ArticleDiagnostic and prognostic research2026
Accurate assessment of surgical HER2-low status based on core needle biopsy data of breast cancer.
Article in Diagnostic and prognostic research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
backgroundBreast cancer is a leading cause of cancer-related mortality among women globally. Accurate identification of HER2 status, particularly the HER2-low category, is crucial for effective treatment strategies, given the emergence of HER2-targeted therapies for this subgroup. However, discrepancies often arise between core needle biopsy (CNB) and surgical resection tissue (SRT) regarding more refined HER2 status.
methodsThis retrospective study included female patients with invasive breast cancer who underwent surgical resection between January 1, 2020, and December 31, 2024, at a single cancer center in China. We assessed the concordance of HER2 status, alongside other clinicopathological features on both CNB and SRT samples. We then developed a supervised binary modeling framework to predict SRT HER2 status (with metastasis) based on CNB data.
resultsAmong 1,919 patients analyzed, the concordance rate for HER2 classification was 97.0% in a binary system but dropped to 75.9% when using a 3-category system, with discordance noted particularly between HER2-zero and HER2-low categories. Logistic regression identified significant factors impacting concordance. The predictive model utilizing CNB HER2 classification achieved a mean AUC of 0.824, indicating substantial predictive capability for SRT HER2-low status. Integrating CNB clinicopathological variables further improved prediction accuracy (mean AUC from 0.691 to 0.771) for SRT HER2-low with metastasis in clinically relevant regions.
conclusionWhile CNB effectively determines binary HER2 status, establishing HER2-low categorization reveals discrepancies. Our findings highlight the importance of accurate preoperative HER2 assessment, supported by AI-based prediction models that enhance clinical decision-making in breast cancer management targeted toward HER2-low patients.
Indexed as
Identifiers
42855725What 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.