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Immunocytochemistry and Immunohistochemistry

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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Related Experiment Video

Updated: Jun 13, 2025

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Published on: April 8, 2016

Enhanced Immunohistochemistry Interpretation with a Machine Learning-Based Expert System.

Anca Iulia Neagu^1,2, Diana Gina Poalelungi^1,3, Ana Fulga^1,3

¹Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza St., 800010 Galati, Romania.

Diagnostics (Basel, Switzerland)

|September 14, 2024

Summary

This summary is machine-generated.

Machine learning models can accurately predict cancer diagnoses using immunohistochemistry markers. This study achieved an 85.97% precision rate, showing the clinical efficacy of this approach for tumor differentiation.

Keywords:

cancer diagnosis immunohistochemistry machine learning prediction

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Last Updated: Jun 13, 2025

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Published on: June 29, 2022

Area of Science:

Oncology
Computational Biology
Pathology

Background:

Machine learning (ML) advances cancer data management with automated diagnostic tools.
Immunohistochemistry (IHC) identifies cellular origins by analyzing antigen expression in tissues.
Accurate histopathological diagnosis is crucial for effective cancer treatment.

Purpose of the Study:

To develop a predictive model for histopathological diagnoses.
To leverage immunohistochemical marker data for diagnostic accuracy.
To assess the clinical utility of ML in cancer diagnostics.

Main Methods:

Applied the XGBoost machine learning model.
Used histopathological diagnosis as the target variable.
Employed immunohistochemical markers as predictor variables.

Main Results:

Achieved a precision rate of 85.97% on the dataset.
Demonstrated high performance and reliability of the ML model.
Indicated the model's capability for accurate diagnostic predictions.

Conclusions:

Confirmed the feasibility of using ML for cancer diagnosis.
Showcased the clinical efficacy of a probabilistic decision tree algorithm.
Highlighted the potential of IHC profiles in differentiating tumor diagnoses.