Development and validation of a nomogram for predicting overall survival of resected N2 non-small cell lung cancer patients undergoing neoadjuvant radiotherapy.

Abstract

Abstract Introduction Currently, the prognosis of resected N2 non-small cell lung cancer patients undergoing neoadjuvant radiotherapy is poor. The goal of this research was to develop and validate a novel nomogram for exactly predicting the overall survival (OS) of resected N2 NSCLC patients undergoing neoadjuvant radiotherapy.Methods The data applied in our research were downloaded from the Surveillance, Epidemiology, and End Results (SEER) database. We divided selected data into a training cohort and a validation cohort using R software, with a ratio of 7:3. Univariate Cox regression and multivariate Cox regression were utilized to select significant variables to build the nomogram. In order to validate our nomogram, calibration curves, receiver operating characteristic curves (ROC), decision curve analysis (DCA), and Kaplan-Meier survival curves were employed. The nomogram model was also compared with the tumor-node-metastasis (TNM) staging system by utilizing Net reclassification index (NRI) and Integrated Discrimination Improvement (IDI).Results Eight variables—age, sex, operative type, LN removed number, chemotherapy, AJCC stage, M stage, histology—were statistically significant in the multivariate Cox Regression Analysis and were selected to develop our nomogram. Based on ROC curves, calibration curves, and DCA analysis, our novel nomogram demonstrated good predictive accuracy and clinical utility. Using Kaplan-Meier (KM) survival curves and log-rank tests, the risk stratification system was able to stratify patients based on their estimated mortality risk. The nomogram performed better than the TNM staging system based on the NRI and IDI indexes.Conclusions We developed and validated a nomogram to predict prognosis of resected N2 NSCLC patients undergoing neoadjuvant radiotherapy. By using this nomogram, Clinicians may find this nomogram useful in predicting OS of targeted patients and making more appropriate treatment decisions.