Development of machine learning-based personalized predictive models for risk evaluation of hepatocellular carcinoma in hepatitis B virus-related cirrhosis patients with low levels of serum alpha-fetoprotein
The proposed XGBoost demonstrated a promising ability for individualized prediction of HCC in HBV-related cirrhosis patients with low-level AFP.
Authors
- Yingping Yi (ORCID: https://orcid.org/0000-0002-6603-7442)
- Yuan Xu (ORCID: https://orcid.org/0000-0003-0861-5897)
- Xinlei Yang (ORCID: https://orcid.org/0000-0003-0771-7904)
- Hui Hu (ORCID: https://orcid.org/0000-0003-0701-9989)
- Bei Zhang (ORCID: https://orcid.org/0000-0002-4602-5524)
- Fan Zhou (ORCID: https://orcid.org/0000-0002-7154-1600)
- Xiao Ouyang (ORCID: https://orcid.org/0000-0002-2423-9222)
Institutions
- Nanchang University (CN)
- Second Affiliated Hospital of Nanchang University (CN)
- CITIC Group (China) (CN)
Publication Details
- Journal
- Annals of Hepatology
- Published
- 2024-08-15
- DOI
- https://doi.org/10.1016/j.aohep.2024.101540
- Citations
- 8
- Primary Topic
- Liver Disease Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 3.09
Funders
- Jiangsu Provincial Key Laboratory of New Environmental Protection
- Jiangxi Provincial Department of Science and Technology
- Key Research and Development Program of Jiangxi Province
- Key Science and Technology Research Project in Jiangxi Province Department of Education