Accurate assessment of surgical HER2-low status based on core needle biopsy data of breast cancer.

Abstract

Background: Breast 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.

Methods: This 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.

Results: Among 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.

Conclusion: While 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.