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

Autor:	Wong PF; Verily Life Sciences LLC, San Francisco, California., McNeil C; Verily Life Sciences LLC, San Francisco, California. Electronic address: cmcneil@verily.com., Wang Y; Verily Life Sciences LLC, San Francisco, California. Electronic address: wayang@verily.com., Paparian J; Verily Life Sciences LLC, San Francisco, California., Santori C; Verily Life Sciences LLC, San Francisco, California., Gutierrez M; Verily Life Sciences LLC, San Francisco, California., Homyk A; Verily Life Sciences LLC, San Francisco, California., Nagpal K; Google LLC, Mountain View, California., Jaroensri T; Google LLC, Mountain View, California., Wulczyn E; Google LLC, Mountain View, California., Yoshitake T; Verily Life Sciences LLC, San Francisco, California., Sigman J; Verily Life Sciences LLC, San Francisco, California., Steiner DF; Google LLC, Mountain View, California., Rao S; Verily Life Sciences LLC, San Francisco, California., Cameron Chen PH; Google LLC, Mountain View, California., Restorick L; Leica Biosystems, Nussloch, Germany., Roy J; Leica Biosystems, Nussloch, Germany., Cimermancic P; Verily Life Sciences LLC, San Francisco, California.
Jazyk:	angličtina
Zdroj:	Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc [Mod Pathol] 2024 Nov; Vol. 37 (11), pp. 100573. Date of Electronic Publication: 2024 Jul 26.
DOI:	10.1016/j.modpat.2024.100573
Abstrakt:	The tissue diagnosis of adenocarcinoma and intraductal carcinoma of the prostate includes Gleason grading of tumor morphology on the hematoxylin and eosin stain and immunohistochemistry markers on the prostatic intraepithelial neoplasia-4 stain (CK5/6, P63, and AMACR). In this work, we create an automated system for producing both virtual hematoxylin and eosin and prostatic intraepithelial neoplasia-4 immunohistochemistry stains from unstained prostate tissue using a high-throughput hyperspectral fluorescence microscope and artificial intelligence and 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 data set 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. (Copyright © 2024 United States & Canadian Academy of Pathology. Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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