BACKGROUND

Although meta-analyses have demonstrated survival benefits associated with primary tumor resection in MBC, guidelines lack consensus on the survival benefit of postoperative radiation therapy (RT).

METHODS

In this study, we included 1392 patients with de novo metastatic breast cancer (dnMBC) by integrating data from the SEER database (2010-2019) to systematically assess the efficacy of postoperative RT and develop a machine learning-driven prognostic tool. The primary endpoint was overall survival (OS).

RESULTS

Propensity score matching (PSM) results showed that postoperative RT significantly improved OS (HR = 0.573, 95 % CI = 0.475-0.693), but this survival gain showed great heterogeneity among different subgroups. It is found that patients with HR-/HER2-or HR+/HER2-subtypes gained significant OS benefit from (p < 0.001) postoperative RT, whereas patients with HER2+ subtype did not gain any survival benefit since the effect of targeted therapy overshadowed the postoperative RT. Further risk stratification by the random survival forest (RSF) model revealed that high-risk patients with T4/N3 stage, high tumor grade and poor response to chemotherapy had significantly prolonged OS after receiving RT (p < 0.001), while low-risk patients showed no additional benefit. The model had excellent predictive efficacy (training set C-index = 0.741, validation set C-index = 0.720) with key predictors including HER2 status, chemotherapy response and tumor grade. The research team developed an interactive web application (https://lee2287171854.shinyapps.io/RSFshiny/) based on this model, which can generate individualized survival risk scores in real-time to guide clinical decision-making.

CONCLUSION

This study is the first to propose a risk stratification strategy for postoperative RT in dnMBC, and innovatively integrates machine learning and clinical tools to provide a new paradigm for optimizing precision therapy.