Michael Becich, University of Pittsburgh, USA, “Computational Pathology and Improving Predictive Analytics”
Presentation from the Midwest Consortium for Computational Pathology (MCCP) Workshop 1, January 26–28, 2021. Sponsored by The Ohio State University and the Midwest Big Data Innovation Hub, under NSF award # 1916613.
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With the FDA approval of whole-slide imaging (WSI) and digital pathology workflows as part of Pathology practice, there is a significant gap in infrastructure for supporting translational research in cancer centers. Pathology Informatics is key to supporting deep interrogation of WSI and allowing for multidimensional studies combining multiplexed immunohistochemical marker studies of the tumor microenvironment with the rich genomic data provided by single-cell genomic studies. Modern tissue-banking protocols coupled with diagnostic workflows in surgical pathology of patient’s tumors samples are key to supporting both computational pathology and predictive analytics. We will discuss an approach that incorporates multiple sources of pathology data and clinically actionable knowledge to improve Pathology decision-making via predictive analytics fueled by advanced machine learning and causal discovery.
