ArticleJournal of imaging informatics in medicine2024
CT-Based Intratumoral and Peritumoral Radiomics Nomograms for the Preoperative Prediction of Spread Through Air Spaces in Clinical Stage IA Non-small Cell Lung Cancer.
Article in Journal of imaging informatics in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 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
- Machine learning-based preoperative prediction of spread through air spaces in clinical stage IA non-small cell lung cancer: a single-center study integrating clinical and CT imaging characteristics.Journal of thoracic disease · 2026Article
- Multicenter peritumoral radiomics in oncology: advances, challenges, and future directions.Radiation oncology (London, England) · 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
- Optimal peritumoral regions and fusion strategies for prediction of the double-expressor subtype in diffuse large B-cell lymphoma: a multi-region radiomics study.Frontiers in oncology · 2026Article
- Multidisciplinary Perspective of Spread Through Air Spaces in Lung Cancer: A Narrative Review.Cancers · 2025Review
- A PET/CT-based 3D deep learning model for predicting spread through air spaces in stage I lung adenocarcinoma.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Article
- Article
- 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
- 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
- Deep learning model for predicting spread through air spaces of lung adenocarcinoma based on transfer learning mechanism.Translational lung cancer research · 2025Article
- Nomogram model for the preoperative prediction of spread through air spaces in sub-centimeter non-small cell lung cancer.Journal of cardiothoracic surgery · 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
- Gradient-Based Radiomics for Outcome Prediction and Decision-Making in PULSAR: A Preliminary Study.International journal of particle therapy · 2025Article
- Preoperative prognostic assessment using intratumoral and peritumoral adipose tissue radiomics derived from contrast-enhanced CT in cT3-4 gastric cancer.Frontiers in oncology · 2025Article
- Deep Learning Analysis for Predicting Tumor Spread through Air Space in Early-Stage Lung Adenocarcinoma Pathology Images.Cancers · 2024Article
- Habitat-based CT radiomics enhances the ability to predict spread through air spaces in stage T1 invasive lung adenocarcinoma.Frontiers in oncology · 2024Article
- 2.5D peritumoural radiomics predicts postoperative recurrence in stage I lung adenocarcinoma.Frontiers in oncology · 2024Article
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10 authors.
Funding
Abstract
The study aims to investigate the value of intratumoral and peritumoral radiomics and clinical-radiological features for predicting spread through air spaces (STAS) in patients with clinical stage IA non-small cell lung cancer (NSCLC). A total of 336 NSCLC patients from our hospital were randomly divided into the training cohort (n = 236) and the internal validation cohort (n = 100) at a ratio of 7:3, and 69 patients from the other two external hospitals were collected as the external validation cohort. Univariate and multivariate analyses were used to select clinical-radiological features and construct a clinical model. The GTV, PTV5, PTV10, PTV15, PTV20, GPTV5, GPTV10, GPTV15, and GPTV20 models were constructed based on intratumoral and peritumoral (5 mm, 10 mm, 15 mm, 20 mm) radiomics features. Additionally, the radscore of the optimal radiomics model and clinical-radiological predictors were used to construct a combined model and plot a nomogram. Lastly, the ROC curve and AUC value were used to evaluate the diagnostic performance of the model. Tumor density type (OR = 6.738) and distal ribbon sign (OR = 5.141) were independent risk factors for the occurrence of STAS. The GPTV10 model outperformed the other radiomics models, and its AUC values were 0.887, 0.876, and 0.868 in the three cohorts. The AUC values of the combined model constructed based on GPTV10 radscore and clinical-radiological predictors were 0.901, 0.875, and 0.878. DeLong test results revealed that the combined model was superior to the clinical model in the three cohorts. The nomogram based on GPTV10 radscore and clinical-radiological features exhibited high predictive efficiency for STAS status in NSCLC.
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