Imaging advances of predicting spread through air spaces (STAS) in lung cancer: a narrative review

Spread through air spaces (STAS) represents a distinct invasive pattern of lung cancer that is strongly associated with local recurrence and poor prognosis. Accurate preoperative identification of STAS is crucial for optimizing surgical strategies. This review comprehensively summarizes recent advances in imaging-based prediction and diagnosis of STAS, evaluates the diagnostic performance, limitations, and challenges of current imaging features, and discusses potential directions for future research.

All the published papers were obtained from PubMed and Web of Science Core Collection on 1 July 2025. The study included original articles and reviews published in English between July 2017 and May 2025, focusing on research related to STAS and computed tomography (CT).

A total of 48 articles were finally included for this narrative review. This review mainly summarized and analyzed the diagnostic value of preoperative CT and 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) for detecting STAS, and with the development of artificial intelligence, the efficacy of STAS prediction models has been greatly enhanced by radiomics and deep learning methods. Emerging spectral CT enables functional tissue characterization and quantitative parameter analysis, further enhancing precision imaging.

The imaging assessment of STAS has evolved from morphological observation to multimodal and intelligent analysis. Future research should emphasize standardized imaging protocols, multimodal data integration, and multicenter prospective validation to facilitate clinical translation.