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

Zhao, Lifeng; Lin, Ziren; Nong, Shitang; Li, Caixin; Li, Junnan; Lin, Cheng; Safi, Sher Zaman; Huang, Shiqing; Ismail, Ikram Shah Bin

doi:10.31557/APJCP.2024.25.9.3199

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

Document Type : Research Articles

Authors

¹ Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, 533000, China.

² Faculty of medicine, MAHSA University, Jenjarom 42610, Selangor, Malaysia.

³ The People’s Hospital of Baise, Baise, 533000, China.

⁴ Youjiang Medical University for Nationalities, Baise, 533000, China.

⁵ Faculty of Medicine, Bioscience and Nursing, MAHSA University, Bandar Saujana Putra, Jenjarom 42610, Malaysia.

10.31557/APJCP.2024.25.9.3199

Abstract

Background: 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. Methods: 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). Results: 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. Conclusion: 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.

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