Generating a Comprehensive Map of Cancer Morphology in Whole Slide Tissue Specimens

Advanced imaging technologies can capture extremely high-resolution images of tissue specimens, and quantitative analyses of cancer morphology using these images have shown value in a variety of correlative and prognostic studies. Our work on Summit will generate a comprehensive multi-scale mapping of cancer morphology with a dataset of more than 10,000 whole slide tissue images from over 20 cancer types. The work will use a collection of deep learning analysis pipelines we have developed to study, quantify and characterize tissue structure in diseased and normal tissue specimens. These analysis pipelines generate distributions of nuclei and cells and patch-level maps of lymphocyte distributions and segmentations of tumor regions. The analysis results will provide a first-ever representations of lymphocyte maps, nuclear characterizations and characterizations of tumor regions on a dataset of this scale. We expect that studies supported by these rich datasets will enable the development of biomarkers to predict clinical outcome and a better epidemiological understanding of cancer subtypes and how constituent cells contribute to cancer invasion and expansion.