BACKGROUND The spatial distributions of different types of cells could reveal a cancer cell's growth pattern, its relationships with the tumor microenvironment and the immune response of the body, all of which represent key "hallmarks of cancer". However, the process by which pathologists manually recognize and localize all the cells in pathology slides is extremely labor intensive and error prone. METHODS In this study, we developed an automated cell type classification pipeline, ConvPath, which includes nuclei segmentation, convolutional neural network-based tumor cell, stromal cell, and lymphocyte classification, and extraction of tumor microenvironment-related features for lung cancer pathology images. To facilitate users in leveraging this pipeline for their research, all source scripts for ConvPath software are available at https://qbrc.swmed.edu/projects/cnn/. FINDINGS The overall classification accuracy was 92.9% and 90.1% in training and independent testing datasets, respectively. By identifying cells and classifying cell types, this pipeline can convert a pathology image into a "spatial map" of tumor, stromal and lymphocyte cells. From this spatial map, we can extract features that characterize the tumor micro-environment. Based on these features, we developed an image feature-based prognostic model and validated the model in two independent cohorts. The predicted risk group serves as an independent prognostic factor, after adjusting for clinical variables that include age, gender, smoking status, and stage. INTERPRETATION The analysis pipeline developed in this study could convert the pathology image into a "spatial map" of tumor cells, stromal cells and lymphocytes. This could greatly facilitate and empower comprehensive analysis of the spatial organization of cells, as well as their roles in tumor progression and metastasis.

中文翻译：

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ConvPath：卷积神经网络辅助的肺腺癌数字病理图像分析软件工具。

背景技术不同类型细胞的空间分布可以揭示癌细胞的生长模式、其与肿瘤微环境的关系以及机体的免疫反应，所有这些都代表了关键的“癌症标志”。然而，病理学家手动识别和定位病理切片中所有细胞的过程是极其劳动密集型的并且容易出错。方法在本研究中，我们开发了一种自动化细胞类型分类流程ConvPath，其中包括细胞核分割、基于卷积神经网络的肿瘤细胞、基质细胞和淋巴细胞分类，以及提取肺癌病理图像的肿瘤微环境相关特征。为了方便用户利用此管道进行研究，ConvPath 软件的所有源脚本均可在 https://qbrc.swmed.edu/projects/cnn/ 上获取。结果 训练数据集和独立测试数据集的总体分类准确度分别为 92.9% 和 90.1%。通过识别细胞并对细胞类型进行分类，该流程可以将病理图像转换为肿瘤、基质和淋巴细胞的“空间图”。从这个空间图中，我们可以提取表征肿瘤微环境的特征。基于这些特征，我们开发了一个基于图像特征的预后模型，并在两个独立的队列中验证了该模型。在调整年龄、性别、吸烟状况和分期等临床变量后，预测的风险组可作为独立的预后因素。解释本研究开发的分析流程可以将病理图像转换为肿瘤细胞、基质细胞和淋巴细胞的“空间图”。这可以极大地促进和增强对细胞空间组织及其在肿瘤进展和转移中的作用的全面分析。