Chukwudebe Olisaemeka, Lynch Elizabeth, Vira Manish, Vaickus Louis, Khan Anam, Shaheen Cocker Rubina
Department of Pathology Northwell Health, Staten Island University Hospital, Staten Island, New York.
The Arthur Smith Institute for Urology, Lake Success, New York.
J Am Soc Cytopathol. 2025 Jan-Feb;14(1):23-35. doi: 10.1016/j.jasc.2024.09.001. Epub 2024 Sep 19.
The Paris System for Reporting Urine Cytology (TPS) is remarkable for its high predictive value in the detection of high-grade urothelial carcinoma, especially of the bladder. However, universal compliance with TPS-recommended threshold for atypical call rates (15%) and TPS performance in the rarer upper tract urothelial carcinomas (UTUC) are challenging. UTUC diagnosis is compounded by instrumentation artifacts, degenerative changes superimposed on an ambiguous cytology, difficult-to-access location, lack of specific standardized criteria, and a limited number of UTUC-focused studies. We reviewed TPS-applied studies published since 2022, noting up to 50%, exceeding the suggested 15% threshold for atypia. Our examination of ancillary tests for UTUC explored novel approaches including DNA methylation analysis, the detection of overexpressed tumor-linked messenger RNAs, and immunohistochemistry on markers such as CK17. Preliminary evidence from our review suggests that ancillary tests display superior performance over cytology, including in voided samples and low-grade urothelial carcinoma. Importantly, voided samples obviate the risks of ureterorenoscopy. Finally, we explored the future opportunities offered by artificial intelligence and machine learning for a more objective application of TPS criteria on urine samples.
巴黎尿液细胞学报告系统(TPS)在检测高级别尿路上皮癌,尤其是膀胱癌方面具有很高的预测价值。然而,普遍遵守TPS推荐的非典型细胞检出率阈值(15%)以及TPS在较罕见的上尿路尿路上皮癌(UTUC)中的表现具有挑战性。UTUC的诊断因器械伪像、细胞学模糊时叠加的退行性改变、难以到达的位置、缺乏特定的标准化标准以及针对UTUC的研究数量有限而变得复杂。我们回顾了自2022年以来应用TPS的研究,发现高达50%的研究超过了建议的15%非典型细胞阈值。我们对UTUC辅助检查的研究探索了新方法,包括DNA甲基化分析、检测过表达的肿瘤相关信使RNA以及对CK17等标志物进行免疫组织化学检测。我们回顾的初步证据表明,辅助检查在性能上优于细胞学检查,包括在排尿样本和低级别尿路上皮癌中。重要的是,排尿样本避免了输尿管镜检查的风险。最后,我们探索了人工智能和机器学习为更客观地应用TPS标准于尿液样本提供的未来机会。