ArticleYonsei medical journal2024
Computed Tomography Radiomics for Preoperative Prediction of Spread Through Air Spaces in the Early Stage of Surgically Resected Lung Adenocarcinomas.
Article in Yonsei medical journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline 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
- Nodule subtype dependency of CT-based radiomics for preoperative prediction of spread through air spaces in stage I lung adenocarcinoma: a multi-centre study.Quantitative imaging in medicine and surgery · 2026Article
- AI-Driven Preoperative Chest Radiograph Analysis for Prognostic Stratification in Surgically Resected Pathological Stage 1 Non-Small Cell Lung Cancer.Journal of imaging informatics in medicine · 2026Article
- Development of a CT-based integrated model combining clinical features, radiomics, and deep learning to predict STAS in subsolid nodules of stage I lung cancer: a retrospective cohort study.Journal of thoracic disease · 2026Article
- Enhancing cancer prognostics with group penalty models: a comparative study on radiomics feature selection in lung adenocarcinomas and meningiomas.Quantitative imaging in medicine and surgery · 2026Article
- Radiomics to understand pre-treatment tumor biology for resectable non-small cell lung cancer.Frontiers in oncology · 2026Review
- CT-based intratumoral habitat and peritumoral radiomics model to predict spread through air spaces in solid lung adenocarcinoma with diameter ≤ 2 cm: a dual-center study.Frontiers in oncology · 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
- Organ-based tumor distribution for predicting prognosis in small-cell lung cancer using fluorodeoxyglucose positron emission tomography/computed tomography.Scientific reports · 2025Article
- Article
- Stereotactic body radiotherapy for early‑stage non‑small cell lung cancer: Comprehensive analysis of outcomes and recurrence from a single‑center experience.Oncology letters · 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
- 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
- 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
- Effectiveness of artificial intelligence for detecting operable lung cancer on chest radiographs.Translational lung cancer research · 2024Article
- Prediction of Bone Marrow Metastases Using Computed Tomography (CT) Radiomics in Patients with Gastric Cancer: Uncovering Invisible Metastases.Diagnostics (Basel, Switzerland) · 2024Article
- Deep Learning Analysis for Predicting Tumor Spread through Air Space in Early-Stage Lung Adenocarcinoma Pathology Images.Cancers · 2024Article
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10 authors.
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Abstract
purposeTo assess the added value of radiomics models from preoperative chest CT in predicting the presence of spread through air spaces (STAS) in the early stage of surgically resected lung adenocarcinomas using multiple validation datasets. MATERIALS AND
methodsThis retrospective study included 550 early-stage surgically resected lung adenocarcinomas in 521 patients, classified into training, test, internal validation, and temporal validation sets (n=211, 90, 91, and 158, respectively). Radiomics features were extracted from the segmented tumors on preoperative chest CT, and a radiomics score (Rad-score) was calculated to predict the presence of STAS. Diagnostic performance of the conventional model and the combined model, based on a combination of conventional and radiomics features, for the diagnosis of the presence of STAS were compared using the area under the curve (AUC) of the receiver operating characteristic curve.
resultsRad-score was significantly higher in the STAS-positive group compared to the STAS-negative group in the training, test, internal, and temporal validation sets. The performance of the combined model was significantly higher than that of the conventional model in the training set {AUC: 0.784 [95% confidence interval (CI): 0.722-0.846] vs. AUC: 0.815 (95% CI: 0.759-0.872),
conclusionA quantitative CT radiomics model can assist in the non-invasive prediction of the presence of STAS in the early stage of lung adenocarcinomas.
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