A nomogram for predicting breast cancer specific survival in elderly patients with breast cancer: a SEER population-based analysis
Ruoning Yang, Yunhao Wu, Yana Qi, Weijing Liu, Ya Huang, Xin Zhao, Ruixian Chen, Tao He, Xiaorong Zhong, Qintong Li, Li Zhou, Jie Chen
Abstract
BACKGROUND: The number of elderly patients diagnosed with breast cancer is increasing worldwide. However, treatment decisions for these patients are highly variable. Although researchers have identified the effects of surgery, radiotherapy, endocrine therapy, and chemotherapy in elderly patients with breast cancer, clinicians still struggle to make appropriate decisions for these patients. METHODS: We identified 75,525 female breast cancer patients aged ≥ 70 years in the Surveillance, Epidemiology, and End Results (SEER) database treated between January 1, 2010, and December 31, 2016. The patients were further divided into training and testing cohorts. The cumulative occurrence of breast cancer-specific deaths (BCSDs) and other cause-specific deaths (OCSD) was calculated using the cumulative incidence function. In the univariate analysis, risk factors were screened using the Fine-Gray model. In the multivariate analysis for competing risks, the sub-distribution hazard ratio with a 95% confidence interval for each independent predictor associated with BCSD was calculated for the construction of nomograms. Based on the above analyses, a competing risk nomogram was constructed to predict the probability of BCSD in the 1st, 3rd, and 5th years after treatment. During validation, the concordance index (C-index) was selected to quantify the predictive ability of the competing risk model. RESULTS: A total of 33,118 patients were included in this study, with 24,838 in the training group and 8,280 in the testing group. Age, race, marital status, cancer grade, tumor stage, node stage, estrogen receptor status, progesterone receptor status, human epidermal growth factor receptor--2 status, and treatment including surgery, radiation, and chemotherapy were used to establish a nomogram. The C-index of 0.852 (0.842-0.862) in the training cohort and 0.876 (0.868-0.892) in the testing cohort indicated satisfactory discriminative ability of the nomogram. Calibration plots showed favorable consistency between the nomogram predictions and actual observations in both the training and validation cohorts. CONCLUSIONS: Our study identified independent predictors of BCSD in elderly patients with breast cancer. A prognostic nomogram was developed and validated to aid clinical decision-making.