Computational Staining of Pathology Images to Study the Tumor Microenvironment in Lung Cancer.

ABSTRACT: 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 lu

SUBMITTER: Wang S 

PROVIDER: S-EPMC7919065 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature