An artificial intelligence model to predict hepatocellular carcinoma risk in Korean and Caucasian patients with chronic hepatitis B
- Kim, Hwi Young; Lampertico, Pietro; Nam, Joon Yeul; Lee, Hyung-Chul; Kim, Seung Up; Sinn, Dong Hyun; Seo, Yeon Seok; Lee, Han Ah; Park, Soo Young; Lim, Young-Suk; Jang, Eun Sun; Yoon, Eileen L.; Kim, Hyoung Su; Kim, Sung Eun; Ahn, Sang Bong; Shim, Jae-Jun; Jeong, Soung Won; Jung, Yong Jin; Sohn, Joo Hyun; Cho, Yong Kyun; Jun, Dae Won; Dalekos, George N.; Idilman, Ramazan; Sypsa, Vana; Berg, Thomas; Buti, Maria; Calleja, Jose Luis; Goulis, John; Manolakopoulos, Spilios; Janssen, Harry L. A.; Jang, Myoung-jin; Lee, Yun Bin; Kim, Yoon Jun; Yoon, Jung-Hwan; Papatheodoridis, George V.; Lee, Jeong-Hoon
- Issue Date
- Elsevier BV
- liver cancer; deep neural networking; antiviral treatment; chronic hepatitis B; HCC; HBV
- Journal of Hepatology, v.76, no.2, pp.311 - 318
- Journal Title
- Journal of Hepatology
- Start Page
- End Page
- 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.
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.
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.
This GBM-based model provides the best predictive power for HCC risk in Korean and Caucasian patients with CHB treated with entecavir or tenofovir.
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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- 2. Clinical Science > Department of Gastroenterology and Hepatology > 1. Journal Articles
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