{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T06:11:46Z","timestamp":1780467106901,"version":"3.54.1"},"reference-count":38,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["acsjournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Cancer Cytopathology"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Urine cytology is a noninvasive and valuable tool for detecting urothelial carcinoma but suffers from variable sensitivity and observer dependency. Artificial intelligence (AI) may enhance the diagnostic accuracy and efficiency of urine cytology. The objective of this study was to develop and validate an AI\u2010based cytology system for urothelial carcinoma detection in both clinical and screening contexts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In total, 328 retrospective clinical cases and 1489 prospective health screening samples were analyzed. All specimens underwent liquid\u2010based cytology and were digitized at \u00d720 magnification. For model development, ed 269 annotated training slides (56,710 cells) were used. The AI pipeline mimicked cytopathologist workflow, integrating deep learning\u2013based cell detection and segmentation with feature extraction and support vector machine classification into AI\u2010negative, AI\u2010atypical, and AI\u2010positive categories according to The Paris System for Reporting the Urinary Cytology. Agreement was assessed using weighted \u03ba values and prevalence\u2010adjusted, bias\u2010adjusted \u03ba values.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In the clinical cohort, AI achieved 83.0% agreement with histopathology and 82.6% agreement with expert cytology. Weighted \u03ba values (\u03ba, 0.631\u20130.661) reflected substantial agreement, with discrepancies mainly between adjacent categories. In the health screening cohort, the prevalence\u2010adjusted, bias\u2010adjusted \u03ba was 0.647 despite low prevalence. AI demonstrated a high negative predictive value of 99.7% and negative percent agreement of 82.3%, with a minimal false\u2010omission rate (0.25%).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>AI\u2010assisted urine cytology exhibited substantial concordance with expert interpretation and histopathologic standards. Its capability of screen\u2010out and high negative predictive value support its potential as a reliable triage tool, improving workflow efficiency while maintaining diagnostic reliability in large\u2010scale screening.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/cncy.70120","type":"journal-article","created":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T11:57:03Z","timestamp":1780315023000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence\u2013assisted urine cytology based on the Paris System for Reporting Urothelial Carcinoma"],"prefix":"10.1002","volume":"134","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9247-4988","authenticated-orcid":false,"given":"Lan","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Pathology Beijing Hospital National Center of Gerontology Institute of Geriatric Medicine Chinese Academy of Medical Sciences  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyi","family":"Cao","sequence":"additional","affiliation":[{"name":"Department of Medical Development Hangzhou Zhiwei Information Technology Co., Ltd.  Hangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zongyue","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Medical Development Hangzhou Zhiwei Information Technology Co., Ltd.  Hangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Longteng","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Pathology Beijing Hospital National Center of Gerontology Institute of Geriatric Medicine Chinese Academy of Medical Sciences  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingjun","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Pathology Beijing Hospital National Center of Gerontology Institute of Geriatric Medicine Chinese Academy of Medical Sciences  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yulong","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Pathology Beijing Hospital National Center of Gerontology Institute of Geriatric Medicine Chinese Academy of Medical Sciences  Beijing 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