{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T14:24:38Z","timestamp":1787840678044,"version":"build-2784847793"},"reference-count":38,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"3","license":[{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0"}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":["J Cytol"],"published-print":{"date-parts":[[2025,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective:<\/jats:title>\n                    <jats:p>This study conducted an unsupervised learning cluster analysis on urine cytological images of high-grade urothelial carcinoma to assess their explanatory potential.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods:<\/jats:title>\n                    <jats:p>A total of 124 urine cytology specimens of urothelial carcinoma, collected between December 2010 to December 2021 at Gunma University Hospital, were analyzed. Ten cytological image fields per specimen were captured, and pathological T factors were examined using principal component analysis and t-distributed stochastic neighbor embedding (t-SNE) with machine learning (ML) software. Common image features were also verbalized and manually reevaluated.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>In the t-SNE analysis, the T1-dominant region was characterized by \u201cfew cells in the background,\u201d whereas the T2-dominant region showed \u201cmany cells in the image,\u201d \u201cnumerous neutrophils in the image,\u201d and \u201cabundant tumor cells in the image.\u201d Human reassessment identified significant differences related to muscle invasion status for all findings except \u201cabundant tumor cells in the image.\u201d Furthermore, we confirmed that histological neutrophil infiltration was related to the abundance of neutrophils in the cytological specimens.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>This study is noteworthy as the cluster analysis identified previously unreported variations in background cell types and quality linked to muscle invasion status, and it also demonstrated the explainability of ML-derived findings through manual reassessment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.4103\/joc.joc_158_24","type":"journal-article","created":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T17:01:02Z","timestamp":1756486862000},"page":"124-133","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":1,"title":["Machine Learning of Urine Cytology Highlights Increased Neutrophil Count in Muscle-Invasive Urothelial Carcinoma"],"prefix":"10.4103","volume":"42","author":[{"given":"Moe","family":"Kameda","sequence":"first","affiliation":[{"name":"Laboratory of Histopathology and Cytopathology, Department of Laboratory Sciences, Gunma University Graduate School of Health Sciences, Gunma, 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