A clinical nomogram and heat map for assessing survival in patients with stage I non-small cell lung cancer after complete resection

BACKGROUND: Assessing the prognosis of patients with early-stage non-small cell lung cancer (NSCLC) has become a major clinical issue. This study aimed to devise an effective clinical nomogram and heat map for assessing the survival of patients with stage I NSCLC receiving complete resection. METHODS: Nomograms were established based on a retrospective study of 654 patients with stage I NSCLC who underwent radical resection at Sun Yat-Sen University Cancer Center between January 2009 and December 2014. The concordance index (C-index) and calibration curve were used to measure the accuracy and discriminative ability of the final nomogram. Heat maps were constructed with prognostic factors and survival probabilities. Survival curves were depicted using the Kaplan–Meier method, and the log-rank test was used to determine significance. Patients were classified into low- and high-risk subgroups using recursive partitioning analysis based on nomogram scores. RESULTS: In univariate and multivariate analyses, the independent factors for overall survival (OS) and disease-free survival (DFS) were age, sex, tumor size, and visceral pleural invasion, which were all selected in the nomogram. The C-indices of the nomogram for predicting OS and DFS were 0.694 [95% confidence interval (CI) 0.651–0.737] and 0.653 (95% CI 0.61–0.696), respectively. The calibration curves for OS and DFS probabilities showed a good agreement between the nomogram prediction and actual observation. A heat map was generated using the above independent factors for OS and DFS. High-risk patients had shorter OS [hazard ratio (HR) = 3.535, 95% CI 2.444–5.113, p < 0.001] and DFS (HR = 2.607, 95% CI 1.922–3.537, p < 0.001) than low-risk patients. CONCLUSION: We established a prognostic nomogram and heat map that can be useful for evaluating survival in patients with stage I NSCLC after complete resection. The tools resulted in more accurate prediction and may guide clinicians in making treatment decisions.