Risk stratification and adjuvant chemotherapy after radical resection based on the clinical risk scores of patients with stage IB-IIA non-small cell lung cancer

Autor: Qihang Yan, Bei Zhang, Wen-Yu Zhai, Dongxia Li, Shuqin Dai, Jun Ye Wang, Fangfang Duan
Rok vydání: 2022
Předmět:
Zdroj: European Journal of Surgical Oncology. 48:752-760
ISSN: 0748-7983
Popis: Introduction Despite the heterogeneity among patients with stage IB-IIA non-small cell lung cancer (NSCLC), clinically applicable models to identify patients most suitable for receiving adjuvant chemotherapy (ACT) are limited. We aimed to develop a model for risk stratification and the individualized application of ACT. Methods Between January 2008 and March 2018, patients with T2N0M0 NSCLC at Sun Yat-sen University Cancer Center were retrospectively enrolled. Survival curves were estimated by Kaplan-Meier method and compared with log-rank test. Cox regression models were used to identify prognostic factors for disease-free survival (DFS) and overall survival (OS). Propensity score matching (PSM) was implemented. Subgroup analysis was performed based on clinical risk score (CRS) value and epidermal growth factor receptor (EGFR) mutation status. Results Of 1063 patients with T2N0 NSCLC enrolled, 272 patients received ACT. Before PSM, patients with high CRS (>1) had a significantly worse OS and DFS outcomes. In the PSM, the baseline characteristics of the 270 pairs of patients were well matched. ACT was associated with improved OS outcomes for patients with a high CRS, while ACT was associated with improved OS and DFS outcomes in patients with wild-type EGFR. The interaction analysis showed an apparent interaction effect between ACT and EGFR-activating mutations as well as chemotherapy regimens and histology. Conclusions The CRS can predict the prognosis of patients with stage IB-IIA NSCLC. ACT could improve the outcome of patients with a high CRS. Patients with non-squamous cell histology receiving pemetrexed plus platinum might benefit more, but not those with EGFR-activating mutations.
Databáze: OpenAIRE