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Related Experiment Video

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Automated Slide Scanning and Segmentation in Fluorescently-labeled Tissues Using a Widefield High-content Analysis System
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Fully Automated Stain Quantification Framework for IHC Whole Slide Images in Breast Cancer.

Tuo Yin1,2, Frédéric Lifrange3, Zoë Denis4

  • 1Radiophysics and MRI Physics Laboratory, Université Libre de Bruxelles (ULB), Brussels, Belgium.

Technology in Cancer Research & Treatment
|April 3, 2026
PubMed
Summary

This study introduces an automated H-scoring framework for immunohistochemistry (IHC) analysis of breast cancer whole slide images (WSIs). The AI tool provides consistent and reproducible IHC scores comparable to expert pathologists, reducing diagnostic variability.

Keywords:
H-scorebreast cancercomputational pathologydeep learningimmunohistochemistry

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Area of Science:

  • Computational pathology
  • Digital pathology
  • Biomedical image analysis

Background:

  • Manual scoring of immunohistochemistry (IHC) whole slide images (WSIs) in breast cancer is labor-intensive and prone to observer variability.
  • Accurate IHC scoring is critical for diagnosis, treatment selection, and research in breast cancer.

Purpose of the Study:

  • To develop and validate a fully automated, compartment-specific (tumor and stroma) H-scoring framework for IHC analysis.
  • To improve the consistency, reproducibility, and efficiency of IHC scoring in breast cancer.

Main Methods:

  • A deep learning framework with three modules: tumor-stroma segmentation, nuclei segmentation, and H-score estimation.
  • The framework was fine-tuned on expert-annotated patches and validated on whole slide images.

Main Results:

  • Achieved a Spearman's rank correlation of 0.84 in internal validation, outperforming state-of-the-art methods and matching inter-observer variability.
  • Demonstrated 86% accuracy in HER2 classification and a mean absolute error of 21 in CD73 scoring during external validation.
  • The automated framework processes WSIs in minutes, offering significant efficiency gains.

Conclusions:

  • The automated IHC H-scoring framework provides reproducible scores comparable to expert pathologists.
  • This tool has clinical utility in reducing diagnostic variability and supporting consistent breast cancer treatment decisions.
  • The publicly released code and modular design enhance its applicability for various IHC analysis tasks.