Nondestructive 3D Pathology and Analysis: A New Perspective on Cancer

Course overview

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. After this presentation, participants will be able to: - State the advantages of 3D pathology over standard 2D histology - Identify clinical use cases in which 3D pathology could add value - Describe various approaches for AI-assisted analysis of 3D pathology datasets