51. An artificial intelligence model to predict hepatocellular carcinoma risk in Korean and Caucasian patients with chronic hepatitis B
- Author
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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
- 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 (PAGEB, modified PAGE-B, REACH-B, and CU-HCC) in both the Korean (c-index 0.79 vs. 0.64-0.74; all p
- Published
- 2022