Development and Validation of a Prognostic Nomogram Model for HER2-Positive Male Breast Cancer Patients

• Published in: Asian Pacific journal of cancer prevention : APJCP Vol. 25; no. 9; p. 3199
• Main Authors: Zhao, Lifeng, Lin, Ziren, Nong, Shitang, Li, Caixin, Li, Junnan, Lin, Cheng, Safi, Sher Zaman, Huang, Shiqing, Ismail, Ikram Shah Bin
• Format: Journal Article
• Language: English
• Published: Thailand 01.09.2024
• Subjects: Adult; Aged; Biomarkers, Tumor - metabolism; Breast Neoplasms, Male - metabolism; Breast Neoplasms, Male - mortality; Breast Neoplasms, Male - pathology; Breast Neoplasms, Male - therapy; Follow-Up Studies; Humans; Male; Middle Aged; Nomograms; Prognosis; Receptor, ErbB-2 - metabolism; ROC Curve; SEER Program; Survival Rate; SEER database; HER2-positive; Cox regression; nomogram; Male breast cancer
• ISSN: 2476-762X

HER2-positive male breast cancer (MBC) is a rare condition that has a poor prognosis. The purpose of this study was to establish a nomogram model for predicting the prognosis of HER2-positive MBC patients. 240 HER2-positive MBC patients from 2004 to 2015 were retrieved from the surveillance, epidemiology, and end results (SEER) database. All HER2-positive MBC patients were divided randomly into training (n = 144) and validation cohorts (n = 96) according to a ratio of 6:4. Univariate and multivariate Cox regression analyses were used to determine the prognostic factors associated with HER2-positive MBC patients. A clinical prediction model was constructed to predict the overall survival of these patients. The nomogram model was assessed by using receiver operating characteristics (ROC) curves, calibration plots and decision curve analysis (DCA). The Cox regression analysis showed that T-stage, M-stage, surgery and chemotherapy were independent risk factors for the prognosis of HER2-positive MBC patients. The model could also accurately predict the Overall survival (OS) of the patients. In the training and validation cohorts, the C indexes of the OS nomograms were 0.746 (0.677-0.815) and 0.754 (0.679-0.829), respectively. Calibration curves and DCA verified the reliability and accuracy of the clinical prediction model. In conclusion, the predictive model constructed had good clinical utility and can help the clinician to select appropriate treatment strategies for HER2-positive MBC patients.