Including positive lymph node count in the AJCC N staging may be a better predictor of the prognosis of NSCLC patients, especially stage III patients: a large population-based study

• DOI: 10.1007/s10147-019-01483-1
• Publication Date: 2019-06-10T17:02:26Z
• Authors: Yanling Fan; Yanfang Du; Wenqu Sun; Haiyong Wang
• Keywords: Univariate analysis; Log-rank test; Cut-off

The study was designed to explore the value of including positive lymph node count in the TNM staging system of non-small cell lung cancer.The X-tile model was applied to determine the cutoff values of positive lymph node count. Survival curves were generated using the Kaplan-Meier method and differences in survival among subgroups were examined using the log-rank test. The influence of different variables on overall survival and lung cancer-specific survival was further evaluated using univariate and multivariate Cox proportional hazard models. All statistical analyses were performed using SPSS version 22.0 (SPSS, Chicago, IL, USA). All p values were 2-sided and p < 0.05 was considered statistically significant.The overall survival and lung cancer-specific survival between stage IIIA and IIIB classified by the sixth edition TNM staging system show no statistically significant difference (p = 0.479 for overall survival; p = 0.081 for lung cancer specific survival). The X-tile model was used to screen three different cutoff values including nN = 0, nN1-3 and nN4-. The nN value is a significant independent prognostic factor that affects overall survival and lung cancer-specific survival of non-small cell lung cancer patients (all, p < 0.001). We obtained the hypothesized TNM sub-stages based on location and the number of PLN. There were significant differences between the hypothesized stage IIIA and IIIB regarding overall survival and lung cancer-specific survival (all, p < 0.001).It needs to be considered that N stage in combination with positive lymph node count may be used to predict the prognosis of non-small cell lung cancer for stage III cases with increased accuracy than category location-based stage.