{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T00:49:47Z","timestamp":1760316587613,"version":"build-2065373602"},"reference-count":0,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2019,4,5]],"date-time":"2019-04-05T00:00:00Z","timestamp":1554422400000},"content-version":"vor","delay-in-days":4,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["acsjournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Cancer Cytopathology"],"published-print":{"date-parts":[[2019,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>The Paris System for Urine Cytopathology (the Paris System) has succeeded in making the analysis of liquid\u2010based urine preparations more reproducible. Any algorithm seeking to automate this system must accurately estimate the nuclear\u2010to\u2010cytoplasmic (N:C) ratio and produce a qualitative \u201catypia score.\u201d The authors propose a hybrid deep\u2010learning and morphometric model that reliably automates the Paris System.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Whole\u2010slide images (WSI) of liquid\u2010based urine cytology specimens were extracted from 51 negative, 60 atypical, 52 suspicious, and 54 positive cases. Morphometric algorithms were applied to decompose images to their component parts; and statistics, including the NC ratio, were tabulated using segmentation algorithms to create organized data structures, dubbed <jats:italic>rich information matrices<\/jats:italic> (RIMs). These RIM objects were enhanced using deep\u2010learning algorithms to include qualitative measures. The augmented RIM objects were then used to reconstruct WSIs with filtering criteria and to generate pancellular statistical information.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The described system was used to calculate the N:C ratio for all cells, generate object classifications (atypical urothelial cell, squamous cell, crystal, etc), filter the original WSI to remove unwanted objects, rearrange the WSI to an efficient, condensed\u2010grid format, and generate pancellular statistics containing quantitative\/qualitative data for every cell in a WSI. In addition to developing novel techniques for managing WSIs, a system capable of automatically tabulating the Paris System criteria also was generated.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>A hybrid deep\u2010learning and morphometric algorithm was developed for the analysis of urine cytology specimens that could reliably automate the Paris System and provide many avenues for increasing the efficiency of digital screening for urine WSIs and other cytology preparations.<\/jats:p><\/jats:sec>","DOI":"10.1002\/cncy.22116","type":"journal-article","created":{"date-parts":[[2019,4,6]],"date-time":"2019-04-06T05:22:52Z","timestamp":1554528172000},"page":"218-221","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Our 2018 <i>Cancer Cytopathology<\/i> Young Investigator"],"prefix":"10.1002","volume":"127","author":[{"given":"William C.","family":"Faquin","sequence":"first","affiliation":[{"name":"Department of Pathology Massachusetts General Hospital, Harvard Medical School Boston Massachusetts"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2019,4,5]]},"container-title":["Cancer Cytopathology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/cncy.22116","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/cncy.22116","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/acsjournals.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/cncy.22116","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T17:11:55Z","timestamp":1760289115000},"score":1,"resource":{"primary":{"URL":"https:\/\/acsjournals.onlinelibrary.wiley.com\/doi\/10.1002\/cncy.22116"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4]]},"references-count":0,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2019,4]]}},"alternative-id":["10.1002\/cncy.22116"],"URL":"https:\/\/doi.org\/10.1002\/cncy.22116","archive":["Portico"],"relation":{},"ISSN":["1934-662X","1934-6638"],"issn-type":[{"type":"print","value":"1934-662X"},{"type":"electronic","value":"1934-6638"}],"subject":[],"published":{"date-parts":[[2019,4]]},"assertion":[{"value":"2019-02-13","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-02-13","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-04-05","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}