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 | Hur, Moon Haeng | - |
dc.contributor.author | Park, Min Kyung | - |
dc.contributor.author | Yip, Terry Cheuk-Fung | - |
dc.contributor.author | Chen, Chien-Hung | - |
dc.contributor.author | Lee, Hyung-Chul | - |
dc.contributor.author | Choi, Won-Mook | - |
dc.contributor.author | Kim, Seung Up | - |
dc.contributor.author | Lim, Young-Suk | - |
dc.contributor.author | Park, Soo Young | - |
dc.contributor.author | Wong, Grace Lai-Hung | - |
dc.contributor.author | Sinn, Dong Hyun | - |
dc.contributor.author | Jin, Young-Joo | - |
dc.contributor.author | Kim, Sung Eun | - |
dc.contributor.author | Peng, Cheng-Yuan | - |
dc.contributor.author | Shin, Hyun Phil | - |
dc.contributor.author | Chen, Chi-Yi | - |
dc.contributor.author | Kim, Hwi Young | - |
dc.contributor.author | Lee, Han Ah | - |
dc.contributor.author | Seo, Yeon Seok | - |
dc.contributor.author | Jun, Dae Won | - |
dc.contributor.author | Yoon, Eileen L. | - |
dc.contributor.author | Sohn, Joo Hyun | - |
dc.contributor.author | Ahn, Sang Bong | - |
dc.contributor.author | Shim, Jae-Jun | - |
dc.contributor.author | Jeong, Soung Won | - |
dc.contributor.author | Cho, Yong Kyun | - |
dc.contributor.author | Kim, Hyoung Su | - |
dc.contributor.author | Jang, Myoung-jin | - |
dc.contributor.author | Kim, Yoon Jun | - |
dc.contributor.author | Yoon, Jung-Hwan | - |
dc.contributor.author | Lee, Jeong-Hoon | - |
dc.date.accessioned | 2024-08-14T08:02:03Z | - |
dc.date.available | 2024-08-14T08:02:03Z | - |
dc.date.issued | 2023-11 | - |
dc.identifier.issn | 0002-9270 | - |
dc.identifier.issn | 1572-0241 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/26594 | - |
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.format.extent | 10 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | LIPPINCOTT WILLIAMS & WILKINS | - |
dc.title | Personalized Antiviral Drug Selection in Patients With Chronic Hepatitis B Using a Machine Learning Model: A Multinational Study | - |
dc.type | Article | - |
dc.publisher.location | 미국 | - |
dc.identifier.doi | 10.14309/ajg.0000000000002234 | - |
dc.identifier.scopusid | 2-s2.0-85161405227 | - |
dc.identifier.wosid | 001102204200013 | - |
dc.identifier.bibliographicCitation | AMERICAN JOURNAL OF GASTROENTEROLOGY, v.118, no.11, pp 1963 - 1972 | - |
dc.citation.title | AMERICAN JOURNAL OF GASTROENTEROLOGY | - |
dc.citation.volume | 118 | - |
dc.citation.number | 11 | - |
dc.citation.startPage | 1963 | - |
dc.citation.endPage | 1972 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Gastroenterology & Hepatology | - |
dc.relation.journalWebOfScienceCategory | Gastroenterology & Hepatology | - |
dc.subject.keywordPlus | TENOFOVIR DISOPROXIL FUMARATE | - |
dc.subject.keywordPlus | HEPATOCELLULAR-CARCINOMA RISK | - |
dc.subject.keywordPlus | ENTECAVIR | - |
dc.subject.keywordPlus | VALIDATION | - |
dc.subject.keywordPlus | REGRESSION | - |
dc.subject.keywordPlus | INFECTION | - |
dc.subject.keywordPlus | CIRRHOSIS | - |
dc.subject.keywordPlus | GENOTYPES | - |
dc.subject.keywordAuthor | liver cancer | - |
dc.subject.keywordAuthor | antiviral selection | - |
dc.subject.keywordAuthor | deep neural networking | - |
dc.subject.keywordAuthor | random survival forests | - |
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