Advances in the prediction of spread through air spaces with imaging in lung cancer: a narrative review

Abstract

Background and Objective: In 2015, the World Health Organization (WHO) officially defined spread through air spaces (STAS) as the fourth type of lung adenocarcinoma (ADC) invasion. STAS is recognized to have effects on the survival rate and the prognosis of patients who have received lung cancer surgery. Given that postoperative pathological diagnosis is the gold standard for STAS diagnosis, but the pathological findings cannot guide the selection of preoperative surgical plan, it is essential to accurately predict STAS before surgery to achieve optimal outcomes. Methods: A comprehensive, non-systematic review of the latest literature was carried out in order to define the advancement of imaging in predicting STAS. PubMed database was being examined and the last run was on 27 June 2022. Key Content and Findings: In this review, the definition and the clinical significance of predicting STAS for lung cancer patients were being discussed. By summarizing the STAS prediction efficacy from imaging-related research, the results suggest that computed tomography (CT), 18-fluorine-fluorodeoxyglucose positron emission tomography/CT (18F-FDG PET/CT), radiomics and deep learning (DL) are of great value in predicting STAS. Conclusions: STAS is an important invasion type of lung cancer, affecting the survival prognosis of patients. Preoperative CT and 18F-FDG PET/CT have certain value in predicting the status of STAS, assisting clinicians in selecting an optimal surgical approach and postsurgical treatment. The prediction of STAS based on radiomics and DL can represent a future research direction.