{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T21:25:47Z","timestamp":1782336347390,"version":"3.54.5"},"reference-count":34,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T00:00:00Z","timestamp":1710288000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Histopathology"],"published-print":{"date-parts":[[2024,7]]},"abstract":"<jats:sec><jats:title>Aims<\/jats:title><jats:p>Immune checkpoint inhibitors targeting programmed death\u2010ligand 1 (PD\u2010L1) have shown promising clinical outcomes in urothelial carcinoma (UC). The combined positive score (CPS) quantifies PD\u2010L1 22C3 expression in UC, but it can vary between pathologists due to the consideration of both immune and tumour cell positivity.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods and Results<\/jats:title><jats:p>An artificial intelligence (AI)\u2010powered PD\u2010L1 CPS analyser was developed using 1,275,907 cells and 6175.42\u2009mm<jats:sup>2<\/jats:sup> of tissue annotated by pathologists, extracted from 400 PD\u2010L1 22C3\u2010stained whole slide images of UC. We validated the AI model on 543 UC PD\u2010L1 22C3 cases collected from three institutions. There were 446 cases (82.1%) where the CPS results (CPS \u226510 or &lt;10) were in complete agreement between three pathologists, and 486 cases (89.5%) where the AI\u2010powered CPS results matched the consensus of two or more pathologists. In the pathologist's assessment of the CPS, statistically significant differences were noted depending on the source hospital (<jats:italic>P<\/jats:italic>\u2009=\u20090.003). Three pathologists reevaluated discrepancy cases with AI\u2010powered CPS results. After using the AI as a guide and revising, the complete agreement increased to 93.9%. The AI model contributed to improving the concordance between pathologists across various factors including hospital, specimen type, pathologic T stage, histologic subtypes, and dominant PD\u2010L1\u2010positive cell type. In the revised results, the evaluation discordance among slides from different hospitals was mitigated.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This study suggests that AI models can help pathologists to reduce discrepancies between pathologists in quantifying immunohistochemistry including PD\u2010L1 22C3 CPS, especially when evaluating data from different institutions, such as in a telepathology setting.<\/jats:p><\/jats:sec>","DOI":"10.1111\/his.15176","type":"journal-article","created":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T10:30:56Z","timestamp":1710325856000},"page":"81-91","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["An artificial intelligence\u2010powered <scp>PD\u2010L1<\/scp> combined positive score (<scp>CPS<\/scp>) analyser in urothelial carcinoma alleviating interobserver and intersite variability"],"prefix":"10.1111","volume":"85","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2801-9072","authenticated-orcid":false,"given":"Kyu Sang","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Pathology, Seoul National University Bundang Hospital Seoul National University College of Medicine  Seongnam\u2010si Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9284-9276","authenticated-orcid":false,"given":"Euno","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Pathology Ewha Womans University Mokdong Hospital Ewha Womans University College of Medicine  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3414-9869","authenticated-orcid":false,"given":"Soo Ick","family":"Cho","sequence":"additional","affiliation":[{"name":"Lunit  Seoul Republic of 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