Computer-assisted scatter plot analysis of cell and nuclear areas distinguishes urothelial carcinoma in urine cytology specimens.

Objective: Image analysis in urine cytology typically focuses on individual cells, particularly nuclear features. This study aimed to analyze non-tumor and urothelial carcinoma cases by examining scatter plots of cell or cell cluster areas and the maximum nuclear area within them.

Material and methods: The study included 192 cases: 52 negative and 140 positive. Whole slide images were generated using a virtual slide scanner, and image analysis was conducted with cytological analysis software. Scatter plots were created for cells/cell cluster areas and the largest connected nuclear areas (scatter plot for cells/cell cluster), as well as for nuclear area and perimeter (scatter plot for nucleus).

Results: In the scatter plot for the nucleus, significant differences were noted between cytology-negative and cytology-positive groups (P = 0.0134). However, when divided into cytology-negative, non-invasive, and invasive groups, a significant difference was only found between negative and non-invasive groups (P = 0.0281), not between negative and invasive groups (P = 0.1266). In the scatter plot for cell/cell cluster, plotting cell cluster area (X-axis) and maximum nuclear area (Y-axis) revealed three distribution patterns: horizontal (X-axis), vertical (Y-axis), and diagonal. Cytology-negative cases mainly showed horizontal patterns, while cytology-positive cases exhibited vertical patterns. In the non-tumor group, horizontal patterns were dominant, while vertical patterns were common in non-invasive and invasive tumor groups. The pTa low-grade group mainly showed diagonal patterns, whereas the pTa high-grade, pTis, and pTis + pTa groups predominantly showed vertical patterns. The percentage of cell/cell clusters in tumor-rich areas (along with Y-axis) was significantly higher in non-invasive and invasive tumors compared to non-tumor cases (P < 0.0001), although lower in invasive tumors compared to non-invasive ones (P = 0.0299). In addition, neutrophil-rich images were significantly more common in stromal and muscle invasion groups than in non-invasion groups.

Conclusion: In urine cytology, cellular overlap and cluster density were key factors for distinguishing malignant from benign cells. This image analysis algorithm was useful in identifying malignant clusters with large, connected nuclear regions. The algorithm could potentially detect both invasive and early-stage tumors, highlighting the need for further development of such tools for routine diagnosis.