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An artificial intelligence model to predict hepatocellular carcinoma risk in Korean and Caucasian patients with chronic hepatitis B
- Title
- An artificial intelligence model to predict hepatocellular carcinoma risk in Korean and Caucasian patients with chronic hepatitis B
- Authors
- Kim H.Y.; Lampertico P.; Nam J.Y.; Lee H.-C.; Kim S.U.; Sinn D.H.; Seo Y.S.; Lee H.A.; Park S.Y.; Lim Y.-S.; Jang E.S.; Yoon E.L.; Kim H.S.; Kim S.E.; Ahn S.B.; Shim J.-J.; Jeong S.W.; Jung Y.J.; Sohn J.H.; Cho Y.K.; Jun D.W.; Dalekos G.N.; Idilman R.; Sypsa V.; Berg T.; Buti M.; Calleja J.L.; Goulis J.; Manolakopoulos S.; Janssen H.L.A.; Jang M.-J.; Lee Y.B.; Kim Y.J.; Yoon J.-H.; Papatheodoridis G.V.; Lee J.-H.
- Ewha Authors
- 김휘영
- SCOPUS Author ID
- 김휘영
- Issue Date
- 2022
- Journal Title
- Journal of Hepatology
- ISSN
- 0168-8278
- Citation
- Journal of Hepatology vol. 76, no. 2, pp. 311 - 318
- Keywords
- antiviral treatment; chronic hepatitis B; deep neural networking; HBV; HCC; liver cancer
- Publisher
- Elsevier B.V.
- Indexed
- SCIE; SCOPUS
- Document Type
- Article
- Abstract
- Background & Aims: Several models have recently been developed to predict risk of hepatocellular carcinoma (HCC) in patients with chronic hepatitis B (CHB). Our aims were to develop and validate an artificial intelligence-assisted prediction model of HCC risk. Methods: Using a gradient-boosting machine (GBM) algorithm, a model was developed using 6,051 patients with CHB who received entecavir or tenofovir therapy from 4 hospitals in Korea. Two external validation cohorts were independently established: Korean (5,817 patients from 14 Korean centers) and Caucasian (1,640 from 11 Western centers) PAGE-B cohorts. The primary outcome was HCC development. Results: In the derivation cohort and the 2 validation cohorts, cirrhosis was present in 26.9%–50.2% of patients at baseline. A model using 10 parameters at baseline was derived and showed good predictive performance (c-index 0.79). This model showed significantly better discrimination than previous models (PAGE-B, modified PAGE-B, REACH-B, and CU-HCC) in both the Korean (c-index 0.79 vs. 0.64–0.74; all p <0.001) and Caucasian validation cohorts (c-index 0.81 vs. 0.57–0.79; all p <0.05 except modified PAGE-B, p = 0.42). A calibration plot showed a satisfactory calibration function. When the patients were grouped into 4 risk groups, the minimal-risk group (11.2% of the Korean cohort and 8.8% of the Caucasian cohort) had a less than 0.5% risk of HCC during 8 years of follow-up. Conclusions: This GBM-based model provides the best predictive power for HCC risk in Korean and Caucasian patients with CHB treated with entecavir or tenofovir. Lay summary: Risk scores have been developed to predict the risk of hepatocellular carcinoma (HCC) in patients with chronic hepatitis B. We developed and validated a new risk prediction model using machine learning algorithms in 13,508 antiviral-treated patients with chronic hepatitis B. Our new model, based on 10 common baseline characteristics, demonstrated superior performance in risk stratification compared with previous risk scores. This model also identified a group of patients at minimal risk of developing HCC, who could be indicated for less intensive HCC surveillance. © 2021 European Association for the Study of the Liver
- DOI
- 10.1016/j.jhep.2021.09.025
- Appears in Collections:
- 의과대학 > 의학과 > Journal papers
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