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A Paradigm Shift in Nuclear Chromatin Interpretation: From Qualitative Intuitive Recognition to Quantitative Texture Analysis of Breast Cancer Cell Nuclei
Cytometry Part A ( IF 3.7 ) Pub Date : 2020-11-07 , DOI: 10.1002/cyto.a.24260
Hye-Kyung Lee 1 , Cho-Hee Kim 2 , Subrata Bhattacharjee 3 , Hyeon-Gyun Park 3 , Deekshitha Prakash 3 , Heung-Kook Choi 3
Affiliation  

Assessing the pattern of nuclear chromatin is essential for pathological investigations. However, the interpretation of nuclear pattern is subjective. In this study, we performed the texture analysis of nuclear chromatin in breast cancer samples to determine the nuclear pleomorphism score thereof. We used three different algorithms for extracting high-level texture features: the gray-level co-occurrence matrix (GLCM), gray-level run length matrix (GLRLM), and gray-level size zone matrix (GLSZM). Using these algorithms, 12 GLCM, 11 GLRLM, and 16 GLSZM features were extracted from three scores of breast carcinoma (Scores 1–3). Classification accuracy was assessed using the support vector machine (SVM) and k-nearest neighbor (KNN) classification models. Three features of GLCM, 11 of GLRLM, and 12 of GLSZM were consistent across the three nuclear pleomorphism scores of breast cancer. Comparing Scores 1 and 3, the GLSZM feature large zone high gray-level emphasis showed the largest difference among breast cancer nuclear scores among all features of the three algorithms. The SVM and KNN classifiers showed favorable results for all three algorithms. A multiclass classification was performed to compare and distinguish between the scores of breast cancer. Texture features of nuclear chromatin can provide useful information for nuclear scoring. However, further validation of the correlations of histopathologic features, and standardization of the texture analysis process, are required to achieve better classification results. © 2021 The Authors. Cytometry Part A published by Wiley Periodicals LLC on behalf of International Society for Advancement of Cytometry.

中文翻译:

核染色质解释的范式转变:从乳腺癌细胞核的定性直观识别到定量纹理分析

评估核染色质的模式对于病理研究至关重要。然而,核模式的解释是主观的。在本研究中,我们对乳腺癌样本中的核染色质进行了质地分析,以确定其核多形性评分。我们使用了三种不同的算法来提取高级纹理特征:灰度共生矩阵 (GLCM)、灰度运行长度矩阵 (GLRLM) 和灰度大小区域矩阵 (GLSZM)。使用这些算法,从乳腺癌的三个评分(评分 1-3)中提取了 12 个 GLCM、11 个 GLRLM 和 16 个 GLSZM 特征。使用支持向量机 (SVM) 和 k 最近邻 (KNN) 分类模型评估分类精度。GLCM的三个特点,GLRLM的11个,和 12 个 GLSZM 在乳腺癌的三个核多形性评分中是一致的。比较分数 1 和 3,GLSZM 特征在三种算法的所有特征中,大区域高灰度强调显示了乳腺癌核评分之间的最大差异。SVM 和 KNN 分类器对所有三种算法都显示出良好的结果。进行多类分类以比较和区分乳腺癌的评分。核染色质的纹理特征可以为核评分提供有用的信息。然而,需要进一步验证组织病理学特征的相关性和纹理分析过程的标准化,以获得更好的分类结果。© 2021 作者。细胞学 A 部分由 Wiley Periodicals LLC 代表国际细胞学促进会出版。
更新日期:2020-11-07
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