A nomogram for survival prediction in 275,812 U.S. patients with breast cancer: a population-based cohort study based on the SEER database.

Background: Nomograms can assess the risk of clinicopathological features by quantifying the biological and clinical variables of cancer patients. However, the nomogram based on significant factors that influence the survival of breast cancer in a large population has been rarely explored. This study was to investigate the predictive effectiveness of a nomogram for the survival of patients with breast cancer. Methods: Demographic and clinical data of 275,812 breast cancer patients were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. All patients aged ≥20 years in this retrospective cohort study were classified as two groups in a random manner, namely the training set (n=193,069) and validation set (n=82,743). The outcomes of our study were the 3- and 5-year survival of breast cancer. The potential predictors of cancer mortality were screened by univariate and multivariable Cox regression analyses. The nomogram was conducted based on the predictors. Harrell's concordance index (C-index), receiver operating characteristic (ROC) curves and calibration curve was utilized to evaluate the performance of the nomogram. Results: The age at diagnosis, race, marital status, tumor size, first malignant primary indicator, American Joint Committee on Cancer (AJCC) T stage, M stage, tumor grade, and number of malignant tumors were independent predictors for the death of patients with breast cancer. The C-indexes of the training set and the validation set were 0.782 and 0.778, respectively. The area under the curve (AUC) values of the nomogram for predicting the 3- and 5-year survival of breast cancer were 0.770 and 0.756, respectively. Furthermore, the C-index values of our nomogram were 0.816, 0.775, 0.773, 0.734, and 0.750 for predicting survival in Asian, White, Hispanic, American Indian, and Black populations, respectively. Conclusions: The nomogram may have predictive performance for predicting the 3- and 5-year survival of breast cancer patients, and future studies need to validate our findings.