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 ( 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.