{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T11:56:48Z","timestamp":1791201408661,"version":"4.2.0"},"reference-count":33,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T00:00:00Z","timestamp":1780444800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T00:00:00Z","timestamp":1780444800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100009619","name":"Japan Agency for Medical Research and Development","doi-asserted-by":"publisher","award":["JP25fk0210130"],"award-info":[{"award-number":["JP25fk0210130"]}],"id":[{"id":"10.13039\/100009619","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009619","name":"Japan Agency for Medical Research and Development","doi-asserted-by":"publisher","award":["JP25fk0210149"],"award-info":[{"award-number":["JP25fk0210149"]}],"id":[{"id":"10.13039\/100009619","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003478","name":"Ministry of Health, Labour and Welfare","doi-asserted-by":"publisher","award":["23HC2001"],"award-info":[{"award-number":["23HC2001"]}],"id":[{"id":"10.13039\/501100003478","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Hepatology Research"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>Steatotic liver disease (SLD) encompasses a heterogeneous spectrum with varying risks of hepatocellular carcinoma (HCC). Limited sample sizes limit the development of predictive models, particularly for rare outcomes. This study evaluated whether generative artificial intelligence (AI)\u2010based synthetic data augmentation can enhance HCC risk prediction in SLD patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      A random survival forest (RSF) model was developed using data from 406 patients with biopsy\u2010confirmed SLD. The dataset was divided into training (\n                      <jats:italic>n<\/jats:italic>\n                      \u00a0=\u00a0284) and testing (\n                      <jats:italic>n<\/jats:italic>\n                      \u00a0=\u00a0122) cohorts. Two synthetic data generation approaches, the conditional tabular generative adversarial network (CTGAN; a type of generative adversarial network [GAN]) and diffusion models, augmented the training dataset from 284 to 1000 samples. Model performance was assessed using Harrell's C\u2010index and integrated Brier score (IBS). Synthetic data quality was evaluated using maximum mean discrepancy (MMD) and Wasserstein distance.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>During the mean follow\u2010up of 5.9\u00a0years, 12 (3.0%) patients developed HCC. In the test cohort, the baseline RSF model achieved a C\u2010index of 0.912. Following augmentation, the diffusion\u2010augmented model improved to 0.949, whereas the GAN\u2010augmented model decreased to 0.818. Diffusion\u2010generated data showed superior fidelity with a lower MMD (0.0303 vs. 0.0762) and Wasserstein distance (0.0467 vs. 0.1145) than GAN\u2010generated data. Both augmentation approaches improved calibration (IBS: diffusion, 0.0103; GAN, 0.0108 vs. baseline, 0.0114).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Diffusion\u2010based synthetic data augmentation improved HCC risk prediction in the test cohort, whereas GAN augmentation reduced model accuracy. These findings suggest that diffusion models may help address the data scarcity challenges in hepatology research, potentially providing a useful approach for developing predictive models in limited cohorts.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/hepr.70213","type":"journal-article","created":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T11:44:36Z","timestamp":1780487076000},"page":"1466-1474","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Toward a Synthetic Data Revolution: Diffusion Model\u2010Enhanced Hepatocellular Carcinoma Prediction in Steatotic Liver Disease"],"prefix":"10.1111","volume":"56","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5423-9664","authenticated-orcid":false,"given":"Masaya","family":"Sato","sequence":"first","affiliation":[{"name":"Department of Gastroenterology, Graduate School of Medicine The University of Tokyo  Tokyo Japan"},{"name":"Department of Clinical Laboratory Medicine, Graduate School of Medicine The University of Tokyo  Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5727-5385","authenticated-orcid":false,"given":"Takuma","family":"Nakatsuka","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Graduate School of Medicine The University of Tokyo  Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuki","family":"Matsushita","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Graduate School of Medicine The University of Tokyo  Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takuma","family":"Kaneko","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Graduate School of Medicine The University of Tokyo  Tokyo 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