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 LA; Jang, Myoung-Jin; Lee, Yun Bin; Kim, Yoon Jun; Yoon, Jung-Hwan; Papatheodoridis, George V; Lee, Jeong-Hoon
Journal of hepatology
2021Oct ; 374 ( 6563 ) :.
PMID : 34606915
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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 LA -
Jang, Myoung-Jin -
Lee, Yun Bin -
Kim, Yoon Jun -
Yoon, Jung-Hwan -
Papatheodoridis, George V -
Lee, Jeong-Hoon -
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. CI - Copyright ??2021 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.
keyword
antiviral treatment; deep neural networking; liver cancer
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