Establishment of Prognostic Nomogram for Male Breast Cancer Patients: A Surveillance, Epidemiology and End Results Database Analysis

BACKGROUND: Male breast cancer (MBC) represents a rare subtype of breast cancer, with limited prognostic factor studies available. The purpose of this research was to develop a unique nomogram for predicting MBC patient overall survival (OS) and breast cancer-specific survival (BCSS). METHODS: From 2010 to 2020, clinical characteristics of male breast cancer patients were obtained from the Surveillance, Epidemiology and End Results (SEER) database. Following univariate and multivariate analyses, nomograms for OS and BCSS were created. Kaplan-Meier plots were further generated to illustrate the relationship between independent risk variables and survival. The nomogram's ability to discriminate was measured by employing the area under a time-dependent receiver operating characteristic curve (AUC) and calibration curves. Additionally, when the nomogram was used to direct clinical practice, we also used decision curve analysis (DCA) to evaluate the clinical usefulness and net clinical benefits. RESULTS: A total of 2143 patients were included in this research. Univariate and multivariate analysis showed that age, grade, surgery, chemotherapy status, brain metastasis status, subtype, marital status, race, and AJCC-T, AJCC-N, and AJCC-M stages were significantly correlated with OS. Lung metastasis, age, marital status, grade, surgery, and AJCC-T, AJCC-N, and AJCC-M stages were significantly correlated with BCSS. By comprising these variables, a predictive nomogram was constructed in the SEER cohort. Then, it could be validated well in the validation cohort by receiver operating characteristics (ROCs) curve and calibration plot. Furthermore, the nomogram demonstrated better decision curve analysis (DCA) results, indicating the ability to forecast survival probability with greater accuracy. CONCLUSION: We created and validated a unique nomogram that can assist clinicians in identifying MBC patients at high risk and forecasting their OS/BCSS.