Long-term trends in the incidence of male breast cancer and nomogram for predicting survival in male breast cancer patients: a population-based epidemiologic study

Male breast cancer (MBC) is rare, and due to the absence of male-specific screening programs, many patients are diagnosed at advanced stages and older ages. This study aims to analyze the long-term trend of MBC incidence and develop a competing risk model to improve survival rates. MBC data from the Surveillance, Epidemiology, and End Results (SEER) database (1975-2019) were analyzed using the Age-Period-Cohort (APC) model to examine trends in age, period, and birth cohort effects of MBC incidence. A competing risk model was used to build a nomogram predicting breast cancer-specific survival (BCSS) for MBC patients, with model accuracy assessed using the concordance index (C-index) and calibration curves. These results were compared with the nomogram established by Cox model. Comparisons were made with traditional AJCC staging system using net reclassification improvement (NRI) and integrated discrimination improvement (IDI). APC analysis showed MBC incidence increases with age, while period and birth cohort effects were not significant. A total of 2,057 patients were included in the competing risk analysis, which identified factors like older age, estrogen receptor (ER) negative, progesterone receptor (PR) negative, and advanced AJCC stage as being associated with shorter survival. The competing risk model demonstrated excellent predictive ability, surpassing the AJCC staging system in both accuracy and clinical utility. In contrast, the Cox regression model overestimated the risk of endpoint events and failed to provide accurate effect estimates. The high incidence and poor prognosis of MBC in the elderly population emphasize the need for improved screening and early diagnosis in high-risk groups. Our competing risk model, compared to the Cox model, more accurately reflects real-world conditions. Additionally, we have developed a competing risk nomogram to assist in identifying high-risk individuals and guiding clinical decision-making.