Connect Sign InNondestructive 3D Pathology and Analysis: A New Perspective on Cancer
We are developing nondestructive, slide-free 3D pathology methods for clinical decision support and surgical guidance. In comparison to conventional slide-based pathology, 3D pathology provides: (1) vastly greater sampling of tissue specimens, including whole biopsies and surgical margins; (2) volumetric imaging of cell distributions and 3D tissue structures that are prognostic and predictive; and (3) a nondestructive and reversible workflow that preserves valuable specimens for downstream molecular assays. Due to the immense size of feature-rich 3D pathology datasets, new challenges exist in terms of data management, human visualization, and computer-aided interpretation. We have been working on a full stack of technologies to facilitate the clinical adoption of 3D pathology, from sample preparation (e.g., reversible optical clearing and fluorescence labeling), high-throughput imaging with open-top light-sheet (OTLS) microscopes developed in our lab, to data processing and artificial intelligence (AI)-based image triage and analysis. For AI analyses, we are developing both traditional machine classifiers based on intuitive “handcrafted” 3D features, and deep-learning classifiers based on subvisual 3D features. Our nondestructive, large-volume digital pathology methods are synergistic with the growing fields of radiomics and genomics, which collectively have the potential to improve treatment decisions for diverse patient populations.
Jonathan Liu is the Arvid and Marianne Peterson endowed professor of Mechanical Engineering, Bioengineering, and of Laboratory Medicine & Pathology at the University of Washington, where his molecular biophotonics laboratory develops high-resolution optical-imaging devices and computational-analysis strategies for guiding treatment decisions. Dr. Liu is a cofounder and board member of Alpenglow Biosciences, Inc., which has commercialized the nondestructive 3D pathology technologies developed in his lab. Dr. Liu’s work is funded by the NCI, NIBIB, NIDDK, DoD, NSF, ARPA-H, and various foundations.
