Diagnostic challenges in urinary cytology: Practical insights from The Paris System for Reporting Urinary Cytology

Abstract

Urinary cytology is an important tool for diagnosing high-grade urothelial carcinoma (HGUC) and plays a vital role in monitoring for disease recurrence. However, its diagnostic utility is often complicated by interpretive challenges, particularly when degenerative artifacts, sparse cellularity, or reactive atypia obscure key cytologic features. The Paris System for Reporting Urinary Cytology has significantly enhanced diagnostic reproducibility by establishing standardized criteria for the diagnosis of HGUC, but misclassification remains a risk, especially when evaluating subtle atypia, tissue fragments, or confounding factors like degenerative changes. One of the most frequent pitfalls in urinary cytology is differentiating HGUC from benign mimics, particularly in specimens affected by prolonged urine exposure, inflammation, or instrumentation artifacts. Similarly, the classification of atypical urothelial cells presents a diagnostic gray zone because its predictive value varies widely, depending on clinical context. Low-grade urothelial neoplasms further complicate risk stratification because these tumors infrequently exfoliate in voided specimens and can appear indistinguishable from reactive urothelial cells in these samples. Consequently, the goal of The Paris System for Reporting Urinary Cytology is to focus on the detection of HGUC to preserve the specificity and the positive predictive value of urinary cytology. Advancements in molecular profiling and artificial intelligence-driven cytopathology promise enhanced reproducibility and risk stratification, refining the role of urinary cytology in precision medicine. However, the success of urinary cytology remains rooted in a balanced approach, integrating morphologic expertise, molecular insights, and clinical data. By applying these essential practices, cytopathologists can improve diagnostic accuracy, reduce misclassification, and optimize patient management.