Nomogram for predicting the risk and prognosis of lung metastasis of four subtypes of breast cancer: A population-based study from SEER

Abstract

Background

Breast cancer (BC) is the most diagnosed cancer worldwide, and patients' survival decreases with metastasis. We conducted a retrospective study using data derived from the Surveillance, Epidemiology, and End Results (SEER) database and clinicopathological data to construct a clinical predictive model to predict the risk and prognosis of lung metastasis (LM) in patients with different subtypes of BC and validate its performance.

Methods

A total of 1650 patients from the SEER database between 2011 and 2015 were enrolled in this study. Cox regression analysis was performed to identify prognostic factors for breast cancer lung metastasis (BCLM). A nomogram was constructed using the independent prognostic factors. The concordance index (C-index), area under the curve (AUC) value, calibration curve, and decision curve analysis (DCA) were used to test the prediction accuracy of the nomogram. External validation (n = 112) was performed using clinical data from the Affiliated Hospital of Qinghai University and the General Hospital of Ningxia Medical University.

Results

Multivariate Cox regression analyses suggested that age, grade, surgery, chemotherapy, subtype, and liver, bone, and brain metastases were independent prognostic factors for overall survival (OS). Kaplan–Meier survival analysis showed that the median survival times of patients with human epidermal growth factor receptor 2 (HER2)-positive, luminal A, luminal B, and triple-negative BC were 25 (95% confidence interval [CI], 20–37), 27 (95% CI, 23–29), 35 (95% CI, 30–44), and 12 (95% CI, 11–14), respectively. The C-indexes of the nomogram for predicting OS of the SEER training, SEER validation, and clinical validation cohorts were 0.7, 0.6, and 0.6, respectively, and the calculated AUCs at 3 years were 0.765, 0.794, and 0.799, respectively. The calibration curve indicates that the nomogram possessed a high level of accuracy.

Conclusions

Our nomogram demonstrates significant predictive value, indicating that molecular subtypes, brain metastasis, and liver metastasis are closely associated with the prognosis of patients with LM. This information can guide clinical practice.