Nomogram for predicting overall survival in patients with triple-negative apocrine breast cancer: Surveillance, epidemiology, and end results-based analysis
Yinggang Xu,
Weiwei Zhang,
Jinzhi He,
Ye Wang,
Rui Chen,
Wenjie Shi,
Xinyu Wan,
Xiaoqing Shi,
Xiaofeng Huang,
Jue Wang,
Xiaoming Zha
Affiliations
Yinggang Xu
Department of Breast Disease, The First Affiliated Hospital of Nanjing Medical University, No.300 Guangzhou Road, Nanjing, 210000, China
Weiwei Zhang
Department of Breast Disease, The First Affiliated Hospital of Nanjing Medical University, No.300 Guangzhou Road, Nanjing, 210000, China
Jinzhi He
Department of Breast Disease, The First Affiliated Hospital of Nanjing Medical University, No.300 Guangzhou Road, Nanjing, 210000, China
Ye Wang
Department of Breast Disease, The First Affiliated Hospital of Nanjing Medical University, No.300 Guangzhou Road, Nanjing, 210000, China
Rui Chen
Department of Breast Disease, The First Affiliated Hospital of Nanjing Medical University, No.300 Guangzhou Road, Nanjing, 210000, China
Wenjie Shi
Department of Breast Disease, The First Affiliated Hospital of Nanjing Medical University, No.300 Guangzhou Road, Nanjing, 210000, China
Xinyu Wan
Department of Breast Disease, The First Affiliated Hospital of Nanjing Medical University, No.300 Guangzhou Road, Nanjing, 210000, China
Xiaoqing Shi
Department of Breast Disease, The First Affiliated Hospital of Nanjing Medical University, No.300 Guangzhou Road, Nanjing, 210000, China
Xiaofeng Huang
Department of Breast Disease, The First Affiliated Hospital of Nanjing Medical University, No.300 Guangzhou Road, Nanjing, 210000, China
Jue Wang
Department of Breast Disease, The First Affiliated Hospital of Nanjing Medical University, No.300 Guangzhou Road, Nanjing, 210000, China; Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, 210000, China; Corresponding author. Department of Breast disease, the First Affiliated Hospital of Nanjing Medical University, No.300 Guangzhou Road, Nanjing 210000, China.
Xiaoming Zha
Department of Breast Disease, The First Affiliated Hospital of Nanjing Medical University, No.300 Guangzhou Road, Nanjing, 210000, China; Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, 210000, China; Corresponding author. Department of Breast disease, the First Affiliated Hospital of Nanjing Medical University, No.300 Guangzhou Road, Nanjing 210000, China.
Purpose: Triple-negative apocrine carcinoma (TNAC) is a sort of triple-negative breast cancer (TNBC) that is rare and prognosis of these patients is unclear. The present study constructed an effective nomogram to assist in predicting TNAC patients overall survival (OS). Methods: A total of 373 TNAC patients from the surveillance, epidemiology, and end results (SEER) got extracted from 2010 to 2016 and were divided into training (n = 261) and external validation (n = 112) groups (split ratio, 7:3) randomly. A Cox regression model was utilized to creating a nomogram according to the risk factors affecting prognosis. The predictive capability of the nomogram was estimated with receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). Results: Multivariate Cox regression analysis revealed age, surgery, chemotherapy, stage, and first malignant primary as independent predictors of OS. A prediction model was constructed and virtualized using the nomogram. The time-dependent area under the curve (AUC) showed satisfactory discrimination of the nomogram. Good consistency was shown on the calibration curves in OS between actual observations and the nomogram prediction. What's more, DCA showed that the nomogram had incredible clinical utility. Through separating the patients into groups of low and high risk group that connects with the risk system that shows a huge difference between the low-risk and high risk OS (P < 0.001). Conclusion: To predict the OS in TNAC patients, the nomogram utilizing the risk stratification system that is corresponding. These tools may help to evaluate patient prognosis and guide treatment decisions.
