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, Eileen Laurel; Kim, H.S.; Kim, S.E.; Ahn, S.B.; Shim, J.-J.; Jeong, S.W.; Jung, Y.J.; Sohn, Joo Hyun; Cho, Y.K.; Jun, Dae Won; 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.
- Issue Date
- Feb-2022
- Publisher
- Elsevier B.V.
- Keywords
- antiviral treatment; chronic hepatitis B; deep neural networking; HBV; HCC; liver cancer
- Citation
- Journal of Hepatology, v.76, no.2, pp.311 - 318
- Indexed
- SCOPUS
- Journal Title
- Journal of Hepatology
- Volume
- 76
- Number
- 2
- Start Page
- 311
- End Page
- 318
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/139640
- DOI
- 10.1016/j.jhep.2021.09.025
- ISSN
- 0168-8278
- 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.
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