ArticleFrontiers in oncology2020
Predictors of CT Morphologic Features to Identify Spread Through Air Spaces Preoperatively in Small-Sized Lung Adenocarcinoma.
Article in Frontiers in oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- Nomogram based on clinical characteristics and radiological features for the preoperative prediction of spread through air spaces in patients with clinical stage IA non-small cell lung cancer: a multicenter study.Diagnostic and interventional radiology (Ankara, Turkey) · 2023Trial
- Dual-layer spectral CT for predicting spread through air spaces in lung adenocarcinoma: a dual-center study.European radiology · 2026Article
- The clinicoradiological and pathological-molecular characteristics associated with spread through air spaces in stage IA invasive lung adenocarcinoma.Insights into imaging · 2026Article
- STAS More than a Prognostic Marker-An Evolving Factor in Operative and Adjuvant Treatment Decisions in Early-Stage NSCLC.Cancers · 2026Review
- Imaging advances of predicting spread through air spaces (STAS) in lung cancer: a narrative review.Journal of thoracic disease · 2026Review
- Incidental pulmonary findings on CT in daily practice: the nodule and the interstitial lung abnormalities - what's old, what's new.La Radiologia medica · 2026Review
- Factors predicting spread through air spaces (STAS) preoperative for T1a-b non-small cell lung cancer.Discover oncology · 2026Review
- Nomogram combining spectral dual-layer detector CT radiomics and deep learning features predicts alveolar tumor spread.Frontiers in oncology · 2026Article
- Nomogram based on computed tomography fractal dimension for predicting spread through air spaces in lung adenocarcinoma.Frontiers in medicine · 2026Article
- Article
- Development and internal validation of predictive models for spread through air spaces in clinical stage IA lung adenocarcinoma.General thoracic and cardiovascular surgery · 2025Article
- Impact of robotic and non-robotic approach on spread through air spaces in early-stage lung cancer.Journal of robotic surgery · 2025Article
- Predicting spread through air space of lung adenocarcinoma based on deep learning and machine learning models.Journal of cardiothoracic surgery · 2025Article
- CT feature-based nomogram for predicting tumor spread through air spaces in stage IA lung adenocarcinoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- A Bayesian deep learning model with consolidation-to-tumor ratio (CTR) prior revolutionizes the prediction of spread through air spaces (STAS) in stage IA lung adenocarcinoma: a large-scale diagnostic study.Translational lung cancer research · 2025Article
- A narrative review of preoperative CT for predicting spread through air spaces of lung cancer.Translational lung cancer research · 2025Review
- Tumor peripheral bronchial signature predicts spread through air spaces in resected invasive mucinous adenocarcinoma of the lung.Journal of thoracic disease · 2025Article
- A nomogram integrating the clinical and CT imaging characteristics for assessing spread through air spaces in clinical stage IA lung adenocarcinoma.Frontiers in immunology · 2025Article
- Development and validation of machine learning models for predicting STAS in stage I lung adenocarcinoma with part-solid and solid nodules: a two-center study.Frontiers in oncology · 2025Article
- Performance of deep learning model and radiomics model for preoperative prediction of spread through air spaces in the surgically resected lung adenocarcinoma: a two-center comparative study.Translational lung cancer research · 2024Article
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6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis study aimed to explore the predictive CT features of spread through air spaces (STAS) in patients with small-sized lung adenocarcinoma.
methodsFrom January 2017 to May 2019, patients with confirmed pathology of small-sized lung adenocarcinoma (less than or equal to 2 cm) and who underwent surgery were retrospectively analyzed. The clinical, pathological, and surgical information and CT features were analyzed.
resultsA total of 47 patients with STAS (males, 61.7%; mean age, 56 ± 8years) and 143 patients without STAS (males, 58%; mean age, 53 ± 11 years) were included. Pathologically, papillary, micropapillary, solid predominant subtypes, and vascular and pleural invasion were most commonly observed features in the STAS group. Radiologically, higher consolidation tumor ratio (CTR), presence of spiculation, satellites, ground glass ribbon sign, pleural attachment, and unclear tumor-lung interface were more commonly observed features in the STAS group. CTR, presence of ground glass ribbons and pleural connection, and absence of cystic airspaces were considered as stable predictors of STAS in multivariate logistic models. The receiver operating characteristic curve (ROC) analysis for predicting STAS demonstrated higher area under the curve (AUC) in the model that used CTR (0.760, 95% confidence interval, 0.69-0.83) for predicting STAS than in the model that used long diameter of entire lesion (0.640).
conclusionsCTR is the best CT sign for predicting STAS in small-sized lung adenocarcinoma. The ground glass ribbon is a newly found indicator and has the potential for predicting STAS.
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