A machine learning model for predicting hepatocellular carcinoma risk in patients with chronic hepatitis B
- Authors
- Lee, Hye Won; Kim, Hwiyoung; Park, Taeyun; Park, Soo Young; Chon, Young Eun; Seo, Yeon Seok; Lee, Jae Seung; Park, Jun Yong; Kim, Do Young; Ahn, Sang Hoon; Kim, Beom Kyung; Kim, Seung Up
- Issue Date
- Aug-2023
- Publisher
- Blackwell Publishing Inc.
- Keywords
- antiviral therapy; chronic hepatitis B; entecavir; hepatocellular carcinoma; machine learning; performance; prediction; prognosis; risk prediction; tenofovir
- Citation
- Liver International, v.43, no.8, pp 1813 - 1821
- Pages
- 9
- Indexed
- SCIE
SCOPUS
- Journal Title
- Liver International
- Volume
- 43
- Number
- 8
- Start Page
- 1813
- End Page
- 1821
- URI
- https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/64125
- DOI
- 10.1111/liv.15597
- ISSN
- 1478-3223
1478-3231
- Abstract
- Background Machine learning (ML) algorithms can be used to overcome the prognostic performance limitations of conventional hepatocellular carcinoma (HCC) risk models. We established and validated an ML-based HCC predictive model optimized for patients with chronic hepatitis B (CHB) infections receiving antiviral therapy (AVT).Methods Treatment-naive CHB patients who were started entecavir (ETV) or tenofovir disoproxil fumarate (TDF) were enrolled. We used a training cohort (n = 960) to develop a novel ML model that predicted HCC development within 5 years and validated the model using an independent external cohort (n = 1937). ML algorithms consider all potential interactions and do not use predefined hypotheses.Results The mean age of the patients in the training cohort was 48 years, and most patients (68.9%) were men. During the median 59.3 (interquartile range 45.8-72.3) months of follow-up, 69 (7.2%) patients developed HCC. Our ML-based HCC risk prediction model had an area under the receiver-operating characteristic curve (AUC) of 0.900, which was better than the AUCs of CAMD (0.778) and REAL B (0.772) (both p < .05). The better performance of our model was maintained (AUC = 0.872 vs. 0.788 for CAMD and 0.801 for REAL B) in the validation cohort. Using cut-off probabilities of 0.3 and 0.5, the cumulative incidence of HCC development differed significantly among the three risk groups (p < .001).Conclusions Our new ML model performed better than models in terms of predicting the risk of HCC development in CHB patients receiving AVT.
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- Appears in
Collections - 2. Clinical Science > Department of Gastroenterology and Hepatology > 1. Journal Articles
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