An artificial intelligence model to predict hepatocellular carcinoma risk in Korean and Caucasian patients with chronic hepatitis B

Hwi Young Kim, Pietro Lampertico, Joon Yeul Nam, Hyung Chul Lee, Seung Up Kim, Dong Hyun Sinn, Yeon Seok Seo, Han Ah Lee, Soo Young Park, Young Suk Lim, Eun Sun Jang, Eileen L. Yoon, Hyoung Su Kim, Sung Eun Kim, Sang Bong Ahn, Jae Jun Shim, Soung Won Jeong, Yong Jin Jung, Joo Hyun Sohn, Yong Kyun ChoDae Won Jun, George N. Dalekos, Ramazan Idilman, Vana Sypsa, Thomas Berg, Maria Buti, Jose Luis Calleja, John Goulis, Spilios Manolakopoulos, Harry L.A. Janssen, Myoung jin Jang, Yun Bin Lee, Yoon Jun Kim, Jung Hwan Yoon, George V. Papatheodoridis, Jeong Hoon Lee

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

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.

Original languageEnglish
Pages (from-to)311-318
Number of pages8
JournalJournal of Hepatology
Volume76
Issue number2
DOIs
StatePublished - Feb 2022

Keywords

  • HBV
  • HCC
  • antiviral treatment
  • chronic hepatitis B
  • deep neural networking
  • liver cancer

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