Digital Assessment of Stained Breast Tissue Images for Comprehensive Tumor and Microenvironment Analysis.

ABSTRACT: Current histopathological diagnosis involves human expert interpretation of stained images for diagnosis. This process is prone to inter-observer variability, often leading to low concordance rates amongst pathologists across many types of tissues. Further, since structural features are mostly just defined for epithelial alterations during tumor progression, the use of associated stromal changes is limited. Here we sought to examine whether digital analysis of commonly used hematoxylin and eosin-stained images could provide precise and quantitative metrics of disease from both epithelial and stromal cells. We developed a convolutional neural network approach to identify epithelial breast cells from their microenvironment. Second, we analyzed the microenvironment to further observe differen

SUBMITTER: Mittal S 

PROVIDER: S-EPMC6797859 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature