{"title":"评估人工智能增强型数字尿液细胞学在膀胱癌诊断中的应用。","authors":"Tien-Jen Liu, Wen-Chi Yang, Shin-Min Huang, Wei-Lei Yang, Hsing-Ju Wu, Hui Wen Ho, Shih-Wen Hsu, Cheng-Hung Yeh, Ming-Yu Lin, Yi-Ting Hwang, Pei-Yi Chu MD, PhD","doi":"10.1002/cncy.22884","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>This study evaluated the diagnostic effectiveness of the AIxURO platform, an artificial intelligence–based tool, to support urine cytology for bladder cancer management, which typically requires experienced cytopathologists and substantial diagnosis time.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>One cytopathologist and two cytotechnologists reviewed 116 urine cytology slides and corresponding whole-slide images (WSIs) from urology patients. They used three diagnostic modalities: microscopy, WSI review, and AIxURO, per The Paris System for Reporting Urinary Cytology (TPS) criteria. Performance metrics, including TPS-guided and binary diagnosis, inter- and intraobserver agreement, and screening time, were compared across all methods and reviewers.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>AIxURO improved diagnostic accuracy by increasing sensitivity (from 25.0%–30.6% to 63.9%), positive predictive value (PPV; from 21.6%–24.3% to 31.1%), and negative predictive value (NPV; from 91.3%–91.6% to 95.3%) for atypical urothelial cell (AUC) cases. For suspicious for high-grade urothelial carcinoma (SHGUC) cases, it improved sensitivity (from 15.2%–27.3% to 33.3%), PPV (from 31.3%–47.4% to 61.1%), and NPV (from 91.6%–92.7% to 93.3%). Binary diagnoses exhibited an improvement in sensitivity (from 77.8%–82.2% to 90.0%) and NPV (from 91.7%–93.4% to 95.8%). Interobserver agreement across all methods showed moderate consistency (κ = 0.57–0.61), with the cytopathologist demonstrating higher intraobserver agreement than the two cytotechnologists across the methods (κ = 0.75–0.88). AIxURO significantly reduced screening time by 52.3%–83.2% from microscopy and 43.6%–86.7% from WSI review across all reviewers. Screening-positive (AUC+) cases required more time than negative cases across all methods and reviewers.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>AIxURO demonstrates the potential to improve both sensitivity and efficiency in bladder cancer diagnostics via urine cytology. Its integration into the cytopathological screening workflow could markedly decrease screening times, which would improve overall diagnostic processes.</p>\n </section>\n </div>","PeriodicalId":9410,"journal":{"name":"Cancer Cytopathology","volume":"132 11","pages":"686-695"},"PeriodicalIF":2.6000,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cncy.22884","citationCount":"0","resultStr":"{\"title\":\"Evaluating artificial intelligence–enhanced digital urine cytology for bladder cancer diagnosis\",\"authors\":\"Tien-Jen Liu, Wen-Chi Yang, Shin-Min Huang, Wei-Lei Yang, Hsing-Ju Wu, Hui Wen Ho, Shih-Wen Hsu, Cheng-Hung Yeh, Ming-Yu Lin, Yi-Ting Hwang, Pei-Yi Chu MD, PhD\",\"doi\":\"10.1002/cncy.22884\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>This study evaluated the diagnostic effectiveness of the AIxURO platform, an artificial intelligence–based tool, to support urine cytology for bladder cancer management, which typically requires experienced cytopathologists and substantial diagnosis time.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>One cytopathologist and two cytotechnologists reviewed 116 urine cytology slides and corresponding whole-slide images (WSIs) from urology patients. They used three diagnostic modalities: microscopy, WSI review, and AIxURO, per The Paris System for Reporting Urinary Cytology (TPS) criteria. Performance metrics, including TPS-guided and binary diagnosis, inter- and intraobserver agreement, and screening time, were compared across all methods and reviewers.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>AIxURO improved diagnostic accuracy by increasing sensitivity (from 25.0%–30.6% to 63.9%), positive predictive value (PPV; from 21.6%–24.3% to 31.1%), and negative predictive value (NPV; from 91.3%–91.6% to 95.3%) for atypical urothelial cell (AUC) cases. For suspicious for high-grade urothelial carcinoma (SHGUC) cases, it improved sensitivity (from 15.2%–27.3% to 33.3%), PPV (from 31.3%–47.4% to 61.1%), and NPV (from 91.6%–92.7% to 93.3%). Binary diagnoses exhibited an improvement in sensitivity (from 77.8%–82.2% to 90.0%) and NPV (from 91.7%–93.4% to 95.8%). Interobserver agreement across all methods showed moderate consistency (κ = 0.57–0.61), with the cytopathologist demonstrating higher intraobserver agreement than the two cytotechnologists across the methods (κ = 0.75–0.88). AIxURO significantly reduced screening time by 52.3%–83.2% from microscopy and 43.6%–86.7% from WSI review across all reviewers. Screening-positive (AUC+) cases required more time than negative cases across all methods and reviewers.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusions</h3>\\n \\n <p>AIxURO demonstrates the potential to improve both sensitivity and efficiency in bladder cancer diagnostics via urine cytology. Its integration into the cytopathological screening workflow could markedly decrease screening times, which would improve overall diagnostic processes.</p>\\n </section>\\n </div>\",\"PeriodicalId\":9410,\"journal\":{\"name\":\"Cancer Cytopathology\",\"volume\":\"132 11\",\"pages\":\"686-695\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2024-07-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cncy.22884\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cancer Cytopathology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/cncy.22884\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cancer Cytopathology","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/cncy.22884","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ONCOLOGY","Score":null,"Total":0}
Evaluating artificial intelligence–enhanced digital urine cytology for bladder cancer diagnosis
Background
This study evaluated the diagnostic effectiveness of the AIxURO platform, an artificial intelligence–based tool, to support urine cytology for bladder cancer management, which typically requires experienced cytopathologists and substantial diagnosis time.
Methods
One cytopathologist and two cytotechnologists reviewed 116 urine cytology slides and corresponding whole-slide images (WSIs) from urology patients. They used three diagnostic modalities: microscopy, WSI review, and AIxURO, per The Paris System for Reporting Urinary Cytology (TPS) criteria. Performance metrics, including TPS-guided and binary diagnosis, inter- and intraobserver agreement, and screening time, were compared across all methods and reviewers.
Results
AIxURO improved diagnostic accuracy by increasing sensitivity (from 25.0%–30.6% to 63.9%), positive predictive value (PPV; from 21.6%–24.3% to 31.1%), and negative predictive value (NPV; from 91.3%–91.6% to 95.3%) for atypical urothelial cell (AUC) cases. For suspicious for high-grade urothelial carcinoma (SHGUC) cases, it improved sensitivity (from 15.2%–27.3% to 33.3%), PPV (from 31.3%–47.4% to 61.1%), and NPV (from 91.6%–92.7% to 93.3%). Binary diagnoses exhibited an improvement in sensitivity (from 77.8%–82.2% to 90.0%) and NPV (from 91.7%–93.4% to 95.8%). Interobserver agreement across all methods showed moderate consistency (κ = 0.57–0.61), with the cytopathologist demonstrating higher intraobserver agreement than the two cytotechnologists across the methods (κ = 0.75–0.88). AIxURO significantly reduced screening time by 52.3%–83.2% from microscopy and 43.6%–86.7% from WSI review across all reviewers. Screening-positive (AUC+) cases required more time than negative cases across all methods and reviewers.
Conclusions
AIxURO demonstrates the potential to improve both sensitivity and efficiency in bladder cancer diagnostics via urine cytology. Its integration into the cytopathological screening workflow could markedly decrease screening times, which would improve overall diagnostic processes.
期刊介绍：
Cancer Cytopathology provides a unique forum for interaction and dissemination of original research and educational information relevant to the practice of cytopathology and its related oncologic disciplines. The journal strives to have a positive effect on cancer prevention, early detection, diagnosis, and cure by the publication of high-quality content. The mission of Cancer Cytopathology is to present and inform readers of new applications, technological advances, cutting-edge research, novel applications of molecular techniques, and relevant review articles related to cytopathology.
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