Development and validation of a survival-predicting nomogram for HER2-negative T1–3N0–1 breast cancer treated with breast-conserving surgery: a Surveillance, Epidemiology, and End Results (SEER) database analysis

Background: Human epidermal growth factor receptor 2 (HER2)-negative early-stage breast cancer (BC) exhibits significant heterogeneity, complicating personalized treatment decisions after breast-conserving surgery (BCS). Robust tools integrating baseline risk, treatment response, and sociodemographic factors are needed to optimize survival while minimizing unnecessary toxicity. This study aimed to create a clinical decision-support tool that leverages these multifaceted factors to optimize survival outcomes and minimize treatment toxicity for these patients. Methods: Utilizing population-level data from the Surveillance, Epidemiology, and End Results (SEER) program (cases from 2010 to 2016; n=8,384), we constructed and validated prognostic nomograms for overall survival (OS) and cancer-specific survival (CSS) in a cohort of HER2-negative, T1-3N0-1 BC patients who underwent BCS followed by radiotherapy. Key prognostic variables were identified through multivariable Cox proportional hazards regression. The performance of the nomograms was rigorously assessed using the concordance index (C-index), time-dependent receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis (DCA). Finally, risk stratification was performed by applying optimal cut-off points determined via X-tile software. Results: 0.63). Patients were stratified into low- (33%), middle-, and high-risk (27%) groups. Chemotherapy provided no OS/CSS benefit in low-risk patients but substantially improved outcomes in high-risk patients (P