The prognosis for BCLM is extremely poor, with median survival ranging from only 3 to 15 months and a 5-year survival rate as low as 8.5% [1, 5, 6]. Our study, leveraging CS analysis, revealed a previously underappreciated dynamic shift in survival outcomes over time. Notably, we observed that the 10-year survival probability increased substantially among patients who survived beyond the initial high-risk period, with the first post-diagnosis year marked by the steepest mortality decline (approximately 30% of deaths occurring within this window). Beyond this critical threshold, mortality rates diminished progressively, highlighting the inadequacy of static survival estimates. To address this temporal heterogeneity, we developed a CS-integrated nomogram model capable of dynamically recalibrating survival predictions based on accrued survivorship, thereby offering real-time, individualized prognostic stratification.

Traditional survival analyses, anchored to the time of diagnosis, inherently assume static risk profiles, failing to account for the evolving biological and clinical trajectories of survivors [20,21,22]. This approach systematically overestimates early mortality risks while underestimating long-term survival potential, particularly in malignancies like BCLM where prognosis improves with elapsed survival time. Our findings underscored the clinical necessity of dynamic prognostic tools: by quantifying how survival probabilities recalibrate annually, clinicians can tailor surveillance intensity, optimize therapeutic sequencing (e.g., transitioning from aggressive palliation to survivorship-focused care), and provide patients with updated, hope-infused prognostic narratives [22].

Our statistical findings have important clinical implications. The sharp drop in mortality after the first year suggested that key survival milestones (such as 1-year survival) could serve as crucial decision points. For instance, during the first year post-diagnosis, patients may require more aggressive treatment strategies and intensified surveillance to navigate the high-risk period. However, once they surpass this critical threshold, updated prognostic information can help refine treatment plans and tailored long-term management. Additionally, CS analysis enhanced prognostic clarity: a patient’s 5-year survival probability, when recalculated after surviving 3 years, may double compared to the initial estimate, fundamentally reshaping treatment goals and patient-clinician communication. This dynamic risk stratification aligns with the principles of precision oncology, recognizing that survival probabilities evolve over time and necessitate flexible, adaptive prognostic models.

Finally, by incorporating a multivariable feature selection strategy—including patient characteristics, tumor features (e.g., hormone receptor status), metastatic burden, and treatment approaches—our CS-based nomogram overcame the limitations of traditional survival evaluation. This model dynamically updated survival predictions based on accumulated survival time, allowing for more precise risk stratification. It helped distinguish patients who have a higher chance of long-term survival from those who remain at high risk. Clinically, this supports personalized resource allocation: high-risk patients may benefit from experimental treatments or clinical trials, while low-risk patients can transition to long-term survivorship programs. Additionally, the nomogram’s user-friendly visual design enables patients to take an active role in shared decision-making, promoting a treatment plan that is both realistic and hopeful. In our variable selection process, we also noted that socioeconomic factors significantly impacted the prognosis of BCLM, particularly marital status and income level. Numerous studies have highlighted the psychological and physiological benefits of marriage and higher income for cancer patients [23,24,25]. Married individuals tend to experience lower levels of distress, depression, and anxiety following a cancer diagnosis, largely due to the emotional and social support provided by a partner. Reduced depression is associated with better adherence to medical treatment, thereby improving survival outcomes [24]. From a physiological perspective, marriage has been associated with improved cardiovascular, endocrine, and immune function. Adequate social support may lower cortisol levels and positively influence immune responses, potentially enhancing cancer survival [24]. Similarly, household income is a key determinant of access to treatment and breast cancer care. It influences various aspects such as patient awareness, health literacy, insurance coverage, and even physician biases [26]. These socioeconomic disparities underscored the importance of incorporating such factors into prognostic models for more accurate and equitable outcome predictions.

While our study leveraged the robust, population-level data from the SEER database, several limitations must be acknowledged. First, as a retrospective dataset, SEER lacks granular details on systemic treatments (e.g., specific regimens, treatment responses) and molecular characteristics, which may impact the precision of survival estimates. Second, although CS analysis mitigated immortal time bias, some residual bias may persist in subgroups with varying treatment access. Third, in oncological research, reporting both OS and cancer-specific survival (CSS) provides a more comprehensive assessment of patient outcomes. While OS captures all-cause mortality and is straightforward to interpret, it lacks specificity. In populations with competing risks—such as elderly or advanced-stage patients—OS may not accurately reflect the benefit of cancer-directed therapies. Some treatments may improve CSS without affecting non-cancer mortality, leading OS to underestimate their efficacy. Conversely, if a treatment increases non-cancer-related deaths, OS may overstate its benefit. Moreover, OS-based models may overemphasize variables like age, which influence non-cancer mortality, thereby obscuring cancer-specific prognostic factors. Our study focused on OS and did not account for competing risks, which may introduce potential bias. Future research should clearly define endpoints when constructing prognostic models to enhance both interpretability and clinical relevance. Furthermore, the generalizability of our nomogram requires further validation in prospective cohorts to ensure broader applicability. Future studies should integrate multi-omics biomarkers and real-world treatment data to enhance dynamic prognostic models, ultimately bridging the gap between large-scale population statistics and personalized cancer care.