A Nomogram for Predicting Overall Survival in Male Breast Cancer Patients: A SEER Based-Study and an External Validation
ABSTRACT
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 MBC and externally validate instances in China.
Methods:
On the basis of 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 then randomly assigned to either a training set (n = 1610) or a validation set (n = 713) at a ratio of 7:3. Cox regression risk regression model was used to screen for significant risk variables and construct a prediction model and nomogram for MBC survival. Information collected from the test set were applied to validate the model. Twenty-two MBC cases diagnosed in the First Affiliated Hospital of Guangxi Medical University between January 2013 and June 2021 were used for external validation, and the follow-up end point was June 10, 2023.
Results:
A total of 2301 MBC patients in the SEER database were included, and 22 MBC patients were collected in the study hospital. The predictive model was constructed to include seven variables: age (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), T 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 were 0.72, 0.747, and 0.981 in the training set, internal validation set, and external validation set, respectively. The nomogram was accurately calibrated, and the ROC curve confirmed the advantage of the model in clinical validity. DCA showed that the model has good clinical application value. Additionally, the nomogram classification allowed for more accurate differentiation of risk subgroups, and patients with low-risk breast cancer demonstrated substantially improved survival outcomes compared with medium- and high- risk patients (P < .001).
Conclusions:
A survival prognosis prediction model for MBC patients was constructed in this study. The model can predict the survival outcome of these patients, and thus can provide a scientific basis for clinical diagnosis and treatment. Clinical Trial: NA
Citation
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