{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,8]],"date-time":"2026-11-08T08:09:30Z","timestamp":1794125370390,"version":"4.8.0"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,7,9]],"date-time":"2025-07-09T00:00:00Z","timestamp":1752019200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,7,9]],"date-time":"2025-07-09T00:00:00Z","timestamp":1752019200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Commun Med"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Organ shortages require prioritizing hepatocellular carcinoma (HCC) patients with the highest survival benefit for allografts. While traditional models like AFP, MORAL, and HALT-HCC are commonly used for recurrence risk prediction, the TRIUMPH model, which uses machine learning, has shown superior performance. This study aims to externally validate the model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The cohort included 2844 HCC patients who underwent liver transplantation at six international centers from 2000-2022. The TRIUMPH model utilized a regularized Cox proportional hazards approach with a penalty term for coefficient adjustment. Discrimination was assessed using the c-index, and clinical utility was evaluated via decision curve analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The most common liver diseases are hepatitis C (49%) and hepatitis B (27%). At listing, 84% meets the Milan criteria, and 91% are within criteria at transplant. Median model for end-stage liver disease score is 10 (IQR:8\u201314), alpha-fetoprotein level 8\u2009ng\/mL (IQR:4\u201325), and tumor size 2\u2009cm (IQR:1.1\u20133.0). Living donor grafts are used in 24% of cases. Recurrence rate is 9.1% with a median time to recurrence of 17.5 months. Recurrence-free survival rates at 1\/3\/5 years are 95.7%\/89.5%\/87.7%, respectively. The TRIUMPH model achieves the highest c-index (0.71), outperforming MORAL (0.61,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.049) and AFP (0.61,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.04), though not significantly better than HALT-HCC (0.67,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.28). TRIUMPH shows superior clinical utility up to a threshold of 0.6.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The TRIUMPH model demonstrates good accuracy and clinical utility in predicting post-transplant HCC recurrence. Its integration into organ allocation could improve transplantation outcomes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1038\/s43856-025-00994-5","type":"journal-article","created":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T08:44:36Z","timestamp":1752137076000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Validation of the Toronto recurrence inference using machine-learning for post-transplant hepatocellular carcinoma model"],"prefix":"10.1038","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5654-7043","authenticated-orcid":false,"given":"Zhihao","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Itsuko Chih-Yi","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9371-7411","authenticated-orcid":false,"given":"Leonardo","family":"Centonze","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4570-949X","authenticated-orcid":false,"given":"Christian T. 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G.S. has received financial compensation for talks for Roche, Astra-Zeneca, Chiesi, and Integra. G.S. has received a grant from Roche. N.M. discloses consultancy for Genentech, FujiFilm Wako, Merck, Eisai, Exact Sciences. N.M. has received a grant from Genentech, FujiFilm Wako, TARGET Pharmasolutions, Exact Sciences. A.V. had been directly paid honoraria and has been paid consulting or advisory roles for AstraZeneca, BeiGene, Boehringer Mannheim, BMS, BTG, EISAI, GSK, Incyte, Ipsen, MSD, Hoffmann-La Roche, Servier, Sirtex, and Taiho. G.O\u2019.K. has received grants from Roche and AstraZeneca; consulting fees from AstraZeneca, Servier, and Incyte; payment for lectures from Roche, and support for attending meetings from MSD and Roche. None of the other authors have any competing interests to declare.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"284"}}