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Related Experiment Videos

Ingrid Garberis1, Fabrice Andre2, Magali Lacroix-Triki3

  • 1Inserm UMR 981, Gustave Roussy Cancer Campus, Villejuif, France; Université Paris-Saclay, 94270 Le Kremlin-Bicêtre, France.

Bulletin Du Cancer
|December 31, 2021
PubMed
Summary
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HER2 testing in breast cancer is crucial for targeted treatment. New "HER2-low" categories require advanced methods like machine learning and digital pathology for accurate assessment and reproducible results.

Area of Science:

  • Oncology
  • Biomarker Detection
  • Digital Pathology

Background:

  • Human Epidermal growth factor Receptor 2 (HER2) is a key prognostic and predictive biomarker in breast cancer, guiding targeted therapy selection.
  • Current HER2 assessment methods, primarily immunohistochemistry (IHC), are established for positive/negative categorization but face challenges with the emerging HER2-low category regarding scoring and reproducibility.

Purpose of the Study:

  • To review current HER2 testing methodologies in breast cancer.
  • To explore the application of machine learning (ML) and artificial intelligence (AI) in enhancing HER2 determination accuracy and reproducibility.
  • To discuss the challenges and opportunities associated with integrating digital pathology and AI in clinical practice for HER2 assessment.

Main Methods:

  • Review of existing HER2 testing protocols, including immunohistochemistry (IHC).
Keywords:
Apprentissage profondArtificial intelligenceBreast pathologyDeep learningDiagnosisDiagnosticHER2Intelligence artificiellePathologie mammaire

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  • Exploration of machine learning algorithms and artificial intelligence applications for quantitative HER2 analysis.
  • Discussion of digital pathology workflows and their role in AI-driven biomarker assessment.

    • Main Results:

    • Standard HER2 IHC is well-established but may lack precision for nuanced categories like HER2-low.
    • Machine learning techniques show promise in improving the accuracy and reproducibility of HER2 scoring.
    • Digital pathology platforms are essential for enabling AI-based analysis and large-scale implementation.

    Conclusions:

    • Accurate HER2 status determination is vital for personalized breast cancer treatment. The introduction of HER2-low necessitates advanced diagnostic approaches.
    • Machine learning and digital pathology offer significant potential to overcome current limitations in HER2 testing, improving diagnostic consistency.
    • The integration of AI and digital pathology into routine clinical workflows presents opportunities for more precise and reliable HER2 biomarker assessment in breast cancer.