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首页> 外文期刊>Intelligence: A Multidisciplinary Journal >Computational Staining of Pathology Images to Study the Tumor Microenvironment in Lung Cancer
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Computational Staining of Pathology Images to Study the Tumor Microenvironment in Lung Cancer

机译：病理学图像的计算染色研究肺癌肿瘤微环境

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The spatial organization of different types of cells in tumor tissues reveals important information about the tumor microenvironment (TME). To facilitate the study of cellular spatial organization and interactions, we developed Histology-based Digital-Staining, a deep learning-based computation model, to segment the nuclei of tumor, stroma, lymphocyte, macrophage, karyorrhexis, and red blood cells from standard hematoxylin and eosin-stained pathology images in lung adenocarcinoma. Using this tool, we identified and classified cell nuclei and extracted 48 cell spatial organization-related features that characterize the TME. Using these features, we developed a prognostic model from the National Lung Screening Trial dataset, and independently validated the model in The Cancer Genome Atlas lung adenocarcinoma dataset, in which the predicted high-risk group showed significantly worse survival than the low-risk group (P = 0.001), with a HR of 2.23 (1.37-3.65) after adjusting for clinical variables. Further-more, the image-derived TME features significantly correlated with the gene expression of biological pathways. For example, transcriptional activation of both the T-cell receptor and programmed cell death protein 1 pathways positively correlated with the density of detected lymphocytes in tumor tissues, while expression of the extracellular matrix organization pathway positively correlated with the density of stromal cells. In summary, we demonstrate that the spatial organization of different cell types is predictive of patient survival and associated with the gene expression of biological pathways.
机译：肿瘤组织中不同类型细胞的空间组织揭示了有关肿瘤微环境（TME）的重要信息。为了促进蜂窝空间组织和相互作用的研究，我们开发了基于组织学的数字染色，基于深度学习的计算模型，分段肿瘤，基质，淋巴细胞，巨噬细胞，karyorrhexis和来自标准血液氧基的红细胞和肺腺癌中的嗜酸盐染色的病理学图像。使用此工具，我们识别和分类细胞核，并提取了特征TME的48个细胞空间组织相关特征。使用这些特征，我们开发了来自国家肺筛查试验数据集的预后模型，独立验证了癌症基因组地图集肺腺癌数据集的模型，其中预测的高风险组显示出比低风险群体的存活率显着更差（ P = 0.001），调整临床变量后，HR为2.23（1.37-3.65）。更重要的是，图像衍生的TME特征与生物途径的基因表达显着相关。例如，T细胞受体和编程的细胞死亡蛋白1的转录激活与肿瘤组织中检测到的淋巴细胞的密度呈正相关，同时表达与基质细胞的密度呈正相关的细胞外基质组织途径。总之，我们证明了不同细胞类型的空间组织是预测患者存活和与生物途径的基因表达相关。

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