{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T08:26:32Z","timestamp":1793694392996,"version":"4.5.1"},"reference-count":30,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T00:00:00Z","timestamp":1788134400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T00:00:00Z","timestamp":1788134400000},"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 Oral Pathology Medicine"],"published-print":{"date-parts":[[2026,11]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Head and neck squamous cell carcinoma (HNSCC) is a common malignancy with increasing incidence. Histopathological assessment remains the diagnostic gold standard, but is labor\u2010intensive and affected by a growing shortage of specialized pathologists. Limited availability of annotated data poses a major challenge for AI\u2010based support tools in digital pathology.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We evaluated the transfer performance of a domain\u2010specific foundation model, Tissue Concepts, compared to standard ImageNet pretraining for HNSCC detection in histopathological whole slide images. A multicentric, retrospective dataset comprising digitized H&amp;E\u2010stained slides from The Cancer Imaging Archive was used (390 slides, 268 HNSCC cases, 112 patients). Tumor regions were annotated by expert pathologists. Model representations were assessed without task\u2010specific fine\u2010tuning using a standardized classifier probe. Sample efficiency was investigated on patch\u2010based and patient\u2010based levels to estimate the number of annotated cases required for robust performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The foundation model consistently outperformed ImageNet pretraining across all experiments. On a patch\u2010based level, Tissue Concepts achieved substantially higher discrimination performance with minimal training data (AUROC\u2009=\u20090.70 vs.\u2009=\u20090.50 using only 1 patch). On a patient\u2010based level, the foundation model reached robust performance with a limited number of annotated cases (AUROC\u2009=\u20090.90 with 10 cases), whereas ImageNet\u2010based representations showed slightly inferior performance despite substantially larger training sets (AUROC\u2009=\u20090.87 with 50 cases). Performance improvements plateaued beyond 10 cases, indicating diminishing returns with additional annotations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Domain\u2010specific foundation models enable competitive HNSCC detection under annotation\u2010scarce conditions. However, performance depends on the representativeness of annotated cases and tumor heterogeneity across anatomical sites and institutions. Further multicenter and prospective studies are required to assess generalizability and integration into routine histopathological workflows.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/jop.70181","type":"journal-article","created":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T06:52:32Z","timestamp":1788245552000},"page":"1113-1120","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Sample\u2010Efficient Transfer Learning for Histopathological Detection of\n                    <scp>HNSCC<\/scp>"],"prefix":"10.1111","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-6817-3766","authenticated-orcid":false,"given":"Till","family":"Nicke","sequence":"first","affiliation":[{"name":"Fraunhofer Institute for Digital Medicine MEVIS  Bremen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fleming Wolf\u2010Eli","family":"Michelsen","sequence":"additional","affiliation":[{"name":"Medical Center\u2014University of Freiburg, Department of Oral and Maxillofacial Surgery, Faculty of Medicine University of Freiburg  Freiburg Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jan\u2010Raphael","family":"Sch\u00e4fer","sequence":"additional","affiliation":[{"name":"Fraunhofer Institute for Digital Medicine MEVIS  Bremen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1807-2531","authenticated-orcid":false,"given":"Jonas","family":"Wuester","sequence":"additional","affiliation":[{"name":"Medical Center\u2014University of Freiburg, Department of Oral and Maxillofacial Surgery, Faculty of Medicine University of Freiburg  Freiburg Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4893-7594","authenticated-orcid":false,"given":"Marc Christian","family":"Metzger","sequence":"additional","affiliation":[{"name":"Medical Center\u2014University of Freiburg, Department of Oral and Maxillofacial Surgery, Faculty of Medicine University of Freiburg  Freiburg Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rainer","family":"Schmelzeisen","sequence":"additional","affiliation":[{"name":"Medical Center\u2014University of Freiburg, Department of Oral and Maxillofacial Surgery, Faculty of Medicine University of Freiburg  Freiburg Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0587-9742","authenticated-orcid":false,"given":"Gian","family":"Kayser","sequence":"additional","affiliation":[{"name":"Institute of Pathology Naehrig Mattern Kayser  Freiburg Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"Weber\u2010Steinhilber","sequence":"additional","affiliation":[{"name":"Institute for Surgical Pathology, Medical Center\u2014University of Freiburg, Faculty of Medicine University of Freiburg  Freiburg Germany"},{"name":"Core Facility for Histopathology and Digital Pathology, Medical Center University of Freiburg  Freiburg Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joachim","family":"Georgii","sequence":"additional","affiliation":[{"name":"Fraunhofer Institute for Digital Medicine MEVIS  Bremen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3387-2596","authenticated-orcid":false,"given":"Johannes","family":"Lotz","sequence":"additional","affiliation":[{"name":"Fraunhofer Institute for Digital Medicine MEVIS  Bremen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4697-5287","authenticated-orcid":false,"given":"Leonard Simon","family":"Brandenburg","sequence":"additional","affiliation":[{"name":"Medical Center\u2014University of Freiburg, Department 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