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. Furthermore, 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. SIGNIFICANCE: These findings present a deep learning-based analysis tool to study the TME in pathology images and demonstrate that the cell spatial organization is predictive of patient survival and is associated with gene expression.See related commentary by Rodriguez-Antolin, p. 1912.

中文翻译：

---

病理图像的计算染色研究肺癌的肿瘤微环境。


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。为了促进细胞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，这是一种基于深度学习的计算模型，用于从标准苏木精中分割肿瘤、基质、淋巴细胞、巨噬细胞、核碎裂和红细胞的细胞核和肺腺癌伊红染色的病理图像。使用该工具，我们对细胞核进行了识别和分类，并提取了 48 个表征 TME 的细胞空间组织相关特征。利用这些特征，我们根据国家肺筛查试验数据集开发了一个预后模型，并在癌症基因组图谱肺腺癌数据集中独立验证了该模型，其中预测的高风险组的生存率明显低于低风险组（ P = 0.001），调整临床变量后 HR 为 2.23 (1.37-3.65)。此外，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细胞受体和程序性细胞死亡蛋白1途径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，而细胞外基质组织途径的表达与基质细胞的密度呈正相关。总之，我们证明不同细胞类型的空间组织可以预测患者的生存并与生物途径的基因表达相关。 意义：这些发现提供了一种基于深度学习的分析工具，用于研究病理图像中的 TME，并证明细胞空间组织可以预测患者的生存并与基因表达相关。请参阅 Rodriguez-Antolin 的相关评论，第 17 页。 1912年。