
Sang Hoon Ahn, Liver Cirrhosis Clinical Research Center, Seoul 03722, South Korea
Sang Hoon Ahn, Brain Korea 21 Project for Medical Science, Seoul 03722, South Korea
Author contributions: Ahn SH contribution to the conception of the manuscript; Lee HW and Ahn SH designed the study; and contribution to the data acquisition, responsibility for writing the paper; all authors reviewed the paper and approved the final version.
Conflict-of-interest statement: The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript.
Open-Access: This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: http://creativecommons.org/licenses/by-nc/4.0/
Correspondence to: Sang Hoon Ahn, MD, PhD, Department of Internal Medicine, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, South Korea. ahnsh@yuhs.ac
Telephone: +82-2-22281 Fax: +82-2-393688
Received: June 28, 2016 Peer-review started: June 29, 2016 First decision: July 29, 2016 Revised: August 3, 2016 Accepted: August 23, 2016 Article in press: August 23, 2016 Published online: October 7, 2016
Abstract
Chronic hepatitis B virus (HBV) infection is a major cause of cirrhosis and hepatocellular carcinoma (HCC). Applying the same strategies for antiviral therapy and HCC surveillance to all chronic hepatitis B (CHB) patients would be a burden worldwide. To properly manage CHB patients, it is necessary to identify and classify the risk for HCC development in such patients. Several HCC risk scores based on risk factors such as cirrhosis, age, male gender, and high viral load have been used, and have negative predictive values of ≥ 95%. Most of these have been derived from, and internally validated in, treatment-naïve Asian CHB patients. Herein, we summarized various HCC prediction models, including IPM (Individual Prediction Model), CU-HCC (Chinese University-HCC), GAG-HCC (Guide with Age, Gender, HBV DNA, Core Promoter Mutations and Cirrhosis-HCC), NGM-HCC (Nomogram-HCC), REACH-B (Risk Estimation for Hepatocellular Carcinoma in Chronic Hepatitis B), and Page-B score. To develop a noninvasive test of liver fibrosis, we also introduced a new scoring system that uses liver stiffness values from transient elastography, including an LSM (Liver Stiffness Measurement)-based model, LSM-HCC, and mREACH-B (modified REACH-B).
