An artificial intelligence model to predict hepatocellular carcinoma risk in Korean and Caucasian patients with chronic hepatitis B
Authors
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 Cho ; Dae 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
Citation
JOURNAL OF HEPATOLOGY, Vol.76(2) : 311-318, 2022-02
Adult ; Antiviral Agents / pharmacology ; Antiviral Agents / therapeutic use ; Artificial Intelligence / standards* ; Artificial Intelligence / statistics & numerical data ; Asian People / ethnology ; Asian People / statistics & numerical data ; Carcinoma, Hepatocellular / etiology ; Carcinoma, Hepatocellular / physiopathology* ; Cohort Studies ; Computer Simulation / standards ; Computer Simulation / statistics & numerical data ; Female ; Follow-Up Studies ; Guanine / analogs & derivatives ; Guanine / pharmacology ; Guanine / therapeutic use ; Hepatitis B, Chronic / complications* ; Hepatitis B, Chronic / physiopathology ; Humans ; Liver Neoplasms / complications ; Liver Neoplasms / physiopathology ; Male ; Middle Aged ; Republic of Korea / ethnology ; Tenofovir / pharmacology ; Tenofovir / therapeutic use ; White People / ethnology ; White People / statistics & numerical data
Keywords
HBV ; HCC ; antiviral treatment ; chronic hepatitis B ; deep neural networking ; liver cancer
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.