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Lookup NU author(s): Dr Dina Tiniakos, Professor Alastair BurtORCiD
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Copyright © 2023 United States & Canadian Academy of Pathology. Published by Elsevier Inc. All rights reserved. Conventional histopathology involves expensive and labor-intensive processes that often consume tissue samples, rendering them unavailable for other analyses. We present a novel end-to-end workflow for pathology powered by hyperspectral microscopy and deep learning. First, we developed a custom hyperspectral microscope to nondestructively image the autofluorescence of unstained tissue sections. We then trained a deep learning model to use autofluorescence to generate virtual histologic stains, which avoids the cost and variability of chemical staining procedures and conserves tissue samples. We showed that the virtual images reproduce the histologic features present in the real-stained images using a randomized nonalcoholic steatohepatitis (NASH) scoring comparison study, where both real and virtual stains are scored by pathologists (D.T., A.D.B., R.K.P.). The test showed moderate-to-good concordance between pathologists' scoring on corresponding real and virtual stains. Finally, we developed deep learning-based models for automated NASH Clinical Research Network score prediction. We showed that the end-to-end automated pathology platform is comparable with an independent panel of pathologists for NASH Clinical Research Network scoring when evaluated against the expert pathologist consensus scores. This study provides proof of concept for this virtual staining strategy, which could improve cost, efficiency, and reliability in pathology and enable novel approaches to spatial biology research.
Author(s): McNeil C, Wong PF, Sridhar N, Wang Y, Santori C, Wu C-H, Homyk A, Gutierrez M, Behrooz A, Tiniakos D, Burt AD, Pai RK, Tekiela K, Patel H, Cameron Chen P-H, Fischer L, Martins EB, Seyedkazemi S, Freedman D, Kim CC, Cimermancic P
Publication type: Article
Publication status: Published
Journal: Modern Pathology
Year: 2024
Volume: 37
Issue: 2
Print publication date: 07/02/2024
Online publication date: 04/11/2023
Acceptance date: 30/10/2023
ISSN (print): 0893-3952
ISSN (electronic): 1530-0285
Publisher: Elsevier
URL: https://doi.org/10.1016/j.modpat.2023.100377
DOI: 10.1016/j.modpat.2023.100377
PubMed id: 37926422
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