Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

Abstract

In order to catalyze a digital pathology transformation to improve clinical decisions and patient outcomes, a novel technological approach is needed that offers significant advantages over traditional “gold-standard” histopathology in terms of accuracy and throughput. We have developed an open-top light-sheet (OTLS) microscopy platform for slide-free 3D pathology of large clinical specimens, enabling whole biopsies and surgical specimens to be non-destructively imaged in toto. Using machine-learning techniques, we are quantifying 3D spatial and molecular biomarkers for prognosticating patient outcomes (indolent vs. aggressive disease) and for predicting treatment response. These non-destructive 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.

Biography

Professor Jonathan T.C. LiuJonathan T.C. Liu is a professor of mechanical engineering, bioengineering, and 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 received his BSE from Princeton and his PhD from Stanford before becoming a postdoc and instructor in the Molecular Imaging Program at Stanford. Dr. Liu is a co-founder and board member of Alpenglow Biosciences Inc. (formerly Lightspeed Microscopy), which has commercialized the non-destructive 3D pathology technologies developed in his lab. Dr. Liu’s work is funded by the NIH, DoD, NSF, and various foundations.

For more information, please visit the Molecular Biophotonics Laboratory group web page. 

 

 

 

Online joining instructions

Zoom link

Meeting ID: 954 8366 5141, Passcode: 017222