Full metadata record
DC Field | Value | Language |
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dc.contributor.author | 김휘영 | * |
dc.date.accessioned | 2022-03-08T16:31:38Z | - |
dc.date.available | 2022-03-08T16:31:38Z | - |
dc.date.issued | 2022 | * |
dc.identifier.issn | 0168-8278 | * |
dc.identifier.other | OAK-30712 | * |
dc.identifier.uri | https://dspace.ewha.ac.kr/handle/2015.oak/260831 | - |
dc.description.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. © 2021 European Association for the Study of the Liver | * |
dc.language | English | * |
dc.publisher | Elsevier B.V. | * |
dc.subject | antiviral treatment | * |
dc.subject | chronic hepatitis B | * |
dc.subject | deep neural networking | * |
dc.subject | HBV | * |
dc.subject | HCC | * |
dc.subject | liver cancer | * |
dc.title | An artificial intelligence model to predict hepatocellular carcinoma risk in Korean and Caucasian patients with chronic hepatitis B | * |
dc.type | Article | * |
dc.relation.issue | 2 | * |
dc.relation.volume | 76 | * |
dc.relation.index | SCIE | * |
dc.relation.index | SCOPUS | * |
dc.relation.startpage | 311 | * |
dc.relation.lastpage | 318 | * |
dc.relation.journaltitle | Journal of Hepatology | * |
dc.identifier.doi | 10.1016/j.jhep.2021.09.025 | * |
dc.identifier.wosid | WOS:000752560300009 | * |
dc.identifier.scopusid | 2-s2.0-85120864891 | * |
dc.author.google | Kim H.Y. | * |
dc.author.google | Lampertico P. | * |
dc.author.google | Nam J.Y. | * |
dc.author.google | Lee H.-C. | * |
dc.author.google | Kim S.U. | * |
dc.author.google | Sinn D.H. | * |
dc.author.google | Seo Y.S. | * |
dc.author.google | Lee H.A. | * |
dc.author.google | Park S.Y. | * |
dc.author.google | Lim Y.-S. | * |
dc.author.google | Jang E.S. | * |
dc.author.google | Yoon E.L. | * |
dc.author.google | Kim H.S. | * |
dc.author.google | Kim S.E. | * |
dc.author.google | Ahn S.B. | * |
dc.author.google | Shim J.-J. | * |
dc.author.google | Jeong S.W. | * |
dc.author.google | Jung Y.J. | * |
dc.author.google | Sohn J.H. | * |
dc.author.google | Cho Y.K. | * |
dc.author.google | Jun D.W. | * |
dc.author.google | Dalekos G.N. | * |
dc.author.google | Idilman R. | * |
dc.author.google | Sypsa V. | * |
dc.author.google | Berg T. | * |
dc.author.google | Buti M. | * |
dc.author.google | Calleja J.L. | * |
dc.author.google | Goulis J. | * |
dc.author.google | Manolakopoulos S. | * |
dc.author.google | Janssen H.L.A. | * |
dc.author.google | Jang M.-J. | * |
dc.author.google | Lee Y.B. | * |
dc.author.google | Kim Y.J. | * |
dc.author.google | Yoon J.-H. | * |
dc.author.google | Papatheodoridis G.V. | * |
dc.author.google | Lee J.-H. | * |
dc.contributor.scopusid | 김휘영(56493773500) | * |
dc.date.modifydate | 20240429140130 | * |