An artificial intelligence model to predict hepatocellular carcinoma risk in Korean and Caucasian patients with chronic hepatitis B
DC Field | Value | Language |
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dc.contributor.author | Kim, H.Y. | - |
dc.contributor.author | Lampertico, P. | - |
dc.contributor.author | Nam, J.Y. | - |
dc.contributor.author | Lee, H.-C. | - |
dc.contributor.author | Kim, S.U. | - |
dc.contributor.author | Sinn, D.H. | - |
dc.contributor.author | Seo, Y.S. | - |
dc.contributor.author | Lee, H.A. | - |
dc.contributor.author | Park, S.Y. | - |
dc.contributor.author | Lim, Y.-S. | - |
dc.contributor.author | Jang, E.S. | - |
dc.contributor.author | Yoon, Eileen Laurel | - |
dc.contributor.author | Kim, H.S. | - |
dc.contributor.author | Kim, S.E. | - |
dc.contributor.author | Ahn, S.B. | - |
dc.contributor.author | Shim, J.-J. | - |
dc.contributor.author | Jeong, S.W. | - |
dc.contributor.author | Jung, Y.J. | - |
dc.contributor.author | Sohn, Joo Hyun | - |
dc.contributor.author | Cho, Y.K. | - |
dc.contributor.author | Jun, Dae Won | - |
dc.contributor.author | Dalekos, G.N. | - |
dc.contributor.author | Idilman, R. | - |
dc.contributor.author | Sypsa, V. | - |
dc.contributor.author | Berg, T. | - |
dc.contributor.author | Buti, M. | - |
dc.contributor.author | Calleja, J.L. | - |
dc.contributor.author | Goulis, J. | - |
dc.contributor.author | Manolakopoulos, S. | - |
dc.contributor.author | Janssen, H.L.A. | - |
dc.contributor.author | Jang, M.-J. | - |
dc.contributor.author | Lee, Y.B. | - |
dc.contributor.author | Kim, Y.J. | - |
dc.contributor.author | Yoon, J.-H. | - |
dc.contributor.author | Papatheodoridis, G.V. | - |
dc.contributor.author | Lee, J.-H. | - |
dc.date.accessioned | 2022-07-06T10:22:53Z | - |
dc.date.available | 2022-07-06T10:22:53Z | - |
dc.date.created | 2022-01-06 | - |
dc.date.issued | 2022-02 | - |
dc.identifier.issn | 0168-8278 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/139640 | - |
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. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Elsevier B.V. | - |
dc.title | An artificial intelligence model to predict hepatocellular carcinoma risk in Korean and Caucasian patients with chronic hepatitis B | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Yoon, Eileen Laurel | - |
dc.contributor.affiliatedAuthor | Sohn, Joo Hyun | - |
dc.contributor.affiliatedAuthor | Jun, Dae Won | - |
dc.identifier.doi | 10.1016/j.jhep.2021.09.025 | - |
dc.identifier.scopusid | 2-s2.0-85120864891 | - |
dc.identifier.wosid | 000752560300009 | - |
dc.identifier.bibliographicCitation | Journal of Hepatology, v.76, no.2, pp.311 - 318 | - |
dc.relation.isPartOf | Journal of Hepatology | - |
dc.citation.title | Journal of Hepatology | - |
dc.citation.volume | 76 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 311 | - |
dc.citation.endPage | 318 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Gastroenterology & Hepatology | - |
dc.relation.journalWebOfScienceCategory | Gastroenterology & Hepatology | - |
dc.subject.keywordPlus | ENTECAVIR TREATMENT | - |
dc.subject.keywordPlus | SCORING SYSTEM | - |
dc.subject.keywordPlus | VALIDATION | - |
dc.subject.keywordPlus | LAMIVUDINE | - |
dc.subject.keywordPlus | CIRRHOSIS | - |
dc.subject.keywordPlus | THERAPY | - |
dc.subject.keywordPlus | HISTORY | - |
dc.subject.keywordPlus | SCORES | - |
dc.subject.keywordAuthor | antiviral treatment | - |
dc.subject.keywordAuthor | chronic hepatitis B | - |
dc.subject.keywordAuthor | deep neural networking | - |
dc.subject.keywordAuthor | HBV | - |
dc.subject.keywordAuthor | HCC | - |
dc.subject.keywordAuthor | liver cancer | - |
dc.identifier.url | https://www.sciencedirect.com/science/article/pii/S0168827821020870?via%3Dihub | - |
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