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

Hengrui Liang; Zhichao Liu; Jun Huang; Jun Liu; Wei Wang; Jianfu Li; Shan Xiong; Caichen Li; Bo Cheng; Yi Zhao; Fei Cui; Jianxing He; Wenhua Liang

doi:10.21037/tlcr-20-709

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

Transl Lung Cancer Res. 2021 Jan;10(1):279-291. doi: 10.21037/tlcr-20-709.

Authors

Hengrui Liang¹, Zhichao Liu^{2

3}, Jun Huang¹, Jun Liu¹, Wei Wang¹, Jianfu Li¹, Shan Xiong¹, Caichen Li¹, Bo Cheng¹, Yi Zhao¹, Fei Cui¹, Jianxing He¹, Wenhua Liang¹

Affiliations

¹ Department of Thoracic Surgery and Oncology, The First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, Guangzhou, China.
² Nanshan School, Guangzhou Medical University, Guangzhou, China.
³ Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.

Abstract

Background: A 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.

Methods: Patients 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.

Results: A 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.

Conclusions: A 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.

Keywords: SEER database; Stage IV non-small cell lung cancer; nomogram; predictive model; surgery.