A Machine Learning Model to Predict De Novo Hepatocellular Carcinoma Beyond Year 5 of Antiviral Therapy in Patients With Chronic Hepatitis B
The proposed ML model excellently predicted HCC risk beyond year 5 of ETV/TFV therapy and, therefore, could facilitate individualised HCC surveillance based on risk stratification.
Authors
- Yeonjung Ha (ORCID: https://orcid.org/0000-0002-3594-3688)
- Kwan Sik Lee (ORCID: https://orcid.org/0000-0002-3672-1198)
- Han Chu Lee (ORCID: https://orcid.org/0000-0002-7631-4124)
- Jihye Lim (ORCID: https://orcid.org/0000-0002-1878-0215)
- Dong Keon Yon (ORCID: https://orcid.org/0000-0003-1628-9948)
- Ju Hyun Shim (ORCID: https://orcid.org/0000-0002-7336-1371)
- Seung‐Seok Lee (ORCID: https://orcid.org/0000-0001-8066-3039)
- Young Eun Chon (ORCID: https://orcid.org/0000-0002-7716-4850)
- Danbi Lee (ORCID: https://orcid.org/0000-0001-6056-0702)
- Kang Mo Kim (ORCID: https://orcid.org/0000-0002-3180-3178)
- Jinseok Lee (ORCID: https://orcid.org/0000-0002-8580-490X)
- Joo Ho Lee (ORCID: https://orcid.org/0000-0001-7248-3214)
- KJ Lee (ORCID: https://orcid.org/0000-0002-6178-2400)
Institutions
- Ulsan College (KR)
- Asan Medical Center (KR)
- Kyung Hee University (KR)
- University of Ulsan (KR)
- Kyung Hee University Medical Center (KR)
- CHA University Bundang Medical Center (KR)
- CHA University (KR)
- Catholic University of Korea (KR)
Publication Details
- Journal
- Liver International
- Published
- 2024-12-18
- DOI
- https://doi.org/10.1111/liv.16139
- Citations
- 3
- Primary Topic
- Hepatitis B Virus Studies
- Type
- article
- Field-Weighted Citation Impact
- 1.16