TY - JOUR T1 - Topological Feature Extraction and Visualization of Whole Slide Images using Graph Neural Networks JF - bioRxiv DO - 10.1101/2020.08.01.231639 SP - 2020.08.01.231639 AU - Joshua Levy AU - Christian Haudenschild AU - Clark Barwick AU - Brock Christensen AU - Louis Vaickus Y1 - 2020/01/01 UR - http://biorxiv.org/content/early/2020/08/03/2020.08.01.231639.abstract N2 - Whole-slide images (WSI) are digitized representations of thin sections of stained tissue from various patient sources (biopsy, resection, exfoliation, fluid) and often exceed 100,000 pixels in any given spatial dimension. Deep learning approaches to digital pathology typically extract information from sub-images (patches) and treat the sub-images as independent entities, ignoring contributing information from vital large-scale architectural relationships. Modeling approaches that can capture higher-order dependencies between neighborhoods of tissue patches have demonstrated the potential to improve predictive accuracy while capturing the most essential slide-level information for prognosis, diagnosis and integration with other omics modalities. Here, we review two promising methods for capturing macro and micro architecture of histology images, Graph Neural Networks, which contextualize patch level information from their neighbors through message passing, and Topological Data Analysis, which distills contextual information into its essential components. We introduce a modeling framework, WSI-GTFE that integrates these two approaches in order to identify and quantify key pathogenic information pathways. To demonstrate a simple use case, we utilize these topological methods to develop a tumor invasion score to stage colon cancer.Competing Interest StatementThe authors have declared no competing interest. ER -