Department of Medical Cell Biology, Institute of Molecular Embryology and Genetics, Kumamoto University, Kumamoto, Japan.
Department of Health Science, Faculty of Medical Science, Kyushu University, Fukuoka, Japan.
Cancer Med. 2020 Mar;9(6):2223-2234. doi: 10.1002/cam4.2885. Epub 2020 Feb 3.
Biological morphologies of cells and tissues represent their physiological and pathological conditions. The importance of quantitative assessment of morphological information has been highly recognized in clinical diagnosis and therapeutic strategies. In this study, we used a supervised machine learning algorithm wndchrm to classify hematoxylin and eosin (H&E)-stained images of human gastric cancer tissues. This analysis distinguished between noncancer and cancer tissues with different histological grades. We then classified the H&E-stained images by expression levels of cancer-associated nuclear ATF7IP/MCAF1 and membranous PD-L1 proteins using immunohistochemistry of serial sections. Interestingly, classes with low and high expressions of each protein exhibited significant morphological dissimilarity in H&E images. These results indicated that morphological features in cancer tissues are correlated with expression of specific cancer-associated proteins, suggesting the usefulness of biomolecular-based morphological classification.
细胞和组织的生物形态代表了它们的生理和病理状况。在临床诊断和治疗策略中，定量评估形态信息的重要性得到了高度认可。在这项研究中，我们使用有监督的机器学习算法 wndchrm 对人胃癌组织的苏木精和伊红（H&E）染色图像进行分类。该分析区分了具有不同组织学分级的非癌组织和癌组织。然后，我们通过免疫组织化学对连续切片中癌相关核 ATF7IP/MCAF1 和膜 PD-L1 蛋白的表达水平对 H&E 染色图像进行分类。有趣的是，每种蛋白低表达和高表达的类在 H&E 图像中表现出显著的形态差异。这些结果表明，癌组织中的形态特征与特定癌相关蛋白的表达相关，提示基于生物分子的形态分类的有用性。
