Research progress on the effect of tumor spread through air spaces in sublobar resec-tion for early-stage non-small cell lung cancer
10.12354/j.issn.1000-8179.2025.20241518
- VernacularTitle:气腔扩散对早期非小细胞肺癌亚肺叶切除影响的研究进展
- Author:
Peng LAN
1
;
Tang DONGXIN
;
Yang ZHU
;
Wu JIAO
;
Li GAO
;
Yang BING
;
Luo ZHUMIN
;
Xia ZIHAN
;
Xu JIADONG
;
Wu WENYU
Author Information
1. 贵州中医药大学(贵阳市 550002)
- Publication Type:Journal Article
- Keywords:
non-small cell lung cancer(NSCLC);
spread through air spaces(STAS);
sublobar resection;
tumor recurrence;
molecular mechanisms
- From:
Chinese Journal of Clinical Oncology
2025;52(1):34-39
- CountryChina
- Language:Chinese
-
Abstract:
Non-small cell lung cancer(NSCLC)is one of the most common and deadly malignant tumors worldwide,with surgical resection being the primary treatment for early-stage NSCLC.Tumor spread through air spaces(STAS)is a novel pattern of tumor dissemination into the air spaces in the lung.Its occurrence after sublobar resection is closely associated with recurrence and distant metastasis,making its con-sideration a vital factor in surgical strategy selection and prognostic evaluation.Patients with STAS-positive status exhibit significantly higher postoperative recurrence rates than do STAS-negative patients,with molecular mechanisms involving tumor microenvironment remodeling,specific genetic mutations,and epithelial-mesenchymal transition(EMT).Imaging techniques including computed tomography(CT)and positron emission tomography/CT have shown potential for preoperative STAS prediction,although their accuracy and practicality require improvement.This paper reviews the definition,pathological characteristics,and related mechanisms of STAS,with a focus on surgical ap-proach selection for STAS-positive patients and its role in cancer recurrence after sublobar resection of early-stage NSCLC.Future research directions include optimization of preoperative diagnostic methods for STAS,exploration of molecular targeted therapies,and development of imaging-based precision prediction models.
