{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T05:38:31Z","timestamp":1776836311386,"version":"3.51.2"},"reference-count":28,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T00:00:00Z","timestamp":1776816000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2023QH328"],"award-info":[{"award-number":["ZR2023QH328"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Liver biopsy is the gold standard for diagnosing liver lesions but is hampered by the time-consuming nature of conventional pathology. Artificial Intelligence-assisted Rapid On-Site Evaluation (AI-ROSE) offers a promising solution for real-time assessment of biopsy samples. This study aims to evaluate the diagnostic performance of AI-ROSE in liver biopsies using histopathology as the reference standard.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Fifty-eight patients with liver lesions undergoing CT-guided percutaneous biopsy were prospectively enrolled. Each biopsy sample underwent triple assessment: AI-ROSE analysis, exfoliative cytology, and histopathological examination. Diagnostic sensitivity, specificity, accuracy, and predictive values were calculated. Serial (both tests positive) and parallel (either test positive) combinations of AI-ROSE and cytology were also analyzed. The agreement between methods was assessed using the Kappa statistic.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>AI-ROSE showed a sensitivity of 92.31% (95% CI: 81.8% - 97.1%) and an accuracy of 87.93% (95% CI: 77.2% - 94.2%), outperforming exfoliative cytology (sensitivity: 86.54%; accuracy: 84.48%). The parallel application of AI-ROSE and cytology achieved the highest sensitivity of 96.15% (95% CI: 87.02-100.00) and significantly improved agreement with the gold standard (Kappa = 0.498, p\u00a0= 0.0015).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study shows AI-ROSE not only has better sensitivity and accuracy in single-item diagnosis than exfoliative cytology in liver biopsy, but demonstrates excellent synergistic value in parallel application, which can greatly improve the reliability of intraoperative diagnosis. This indicates AI-ROSE has important application prospects in optimizing the clinical decision-making process and reducing the risk of intraoperative missed diagnosis.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2026.1740247","type":"journal-article","created":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T04:40:17Z","timestamp":1776832817000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence-assisted rapid on-site evaluation in liver biopsy: a diagnostic accuracy study"],"prefix":"10.3389","volume":"16","author":[{"given":"Cheng-Cheng","family":"Du","sequence":"first","affiliation":[{"name":"Department of Radiology, Qilu Hospital of Shandong University","place":["Jinan, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu-Xian","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Radiology, Qilu Hospital of Shandong 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