Construction and Validation of a Nomogram Predicting the Overall Survival Benefit of Unilateral Breast Cancer Patients Undergoing Contralateral Prophylactic Mastectomy

Currently, research on the prognostic factors of unilateral breast cancer (UBC) patients receiving contralateral prophylactic mastectomy (CPM) is limited. This study aimed to construct a new nomogram to predict these patients' overall survival (OS). In this retrospective study, 88,477 patients who underwent CPM or unilateral mastectomy (UM) were selected from the Surveillance, Epidemiology, and End Results database. Kaplan-Meier curves and Cox regression analyses were used to determine the difference in the impact of the 2 surgical methods on the prognosis. Multivariate Cox analysis was used to determine the best prognostic variable and construct a nomogram. The concordance index (C-index), receiver operating characteristic (ROC) curve, calibration curve, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were used to evaluate the discrimination capability and clinical effectiveness of the nomogram. The prognosis of patients receiving CPM and UM was significantly different. The DCA curves indicated that the nomogram could provide more excellent clinical net benefits for these patients. The NRI and IDI of the nomogram demonstrated that its performance was better than that of the classical tumor-node-metastasis (TNM) staging system. This study developed and validated a practical nomogram to predict the OS of UBC patients undergoing CPM, which provided a beneficial tool for clinical decision-making management. This study aimed to construct a new nomogram to predict these patients' overall survival. We selected 88,477 patients who underwent contralateral prophylactic mastectomy or unilateral mastectomy from the Surveillance, Epidemiology, and End Results database. The results showed that the nomogram constructed in this study had excellent performance and provided a beneficial tool for clinical decision-making management.