A large-scale internal validation study of unsupervised virtual trichrome staining technologies on nonalcoholic steatohepatitis liver biopsies.

Journal: Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
PMID: 33299110

Abstract

Non-alcoholic steatohepatitis (NASH) is a fatty liver disease characterized by accumulation of fat in hepatocytes with concurrent inflammation and is associated with morbidity, cirrhosis and liver failure. After extraction of a liver core biopsy, tissue sections are stained with hematoxylin and eosin (H&E) to grade NASH activity, and stained with trichrome to stage fibrosis. Methods to computationally transform one stain into another on digital whole slide images (WSI) can lessen the need for additional physical staining besides H&E, reducing personnel, equipment, and time costs. Generative adversarial networks (GAN) have shown promise for virtual staining of tissue. We conducted a large-scale validation study of the viability of GANs for H&E to trichrome conversion on WSI (n = 574). Pathologists were largely unable to distinguish real images from virtual/synthetic images given a set of twelve Turing Tests. We report high correlation between staging of real and virtual stains ([Formula: see text]; 95% CI: 0.84-0.88). Stages assigned to both virtual and real stains correlated similarly with a number of clinical biomarkers and progression to End Stage Liver Disease (Hazard Ratio HR = 2.06, 95% CI: 1.36-3.12, p < 0.001 for real stains; HR = 2.02, 95% CI: 1.40-2.92, p < 0.001 for virtual stains). Our results demonstrate that virtual trichrome technologies may offer a software solution that can be employed in the clinical setting as a diagnostic decision aid.

Authors

  • Joshua J Levy
    DOE Joint Genome Institute, 2800 Mitchell Dr, Walnut Creek, CA, 94598, USA.
  • Nasim Azizgolshani
    Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, 03756, USA.
  • Michael J Andersen
    Emerging Diagnostic and Investigative Technologies, Clinical Genomics and Advanced Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH, 03756, USA.
  • Arief Suriawinata
    Lia Harrington, Todd MacKenzie, and Saeed Hassanpour, Geisel School of Medicine at Dartmouth College, Hanover; Roberta diFlorio-Alexander, Katherine Trinh, and Arief Suriawinata, Dartmouth-Hitchcock Medical Center, Lebanon, NH.
  • Xiaoying Liu
    Department of Pathology and Laboratory Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire.
  • Mikhail Lisovsky
    Department of Pathology and Laboratory Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire.
  • Bing Ren
    Ludwig Institute for Cancer Research, La Jolla, CA, 92093, USA.
  • Carly A Bobak
    Program in Quantitative Biomedical Sciences, Geisel School of Medicine at Dartmouth, Lebanon, NH, 03756, USA.
  • Brock C Christensen
    Department of Epidemiology, Lebanon, USA. Brock.C.Christensen@dartmouth.edu.
  • Louis J Vaickus
    Department of Pathology and Laboratory Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire.

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