Cited 51 times in
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 | 김승업 | - |
dc.date.accessioned | 2023-03-21T07:38:33Z | - |
dc.date.available | 2023-03-21T07:38:33Z | - |
dc.date.issued | 2022-02 | - |
dc.identifier.issn | 0168-8278 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/193485 | - |
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.description.statementOfResponsibility | restriction | - |
dc.language | English | - |
dc.publisher | Elsevier | - |
dc.relation.isPartOf | JOURNAL OF HEPATOLOGY | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.subject.MESH | Adult | - |
dc.subject.MESH | Antiviral Agents / pharmacology | - |
dc.subject.MESH | Antiviral Agents / therapeutic use | - |
dc.subject.MESH | Artificial Intelligence / standards* | - |
dc.subject.MESH | Artificial Intelligence / statistics & numerical data | - |
dc.subject.MESH | Asian People / ethnology | - |
dc.subject.MESH | Asian People / statistics & numerical data | - |
dc.subject.MESH | Carcinoma, Hepatocellular / etiology | - |
dc.subject.MESH | Carcinoma, Hepatocellular / physiopathology* | - |
dc.subject.MESH | Cohort Studies | - |
dc.subject.MESH | Computer Simulation / standards | - |
dc.subject.MESH | Computer Simulation / statistics & numerical data | - |
dc.subject.MESH | Female | - |
dc.subject.MESH | Follow-Up Studies | - |
dc.subject.MESH | Guanine / analogs & derivatives | - |
dc.subject.MESH | Guanine / pharmacology | - |
dc.subject.MESH | Guanine / therapeutic use | - |
dc.subject.MESH | Hepatitis B, Chronic / complications* | - |
dc.subject.MESH | Hepatitis B, Chronic / physiopathology | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Liver Neoplasms / complications | - |
dc.subject.MESH | Liver Neoplasms / physiopathology | - |
dc.subject.MESH | Male | - |
dc.subject.MESH | Middle Aged | - |
dc.subject.MESH | Republic of Korea / ethnology | - |
dc.subject.MESH | Tenofovir / pharmacology | - |
dc.subject.MESH | Tenofovir / therapeutic use | - |
dc.subject.MESH | White People / ethnology | - |
dc.subject.MESH | White People / statistics & numerical data | - |
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.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Internal Medicine (내과학교실) | - |
dc.contributor.googleauthor | Hwi Young Kim | - |
dc.contributor.googleauthor | Pietro Lampertico | - |
dc.contributor.googleauthor | Joon Yeul Nam | - |
dc.contributor.googleauthor | Hyung-Chul Lee | - |
dc.contributor.googleauthor | Seung Up Kim | - |
dc.contributor.googleauthor | Dong Hyun Sinn | - |
dc.contributor.googleauthor | Yeon Seok Seo | - |
dc.contributor.googleauthor | Han Ah Lee | - |
dc.contributor.googleauthor | Soo Young Park | - |
dc.contributor.googleauthor | Young-Suk Lim | - |
dc.contributor.googleauthor | Eun Sun Jang | - |
dc.contributor.googleauthor | Eileen L Yoon | - |
dc.contributor.googleauthor | Hyoung Su Kim | - |
dc.contributor.googleauthor | Sung Eun Kim | - |
dc.contributor.googleauthor | Sang Bong Ahn | - |
dc.contributor.googleauthor | Jae-Jun Shim | - |
dc.contributor.googleauthor | Soung Won Jeong | - |
dc.contributor.googleauthor | Yong Jin Jung | - |
dc.contributor.googleauthor | Joo Hyun Sohn | - |
dc.contributor.googleauthor | Yong Kyun Cho | - |
dc.contributor.googleauthor | Dae Won Jun | - |
dc.contributor.googleauthor | George N Dalekos | - |
dc.contributor.googleauthor | Ramazan Idilman | - |
dc.contributor.googleauthor | Vana Sypsa | - |
dc.contributor.googleauthor | Thomas Berg | - |
dc.contributor.googleauthor | Maria Buti | - |
dc.contributor.googleauthor | Jose Luis Calleja | - |
dc.contributor.googleauthor | John Goulis | - |
dc.contributor.googleauthor | Spilios Manolakopoulos | - |
dc.contributor.googleauthor | Harry L A Janssen | - |
dc.contributor.googleauthor | Myoung-Jin Jang | - |
dc.contributor.googleauthor | Yun Bin Lee | - |
dc.contributor.googleauthor | Yoon Jun Kim | - |
dc.contributor.googleauthor | Jung-Hwan Yoon | - |
dc.contributor.googleauthor | George V Papatheodoridis | - |
dc.contributor.googleauthor | Jeong-Hoon Lee | - |
dc.identifier.doi | 10.1016/j.jhep.2021.09.025 | - |
dc.contributor.localId | A00654 | - |
dc.relation.journalcode | J01441 | - |
dc.identifier.eissn | 1600-0641 | - |
dc.identifier.pmid | 34606915 | - |
dc.identifier.url | https://www.sciencedirect.com/science/article/pii/S0168827821020870 | - |
dc.subject.keyword | HBV | - |
dc.subject.keyword | HCC | - |
dc.subject.keyword | antiviral treatment | - |
dc.subject.keyword | chronic hepatitis B | - |
dc.subject.keyword | deep neural networking | - |
dc.subject.keyword | liver cancer | - |
dc.contributor.alternativeName | Kim, Seung Up | - |
dc.contributor.affiliatedAuthor | 김승업 | - |
dc.citation.volume | 76 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 311 | - |
dc.citation.endPage | 318 | - |
dc.identifier.bibliographicCitation | JOURNAL OF HEPATOLOGY, Vol.76(2) : 311-318, 2022-02 | - |
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