Prognostic value of log odds of positive lymph nodes in node-positive lung squamous cell carcinoma patients after surgery: a SEER population-based study.

Abstract

BackgroundLog odds of positive lymph nodes (LODDS) is a novel and promising ratio-based lymph node (LN) staging system in many malignancies. This study aimed to evaluate the prognostic value of LODDS, and comprehensively compare the prognostic predictive performance of LODDS with the American Joint Committee on Cancer (AJCC) N classification, number of positive lymph node (NPLN), and lymph node ratio (LNR) among node-positive lung squamous cell carcinoma (SCC) patients after surgery.MethodsWe identified 2,561 patients with N1/N2 stage SCC diagnosed between 2004 and 2014 from the Surveillance, Epidemiology, and End Results (SEER) database. X-tile analysis was used to calculate the optimal cut-off value for each staging system. Univariable and Multivariable Cox regression analyses were used to assess the association of cancer-specific survival (CSS), and overall survival (OS) with N, NPLN, LNR, and LODDS, separately, and integrally. Moreover, linear trend χ2 score, likelihood ratio (LR) test, Akaike information criterion (AIC), and Harrell concordance index (C-index) were adopted as criteria for assessing the predictive ability of each model.ResultsThe optimal cut-off values for NPLN, LNR, and LODDS were 3, 0.28, and −0.37, respectively. N, NPLN, LNR, and LODDS were identified as independent prognostic predictors for CSS and OS in patients with SCC when each of them was incorporated into multivariable Cox model separately. Additionally, LODDS had the higher linear trend χ2 score, higher LR χ2 test score, lower AIC, and higher C-index compared to the other three systems. Moreover, a combination of N, NPLN, and LODDS was superior to any staging system alone for predicting prognosis.ConclusionsLODDS showed better predictive performance than N, NPLN, and LNR among patients with node-positive SCC after surgery. A combination of LODDS and the current AJCC TNM classification has the potential for becoming a better staging method to more precisely predicting prognosis.