背景:不同类型细胞的空间分布可以揭示癌细胞的生长模式,它与肿瘤微环境和机体免疫反应的关系,所有这些都代表关键的“癌症标志”。然而,病理学家手动识别和定位病理载玻片中的所有细胞的过程是非常劳动密集和容易出错的。
方法:在本研究中,我们开发了一个自动化的细胞类型分类管道,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,并提取肺癌病理图像的肿瘤微环境相关特征。为了方便用户利用这个管道进行研究,ConvPath软件的所有源脚本均可在https://qbrc获得。swmed.教育/项目/cnn/。
结果:训练和独立测试数据集的总体分类准确率分别为92.9%和90.1%,分别。通过识别细胞和分类细胞类型,该管道可以将病理图像转换为肿瘤的“空间图”,基质细胞和淋巴细胞。从这张空间地图上看,我们可以提取表征肿瘤微环境的特征。基于这些特征,我们建立了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.预测的风险组是一个独立的预后因素,在调整了包括年龄在内的临床变量后,性别,吸烟状况,和舞台。
结论:本研究中开发的分析管道可以将病理图像转换为肿瘤细胞的“空间图”,基质细胞和淋巴细胞。这可以极大地促进和增强对细胞空间组织的全面分析,以及它们在肿瘤进展和转移中的作用。
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/.
RESULTS: 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.
CONCLUSIONS: 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.