Monday, May 21, 2018 | 5:00-7:30 PM
Wyndham Grand Pittsburgh Hotel | King’s Garden 2/3
Moderator: Anil Parwani, MD, PhD, MBA
This presentation will cover the following topics: 1) What resources does one need to start doing computational pathology? 2) What infrastructure elements are needed to begin doing computational pathology within your pathology practice/department? 3) What costs should you consider prior to implementing computational pathology, and 4) What are some of the expected barriers to adoption by pathologists and clinicians?
THRIVE: Platform for Quantitative Evaluation of Spatial Intratumor Heterogeneity in Multiplexed Fluorescence Images by Chakra Chennubhotla, PhD
Spatial intratumoral heterogeneity (ITH), quantified as the number and variation of cell phenotypes, as well as the spatial relationships between cells and extracellular molecules within a tumor microenvironment (TME), is of high prognostic and diagnostic value. The acknowledgement of spatial ITH as a key factor in tumor progression has identified a need for new informatics tools to quantify spatial heterogeneity in cancer research applications. Toward this end, we have created an open-source tool, THRIVE (Tumor Heterogeneity Research Interactive Visualization Environment), which (i) permits visualization of large cohorts of whole-slide images and tissue microarrays; (ii) performs interactive image analysis tasks such as cell segmentation, cell phenotyping, and tumor microdomain discovery via ITH; and (iii) contains statistical inference tools to aid in cancer-specific hypothesis testing.
Computational Cytopathology: Past & Prospective Apps by Liron Pantanowitz, MD
Considerable effort is being focused on developing image algorithms in pathology. The majority of this work is targeting histopathology. However, image analysis was successfully applied to automating the Pap smear more than a decade ago. This talk will present several lessons learned from this accomplishment. Additionally, this presentation will deal with specific promises and pitfalls related to Computational Cytopathology, and highlight several exciting apps in this area that researchers have been working on.
Chakra Chennubhotla, PhD
Dr. Chennubhotla is an Associate Professor in the Department of Computational and Systems Biology in the School of Medicine at the University of Pittsburgh. His group investigates the molecular and cellular origins of human epithelial malignancies through computational models. A major thrust of his research is in computational pathology and spatial tumor biology.
Thomas Fuchs, PhD
Thomas Fuchs heads the Computational Pathology and Medical Machine Learning Lab at Memorial Sloan Kettering Cancer Center and teaches biomedical machine learning as associate professor at Weill-Cornell in New York City. He is director of The Warren Alpert Center for Digital and Computational Pathology. His passion for the tremendous potential of artificial intelligence in medicine resulted in more than 90 publications spanning a range of topics from novel deep learning and Bayesian approaches for quantification to real-world applications in the clinic. Previously, Thomas was a rocket scientist at NASA's Jet Propulsion Laboratory, where he developed autonomous computer vision systems for space exploration. He completed his Postdoc at the California Institute of Technology after receiving his PhD in machine learning from ETH Zurich.
David McClintock, MD
David McClintock, MD, is an Associate CMIO of Michigan Medicine (Pathology Informatics), Director of Digital Pathology, and Associate Professor at the University of Michigan. His primary clinical interests comprise operational pathology and clinical laboratory informatics including workflow analysis, laboratory information system optimization, and improved integration of pathology and clinical laboratory data within the EHR and clinical research data warehouses. His research interests include understanding the role and effects of whole slide imaging and digital pathology within the clinical laboratories, the effects of computational pathology and machine learning on diagnostics testing and patient outcomes, and how to enable laboratory data analytics in order to provide both pathologists and clinicians opportunities to better optimize patient care and clinical decision-making. He is also currently serving as President of the Association of Pathology Informatics for 2018.
Liron Pantanowitz, MD
Dr. Liron Pantanowitz is a Professor of Pathology and of Biomedical Informatics at the University of Pittsburgh in the USA. He is the Director of the Pathology Informatics Division and Director of the Pathology Informatics Fellowship at the University of Pittsburgh Medical Center (UPMC). He is also the Director of the Cytopathology Division at UPMC Shadyside. Dr. Pantanowitz is an Editor-in-Chief of the Journal of Pathology Informatics. He is a member of the board of directors for the Digital Pathology Association (DPA), executive board of the American Society of Cytopathology (ASC), serves as a council member for the Association for Pathology Informatics (API) and is a member of the College of American Pathologists (CAP) Digital Pathology committee. He is widely published in the field of pathology informatics including digital imaging and its application to pathology.