Trial reportScientific reports2022
Peritumoral radiomics features on preoperative thin-slice CT images can predict the spread through air spaces of lung adenocarcinoma.
Trial report in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
23 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Diagnostic performance of radiomics analysis for pulmonary cancer airway spread: a systematic review and meta-analysis.Diagnostic and interventional radiology (Ankara, Turkey) · 2025Pooled it
- Research hotspots and trends in lung cancer STAS: a bibliometric and visualization analysis.Frontiers in oncology · 2024Pooled it
- Imaging advances of predicting spread through air spaces (STAS) in lung cancer: a narrative review.Journal of thoracic disease · 2026Review
- Computed tomography-based tumoral and peritumoral radiomics models for preoperative prediction of the spread through air spaces in patients with clinical stage I pure solid invasive lung adenocarcinoma: a multicenter study.Journal of thoracic disease · 2026Article
- Prediction of Lymphovascular Invasion in Early-Stage Lung Adenocarcinoma Using Artificial Intelligence-Based Radiomics.Cancers · 2025Article
- Development and internal validation of predictive models for spread through air spaces in clinical stage IA lung adenocarcinoma.General thoracic and cardiovascular surgery · 2025Article
- Multidisciplinary Perspective of Spread Through Air Spaces in Lung Cancer: A Narrative Review.Cancers · 2025Review
- CT-based intratumoral and peritumoral radiomics nomogram to predict spread through air spaces in lung adenocarcinoma with diameter ≤ 3 cm: A multicenter study.European journal of radiology open · 2025Article
- Bridging surgical oncology and personalized medicine: the role of artificial intelligence and machine learning in thoracic surgery.Annals of medicine and surgery (2012) · 2025Review
- 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
- Deep learning model for predicting spread through air spaces of lung adenocarcinoma based on transfer learning mechanism.Translational lung cancer research · 2025Article
- STAS: New explorations and challenges for thoracic surgeons.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Review
- Prediction of early lung adenocarcinoma spread through air spaces by machine learning radiomics: a cross-center cohort study.Translational lung cancer research · 2024Article
- Deep Learning Analysis for Predicting Tumor Spread through Air Space in Early-Stage Lung Adenocarcinoma Pathology Images.Cancers · 2024Article
- Computed Tomography Radiomics for Preoperative Prediction of Spread Through Air Spaces in the Early Stage of Surgically Resected Lung Adenocarcinomas.Yonsei medical journal · 2024Article
- Predictive value of multiple imaging predictive models for spread through air spaces of lung adenocarcinoma: A systematic review and network meta‑analysis.Oncology letters · 2024Article
- Habitat-based CT radiomics enhances the ability to predict spread through air spaces in stage T1 invasive lung adenocarcinoma.Frontiers in oncology · 2024Article
- Advancing presurgical non-invasive spread through air spaces prediction in clinical stage IA lung adenocarcinoma using artificial intelligence and CT signatures.Frontiers in surgery · 2024Article
- Improving the prediction of Spreading Through Air Spaces (STAS) in primary lung cancer with a dynamic dual-delta hybrid machine learning model: a multicenter cohort study.Biomarker research · 2023Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The spread through air spaces (STAS) is recognized as a negative prognostic factor in patients with early-stage lung adenocarcinoma. The present study aimed to develop a machine learning model for the prediction of STAS using peritumoral radiomics features extracted from preoperative CT imaging. A total of 339 patients who underwent lobectomy or limited resection for lung adenocarcinoma were included. The patients were randomly divided (3:2) into training and test cohorts. Two prediction models were created using the training cohort: a conventional model based on the tumor consolidation/tumor (C/T) ratio and a machine learning model based on peritumoral radiomics features. The areas under the curve for the two models in the testing cohort were 0.70 and 0.76, respectively (P = 0.045). The cumulative incidence of recurrence (CIR) was significantly higher in the STAS high-risk group when using the radiomics model than that in the low-risk group (44% vs. 4% at 5 years; P = 0.002) in patients who underwent limited resection in the testing cohort. In contrast, the 5-year CIR was not significantly different among patients who underwent lobectomy (17% vs. 11%; P = 0.469). In conclusion, the machine learning model for STAS prediction based on peritumoral radiomics features performed better than the C/T ratio model.
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