Urine cytology: Updates and challenges in reporting systems, ancillary studies, and artificial intelligence

Human Pathology Reports Pub Date : 2024-02-28 DOI:10.1016/j.hpr.2024.300733

Juan Xing , Jordan P. Reynolds , Xiaoying Liu , Liron Pantanowitz

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Abstract

Several urine cytology classifications have been published in the literature. However, global acceptance in the field of urine cytology was only gained in 2016 after The Paris System for reporting urinary cytology was published. Despite this Paris System and its shifted focus toward the detection of high-grade urothelial carcinoma, the perceived weakness of low sensitivity and indeterminate diagnoses when screening with urine cytology remains unresolved. To overcome these shortcomings, investigators have studied a variety of emerging ancillary tests to augment urine cytology (UroVysion, ImmunoCyt/uCyte+, BTA-stat/TRAK, NMP22, SCD-A7, URO17, CellDetect, UroMark, UroSEEK). Furthermore, with the advent of digital cytology, the creation of artificial intelligence tools has created innovative opportunities to aid with urine cytology. This review article discusses the lessons learned in the evolution of reporting systems, explores the merit and challenges of ancillary tests, and calls attention to potential utility of applying artificial intelligence in urine cytology.

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尿液细胞学：报告系统、辅助研究和人工智能的更新与挑战

文献中已发表了多种尿液细胞学分类方法。然而，直到 2016 年尿液细胞学报告巴黎体系（The Paris System for reporting urinary cytology）发布后，尿液细胞学领域才获得全球认可。尽管有了巴黎系统，并且其重点转向检测高级别尿路上皮癌，但尿液细胞学筛查中灵敏度低和诊断不确定的弱点仍未得到解决。为了克服这些缺点，研究人员研究了各种新出现的辅助检测方法来增强尿液细胞学检查（UroVysion、ImmunoCyt/uCyte+、BTA-stat/TRAK、NMP22、SCD-A7、URO17、CellDetect、UroMark、UroSEEK）。此外，随着数字细胞学技术的出现，人工智能工具的创造也为尿液细胞学检查带来了创新机会。这篇综述文章讨论了报告系统发展过程中的经验教训，探讨了辅助检验的优点和挑战，并呼吁人们关注人工智能在尿液细胞学中应用的潜在效用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Human Pathology Reports

CiteScore

0.60

自引率

0.00%

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