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Abstract Background The current AJCC staging system for breast cancer categorizes all distant metastases under a monolithic M1 designation, failing to capture profound heterogeneity in outcomes. This study aimed to develop and validate a refined M-stage classification that integrates metastatic burden, anatomic site, and brain metastasis status to improve prognostic stratification. Methods In this multi-center, retrospective study, 2,615 patients with metastatic breast cancer (diagnosed 2005–2020) were classified into three proposed categories: M1a (Oligometastasis: 1–5 lesions, ≤ 2 organs, no brain metastases), M1b (Oligometastasis: 1–5 lesions, ≤ 2 organs, includes 1–3 treatable brain metastases), and M1c (Extensive Metastasis: > 5 lesions, ≥ 3 organs, or untreatable brain metastases). Overall survival (OS) was compared using Kaplan–Meier curves and Cox proportional hazards models. Prognostic performance was assessed using Harrell’s C-index and compared to the traditional binary M1 stage. Results The cohort was stratified into M1a (n = 672, 25.7%), M1b (n = 418, 16.0%), and M1c (n = 1,525, 58.3%). A clear prognostic gradient was observed: median OS was 98.3 months for M1a, 62.1 months for M1b, and 32.7 months for M1c (log-rank p < 0.001). The proposed classification was an independent prognostic factor on multivariable analysis (M1a vs. M1c adjusted HR = 0.49, p < 0.001). It demonstrated superior prognostic discrimination compared to the traditional M1 stage (C-index 0.67 vs. 0.59, Δ = 0.08, p < 0.001), reclassifying 41.7% of patients into more precise risk categories. Conclusion This novel three-tiered M-stage classification robustly stratifies metastatic breast cancer into prognostically distinct groups, outperforming the current binary system. It provides a practical framework for refining prognostication, personalizing therapy, and enriching future clinical trials.
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Shanqing Liu
Brown University
Y W Li
Henan Cancer Hospital
Yan Shen
First People's Hospital of Yuhang District
BMC Cancer
Henan Cancer Hospital
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Liu et al. (Thu,) studied this question.
synapsesocial.com/papers/6a0a062c16dfdfe7ed348d07 — DOI: https://doi.org/10.1186/s12885-026-15861-2
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