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$125,000 Award

Project Overview

Microscopic review of histologically stained tissue samples is a mainstay of pathology and oncology diagnostics but has remained largely unchanged for decades. Recent digital pathology and artificial intelligence approaches promise to upend the traditional workflow, including machine-learning based virtual staining techniques. One such method, developed by PictorLabs (Los Angeles, CA), can improve diagnostics by allowing a comprehensive biomarker workup to be performed nondestructively and extremely rapidly on limited tissue samples. By leveraging artificial intelligence algorithms, the histology workflow can be compressed, waste generation can be reduced, turnaround time can be improved and oncologists can get more rapid answers to expediently place patients on definitive treatment. The University of Maryland Department of Pathology has previously demonstrated a proof-of-concept to deploy virtual H&E and IHC stains and is now poised to expand these capabilities into clinical use. If successful, this breakthrough innovation will allow the University of Maryland to deploy the first clinical virtual staining workflow in the world.

Project Team

  • Michael E. Kallen, MD – Principal Investigator
  • Kathryn Rice, MD – Research Associate
  • Autumn LaRocque, MD – Research Associate
  • Elba Vidal – Histology Supervisor
  • Laura Wake, MD – Hematopathology
  • Rima Koka, MD, PhD – Section Head, Hematopathology
  • Serge Alexanian, MD – VP of Medical Affairs, Clinical & Regulatory, PictorLabs
 

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