ArticleModern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc2021
A large-scale internal validation study of unsupervised virtual trichrome staining technologies on nonalcoholic steatohepatitis liver biopsies.
Article in Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 50 citations in OpenAlex.
- Hardware-Attentive Programmable Fourier Ptychography Enables Task-Adaptive Label-Free Virtual Staining.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- From unlabeled to labeled: self-supervised deep learning in computational pathology.BioData mining · 2026Article
- Virtual Evaluation of Hematoxylin & Eosin via Digital Pathology Survey (VEED) Project: Results from a Non-Inferiority Study of a Tabs-Based Staining Method.Diagnostics (Basel, Switzerland) · 2026Article
- Hepatocarcinogenesis prediction by liver fibrosis patterns in metabolic dysfunction-associated steatotic liver disease biopsies.Medical molecular morphology · 2026Article
- Assessing Tumour Budding in Lung Squamous Cell Carcinoma: A Comparative Analysis of Digital Whole-Slide Imaging and Light Microscopy.Pathobiology : journal of immunopathology, molecular and cellular biology · 2026Article
- Masson's Trichrome Staining Technique to Evaluate Tissue Fibrosis.Methods in molecular biology (Clifton, N.J.) · 2026Article
- A robust and scalable framework for hallucination detection in virtual tissue staining and digital pathology.Nature biomedical engineering · 2025Article
- Hierarchical Micro-Nano Surface Roughness Wax-Impregnated Cotton Fabrics Platform for Cell-Based Diagnostics.ACS omega · 2025Article
- AI-driven 3D bioprinting for regenerative medicine: From bench to bedside.Bioactive materials · 2025Review
- An initial game-theoretic assessment of enhanced tissue preparation and imaging protocols for improved deep learning inference of spatial transcriptomics from tissue morphology.Briefings in bioinformatics · 2024Article
- The Use of Artificial Intelligence in the Liver Histopathology Field: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Intraoperative margin assessment for basal cell carcinoma with deep learning and histologic tumor mapping to surgical site.NPJ precision oncology · 2024Article
- Deep-learning-based cross-modality translation from Stokes image to bright-field contrast.Journal of biomedical optics · 2023Article
- A novel role of RNase L in the development of nonalcoholic steatohepatitis.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2023Article
- Deep learning-enabled virtual histological staining of biological samples.Light, science & applications · 2023Review
- VSGD-Net: Virtual Staining Guided Melanocyte Detection on Histopathological Images.IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision · 2023Article
- Multi-modality artificial intelligence in digital pathology.Briefings in bioinformatics · 2022Review
- Simple Code Implementation for Deep Learning-Based Segmentation to Evaluate Central Serous Chorioretinopathy in Fundus Photography.Translational vision science & technology · 2022Article
- Topological Feature Extraction and Visualization of Whole Slide Images using Graph Neural Networks.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2021Review
- Deep Learning Approaches and Applications in Toxicologic Histopathology: Current Status and Future Perspectives.Journal of pathology informatics · 2021Review
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
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.
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