Prognostic Impact of Tumor Spread Through Air Spaces in Sublobar Resection for 1A Lung Adenocarcinoma Patients

Overview

Journal Ann Surg Oncol

Publisher Springer

Specialty Oncology

Date 2019 Mar 20

PMID 30887374

Citations 35

Authors

Yijiu Ren

Huikang Xie

Chenyang Dai

Yunlang She

Hang Su

Dong Xie

Hui Zheng

Liping Zhang

Gening Jiang

Chunyan Wu

Chang Chen

Affiliations

Soon will be listed here.

Abstract

Background: This study aimed to clarify differences in the prognostic impact of tumor spread through air spaces (STAS) in lobectomy versus sublobar resection (SR). The study also investigated the frequency and significance of STAS in residual lung segments.

Methods: This study identified 752 patients with p-stage 1A non-small cell lung cancer (NSCLC) from 2010 to 2012. Recurrence-free survival (RFS) and overall survival (OS) were compared. For proactive simulation of SR, 100 consecutive lobectomy specimens of p-stage 1A NSCLC were selected.

Results: The study found STAS in 182 (28.7%) of 634 lobectomy cases and 43 (36.4%) of 118 SR cases. Multivariable analysis showed that STAS was not a prognostic factor in the lobectomy group, but showed a significantly worse prognostic effect for the SR group (RFS, P < 0.001; OS, P < 0.001). In 9 of 100 simulated cases, STAS occurred in residual lung segments. The patients with T1c category disease had a significantly increased risk for the development of STAS in residual lung segments (P = 0.033).

Conclusions: For patients with p-stage 1A lung cancer who have undergone SR, STAS is a prognostic indicator of poor outcomes. The presence of STAS does occasionally exist in the residual lung segments.

Citing Articles

Prediction of tumor spread through air spaces with an automatic segmentation deep learning model in peripheral stage I lung adenocarcinoma.

Liu C, Wang Y, Gong P, Xue X, Zhao H, Qian H Respir Res. 2025; 26(1):94.

PMID: 40057743 PMC: 11890504. DOI: 10.1186/s12931-025-03174-0.

A PET/CT-based 3D deep learning model for predicting spread through air spaces in stage I lung adenocarcinoma.

Zheng C, Cai Y, Miao J, Zheng B, Gao Y, Shen C Clin Transl Oncol. 2025; .

PMID: 39994163 DOI: 10.1007/s12094-025-03870-9.

Advancing presurgical non-invasive spread through air spaces prediction in clinical stage IA lung adenocarcinoma using artificial intelligence and CT signatures.

Ye G, Wu G, Li Y, Zhang C, Qin L, Wu J Front Surg. 2025; 11:1511024.

PMID: 39877310 PMC: 11772258. DOI: 10.3389/fsurg.2024.1511024.

Prediction of early lung adenocarcinoma spread through air spaces by machine learning radiomics: a cross-center cohort study.

Liu C, Meng A, Xue X, Wang Y, Jia C, Yao D Transl Lung Cancer Res. 2025; 13(12):3443-3459.

PMID: 39830767 PMC: 11736589. DOI: 10.21037/tlcr-24-565.

Improving prediction accuracy of spread through air spaces in clinical-stage T1N0 lung adenocarcinoma using computed tomography imaging models.

Dou S, Li Z, Qiu Z, Zhang J, Chen Y, You S JTCVS Open. 2024; 21:290-303.

PMID: 39534334 PMC: 11551290. DOI: 10.1016/j.xjon.2024.07.018.