ArticleGland surgery2025
Artificial intelligence-assisted HER2 interpretation for breast cancers in a multi-laboratory study.
Article in Gland surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- HER2-Low Breast Cancer at the Interface of Pathology and Technology: Toward Precision Management.Biomedicines · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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% 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.