Does artificial intelligence redefine nuclear-to-cytoplasmic ratio threshold for diagnosing high-grade urothelial carcinoma?

BACKGROUND

The Paris System (TPS) introduced standardized nuclear-to-cytoplasmic (N/C) ratio thresholds for urine cytology to improve high-grade urothelial carcinoma (HGUC) detection, but these criteria remain subjective. This study used AIxURO, an artificial intelligence-based model, to measure N/C ratio and nuclear area to identify abnormal cells in whole slide images (WSIs).

METHODS

A total of 106 urine cytology slides from 46 lower urinary tract (LUT) and 60 upper urinary tract (UUT) cancer cases, diagnosed as atypical urothelial cell (15.1%), suspicious for high-grade urothelial carcinoma (SHGUC) (23.6%), and HGUC (61.3%), with biopsy-confirmed HGUC or carcinoma in situ (CIS), were digitized and analyzed by AIxURO. The model quantified suspicious and atypical cells, N/C ratios, and nuclear areas, with statistical differences assessed using Kruskal-Wallis tests.

RESULTS

AIxURO identified fewer suspicious cells than atypical cells (20.5 vs. 242.0, p < .001). Suspicious cells had higher N/C ratios (0.66 vs. 0.58, p < .001) and larger nuclear areas (102.3 vs. 85.7 µm, p < .001). Although N/C ratios did not differ significantly between UUT and LUT cases, nuclear areas varied among abnormal cells (CIS: 101.5 µm; HGUC: 83.5 µm). In HGUC cytology cases, the CIS category had larger nuclear areas than HGUC for both suspicious (116.3 vs. 100.4 µm) and atypical cells (101.5 vs. 82.2 µm).

CONCLUSIONS

AIxURO provides objective quantification of N/C ratios and nuclear areas, refining TPS criteria for distinguishing suspicious from atypical cells. A lower N/C ratio cutoff (0.66) for SHGUC/HGUC may be more appropriate than the TPS threshold (>0.7). Findings support using consistent N/C ratio criteria across UUT and LUT cases.