Hwi Young Kim1, Pietro Lampertico2, Joon Yeul Nam3, Hyung-Chul Lee4, Seung Up Kim5, Dong Hyun Sinn6, Yeon Seok Seo7, Han Ah Lee8, Soo Young Park9, Young-Suk Lim10, Eun Sun Jang11, Eileen L Yoon12, Hyoung Su Kim13, Sung Eun Kim14, Sang Bong Ahn15, Jae-Jun Shim16, Soung Won Jeong17, Yong Jin Jung18, Joo Hyun Sohn19, Yong Kyun Cho20, Dae Won Jun21, George N Dalekos22, Ramazan Idilman23, Vana Sypsa24, Thomas Berg25, Maria Buti26, Jose Luis Calleja27, John Goulis28, Spilios Manolakopoulos29, Harry L A Janssen30, Myoung-Jin Jang31, Yun Bin Lee3, Yoon Jun Kim3, Jung-Hwan Yoon3, George V Papatheodoridis32, Jeong-Hoon Lee33. 1. Department of Internal Medicine, College of Medicine, Ewha Womans University, Seoul, Republic of Korea. 2. Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Division of Gastroenterology and Hepatology, Milan, Italy; CRC "A. M. and A. Migliavacca" Center for Liver Disease, Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy. 3. Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, Seoul, Republic of Korea. 4. Department of Anesthesiology, Seoul National University College of Medicine, Seoul, Republic of Korea. 5. Department of Internal Medicine and Yonsei Liver Center, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea. 6. Department of Internal Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea. 7. Department of Internal Medicine, Korea University Anam Hospital, Korea University College, Republic of Korea. 8. Department of Internal Medicine, Korea University Anam Hospital, Korea University College, Republic of Korea; Department of Internal Medicine, Sanggye Paik Hospital, Inje University College of Medicine, Seoul, Republic of Korea. 9. Department of Internal Medicine, School of Medicine, Kyungpook National University, Kyungpook National University Hospital, Daegu, Republic of Korea. 10. Department of Internal Medicine, University of Ulsan College of Medicine, Asan Medical Centre, Seoul, Republic of Korea. 11. Departments of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Republic of Korea. 12. Department of Internal Medicine, Sanggye Paik Hospital, Inje University College of Medicine, Seoul, Republic of Korea; Department of Internal Medicine, Hanyang University Hospital, Hanyang University College of Medicine, Seoul, Republic of Korea. 13. Department of Internal Medicine, Kangdong Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Republic of Korea. 14. Department of Internal Medicine, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang-si, Republic of Korea. 15. Department of Internal Medicine, Nowon Eulji Medical Center, Eulji University College of Medicine, Seoul, Republic of Korea. 16. Department of Internal Medicine, Kyung Hee University School of Medicine, Seoul, Republic of Korea. 17. Department of Internal Medicine, Soonchunhyang University College of Medicine, Soonchunhyang University Seoul Hospital, Seoul, Republic of Korea. 18. Department of Internal Medicine, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Republic of Korea. 19. Department of Internal Medicine, Hanyang University Guri Hospital, Hanyang University College of Medicine, Guri-si, Republic of Korea. 20. Department of Internal Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea. 21. Department of Internal Medicine, Hanyang University Hospital, Hanyang University College of Medicine, Seoul, Republic of Korea. 22. Department of Medicine and Research Laboratory of Internal Medicine, National Expertise Center of Greece in Autoimmune Liver Diseases, General University Hospital of Larissa, Larissa, Greece. 23. Department of Gastroenterology, Ankara University School of Medicine, Ankara, Turkey. 24. Department of Hygiene, Epidemiology & Medical Statistics, Medical School of National and Kapodistrian University of Athens, Athens, Greece. 25. Division of Hepatology, Department of Medicine II, Leipzig University Medical Center, Leipzig, Germany. 26. Hospital General Universitario Vall Hebron and Ciberehd, Barcelona, Spain. 27. Hospital U Puerta de Hierro, IDIPHIM CIBERehd, Madrid, Spain. 28. 4th Department of Internal Medicine, Aristotle University of Thessaloniki Medical School, General Hospital of Thessaloniki "Hippokratio", Thessaloniki, Greece. 29. 2nd Department of Internal Medicine, Medical School of National and Kapodistrian University of Athens, General Hospital of Athens "Hippokratio", Athens, Greece. 30. Liver Clinic, Toronto Western & General Hospital, University Health Network, Toronto, ON, Canada. 31. Medical Research Collaboration Center, Seoul National University Hospital, Seoul, Republic of Korea. 32. Department of Gastroenterology, Medical School of National and Kapodistrian University of Athens, General Hospital of Athens "Laiko", Athens, Greece. Electronic address: gepapath@med.uoa.gr. 33. Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, Seoul, Republic of Korea. Electronic address: pindra@empal.com.
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.
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.