51. An artificial intelligence model to predict hepatocellular carcinoma risk in Korean and Caucasian patients with chronic hepatitis B
- Author
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Maria Buti, Jae-Jun Shim, Eileen L. Yoon, Spilios Manolakopoulos, Jose Luis Calleja, Han Ah Lee, Yong Kyun Cho, Myoung-Jin Jang, Yeon Seok Seo, Joo Hyun Sohn, John Goulis, Pietro Lampertico, Eun Sun Jang, Harry La Janssen, Jeong Hoon Lee, Dong Hyun Sinn, Seung Up Kim, Yun Bin Lee, Hwi Young Kim, George V. Papatheodoridis, Ramazan Idilman, Thomas Berg, Soo-Young Park, George N. Dalekos, Soung Won Jeong, Yoon Jun Kim, Hyung-Chul Lee, Sang Bong Ahn, Hyoung Su Kim, Dae Won Jun, Yong Jin Jung, Vana Sypsa, Sung Eun Kim, Young-Suk Lim, Joon Yeul Nam, and Jung Hwan Yoon
- Subjects
Oncology ,Adult ,Male ,medicine.medical_specialty ,Cirrhosis ,Carcinoma, Hepatocellular ,Guanine ,Milan criteria ,medicine.disease_cause ,Antiviral Agents ,White People ,Cohort Studies ,Hepatitis B, Chronic ,Asian People ,Artificial Intelligence ,Internal medicine ,Republic of Korea ,medicine ,Humans ,Computer Simulation ,Tenofovir ,Hepatitis B virus ,Hepatology ,business.industry ,Liver Neoplasms ,Entecavir ,Middle Aged ,medicine.disease ,HBeAg ,Hepatocellular carcinoma ,Female ,Alpha-fetoprotein ,Liver cancer ,business ,medicine.drug ,Follow-Up Studies - Abstract
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 p0.001) and Caucasian validation cohorts (c-index 0.81 vs. 0.57-0.79; all p0.05 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.
- Published
- 2021