{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T10:09:08Z","timestamp":1784887748627,"version":"3.55.0"},"reference-count":82,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T00:00:00Z","timestamp":1782518400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T00:00:00Z","timestamp":1782518400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["acsjournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Cancer Cytopathology"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Bladder cancer is a common and highly recurrent malignancy requiring lifelong surveillance. Urine cytology serves as a noninvasive triage tool to guide cystoscopy but is limited by variable sensitivity and manual review. Although deep learning enables quantitative cell\u2010level assessment, limited work has examined three\u2010dimensional urine cytology preparations (e.g.,\u00a0SurePath) containing residual cellular fragments, where restricting analysis to a single nominal focal plane may obscure diagnostically relevant features. This study aimed to quantify focal\u2010plane heterogeneity, measure degradation of nuclear\u2010to\u2010cytoplasmic (NC) ratio and nuclear area estimates off plane, and evaluate focal\u2010plane selection algorithms for performance recovery.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 325 SurePath whole\u2010slide images scanned as 11\u2010plane Z\u2010stacks were analyzed that spanned negative through high\u2010grade urothelial carcinoma cases. A detection model identified cells and clusters across planes, and 343 clusters (2435 urothelial cells) were reannotated at the optimal nuclear and cytoplasmic focal depths. Classical focus metrics and vision\u2010transformer models were evaluated for focal\u2010plane prediction. A U\u2010Net segmentation model generated NC ratios and nuclear areas, and Spearman correlations compared annotated and predicted measurements across optimal, off\u2010plane, and algorithm\u2010selected conditions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Focal\u2010plane prediction accuracy ranged from 42% to 88%, with classical focus metrics outperforming deep\u2010learning approaches. NC ratio correlation was 0.774 at optimal focus and declined progressively off plane (\u223c0.50 at \u00b15 planes). Algorithm\u2010selected planes partially recovered performance (up to 0.748). Similar trends were observed for nuclear area estimation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Accurate focal\u2010plane selection is critical for artificial intelligence\u2013based assessment of three\u2010dimensional urine cytology. Future work will extend this analysis to cluster\u2010 and patient\u2010level outcomes in multi\u2010institutional validation studies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/cncy.70117","type":"journal-article","created":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T10:12:08Z","timestamp":1782555128000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Accurate focal\u2010plane selection is crucial for artificial intelligence assessment of three\u2010dimensional urine cytology specimens for bladder cancer screening and surveillance"],"prefix":"10.1002","volume":"134","author":[{"given":"Yoseph","family":"Sayegh","sequence":"first","affiliation":[{"name":"Department of Pathology Johns Hopkins Medical Center  Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brody","family":"McNutt","sequence":"additional","affiliation":[{"name":"Department of Pathology 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