Abstract
PURPOSE: Risk stratification of patients with non-small cell lung cancer (NSCLC) is crucial to select the appropriate treatments, but available models for patients with complete resection are unsatisfactory. The purpose of this study was to determine a prediction model based on clinical information, routine physical and blood tests, and molecular markers. PATIENTS AND METHODS: This was a retrospective cohort study of patients who underwent surgical resection for lung cancer between 2009 to 2013. Potential prognostic factors were used to build a full prediction model based on a multivariable Cox regression analysis. A nomogram was constructed. The risk stratification cutoffs for clinical use were determined based on the model. RESULTS: A total of 368 NSCLC patients with R0 resection were included. The final multivariable model indicated that low diffusing capacity of the lung for carbon monoxide (HR=1.66, 95% CI: 1.18-2.34), high platelet-to-lymphocyte ratio (HR=1.42, 95% CI: 1.04-1.95), histology type of squamous cell carcinoma and others (squamous cell carcinoma vs adenocarcinoma, HR=1.40, 95% CI: 1.01-1.96; others vs adenocarcinoma, HR=2.36, 95% CI: 1.15-4.84; P trend=0.001), N>0 status (HR=1.96, 95% CI: 1.42-2.70), high serum carcinoembryonic antigen levels (HR=1.61, 95% CI: 1.13-2.27), and postoperative chemotherapy (HR=0.53, 95% CI: 0.33-0.87) were independently associated with poor OS. The patients were classified into four risk groups according to the nomogram, and the OS was different among the four groups (P<0.05). CONCLUSION: A nomogram was successfully constructed based on a multivariable analysis, and the nomogram can discriminate the OS of patients with NSCLC based on risk categories, but external validation is still necessary.