Hoshino Chinami, Kobayashi Sayaka, Nishijima Yoshimi, Arai Seiji, Suzuki Kazuhiro, Saio Masanao
Laboratory of Histopathology and Cytopathology, Department of Laboratory Sciences, Gunma University Graduate School of Health Sciences, Gunma, Japan.
Department of Medical Laboratory Science, Kitasato University School of Health Sciences, Niigata, Japan.
Cytojournal. 2025 Feb 8;22:12. doi: 10.25259/Cytojournal_213_2024. eCollection 2025.
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.
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).
In the scatter plot for the nucleus, significant differences were noted between cytology-negative and cytology-positive groups ( = 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 ( = 0.0281), not between negative and invasive groups ( = 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 ( < 0.0001), although lower in invasive tumors compared to non-invasive ones ( = 0.0299). In addition, neutrophil-rich images were significantly more common in stromal and muscle invasion groups than in non-invasion groups.
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.
尿液细胞学中的图像分析通常聚焦于单个细胞，尤其是细胞核特征。本研究旨在通过检查细胞或细胞团区域以及其中最大细胞核面积的散点图，对非肿瘤和尿路上皮癌病例进行分析。
该研究纳入192例病例：52例阴性和140例阳性。使用虚拟切片扫描仪生成全切片图像，并使用细胞学分析软件进行图像分析。针对细胞/细胞团区域和最大相连细胞核区域创建散点图（细胞/细胞团散点图），以及针对细胞核面积和周长创建散点图（细胞核散点图）。
在细胞核散点图中，细胞学阴性组和阳性组之间存在显著差异（ = 0.0134）。然而，当分为细胞学阴性、非侵袭性和侵袭性组时，仅在阴性和非侵袭性组之间发现显著差异（ = 0.0281），阴性和侵袭性组之间未发现显著差异（ = 0.1266）。在细胞/细胞团散点图中，绘制细胞团面积（X轴）和最大细胞核面积（Y轴）显示出三种分布模式：水平（X轴）、垂直（Y轴）和对角线。细胞学阴性病例主要表现为水平模式，而细胞学阳性病例表现为垂直模式。在非肿瘤组中，水平模式占主导，而垂直模式在非侵袭性和侵袭性肿瘤组中常见。pTa低级别组主要表现为对角线模式，而pTa高级别、pTis和pTis + pTa组主要表现为垂直模式。与非肿瘤病例相比，非侵袭性和侵袭性肿瘤中富含肿瘤区域（沿Y轴）的细胞/细胞团百分比显著更高（ < 0.0001），尽管侵袭性肿瘤中的该百分比低于非侵袭性肿瘤（ = 0.0299）。此外，富含中性粒细胞的图像在基质和肌肉侵袭组中比在非侵袭组中显著更常见。
在尿液细胞学中，细胞重叠和团块密度是区分恶性细胞和良性细胞的关键因素。这种图像分析算法有助于识别具有大的相连细胞核区域的恶性团块。该算法可能检测到侵袭性肿瘤和早期肿瘤，凸显了进一步开发此类工具用于常规诊断的必要性。
