Περίληψη:
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 (PAGEB, 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. (C) 2021 European Association for the Study
of the Liver. Published by Elsevier B.V. All rights reserved.
Συγγραφείς:
Kim, Hwi Young
Lampertico, Pietro
Nam, Joon Yeul
Lee,
Hyung-Chul
Kim, Seung Up
Sinn, Dong Hyun
Seo, Yeon Seok and
Lee, Han Ah
Park, Soo Young
Lim, Young-Suk
Jang, Eun Sun and
Yoon, Eileen L.
Kim, Hyoung Su
Kim, Sung Eun
Ahn, Sang Bong
and Shim, Jae-Jun
Jeong, Soung Won
Jung, Yong Jin
Sohn, Joo
Hyun
Cho, Yong Kyun
Jun, Dae Won
Dalekos, George N. and
Idilman, Ramazan
Sypsa, Vana
Berg, Thomas
Buti, Maria and
Calleja, Jose Luis
Goulis, John
Manolakopoulos, Spilios and
Janssen, Harry L. A.
Jang, Myoung-jin
Lee, Yun Bin
Kim, Yoon
Jun
Yoon, Jung-Hwan
Papatheodoridis, George V.
Lee,
Jeong-Hoon
