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.
Keywords: Artificial intelligence and machine learning; High-grade urothelial carcinoma; Low-grade urothelial carcinoma; The Paris System; Upper tract urothelial carcinoma; Urothelial carcinoma.
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