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Prediction model of hepatitis B virus-related hepatocellular carcinoma in patients receiving antiviral therapy

Authors
 Beom Kyung Kim  ;  Sang Hoon Ahn 
Citation
 JOURNAL OF THE FORMOSAN MEDICAL ASSOCIATION, Vol.122(12) : 1238-1246, 2023-12 
Journal Title
JOURNAL OF THE FORMOSAN MEDICAL ASSOCIATION
ISSN
 0929-6646 
Issue Date
2023-12
MeSH
Antiviral Agents / therapeutic use ; Artificial Intelligence ; Biomarkers, Tumor ; Carcinoma, Hepatocellular* / drug therapy ; Hepatitis B virus / genetics ; Hepatitis B, Chronic* / complications ; Hepatitis B, Chronic* / diagnosis ; Hepatitis B, Chronic* / drug therapy ; Humans ; Liver Cirrhosis ; Liver Neoplasms* / drug therapy
Keywords
Hepatitis B virus ; Hepatocellular carcinoma ; Model ; Prediction
Abstract
Chronic hepatitis B virus (HBV) infection, which ultimately leads to liver cirrhosis, hepatic decompensation, and hepatocellular carcinoma (HCC), remains a significant disease burden worldwide. Despite the use of antiviral therapy (AVT) using oral nucleos(t)ide analogs (NUCs) with high genetic barriers, the risk of HCC development cannot be completely eliminated. Therefore, bi-annual surveillance of HCC using abdominal ultrasonography with or without tumor markers is recommended for at-risk populations. For a more precise assessment of future HCC risk at the individual level, many HCC prediction models have been proposed in the era of potent AVT with promising results. It allows prognostication according to the risk of HCC development, for example, low-vs. intermediate-vs. high-risk groups. Most of these models have the advantage of high negative predictive values for HCC development, allowing exemption from biannual HCC screening. Recently, non-invasive surrogate markers for liver fibrosis, such as vibration-controlled transient elastography, have been introduced as integral components of the equations, providing better predictive performance in general. Furthermore, beyond the conventional statistical methods that primarily depend on multi-variable Cox regression analyses based on the previous literature, newer techniques using artificial intelligence have also been applied in the design of HCC prediction models. Here, we aimed to review the HCC risk prediction models that were developed in the era of potent AVT and validated among independent cohorts to address the clinical unmet needs, as well as comment on future direction to establish the individual HCC risk more precisely.
Files in This Item:
T202306793.pdf Download
DOI
10.1016/j.jfma.2023.05.029
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
Yonsei Authors
Kim, Beom Kyung(김범경) ORCID logo https://orcid.org/0000-0002-5363-2496
Ahn, Sang Hoon(안상훈) ORCID logo https://orcid.org/0000-0002-3629-4624
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/197502
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