Stepan Alex, Simionescu Cristiana, Pirici Daniel, Ciurea Raluca, Margaritescu Claudiu
Department of Pathology, University of Medicine and Pharmacy of Craiova, Petru Rares Street 2, Dolj, 200349 Craiova, Romania.
Department of Pathology, University of Medicine and Pharmacy of Craiova, Petru Rares Street 2, Dolj, 200349 Craiova, Romania ; Department of Research Methodology, University of Medicine and Pharmacy of Craiova, Petru Rares Street 2, Dolj, 200349 Craiova, Romania.
Anal Cell Pathol (Amst). 2015;2015:250265. doi: 10.1155/2015/250265. Epub 2015 Aug 20.
Pathological diagnosis of prostate adenocarcinoma often requires complementary methods. On prostate biopsy tissue from 39 patients including benign nodular hyperplasia (BNH), atypical adenomatous hyperplasia (AAH), and adenocarcinomas, we have performed combined histochemical-immunohistochemical stainings for argyrophilic nucleolar organizer regions (AgNORs) and glandular basal cells. After ascertaining the pathology, we have analyzed the number, roundness, area, and fractal dimension of individual AgNORs or of their skeleton-filtered maps. We have optimized here for the first time a combination of AgNOR morphological denominators that would reflect best the differences between these pathologies. The analysis of AgNORs' roundness, averaged from large composite images, revealed clear-cut lower values in adenocarcinomas compared to benign and atypical lesions but with no differences between different Gleason scores. Fractal dimension (FD) of AgNOR silhouettes not only revealed significant lower values for global cancer images compared to AAH and BNH images, but was also able to differentiate between Gleason pattern 2 and Gleason patterns 3-5 adenocarcinomas. Plotting the frequency distribution of the FDs for different pathologies showed clear differences between all Gleason patterns and BNH. Together with existing morphological classifiers, AgNOR analysis might contribute to a faster and more reliable machine-assisted screening of prostatic adenocarcinoma, as an essential aid for pathologists.
前列腺腺癌的病理诊断通常需要辅助方法。我们对39例患者的前列腺活检组织进行了研究,这些组织包括良性结节性增生(BNH)、非典型腺瘤样增生(AAH)和腺癌,我们对嗜银核仁组织区(AgNORs)和腺基底细胞进行了组织化学-免疫组织化学联合染色。在确定病理后,我们分析了单个AgNORs或其骨架滤波图的数量、圆度、面积和分形维数。我们首次在此优化了一组AgNOR形态学指标的组合,该组合能最好地反映这些病理之间的差异。对从大的合成图像中平均得到的AgNORs圆度分析显示,与良性和非典型病变相比,腺癌中的值明显更低,但不同Gleason评分之间没有差异。AgNOR轮廓的分形维数(FD)不仅显示与AAH和BNH图像相比,整体癌症图像的值显著更低,而且还能够区分Gleason模式2和Gleason模式3-5腺癌。绘制不同病理的FD频率分布图显示,所有Gleason模式与BNH之间存在明显差异。与现有的形态学分类器一起,AgNOR分析可能有助于更快、更可靠地对前列腺腺癌进行机器辅助筛查,作为病理学家的重要辅助手段。