1. Introduction
MBC, breast cancer
BRAIN METASTASIS, BREAST CANCER, LUNG METASTASIS, PRIMARY TUMOR, BONE METASTASIS, REGRESSION, LIVER METASTASIS, METASTATIC BREAST CANCER
These authors contributed equally to this work.Background The lifespan of patients diagnosed with de novo metastatic breast cancer (dnMBC) has been prolonged. Nonetheless, there remains substantial debate regarding immediate breast reconstruction (IBR) for this particular subgroup of patients. The aim of this study was to construct a nomogram predicting the breast cancer-specific survival (BCSS) of dnMBC patients who underwent IBR. Methods A total of 682 patients initially diagnosed with metastatic breast cancer (MBC) between 2010 and 2018 in the Surveillance, Epidemiology, and End Results (SEER) database were included in this study. All patients were randomly allocated into training and validation groups at a ratio of 7:3. Univariate Cox hazard regression, least absolute shrinkage and selection operator (LASSO), and best subset regression (BSR) were used for initial variable selection, followed by a backward stepwise multivariate Cox regression to identify prognostic factors and construct a nomogram. Following the validation of the nomogram with concordance indexes (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analyses (DCAs), risk stratifications were established. Results Age, marital status, T stage, N stage, breast subtype, bone metastasis, brain metastasis, liver metastasis, lung metastasis, radiotherapy, and chemotherapy were independent prognostic factors for BCSS. The C-indexes were 0.707 [95% confidence interval (CI), 0.666–0.748] in the training group and 0.702 (95% CI, 0.639–0.765) in the validation group. In the training group, the AUCs for BCSS were 0.857 (95% CI, 0.770–0.943), 0.747 (95% CI, 0.689–0.804), and 0.700 (95% CI, 0.643–0.757) at 1 year, 3 years, and 5 years, respectively, while in the validation group, the AUCs were 0.840 (95% CI, 0.733–0.947), 0.763 (95% CI, 0.677–0.849), and 0.709 (95% CI, 0.623–0.795) for the same time points. The calibration curves for BCSS probability prediction demonstrated excellent consistency. The DCA curves exhibited strong discrimination power and yielded substantial net benefits. Conclusions The nomogram, constructed based on prognostic risk factors, has the ability to provide personalized predictions for BCSS in dnMBC patients undergoing IBR and serve as a valuable reference for clinical decision making.About 3–8% of breast cancer patients are detected in stage IV [Some large-scale retrospective studies have shown that surgical removal of the primary tumor provides a survival benefit for dnMBC patients [The precise identification of which subset of dnMBC patients would significantly benefit from IBR remains unclear; thus, a survival prediction model is needed to assist in the selection of suitable candidates. In this study, we explored prognostic factors in dnMBC patients who underwent IBR based on the SEER database, developed a BCSS prediction nomogram, and externally validated the model. Ultimately, we divided de novo metastatic breast cancer patients into three distinct risk categories—low, intermediate, and high—with the aim of enabling the recognition of high-risk individuals and providing valuable insights for clinical decision making. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) was rigidly referred to in our study [