Highlights
- •A new HCC prediction model (PLAN-B) was developed using machine learning algorithms in antiviral-treated patients with chronic hepatitis B.
- •The utility of the model was validated in independent Korean and Caucasian cohorts.
- •PLAN-B comprises 10 baseline parameters: cirrhosis, age, platelet count, ETV/TDF, sex, serum ALT and HBV DNA, albumin and bilirubin levels, and HBeAg status.
- •The PLAN-B model demonstrated satisfactory predictive performance for HCC development and outperformed other risk scores.
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.
Graphical abstract
Graphical Abstract
Keywords
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Article info
Publication history
Published online: October 01, 2021
Accepted:
September 15,
2021
Received in revised form:
September 13,
2021
Received:
April 27,
2021
Footnotes
Author names in bold designate shared co-first authorship
Identification
Copyright
© 2021 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.
