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Multiplex Immunofluorescence Combined with Spatial Image Analysis for the Clinical and Biological Assessment of the Tumor Microenvironment

Multiplex Immunofluorescence Combined with Spatial Image Analysis for the Clinical and Biological Assessment of the Tumor Microenvironment

Published on: June 2, 2023

Immunohistochemical analysis of breast tissue microarray images using contextual classifiers.

Stephen J McKenna¹, Telmo Amaral, Shazia Akbar

¹School of Computing, University of Dundee, Dundee DD1 4HN, UK.

Journal of Pathology Informatics

|June 15, 2013

Summary

This study introduces a novel two-stage automated method for scoring immunohistochemical (IHC) staining in breast cancer tissue microarrays (TMAs). The approach improves accuracy in localizing tumors and scoring protein markers, reducing errors compared to manual methods.

Keywords:

Tissue microarrays immunohistochemical scoring tumor localization

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Area of Science:

Computational pathology
Biomedical image analysis
Translational cancer research

Background:

Tissue microarrays (TMAs) are crucial for translational research in oncology.
Automated immunohistochemical (IHC) scoring of breast cancer TMAs presents significant challenges.

Purpose of the Study:

To develop and validate a two-stage automated method for IHC scoring in breast cancer TMAs.
To improve the accuracy of tumor localization and the precision of IHC scoring.

Main Methods:

A two-stage approach involving tumor region localization and ordinal IHC scoring.
Utilized spin-context algorithm for refining localization by integrating image features and spatial context.
Estimated staining proportion and strength as posterior probabilities for robust scoring.

Main Results:

The method demonstrated reduced scoring errors for progesterone receptor (PR) and estrogen receptor (ER) markers compared to manual pathologist scoring.
Achieved average absolute differences of 0.74 for cell proportion and 0.35 for staining strength for PR.
Spin-context improved tumor localization precision and recall.

Conclusions:

Automated IHC scoring using the proposed method reduces errors in breast cancer TMA analysis.
Integration of context information significantly enhances tumor localization and scoring accuracy.
This approach facilitates more reliable molecular and protein marker examination in translational research.