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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
Artificial intelligence-assisted HER2 interpretation for breast cancers in a multi-laboratory study
Libo Yang1,2, Jie Chen2, Leyi Gao1,2
1Department of Pathology, West China Hospital, Sichuan University, Chengdu, China.
Insights
Manual interpretation of human epidermal growth factor receptor 2 (HER2) immunohistochemistry (IHC) shows low concordance, especially for HER2 1+ cases. Artificial intelligence (AI)-assisted interpretation improves agreement in multi-laboratory studies.
Area of Science:
- Oncology
- Pathology
- Biotechnology
Background:
- Improving concordance in human epidermal growth factor receptor 2 (HER2) examinations across laboratories is a persistent challenge.
- HER2 testing is critical for guiding breast cancer treatment decisions.
Purpose of the Study:
- To assess the concordance of HER2 immunohistochemistry (IHC) examination using manual versus artificial intelligence (AI)-assisted interpretation in a multi-laboratory setting.
- To identify factors contributing to discrepancies in HER2 scoring.
Main Methods:
- A tissue microarray (TMA) of 53 breast cancer samples was distributed to 35 laboratories for manual HER2 IHC scoring.
- Cases with discordant manual interpretations were re-evaluated using an AI-assisted microscope.
Main Results:
- Only 26.4% of cases showed concordant manual HER2 IHC results across all laboratories, with significant issues in HER2 1+ scoring.
- AI-assisted interpretation resolved complete agreement in 35.9% of previously non-concordant cases.
- Discrepancies were significantly more frequent in manually scored 1+ sections compared to 0 sections.
Conclusions:
- Weak staining in HER2 1+ breast cancers contributes to poor manual interpretation agreement.
- AI-assisted HER2 interpretation offers a robust solution to mitigate subjective errors and enhance multi-laboratory study concordance.
Background:
Improving the concordance of human epidermal growth factor receptor 2 (HER2) examinations among laboratories remains a challenge. In this multi-laboratory study, we investigated the concordance of HER2 immunohistochemistry (IHC) examination through manual and artificial intelligence (AI)-assisted interpretation.
Methods:
A tissue microarray (TMA) comprising 53 breast cancer samples was constructed and distributed to 35 participating laboratories. For each sample on every slide, IHC scores of 0, 1+, 2+, and 3+ were recorded. Subsequently, cases that failed to achieve complete agreement during manual interpretation were re-evaluated using an AI-assisted microscope.
Results:
During manual interpretation, 14 out of 53 cases (14/53, 26.4%) demonstrated concordant results across all laboratories, including 13 IHC-0 cases and 1 IHC-3+ case. Notably, cases scored as 1+ in at least one laboratory exhibited a low overall percentage agreement (OPA) and Fleiss Kappa value. Among the 39 cases with non-concordant manual interpretation, 14 cases (14/39, 35.9%) achieved complete agreement through AI-assisted HER2 interpretation. In cases where manual interpretation discrepancies were restricted to scores of 0 and 1+, 69.6% (16/23) of the cases still showed differences between 0 and 1+ in AI-assisted HER2 interpretation. Disagreements between manual and AI-assisted interpretation occurred significantly more frequently in sections manually scored as 1+ compared to those scored as 0 (58.6% vs. 2.1%, P<0.001).
Conclusions:
The weakly staining phenotype leads to poor agreement in the manual interpretation of HER2 IHC-1+ breast cancers. AI-assisted HER2 interpretation offers a viable approach for multi-laboratory studies, effectively avoiding the subjective errors inherent in manual interpretation.
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