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An artificial intelligence model to predict hepatocellular carcinoma risk in Korean and Caucasian patients with chronic hepatitis B

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dc.contributor.authorKim, H.Y.-
dc.contributor.authorLampertico, P.-
dc.contributor.authorNam, J.Y.-
dc.contributor.authorLee, H.-C.-
dc.contributor.authorKim, S.U.-
dc.contributor.authorSinn, D.H.-
dc.contributor.authorSeo, Y.S.-
dc.contributor.authorLee, H.A.-
dc.contributor.authorPark, S.Y.-
dc.contributor.authorLim, Y.-S.-
dc.contributor.authorJang, E.S.-
dc.contributor.authorYoon, Eileen Laurel-
dc.contributor.authorKim, H.S.-
dc.contributor.authorKim, S.E.-
dc.contributor.authorAhn, S.B.-
dc.contributor.authorShim, J.-J.-
dc.contributor.authorJeong, S.W.-
dc.contributor.authorJung, Y.J.-
dc.contributor.authorSohn, Joo Hyun-
dc.contributor.authorCho, Y.K.-
dc.contributor.authorJun, Dae Won-
dc.contributor.authorDalekos, G.N.-
dc.contributor.authorIdilman, R.-
dc.contributor.authorSypsa, V.-
dc.contributor.authorBerg, T.-
dc.contributor.authorButi, M.-
dc.contributor.authorCalleja, J.L.-
dc.contributor.authorGoulis, J.-
dc.contributor.authorManolakopoulos, S.-
dc.contributor.authorJanssen, H.L.A.-
dc.contributor.authorJang, M.-J.-
dc.contributor.authorLee, Y.B.-
dc.contributor.authorKim, Y.J.-
dc.contributor.authorYoon, J.-H.-
dc.contributor.authorPapatheodoridis, G.V.-
dc.contributor.authorLee, J.-H.-
dc.date.accessioned2022-07-06T10:22:53Z-
dc.date.available2022-07-06T10:22:53Z-
dc.date.created2022-01-06-
dc.date.issued2022-02-
dc.identifier.issn0168-8278-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/139640-
dc.description.abstractBackground & 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.language영어-
dc.language.isoen-
dc.publisherElsevier B.V.-
dc.titleAn artificial intelligence model to predict hepatocellular carcinoma risk in Korean and Caucasian patients with chronic hepatitis B-
dc.typeArticle-
dc.contributor.affiliatedAuthorYoon, Eileen Laurel-
dc.contributor.affiliatedAuthorSohn, Joo Hyun-
dc.contributor.affiliatedAuthorJun, Dae Won-
dc.identifier.doi10.1016/j.jhep.2021.09.025-
dc.identifier.scopusid2-s2.0-85120864891-
dc.identifier.wosid000752560300009-
dc.identifier.bibliographicCitationJournal of Hepatology, v.76, no.2, pp.311 - 318-
dc.relation.isPartOfJournal of Hepatology-
dc.citation.titleJournal of Hepatology-
dc.citation.volume76-
dc.citation.number2-
dc.citation.startPage311-
dc.citation.endPage318-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaGastroenterology & Hepatology-
dc.relation.journalWebOfScienceCategoryGastroenterology & Hepatology-
dc.subject.keywordPlusENTECAVIR TREATMENT-
dc.subject.keywordPlusSCORING SYSTEM-
dc.subject.keywordPlusVALIDATION-
dc.subject.keywordPlusLAMIVUDINE-
dc.subject.keywordPlusCIRRHOSIS-
dc.subject.keywordPlusTHERAPY-
dc.subject.keywordPlusHISTORY-
dc.subject.keywordPlusSCORES-
dc.subject.keywordAuthorantiviral treatment-
dc.subject.keywordAuthorchronic hepatitis B-
dc.subject.keywordAuthordeep neural networking-
dc.subject.keywordAuthorHBV-
dc.subject.keywordAuthorHCC-
dc.subject.keywordAuthorliver cancer-
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S0168827821020870?via%3Dihub-
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