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Updated: May 20, 2025
08:29
Validated Immunochemical Assay for Comprehensive Determination of the Human Epidermal Growth Factor Receptor 2 Released from and Bound to Cells
Published on: May 9, 2025
53
Systematic Analysis of Factors Affecting Human Epidermal Growth Factor Receptor 2 Interpretation Consistency:
Chen Jiang1, Mei Li1, Chengyou Zheng1
1State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, P. R. China; Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China.
Summary
Standardizing human epidermal growth factor receptor 2 (HER2) testing improves breast cancer diagnosis. An AI model and new criteria enhance HER2 interpretation consistency, aiding treatment decisions.
Area of Science:
- Oncology
- Pathology
- Biomedical Engineering
Background:
- Accurate human epidermal growth factor receptor 2 (HER2) assessment is crucial for breast cancer treatment, particularly for HER2-targeted therapies.
- Variability in immunohistochemical (IHC) staining protocols and interobserver disagreement compromise the reliability of HER2 status determination.
- Existing classification criteria present challenges in consistently interpreting HER2 status, especially for low and ultra-low expression levels.
Purpose of the Study:
- To investigate factors influencing HER2 interpretation consistency in breast cancer diagnostics.
- To evaluate the impact of different IHC staining protocols and an artificial intelligence (AI) model on HER2 assessment reliability.
- To compare the performance of existing and novel HER2 classification criteria in improving interobserver agreement.
Main Methods:
- Tissue microarrays from 1063 breast carcinoma cases were stained using three distinct IHC protocols.
- A novel AI model was developed to standardize HER2-stained images.
- Five sets of stained tissue microarrays were independently reviewed by eight pathologists, with interobserver agreement measured using Fleiss Kappa and overall agreement rates. Logistic regression analyzed diagnostic accuracy.
Main Results:
- The Nordi QC protocol exhibited the highest interobserver agreement (Kappa 0.754).
- AI-based image standardization significantly improved consistency, particularly for HER2 low cases, aligning results with the Nordi QC standard (P < .001).
- The proposed null, ultra-low/low, positive criteria demonstrated improved reliability over the ASCO/CAP 2023 criteria, especially for challenging HER2-ultra-low cases (Kappa < 0.20).
Conclusions:
- Variability in IHC staining protocols and HER2 classification criteria significantly impacts diagnostic consistency.
- AI-driven image standardization offers a promising approach to enhance HER2 interpretation reliability.
- Adoption of the null, ultra-low/low, positive criteria may refine diagnostic precision for improved breast cancer treatment decisions.
Keywords:
artificial intelligencebreast cancerclassification criteriahuman epidermal growth factor receptor 2immunohistochemistry staininginterobserver agreementMore Related Videos
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