The Johns Hopkins Hospital template for urologic cytology samples: parts II and III: improving the predictability of indeterminate results in urinary cytologic samples: an outcomes and cytomorphologic study.

BACKGROUND

Urine cytology represents a major portion of testing volume in many cytopathology laboratories.

METHODS

The authors previously reported a template designed to standardize urothelial diagnostic categories to enable clinicians to uniformly manage their patients. In this study, they examined the common cytomorphologic features observed in specimens diagnosed with atypical urothelial cells, cannot exclude high-grade urothelial carcinoma (AUC-H), which prove most predictive of high-grade urothelial carcinoma (HGUC).

RESULTS

The most common morphologic features observed in the AUC-H specimens were hyperchromasia, irregular nuclear borders, increased nucleus-to-cytoplasm ratio, and anisonucleosis. Of the 58 patients who had specimens diagnosed with AUC-H, 95% ultimately were diagnosed with HGUC on follow-up biopsy over the study period. The small number of patients who had AUC-H with non-HGUC follow-up did not allow for a statistical comparison to determine the predictive ability of the selected criteria for HGUC. Next, the authors used the same features to examine a subset of urine samples that were diagnosed with atypical urothelial cells of unknown significance (AUC-US) in an attempt to improve the predictive value of this clinically frustrating category. A blind review was performed of 290 urine specimens from 217 patients. In contrast to the AUC-H specimen cohort, the majority of specimens with AUC-US did not contain atypical cells with the 4 common morphologic features. All 4 features significantly predicted HGUC in surveillance patients, but not in patients with hematuria.

CONCLUSIONS

Hyperchromasia was the strongest predictor of HGUC by far in patients who were undergoing surveillance (odds ratio, 9.81). Hyperchromasia remained statistically significant in multivariate analysis, indicating its predictive strength even in the absence of other features.