Identifying optimal candidates for primary tumor resection among metastatic non-small cell lung cancer patients: a population-based predictive model.

Abstract

BackgroundA survival benefit was observed in metastatic non-small cell lung cancer (NSCLC) patients that underwent surgical resection of the primary tumor. We developed a model testing the hypothesis that only certain stage IV patients would benefit from surgery and the potential benefit would vary based on primary tumor characteristics.MethodsPatients with stage IV NSCLC were identified in the Surveillance, Epidemiology and End Results (SEER) database and then divided into surgery and non-surgery groups. A 1:1 Propensity score matching (PSM) was performed to balance characters. We assumed that patients received primary tumor surgery that lived longer than median cancer specific survival (CSS) time of those who didn’t underwent surgery could benefit from the operation. Multivariable Cox model was used to explore the independent factors of CSS in two groups (beneficial and non-beneficial group). Logistic regression was used to build a nomogram based on the significant predictive factors.ResultsA total of 30,342 patients with stage IV NSCLC were identified; 8.03% (2,436) received primary tumor surgery. After PSM, surgical intervention was independently correlated with longer median CSS time (19 vs. 9 months, P<0.001). Among the surgery cohort, 1,374 (56.40%) patients lived longer than 9 months (beneficial group). Differentiated characters (beneficial and non-beneficial group) included age, gender, TNM stage, histologic type, tumor position and differentiation grade, which were integrated as predictors to build a nomogram.ConclusionsA practical predictive model was created and might be used to identify the optimal candidates for surgical resection of the primary tumor among stage IV NSCLC patients.