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Related Concept Videos

Immunocytochemistry and Immunohistochemistry01:22

Immunocytochemistry and Immunohistochemistry

Immunocytochemistry (ICC) and immunohistochemistry (IHC) are techniques that use antibodies to check for specific proteins or antigens in a sample. The technique was first published by Albert Coons in 1941 to detect the presence of pneumococcal antigen in tissue sections from mice infected with Pneumococcus. Immunocytochemistry helps localization of proteins or antigens in individual cells like blood cells, stem cells, etc., while immunohistochemistry does the same for tissue samples.
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Automated H-Scoring in Muscle-Invasive Bladder Cancer IHC: An Internal Validation Study.

Matthew Yap1, Ioana-Maria Mihai1,2, Maram Awadh A Alanazi1,3

  • 1Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC V6T 1Z7, Canada.

Diagnostics (Basel, Switzerland)
|June 12, 2026
PubMed
Summary
This summary is machine-generated.

Automated digital pathology accurately quantifies immunohistochemistry (IHC) H-scores for muscle-invasive bladder cancer (MIBC) subtyping. This validated pipeline offers objective and scalable scoring for biomarker analysis.

Keywords:
H-scoreQuPathStarDistautomated IHC scoringbiomarkercytoplasmic stainingdigital pathologymembranous stainingmuscle-invasive bladder cancer

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Area of Science:

  • Digital pathology
  • Oncology
  • Biomarker analysis

Background:

  • Immunohistochemistry (IHC) is crucial for muscle-invasive bladder cancer (MIBC) subtyping.
  • Current semi-quantitative IHC scoring methods lack objectivity and scalability.
  • Automated digital pathology presents a potential solution requiring validation.

Purpose of the Study:

  • To develop and validate an automated digital pathology pipeline for continuous IHC H-score quantification in MIBC.
  • To compare automated H-scores with expert pathologist consensus.
  • To assess the pipeline's objectivity and scalability for biomarker analyses.

Main Methods:

  • Developed an automated pipeline using QuPath and StarDist for IHC H-score quantification.
  • Utilized tissue microarrays from MIBC patient specimens.
  • Trained a tumor/non-tumor classifier for accurate H-scoring within tumor epithelium.
  • Validated automated scores against consensus scores from three pathologists.

Main Results:

  • Automated H-scores showed strong agreement with pathologist consensus across four markers (CK14, CK20, CK5/6, Uroplakin II).
  • Intraclass correlation coefficients (ICC) ranged from approximately 0.92 to 0.99.
  • The pipeline preserved a broad H-score range, indicating minimal bias.

Conclusions:

  • Established a robust and validated automated pipeline for continuous IHC H-scoring in MIBC.
  • The framework supports scalable external cohort testing.
  • Enables future clinical outcome-associated biomarker analyses.