CITATION: Clinical-Grade Validation of an Autofluorescence Virtual Staining System With Human Experts and a Deep Learning System for Prostate Cancer

Pok FaiWong , Carson McNeil , YangWang , Jack Paparian , Charles Santori , Michael Gutierrez , Andrew Homyk , Kunal Nagpal , Tiam Jaroensri , ElleryWulczyn , David F. Steiner , Po-Hsuan Cameron Chen , Luke Restorick , Jonathan Roy and Peter Cimermancic

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NeoBiotechnologies’ monoclonal antibodies were used in a joint study by Verily Life Sciences (Google) and Leica Biosystems and published in the Journal of Modern Pathology, Volume 37, Issue 11

Abstract

The tissue diagnosis of adenocarcinoma and intraductal carcinoma of the prostate (IDC-P) includes Gleason grading of tumor morphology on the hematoxylin and eosin (H&E) stain, and immunohistochemistry (IHC) markers on the PIN-4 stain (CK5/6, P63, AMACR). In this work, we create an automated system for producing both virtual H&E and PIN-4 IHC stains from unstained prostate tissue using a high-throughput multispectral fluorescence microscope and artificial intelligence & machine learning. We demonstrate that the virtual stainer models can produce high-quality images suitable for diagnosis by genitourinary pathologists. Specifically, we validate our system through extensive human review and computational analysis, using a previously-validated Gleason scoring model, and an expert panel, on a large dataset of test slides. This study extends our previous work on virtual staining from autofluorescence, demonstrates the clinical utility of this technology for prostate cancer, and exemplifies a rigorous standard of qualitative and quantitative evaluation for digital pathology.

Publication History:
Received February 26, 2024; Revised July 3, 2024; Accepted July 22, 2024; Published online July 26, 2024

Footnotes:
These authors contributed equally: Pok Fai Wong, Carson McNeil, Yang Wang, Jack Paparian, Charles Santori, Michael Gutierrez, Andrew Homyk, Kunal Nagpal, Tiam Jaroensri, and Ellery Wulczyn.
DOI: 10.1016/j.modpat.2024.100573
Copyright: © 2024 United States & Canadian Academy of Pathology. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.