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A machine learning model for predicting hepatocellular carcinoma risk in patients with chronic hepatitis B
DC Field | Value | Language |
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dc.contributor.author | 김도영 | - |
dc.contributor.author | 김범경 | - |
dc.contributor.author | 김승업 | - |
dc.contributor.author | 김휘영 | - |
dc.contributor.author | 박준용 | - |
dc.contributor.author | 안상훈 | - |
dc.contributor.author | 이재승 | - |
dc.contributor.author | 이혜원 | - |
dc.date.accessioned | 2023-11-28T03:00:37Z | - |
dc.date.available | 2023-11-28T03:00:37Z | - |
dc.date.issued | 2023-08 | - |
dc.identifier.issn | 1478-3223 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/196719 | - |
dc.description.abstract | Background: Machine learning (ML) algorithms can be used to overcome the prognostic performance limitations of conventional hepatocellular carcinoma (HCC) risk models. We established and validated an ML-based HCC predictive model optimized for patients with chronic hepatitis B (CHB) infections receiving antiviral therapy (AVT). Methods: Treatment-naïve CHB patients who were started entecavir (ETV) or tenofovir disoproxil fumarate (TDF) were enrolled. We used a training cohort (n = 960) to develop a novel ML model that predicted HCC development within 5 years and validated the model using an independent external cohort (n = 1937). ML algorithms consider all potential interactions and do not use predefined hypotheses. Results: The mean age of the patients in the training cohort was 48 years, and most patients (68.9%) were men. During the median 59.3 (interquartile range 45.8-72.3) months of follow-up, 69 (7.2%) patients developed HCC. Our ML-based HCC risk prediction model had an area under the receiver-operating characteristic curve (AUC) of 0.900, which was better than the AUCs of CAMD (0.778) and REAL B (0.772) (both p < .05). The better performance of our model was maintained (AUC = 0.872 vs. 0.788 for CAMD and 0.801 for REAL B) in the validation cohort. Using cut-off probabilities of 0.3 and 0.5, the cumulative incidence of HCC development differed significantly among the three risk groups (p < .001). Conclusions: Our new ML model performed better than models in terms of predicting the risk of HCC development in CHB patients receiving AVT. | - |
dc.description.statementOfResponsibility | restriction | - |
dc.language | English | - |
dc.publisher | Wiley-Blackwell | - |
dc.relation.isPartOf | LIVER INTERNATIONAL | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.subject.MESH | Antiviral Agents / therapeutic use | - |
dc.subject.MESH | Carcinoma, Hepatocellular* / drug therapy | - |
dc.subject.MESH | Carcinoma, Hepatocellular* / epidemiology | - |
dc.subject.MESH | Carcinoma, Hepatocellular* / etiology | - |
dc.subject.MESH | Female | - |
dc.subject.MESH | Hepatitis B, Chronic* / complications | - |
dc.subject.MESH | Hepatitis B, Chronic* / drug therapy | - |
dc.subject.MESH | Hepatitis B, Chronic* / epidemiology | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Liver Neoplasms* / drug therapy | - |
dc.subject.MESH | Liver Neoplasms* / epidemiology | - |
dc.subject.MESH | Liver Neoplasms* / etiology | - |
dc.subject.MESH | Male | - |
dc.subject.MESH | Middle Aged | - |
dc.subject.MESH | Retrospective Studies | - |
dc.subject.MESH | Tenofovir / therapeutic use | - |
dc.title | A machine learning model for predicting hepatocellular carcinoma risk in patients with chronic hepatitis B | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Internal Medicine (내과학교실) | - |
dc.contributor.googleauthor | Hye Won Lee | - |
dc.contributor.googleauthor | Hwiyoung Kim | - |
dc.contributor.googleauthor | Taeyun Park | - |
dc.contributor.googleauthor | Soo Young Park | - |
dc.contributor.googleauthor | Young Eun Chon | - |
dc.contributor.googleauthor | Yeon Seok Seo | - |
dc.contributor.googleauthor | Jae Seung Lee | - |
dc.contributor.googleauthor | Jun Yong Park | - |
dc.contributor.googleauthor | Do Young Kim | - |
dc.contributor.googleauthor | Sang Hoon Ahn | - |
dc.contributor.googleauthor | Beom Kyung Kim | - |
dc.contributor.googleauthor | Seung Up Kim | - |
dc.identifier.doi | 10.1111/liv.15597 | - |
dc.contributor.localId | A00385 | - |
dc.contributor.localId | A00487 | - |
dc.contributor.localId | A00654 | - |
dc.contributor.localId | A05971 | - |
dc.contributor.localId | A01675 | - |
dc.contributor.localId | A02226 | - |
dc.contributor.localId | A05963 | - |
dc.contributor.localId | A03318 | - |
dc.relation.journalcode | J02171 | - |
dc.identifier.eissn | 1478-3231 | - |
dc.identifier.pmid | 37452503 | - |
dc.identifier.url | https://onlinelibrary.wiley.com/doi/10.1111/liv.15597 | - |
dc.subject.keyword | antiviral therapy | - |
dc.subject.keyword | chronic hepatitis B | - |
dc.subject.keyword | entecavir | - |
dc.subject.keyword | hepatocellular carcinoma | - |
dc.subject.keyword | machine learning | - |
dc.subject.keyword | performance | - |
dc.subject.keyword | prediction | - |
dc.subject.keyword | prognosis | - |
dc.subject.keyword | risk prediction | - |
dc.subject.keyword | tenofovir | - |
dc.contributor.alternativeName | Kim, Do Young | - |
dc.contributor.affiliatedAuthor | 김도영 | - |
dc.contributor.affiliatedAuthor | 김범경 | - |
dc.contributor.affiliatedAuthor | 김승업 | - |
dc.contributor.affiliatedAuthor | 김휘영 | - |
dc.contributor.affiliatedAuthor | 박준용 | - |
dc.contributor.affiliatedAuthor | 안상훈 | - |
dc.contributor.affiliatedAuthor | 이재승 | - |
dc.contributor.affiliatedAuthor | 이혜원 | - |
dc.citation.volume | 43 | - |
dc.citation.number | 8 | - |
dc.citation.startPage | 1813 | - |
dc.citation.endPage | 1821 | - |
dc.identifier.bibliographicCitation | LIVER INTERNATIONAL, Vol.43(8) : 1813-1821, 2023-08 | - |
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