May 16 @ 5:30 pm – 6:30 pm PDT
The current diagnostic “gold standard” in medicine is histopathology, a centuries-old process in which tissue biopsies from patients are preserved, embedded in wax blocks, cut into thin tissue sections, mounted on glass slides, and stained so that they can be viewed with analog microscopes similar to what are used in high school biology classes.
Critical treatment decisions for cancer patients are based on what pathologists observe in these limited 2D tissue sections, which often represent only 1% or less of the biopsied specimens. In order 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 (i.e. distinguishing between indolent vs. lethal 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.