Vranes Velicko, Rajković Nemanja, Li Xingyu, Plataniotis Konstantinos N, Todorović Raković Nataša, Milovanović Jelena, Kanjer Ksenija, Radulovic Marko, Milošević Nebojša T
Department of Basic and Environmental Science, Instituto Tecnológico de Santo Domingo (INTEC), Santo Domingo 10602, Dominican Republic.
Department of Biophysics, School of Medicine, University of Belgrade, 11000 Belgrade, Serbia.
Cancers (Basel). 2019 Oct 22;11(10):1615. doi: 10.3390/cancers11101615.
Survival and life quality of breast cancer patients could be improved by more aggressive chemotherapy for those at high metastasis risk and less intense treatments for low-risk patients. Such personalized treatment cannot be currently achieved due to the insufficient reliability of metastasis risk prognosis. The purpose of this study was therefore, to identify novel histopathological prognostic markers of metastasis risk through exhaustive computational image analysis of 80 size and shape subsets of epithelial clusters in breast tumors. The group of 102 patients had a follow-up median of 12.3 years, without lymph node spread and systemic treatments. Epithelial cells were stained by the AE1/AE3 pan-cytokeratin antibody cocktail. The size and shape subsets of the stained epithelial cell clusters were defined in each image by use of the circularity and size filters and analyzed for prognostic performance. Epithelial areas with the optimal prognostic performance were uniformly small and round and could be recognized as individual epithelial cells scattered in tumor stroma. Their count achieved an area under the receiver operating characteristic curve (AUC) of 0.82, total area (AUC = 0.77), average size (AUC = 0.63), and circularity (AUC = 0.62). In conclusion, by use of computational image analysis as a hypothesis-free discovery tool, this study reveals the histomorphological marker with a high prognostic value that is simple and therefore easy to quantify by visual microscopy.
对于高转移风险的乳腺癌患者采用更积极的化疗,对于低风险患者采用强度较低的治疗,可提高患者的生存率和生活质量。由于转移风险预后的可靠性不足,目前尚无法实现这种个性化治疗。因此,本研究的目的是通过对乳腺肿瘤上皮细胞簇的80个大小和形状子集进行详尽的计算机图像分析,确定转移风险的新型组织病理学预后标志物。该组102例患者的中位随访时间为12.3年,无淋巴结转移且未接受全身治疗。上皮细胞用AE1/AE3全细胞角蛋白抗体混合物染色。通过使用圆形度和大小过滤器在每个图像中定义染色上皮细胞簇的大小和形状子集,并分析其预后性能。具有最佳预后性能的上皮区域均匀地小且呈圆形,可被识别为散在肿瘤基质中的单个上皮细胞。它们的计数在受试者工作特征曲线(AUC)下的面积为0.82,总面积(AUC = 0.77),平均大小(AUC = 0.63)和圆形度(AUC = 0.62)。总之,通过使用计算机图像分析作为无假设发现工具,本研究揭示了具有高预后价值的组织形态学标志物,该标志物简单,因此易于通过视觉显微镜进行量化。