{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T13:14:12Z","timestamp":1781615652309,"version":"3.54.5"},"reference-count":34,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2024,11,22]],"date-time":"2024-11-22T00:00:00Z","timestamp":1732233600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82373654"],"award-info":[{"award-number":["82373654"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,4,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>Liver stiffness measurement (LSM) via vibration-controlled transient elastography accurately assesses fibrosis. We aimed to develop a universal risk score for predicting hepatocellular carcinoma (HCC) development in patients with chronic hepatitis.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>We systematically selected predictors and developed the risk prediction model (HCC-LSM) in the hepatitis B virus (HBV) training cohort (n\u2009=\u20092251, median follow-up of 3.2\u2009years). The HCC-LSM model was validated in an independent HBV validation cohort (n\u2009=\u20091191, median follow-up of 5.7\u2009years) and a non-viral chronic liver disease (CLD) extrapolation cohort (n\u2009=\u20091189, median follow-up of 3.3\u2009years). An HCC risk score was then constructed based on a nomogram. An online risk evaluation tool Liver Elastography-Based Hepatocellular Carcinoma Risk Score (LEBER) was developed using ChatGPT4.0.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Eight routinely available predictors were identified, with LSM levels showing a significant dose-response relationship with HCC incidence (P &amp;lt; .001 by log-rank test). The HCC-LSM model exhibited excellent predictive performance in the HBV training cohort (C-index\u2009=\u20090.866) and the HBV validation cohort (C-index\u2009=\u20090.852), with good performance in the extrapolation CLD cohort (C-index\u2009=\u20090.769). The model demonstrated significantly superior discrimination compared to 6 previous models across the 3 cohorts. Cut-off values of 87.2 and 121.1 for the HCC-LSM score categorized participants into low-, medium-, and high-risk groups. An online public risk evaluation tool (LEBER; http:\/\/ccra.njmu.edu.cn\/LEBER669.html) was developed to facilitate the use of HCC-LSM.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>The accessible, reliable risk score based on LSM accurately predicted HCC development in patients with chronic hepatitis, providing an effective risk assessment tool for HCC surveillance strategies.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jnci\/djae304","type":"journal-article","created":{"date-parts":[[2024,11,22]],"date-time":"2024-11-22T16:51:24Z","timestamp":1732294284000},"page":"761-771","source":"Crossref","is-referenced-by-count":6,"title":["Liver elastography-based risk score for predicting hepatocellular carcinoma risk"],"prefix":"10.1093","volume":"117","author":[{"given":"Chan","family":"Tian","sequence":"first","affiliation":[{"name":"Health Management Center, The First Affiliated Hospital of Nanjing Medical University , Nanjing 210029,","place":["China"]},{"name":"Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University , Nanjing 211166, Jiangsu, P. 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