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Personalized Antiviral Drug Selection in Patients With Chronic Hepatitis B Using a Machine Learning Model: A Multinational Study
DC Field | Value | Language |
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dc.contributor.author | 김승업 | - |
dc.date.accessioned | 2024-01-31T05:47:28Z | - |
dc.date.available | 2024-01-31T05:47:28Z | - |
dc.date.issued | 2023-11 | - |
dc.identifier.issn | 0002-9270 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/197898 | - |
dc.description.abstract | INTRODUCTION: Tenofovir disoproxil fumarate (TDF) is reportedly superior or at least comparable to entecavir (ETV) for the prevention of hepatocellular carcinoma (HCC) in patients with chronic hepatitis B; however, it has distinct long-term renal and bone toxicities. This study aimed to develop and validate a machine learning model (designated as Prediction of Liver cancer using Artificial intelligence-driven model for Network-antiviral Selection for hepatitis B [PLAN-S]) to predict an individualized risk of HCC during ETV or TDF therapy.METHODS: This multinational study included 13,970 patients with chronic hepatitis B. The derivation (n = 6,790), Korean validation (n = 4,543), and Hong Kong-Taiwan validation cohorts (n = 2,637) were established. Patients were classified as the TDF-superior group when a PLAN-S-predicted HCC risk under ETV treatment is greater than under TDF treatment, and the others were defined as the TDF-nonsuperior group.RESULTS: The PLAN-S model was derived using 8 variables and generated a c-index between 0.67 and 0.78 for each cohort. The TDF-superior group included a higher proportion of male patients and patients with cirrhosis than the TDF-nonsuperior group. In the derivation, Korean validation, and Hong Kong-Taiwan validation cohorts, 65.3%, 63.5%, and 76.4% of patients were classified as the TDF-superior group, respectively. In the TDF-superior group of each cohort, TDF was associated with a significantly lower risk of HCC than ETV (hazard ratio = 0.60-0.73, all P < 0.05). In the TDF-nonsuperior group, however, there was no significant difference between the 2 drugs (hazard ratio = 1.16-1.29, all P > 0.1). [GRAPHICS] .DISCUSSION: Considering the individual HCC risk predicted by PLAN-S and the potential TDF-related toxicities, TDF and ETV treatment may be recommended for the TDF-superior and TDF-nonsuperior groups, respectively. | - |
dc.description.statementOfResponsibility | restriction | - |
dc.language | English | - |
dc.publisher | Nature Pub. Group | - |
dc.relation.isPartOf | AMERICAN JOURNAL OF GASTROENTEROLOGY | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.subject.MESH | Antiviral Agents / therapeutic use | - |
dc.subject.MESH | Artificial Intelligence | - |
dc.subject.MESH | Carcinoma, Hepatocellular* / complications | - |
dc.subject.MESH | Carcinoma, Hepatocellular* / epidemiology | - |
dc.subject.MESH | Carcinoma, Hepatocellular* / prevention & control | - |
dc.subject.MESH | Hepatitis B virus | - |
dc.subject.MESH | Hepatitis B, Chronic* / complications | - |
dc.subject.MESH | Hepatitis B, Chronic* / drug therapy | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Liver Neoplasms* / complications | - |
dc.subject.MESH | Machine Learning | - |
dc.subject.MESH | Male | - |
dc.subject.MESH | Retrospective Studies | - |
dc.subject.MESH | Tenofovir / therapeutic use | - |
dc.subject.MESH | Treatment Outcome | - |
dc.title | Personalized Antiviral Drug Selection in Patients With Chronic Hepatitis B Using a Machine Learning Model: A Multinational Study | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Internal Medicine (내과학교실) | - |
dc.contributor.googleauthor | Moon Haeng Hur | - |
dc.contributor.googleauthor | Min Kyung Park | - |
dc.contributor.googleauthor | Terry Cheuk-Fung Yip | - |
dc.contributor.googleauthor | Chien-Hung Chen | - |
dc.contributor.googleauthor | Hyung-Chul Lee | - |
dc.contributor.googleauthor | Won-Mook Choi | - |
dc.contributor.googleauthor | Seung Up Kim | - |
dc.contributor.googleauthor | Young-Suk Lim | - |
dc.contributor.googleauthor | Soo Young Park | - |
dc.contributor.googleauthor | Grace Lai-Hung Wong | - |
dc.contributor.googleauthor | Dong Hyun Sinn | - |
dc.contributor.googleauthor | Young-Joo Jin | - |
dc.contributor.googleauthor | Sung Eun Kim | - |
dc.contributor.googleauthor | Cheng-Yuan Peng | - |
dc.contributor.googleauthor | Hyun Phil Shin | - |
dc.contributor.googleauthor | Chi-Yi Chen | - |
dc.contributor.googleauthor | Hwi Young Kim | - |
dc.contributor.googleauthor | Han Ah Lee | - |
dc.contributor.googleauthor | Yeon Seok Seo | - |
dc.contributor.googleauthor | Dae Won Jun | - |
dc.contributor.googleauthor | Eileen L Yoon | - |
dc.contributor.googleauthor | Joo Hyun Sohn | - |
dc.contributor.googleauthor | Sang Bong Ahn | - |
dc.contributor.googleauthor | Jae-Jun Shim | - |
dc.contributor.googleauthor | Soung Won Jeong | - |
dc.contributor.googleauthor | Yong Kyun Cho | - |
dc.contributor.googleauthor | Hyoung Su Kim | - |
dc.contributor.googleauthor | Myoung-Jin Jang | - |
dc.contributor.googleauthor | Yoon Jun Kim | - |
dc.contributor.googleauthor | Jung-Hwan Yoon | - |
dc.contributor.googleauthor | Jeong-Hoon Lee | - |
dc.identifier.doi | 10.14309/ajg.0000000000002234 | - |
dc.contributor.localId | A00654 | - |
dc.relation.journalcode | J00081 | - |
dc.identifier.eissn | 1572-0241 | - |
dc.identifier.pmid | 36881437 | - |
dc.identifier.url | https://journals.lww.com/ajg/fulltext/2023/11000/personalized_antiviral_drug_selection_in_patients.17.aspx | - |
dc.contributor.alternativeName | Kim, Seung Up | - |
dc.contributor.affiliatedAuthor | 김승업 | - |
dc.citation.volume | 118 | - |
dc.citation.number | 11 | - |
dc.citation.startPage | 1963 | - |
dc.citation.endPage | 1972 | - |
dc.identifier.bibliographicCitation | AMERICAN JOURNAL OF GASTROENTEROLOGY, Vol.118(11) : 1963-1972, 2023-11 | - |
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