Cakir Ebru, Kucuk Ulku, Pala Emel Ebru, Sezer Ozlem, Ekin Rahmi Gokhan, Cakmak Ozgur
Department of Pathology, İzmir Katip Celebi University, Ataturk Education and Research Hospital, Izmir, Turkey.
Department of Pathology, Izmir Tepecik Education and Research Hospital, Izmir, Turkey.
APMIS. 2017 May;125(5):431-436. doi: 10.1111/apm.12657. Epub 2017 Feb 22.
Conventional cytomorphologic assessment is the first step to establish an accurate diagnosis in urinary cytology. In cytologic preparations, the separation of low-grade urothelial carcinoma (LGUC) from reactive urothelial proliferation (RUP) can be exceedingly difficult. The bladder washing cytologies of 32 LGUC and 29 RUP were reviewed. The cytologic slides were examined for the presence or absence of the 28 cytologic features. The cytologic criteria showing statistical significance in LGUC were increased numbers of monotonous single (non-umbrella) cells, three-dimensional cellular papillary clusters without fibrovascular cores, irregular bordered clusters, atypical single cells, irregular nuclear overlap, cytoplasmic homogeneity, increased N/C ratio, pleomorphism, nuclear border irregularity, nuclear eccentricity, elongated nuclei, and hyperchromasia (p ˂ 0.05), and the cytologic criteria showing statistical significance in RUP were inflammatory background, mixture of small and large urothelial cells, loose monolayer aggregates, and vacuolated cytoplasm (p ˂ 0.05). When these variables were subjected to a stepwise logistic regression analysis, four features were selected to distinguish LGUC from RUP: increased numbers of monotonous single (non-umbrella) cells, increased nuclear cytoplasmic ratio, hyperchromasia, and presence of small and large urothelial cells (p = 0.0001). By this logistic model of the 32 cases with proven LGUC, the stepwise logistic regression analysis correctly predicted 31 (96.9%) patients with this diagnosis, and of the 29 patients with RUP, the logistic model correctly predicted 26 (89.7%) patients as having this disease. There are several cytologic features to separate LGUC from RUP. Stepwise logistic regression analysis is a valuable tool for determining the most useful cytologic criteria to distinguish these entities.
传统的细胞形态学评估是在尿液细胞学中建立准确诊断的第一步。在细胞学制片中,将低级别尿路上皮癌(LGUC)与反应性尿路上皮增生(RUP)区分开来可能极其困难。回顾了32例LGUC和29例RUP的膀胱冲洗细胞学检查结果。检查细胞学玻片上是否存在28种细胞特征。在LGUC中具有统计学意义的细胞学标准为单调的单个(非伞状)细胞数量增加、无纤维血管核心的三维细胞乳头簇、边界不规则的簇、非典型单个细胞、不规则核重叠、细胞质均匀性、核质比增加、多形性、核边界不规则、核偏心、核拉长和核深染(p<0.05),而在RUP中具有统计学意义的细胞学标准为炎症背景、大小不等的尿路上皮细胞混合、松散的单层聚集物和空泡状细胞质(p<0.05)。当对这些变量进行逐步逻辑回归分析时,选择了四个特征来区分LGUC和RUP:单调的单个(非伞状)细胞数量增加、核质比增加、核深染以及大小不等的尿路上皮细胞的存在(p = 0.0001)。通过该逻辑模型对32例经证实的LGUC病例进行分析,逐步逻辑回归分析正确预测了31例(96.9%)患有该诊断的患者,在29例RUP患者中,逻辑模型正确预测了26例(89.7%)患有该疾病的患者。有几种细胞学特征可用于区分LGUC和RUP。逐步逻辑回归分析是确定区分这些实体最有用的细胞学标准的有价值工具。