{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T20:36:37Z","timestamp":1791923797423,"version":"4.3.4"},"reference-count":42,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T00:00:00Z","timestamp":1787788800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T00:00:00Z","timestamp":1787788800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Gastro and Hepatol"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Accurate risk stratification for hepatocellular carcinoma (HCC) among chronic hepatitis B (CHB) patients remains challenging. Vibration\u2010controlled transient elastography (VCTE) is useful for HCC prediction; however, its nationwide use might be limited in CHB\u2010endemic or resource\u2010limited regions. We developed and validated non\u2010VCTE\u2013based machine learning (ML) models for HCC prediction, integrating routine clinical and laboratory parameters.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We analyzed a multicenter cohort of CHB patients receiving entecavir or tenofovir. ML\u2010based survival models were developed and validated, with their performance compared both with and without VCTE\u2010derived parameters (liver stiffness measurement\/controlled attenuation parameter) to analyze VCTE's role amidst routine variables. Non\u2010VCTE\u2013based ML models were then compared against the modified PAGE\u2010B (mPAGE\u2010B) score. Model performance was assessed by Uno's C\u2010index, area under the ROC curve (AUC), and Brier score at 5\u2010, 6\u2010, and 7\u2010year follow\u2010up using cross\u2010validation and external validation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Among 2736 patients (training\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20091954; validation\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009782), no statistically significant difference in predictive performance was observed between ML\u2010based models with and without VCTE\u2010derived parameters. Non\u2010VCTE\u2013based ML models consistently demonstrated superior overall performance and a statistically significant difference compared to the mPAGE\u2010B score. The non\u2010VCTE\u2013based RSF model demonstrated the highest discrimination (5\u2010year C\u2010index: 0.821; Brier score: 0.0592) in external validation. Statistically significant improvements over the mPAGE\u2010B score were observed across all metrics except for rpCox's AUC up to 5\u2009years.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>ML\u2010based models provide superior long\u2010term HCC risk prediction in CHB patients compared to the mPAGE\u2010B score. Importantly, their performance remains robust even without VCTE\u2010derived parameters, suggesting that effective risk stratification is achievable across diverse clinical settings, especially CHB\u2010endemic or resource\u2010limited regions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/jgh.70686","type":"journal-article","created":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T06:59:16Z","timestamp":1787900356000},"page":"3199-3208","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Comparable Predictive Performance of Non\u2010VCTE\u2013Based Machine Learning Model for HBV\u2010Infected Hepatocellular Carcinoma Risk"],"prefix":"10.1111","volume":"41","author":[{"given":"Hahn","family":"Yi","sequence":"first","affiliation":[{"name":"Department of Convergence Medicine Asan Medical Center, University of Ulsan College of Medicine  Seoul Republic of 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