Patterns and prognostic implications of distant metastasis in breast Cancer based on SEER population data.
Distant metastasis remains the leading cause of mortality in breast cancer, yet comprehensive population-based evaluations of metastatic site combinations and their survival implications are limited. This study aimed to explore the clinicopathological determinants and prognostic outcomes of site-specific and multi-organ metastases in breast cancer using SEER data. A total of 200,558 female breast cancer patients diagnosed between 2014 and 2023 were extracted from the SEER database. Logistic regression was used to assess associations between clinicopathological features and metastatic patterns. Kaplan-Meier analysis and Cox proportional hazards models were applied to evaluate overall survival (OS) across different metastatic site combinations. Among patients with distant metastasis classified into 15 common metastatic patterns, bone was the most common metastatic site (21.3%), followed by lung (16.1%), liver (9.2%), and brain (2.9%). Molecular subtypes showed distinct organotropism: HR+/HER2 - tumors were prone to bone-only metastasis, whereas HER2-positive and triple-negative subtypes were more likely to involve visceral and brain metastases. Multi-organ metastases, especially combinations including the brain (e.g., brain + liver + lung), were associated with the poorest prognosis (median OS: 4.0 months). Younger age (≤ 40 years), higher histological grade (Grade III), and tumor location in the axillary tail or unspecified regions were independently associated with increased metastatic risk. Grade III tumors exhibited broader visceral spread and significantly worse survival compared to lower-grade tumors. This is the first population-based study to systematically characterize 15 metastatic site combinations and their survival outcomes across molecular subtypes. The findings highlight the heterogeneity of breast cancer metastasis and underscore the need for subtype-specific, site-targeted surveillance strategies and prognostic assessment tools.
Authors
Zhang Zhang, Deng Deng, Hu Hu, Chen Chen, Dong Dong, Zhang Zhang, Guo Guo, Yang Yang, Chen Chen
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