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This study presents the first phase of the SALV-Dataset Registry; a nationwide, expertly curated, and fully digitized clinicopathological resource, designed to support research and develop artificial intelligence (AI) tools assisting in salivary gland tumor pathology diagnostics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Salivary gland tumor resections diagnosed at the Leiden University Medical Center (1999\u20132024) were collected through the Dutch national network and registry for histo- and cytopathology (PALGA). In total, 685 cases were included. Hematoxylin- and eosin-stained slides were digitized and independently reviewed by three teams of head and neck pathologists, in line with the 2023 WHO Classification of Head and Neck Tumours. Discordant and ambiguous cases were resolved in consensus meetings, with access to immunohistochemistry, molecular analysis, and clinical data. Interobserver agreement among the three teams was quantified (Fleiss\u2019 kappa), and agreement between the original and consensus diagnosis was determined (Cohen\u2019s kappa).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Of the 685 tumors, 75% were benign, 24% malignant, and 1% of uncertain malignant potential. The parotid gland was most frequently involved (86%), and the highest rate of malignancy was observed in the sublingual gland (100%). Rare entities were represented, although numbers remained limited. Interobserver agreement was substantial (\u03ba of 0.64; 95% 0.60\u20130.67). Agreement between the original and consensus diagnosis was excellent (Cohen\u2019s \u03ba of 0.89; 95% CI 0.86\u20130.92). Forty-three cases (6%) were reclassified following revision, with a change in diagnostic category in 11 cases (2%).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study demonstrates the feasibility of large-scale, expert digital revision of salivary gland tumors, and establishes a robust foundation for future clinicopathological and AI-based research. With national expansion ongoing, the SALV-Dataset Registry will provide a comprehensive resource for AI training, validation, and clinically oriented modeling in salivary gland tumor diagnostics.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s12105-026-01907-1","type":"journal-article","created":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T15:18:38Z","timestamp":1780672718000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["The SALV-Dataset Registry: An Expertly Curated Digital Clinicopathological Dataset for Salivary Gland Tumor Research and AI-Assisted Diagnostic Tools"],"prefix":"10.1007","volume":"20","author":[{"given":"Kimberly S. T.","family":"Burghout","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jurre A. 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Unrestricted research grants from OWKIN, GE Healthcare, Roche, and IBEX. Personal fees were received from DEKRA. All activities are unrelated to the current manuscript. NJR discloses the receipt of a travel grant from Roche Diagnostics, and an advisory board function for AbbVie AG, both outside the scope of the current publication. All other authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"62"}}