Primary Breast Neuroendocrine Tumors: An Analysis of the National Cancer Database
- PMID: 35789311
- PMCID: PMC9464685
- DOI: 10.1245/s10434-022-12123-w
Primary Breast Neuroendocrine Tumors: An Analysis of the National Cancer Database
Abstract
Background: Primary breast neuroendocrine tumors (BNETs) represent < 1% of breast cancers. Diagnosing BNETs can be challenging, and a limited amount of cohort data currently exists in literature. We aimed to describe primary BNET characteristics, treatment modalities, and survival outcomes through the National Cancer Database (NCDB).
Methods: A retrospective cohort analysis was performed using the NCDB from 2004 to 2017. BNET cases were compared with patients with invasive ductal carcinoma (IDC). A matched IDC cohort was created by matching patient age, race, and disease stage. Kaplan-Meier analysis was performed, and hazard ratios (HR) were calculated through the bootstrap sampling method.
Results: A total of 1389 BNET and 1,967,401 IDC cases were identified. When compared with IDC patients, BNET patients were older, had more comorbidities, and were more often male (p < 0.01). BNETs were larger, higher grade, and more frequently hormone receptor negative (p < 0.01). While BNET patients were treated with surgery and radiotherapy (p < 0.01) less often compared with IDC patients, they presented at later disease stage (p < 0.001) and received systemic treatment more frequently (53.5% vs. 40%, p < 0.01). Patients with BNET had increased mortality compared with the matched IDC cohort: stage 1 HR 1.8, stage 2 HR 2.0, stage 3 HR 1.8, and stage 4 HR 1.5 (p < 0.001 for all).
Conclusion: Patients with BNET tend to present at higher clinical stages, are more frequently hormone receptor negative, and have inferior overall survival compared with patients with IDC. Further treatment strategies and studies are needed to elucidate optimal therapies to maximize patient outcomes.
© 2022. Society of Surgical Oncology.
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Comment in
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ASO Author Reflections: Rare Breast Cancer Subtypes and the Role for Precision Oncology.Ann Surg Oncol. 2022 Oct;29(10):6347-6348. doi: 10.1245/s10434-022-12137-4. Epub 2022 Jul 5. Ann Surg Oncol. 2022. PMID: 35789300 No abstract available.
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