ReviewJournal of thoracic disease2026
Imaging advances of predicting spread through air spaces (STAS) in lung cancer: a narrative review.
Review in Journal of thoracic disease, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Objective: 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. Methods: 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). Key Content and Findings: 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. Conclusions: 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.