Ansardamavandi Arian, Tafazzoli-Shadpour Mohammad, Omidvar Ramin, Jahanzad Iisa
Faculty of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran.
Faculty of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran.
J Mech Behav Biomed Mater. 2016 Jul;60:234-242. doi: 10.1016/j.jmbbm.2015.12.028. Epub 2016 Jan 7.
Different behaviors of cells such as growth, differentiation and apoptosis widely differ in case of diseases. The mechanical properties of cells and tissues can be used as a clue for diagnosis of pathological conditions. Here, we implemented Atomic Force Microscopy to evaluate the extent of alteration in mechanical stiffness of tissue layers from patients affected by breast cancer and investigated how data can be categorized based on pathological observations. To avoid predefined categories, Fuzzy-logic algorithm as a novel method was used to divide and categorize the derived Young׳s modulus coefficients (E). Such algorithm divides data among groups in such way that data of each group are mostly similar while dissimilar with other groups. The algorithm was run for different number of categories. Results showed that three (followed by two with small difference) groups categorized data best. Three categories were defined as (E<3000Pa, 3000<E<7000Pa and E>7000Pa) among which data were allocated. The first cluster was assumed as the cellular region while the last cluster was referred to the fibrous parts of the tissue. The intermediate region was due to other non-cellular parts. Results indicated 50% decline of average Young׳s modulus of cellular region of cancerous tissues compared to healthy tissues. The average Young׳s modulus of non-cellular area of normal tissues was slightly lower than that of cancerous tissues, although the difference was not statistically different. Through clustering, the measured Young׳s moduli of different locations of cancerous tissues, a quantified approach was developed to analyze changes in elastic modulus of a spectrum of components of breast tissue which can be applied in diagnostic mechanisms of cancer development, since in cancer progression the softening cell body facilitates the migration of cancerous cells through the original tumor and endothelial junctions.
细胞的不同行为，如生长、分化和凋亡，在疾病情况下有很大差异。细胞和组织的力学特性可作为病理状况诊断的线索。在此，我们采用原子力显微镜来评估乳腺癌患者组织层机械硬度的改变程度，并研究如何根据病理观察对数据进行分类。为避免预定义类别，使用模糊逻辑算法作为一种新方法来划分和分类导出的杨氏模量系数（E）。这种算法以这样的方式将数据划分到不同组中：每组的数据大多相似，而与其他组不同。该算法针对不同数量的类别运行。结果表明，三类（其次是两类，差异较小）对数据的分类最佳。定义了三类（E<3000Pa、3000<E<7000Pa和E>7000Pa）并在其中分配数据。第一个聚类被假定为细胞区域，而最后一个聚类指的是组织的纤维部分。中间区域是由于其他非细胞部分。结果表明，癌组织细胞区域的平均杨氏模量与健康组织相比下降了50%。正常组织非细胞区域的平均杨氏模量略低于癌组织，尽管差异无统计学意义。通过对癌组织不同位置测量的杨氏模量进行聚类，开发了一种量化方法来分析乳腺组织一系列成分的弹性模量变化，这可应用于癌症发展的诊断机制，因为在癌症进展过程中，软化的细胞体有助于癌细胞通过原发肿瘤和内皮连接迁移。
