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Review
. 2025 Apr 30;14(4):1471-1481.
doi: 10.21037/tlcr-24-952. Epub 2025 Apr 27.

A narrative review of preoperative CT for predicting spread through air spaces of lung cancer

Qinling Jiang #  1 , Xiang Wang #  1 , Xin'ang Jiang #  1 , Hongbiao Sun  1 , Qi Chen  2 , Yuxin Cheng  1 , Yunmeng Wang  1 , Tianyi Xing  1 , Xin Zhang  1 , Yi Xiao  1
Affiliations
Review

A narrative review of preoperative CT for predicting spread through air spaces of lung cancer

Qinling Jiang et al. Transl Lung Cancer Res. .

Abstract

Background and objective: Spread through air space (STAS) is a recognized mechanism of lung cancer invasion and is associated with patient prognosis. However, current diagnostic accuracy of bronchial cytology and intraoperative frozen section for STAS remains insufficient to meet clinical needs. Therefore, accurate and non-invasive preoperative prediction of STAS is critical for clinical decision-making. In this paper, we review and summarize recent studies on the role of computed tomography (CT) in predicting STAS in lung cancer, and discuss the limitations and future directions of related research in this field.

Methods: Relevant studies were identified through searches on the Web of Science, PubMed, Cochrane Library, and EMBASE. We included English-language articles published between July 2017 and June 2024, focusing on research related to STAS and CT.

Key content and findings: This review aimed to assess the viability of preoperative CT imaging for predicting STAS. Current studies suggest that traditional imaging signs enable the assessment of STAS, and with the development of artificial intelligence, the efficacy of STAS prediction models has been greatly enhanced by radiomics and deep learning methods. However, risk stratification studies remain limited and require further refinement through more comprehensive pathological definitions of STAS.

Conclusions: Preoperative CT imaging images demonstrated good predictive efficacy of STAS. However, further progress requires a more comprehensive definition of STAS and validation through large-sample, prospective, and multi-center studies to enhance its clinical applicability.

Keywords: Computed tomography (CT); artificial intelligence; invasiveness; lung cancer; spread through air spaces (STAS).

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Conflict of interest statement

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-24-952/coif). The authors have no conflicts of interest to declare.

Figures

Figure 1
Figure 1
Various ROI ranges in different radiomics models. (A) Image of the original lung nodule, (B) tumor area, (C) tumor area and peritumoral area, (D) partial tumor area and peritumoral area. ROI, region of interest.
See this image and copyright information in PMC

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