Predicting Overall Survival in Patients with Male Breast Cancer: Nomogram Development and External Validation Study.

BACKGROUND

Male breast cancer (MBC) is an uncommon disease. Few studies have discussed the prognosis of MBC due to its rarity.

OBJECTIVE

This study aimed to develop a nomogram to predict the overall survival of patients with MBC and externally validate it using cases from China.

METHODS

Based on the Surveillance, Epidemiology, and End Results (SEER) database, male patients who were diagnosed with breast cancer between January 2010, and December 2015, were enrolled. These patients were randomly assigned to either a training set (n=1610) or a validation set (n=713) in a 7:3 ratio. Additionally, 22 MBC cases diagnosed at the First Affiliated Hospital of Guangxi Medical University between January 2013 and June 2021 were used for external validation, with the follow-up endpoint being June 10, 2023. Cox regression analysis was performed to identify significant risk variables and construct a nomogram to predict the overall survival of patients with MBC. Information collected from the test set was applied to validate the model. The concordance index (C-index), receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and a Kaplan-Meier survival curve were used to evaluate the accuracy and reliability of the model.

RESULTS

A total of 2301 patients with MBC in the SEER database and 22 patients with MBC from the study hospital were included. The predictive model included 7 variables: age (hazard ratio [HR] 1.89, 95% CI 1.50-2.38), surgery (HR 0.38, 95% CI 0.29-0.51), marital status (HR 0.75, 95% CI 0.63-0.89), tumor stage (HR 1.17, 95% CI 1.05-1.29), clinical stage (HR 1.41, 95% CI 1.15-1.74), chemotherapy (HR 0.62, 95% CI 0.50-0.75), and HER2 status (HR 2.68, 95% CI 1.20-5.98). The C-index was 0.72, 0.747, and 0.981 in the training set, internal validation set, and external validation set, respectively. The nomogram showed accurate calibration, and the ROC curve confirmed the advantage of the model in clinical validity. The DCA analysis indicated that the model had good clinical applicability. Furthermore, the nomogram classification allowed for more accurate differentiation of risk subgroups, and patients with low-risk MBC demonstrated substantially improved survival outcomes compared with medium- and high-risk patients (P<.001).

CONCLUSIONS

A survival prognosis prediction nomogram with 7 variables for patients with MBC was constructed in this study. The model can predict the survival outcome of these patients and provide a scientific basis for clinical diagnosis and treatment.