Lukianova N, Zadvornyi T, Mushii О, Pyatchanina T, Chekhun V
R.E. Kavetsky Institute of Experimental Pathology, Oncology and Radiobiology, NAS of Ukraine, 03022 Kyiv, Ukraine.
Exp Oncol. 2022 Dec;44(4):281-286. doi: 10.32471/exp-oncology.2312-8852.vol-44-no-4.19137.
The changes in the quantitative parameters and spatial structure of collagen are considered a key diagnostic and prognostic factor associated with the development of many malignant neoplasms, including breast cancer (BCa). The aim of the work was to develop and test an algorithm for the assessment of collagen organization parameters as informative attributes associated with BCa for developing technology of machine learning and building an intelligent system of cancer diagnostics.
Tumor tissue samples of 5 patients with breast fibroadenomas and 20 patients with stage I-II BCa were studied. Collagen was identified histochemically by Mallory method. Photomicrographs of the studied preparations were obtained using a digital microscopy complex AxioScope A1. Morphometric studies were performed using the software CurveAlign v. 4.0. beta and ImageJ.
The algorithm for determining the quantitative characteristics and spatial organization of the collagen matrix in tumor tissue samples has been developed and tested. We showed that collagen fibers in the BCa tissue are characterized by significantly lower values of length (p < 0.001) and width (p < 0.001) as well as higher values of straightness (p < 0.001) and angle (p < 0.05) compared to these in the fibroadenoma tissue. No significant difference was found in the density of collagen fibers in the tissue of benign and malignant neoplasms of the mammary gland.
The algorithm allows assessing a wide range of parameters of collagen fibers in tumor tissue, including their spatial orientation and mutual arrangement, parametric characteristics and density of the three-dimensional fibrillar network.
胶原蛋白定量参数和空间结构的变化被认为是与包括乳腺癌（BCa）在内的许多恶性肿瘤发生发展相关的关键诊断和预后因素。本研究的目的是开发并测试一种算法，用于评估胶原蛋白组织参数，将其作为与BCa相关的信息属性，以发展机器学习技术并构建癌症诊断智能系统。
研究了5例乳腺纤维腺瘤患者和20例I-II期BCa患者的肿瘤组织样本。采用马洛里法对胶原蛋白进行组织化学鉴定。使用数字显微镜系统AxioScope A1获取所研究制剂的显微照片。使用软件CurveAlign v. 4.0. beta和ImageJ进行形态计量学研究。
已开发并测试了用于确定肿瘤组织样本中胶原蛋白基质定量特征和空间组织的算法。我们发现，与纤维腺瘤组织相比，BCa组织中的胶原纤维长度（p < 0.001）和宽度（p < 0.001）值显著更低，而直线度（p < 0.001）和角度（p < 0.05）值更高。在乳腺良性和恶性肿瘤组织中，胶原纤维密度未发现显著差异。
该算法能够评估肿瘤组织中胶原纤维的广泛参数，包括其空间取向和相互排列、三维纤维网络的参数特征和密度。
