ArticleScientific reports2018
Deciphering the immune microenvironment of a tissue by digital imaging and cognition network.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed.
- AI-based virtual immunocytochemistry for rapid and robust fine needle aspiration biopsy diagnosis.Diagnostic pathology · 2025Article
- Therapeutic Efficacy ofPharmaceuticals (Basel, Switzerland) · 2024Article
- Development of an optimized machine learning approach for assessing brain metastatic burden in preclinical models.bioRxiv : the preprint server for biology · 2024Article
- Mouse models in colon cancer, inferences, and implications.iScience · 2023Review
- Digital pathology and artificial intelligence in translational medicine and clinical practice.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2022Review
- Quantitative Image Analysis for Tissue Biomarker Use: A White Paper From the Digital Pathology Association.Applied immunohistochemistry & molecular morphology : AIMM · 2021Article
- Molecular profiling of the intestinal mucosa and immune cells of the colon by multi-parametric histological techniques.Scientific reports · 2021Article
- Imaging methods to evaluate tumor microenvironment factors affecting nanoparticle drug delivery and antitumor response.Cancer drug resistance (Alhambra, Calif.) · 2021Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Evidence has highlighted the importance of immune cells in various gut disorders. Both the quantification and localization of these cells are essential to the understanding of the complex mechanisms implicated in these pathologies. Even if quantification can be assessed (e.g., by flow cytometry), simultaneous cell localization and quantification of whole tissues remains technically challenging. Here, we describe the use of a computer learning-based algorithm created in the Tissue Studio interface that allows for a semi-automated, robust and rapid quantitative analysis of immunofluorescence staining on whole colon sections according to their distribution in different tissue areas. Indeed, this algorithm was validated to characterize gut immune microenvironment. Its application to the preclinical colon cancer APC
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Registered trials
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