Development and validation of a dynamic survival nomogram for metastatic non-small cell lung cancer based on the SEER database and an external validation cohort

BACKGROUND: Limited efficacy and poor prognosis are common in patients with metastatic non-small cell lung cancer (NSCLC). An accurate and useful nomogram helps the clinician predict the prognosis of the patients. However, there has been no previous report on the nomogram specially for predicting the overall survival (OS) of metastatic NSCLC patients. METHODS: A total of 18,343 patients diagnosed with metastatic NSCLC in the Surveillance, Epidemiology, and End Results (SEER) database were included and divided into the training cohort (n=12,840) and the internal validation cohort (n=5,503), and 242 patients in Renji Hospital were additionally enrolled as the external validation cohort. Demographical, clinical, and OS data were collected. A Cox proportional hazards regression model was used to develop a nomogram based on the training cohort. To validate the nomogram, we applied C-indexes, calibration curves, receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and a Kaplan-Meier survival curve. RESULTS: The multivariate Cox regression model found that there were a total of 16 independent risk factors for OS of the patients (all 16 factors showed P<0.001), which were integrated into the nomogram with a C-index of 0.702 [95% confidence interval (CI): 0.684–0.720]. The nomogram also exhibited good prognostic value in the internal validation cohort (C-index =0.699, 95% CI: 0.673–0.725) and external validation cohort (C-index =0.695, 95% CI: 0.653–0.737). The ROC and Kaplan-Meier survival curve analyses demonstrated a high discriminative ability. High-risk patients had significantly less favorable OS than low-risk patients in the SEER population and external validation cohort (both P<0.001). The DCA analysis showed that the nomogram provided better prognosis prediction than the tumor-node-metastasis (TNM) staging system. CONCLUSIONS: We constructed and validated a dynamic nomogram with 16 variables based on a large-scale population of SEER database to predict the prognosis of metastatic NSCLC patients. The nomogram is expected to provide higher predictive ability and accuracy than the TNM staging system.