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Building Up a High-throughput Screening Platform to Assess the Heterogeneity of HER2 Gene Amplification in Breast Cancers
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Building Up a High-throughput Screening Platform to Assess the Heterogeneity of HER2 Gene Amplification in Breast Cancers

Published on: December 5, 2017

Classification and immunohistochemical scoring of breast tissue microarray spots.

Telmo Amaral, Stephen J McKenna, Katherine Robertson

    IEEE Transactions on Bio-Medical Engineering
    |May 30, 2013
    PubMed
    Summary

    This study introduces an automated computational pipeline for classifying and scoring breast cancer tissue microarrays (TMAs). The system accurately assesses nuclear immunostaining, improving workflow efficiency for pathologists.

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    Published on: October 25, 2011

    Area of Science:

    • Computational pathology
    • Digital image analysis
    • Breast cancer research

    Background:

    • Tissue microarrays (TMAs) enable large-scale tumor surveys but manual assessment is time-consuming.
    • Automating analysis of stained TMA sections is crucial for efficient pathology workflows.

    Purpose of the Study:

    • To develop and evaluate a computational pipeline for automated classification and scoring of breast cancer TMA spots.
    • To compare different machine learning models for accurate TMA analysis.

    Main Methods:

    • A bag of visual words approach was used for spot classification.
    • Immunohistochemical scoring involved computing features of epithelial nuclei staining intensity and proportion.
    • Classifiers like multilayer perceptrons, latent topic models, and support vector machines were compared.
    • Gaussian process ordinal regression and linear models were used for scoring.

    Main Results:

    • The pipeline successfully classifies and scores breast cancer TMA spots based on nuclear immunostaining.
    • Comparison of different computational models identified optimal approaches for classification and scoring.
    • Posterior entropy was utilized to flag uncertain cases for review.

    Conclusions:

    • Automated computational pipelines can significantly improve the efficiency of breast cancer TMA analysis.
    • The developed pipeline offers a viable alternative to manual assessment, reducing bottlenecks in pathology.
    • This approach has the potential to enhance diagnostic accuracy and throughput in cancer research.