Objective: This study aimed to design a nomogram survival prediction by means of the figures retrieved from the Surveillance, Epidemiology, and End Results (SEER) source bank, and to predict the overall survival (OS) of patients with stage IIA non-small cell lung cancer (NSCLC) after surgery.
Methods: Data for 4511 patients who had been diagnosed with postoperative stage IIA NSCLC were collected from the SEER databank, while information on 528 patients was acquired from the Chongqing University Cancer Hospital for the external validation cohort. The independent risk factors that affected the prognosis were identified using a multivariate Cox proportional hazards regression model (also used to conduct a nomogram). A survival analysis between the low- and the high-risk groups was performed using the Kaplan–Meier method. Furthermore, a subgroup analysis was conducted of the two groups using the Kaplan–Meier method to determine whether the patients had received adjuvant chemotherapy.
Results: The following five variables were integrated into the nomogram: sex (female: HR 1.73, 95% CI 0.64– 0.83), age (≥ 60: HR 1.61, 95% CI 1.39– 1.87), differentiation grade (grade II: HR 2.19, 95% CI 1.66– 2.88; grade III: HR 2.65, 95% CI 2.00– 3.51; grade IV: HR 3.17, 95% CI 1.99– 5.03), surgery (lobectomy: HR 0.72, 95% CI 0.59– 0.86), and lymph node resection (> 12: HR 0.82, 95% CI 0.70– 0.96). Furthermore, the patients selected were categorized into high- and low-risk groups. The OS rate was significantly lower in the high-risk group than in the low-risk group (P < 0.001). Finally, adjuvant chemotherapy was highly correlated with OS in the high-risk set (P = 0.035); however, adjuvant chemotherapy was not related to OS in the low-risk set.
Conclusion: A nomogram was created as a reliable, convenient scheme that could predict OS, and it was determined that the high-risk feature patients identified by the nomogram gained benefits from adjuvant chemotherapy.
Keywords: nomogram, stage IIA non-small cell lung cancer, adjuvant chemotherapy, OS prediction