The effect of spread through air spaces on postoperative recurrence-free survival in patients with multiple primary lung cancers.

Xie, Hongsheng; Dou, Shihua; Huang, Xiaoxiang; Wen, Yuxin; Yang, Lin

Purpose: The purpose of the study was to investigate the effect of spread through air spaces (STAS) on the postoperative prognosis of patients with multiple primary lung cancers staged from IA to IB based on tumor size. Methods: Clinicopathological and follow-up data of 122 patients with multiple primary lung cancers diagnosed at stages IA-IB and surgically treated at the Department of Thoracic Surgery, Shenzhen people's Hospital from January 2019 to December 2021 were retrospectively analyzed. The study involved 42 males and 80 females. STAS status was used to divide them into two groups (87 cases in STAS (-) and 35 cases in STAS (+)). A logistic regression analysis, univariate and multivariate Cox regression analysis, and Kaplan-Meier curves (K-M) were used to determine how STAS affected recurrence-free survival (RFS) in patients. Results: STAS (+) had a significantly higher recurrence rate than STAS (-). STAS was predicted by smoking history (P = 0.044), main tumor diameter (P = 0.02), and solid nodules on chest CT (P = 0.02). STAS incidence was not significantly different between lobectomy and sublobar resection groups (P = 0.17). Solid nodules on CT, tumor diameter, vascular invasion, pleural invasion, and STAS were significant predictors of recurrence in the univariate Cox regression analysis. Tumor diameter, pleural invasion and STAS were significant prognostic factors for recurrence in the multivariate Cox regression analysis. Furthermore, STAS (+) group was at greater risk of recurrence than STAS (-) group (34% vs. 0%, P < 0.05)。. Conclusion: Stage IA-IB multiple primary lung cancer patients with STAS (+) had a higher recurrence rate and a shorter overall survival rate.

SHENZHEN (Guangdong Sheng, China : East); OVERALL survival; LUNG cancer; SURVIVAL analysis (Biometry); CANCER patients; LOGISTIC regression analysis; REGRESSION analysis

World Journal of Surgical Oncology, 2024, Vol 22, Issue 1, p1

1477-7819

Academic Journal

10.1186/s12957-024-03351-3