ArticleFrontiers in oncology2023
Dual-energy CT-based radiomics for predicting invasiveness of lung adenocarcinoma appearing as ground-glass nodules.
Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Comparative diagnostic accuracy of radiomics, deep learning, and hybrid AI for invasiveness stratification of pulmonary ground-glass nodules: a systematic review and meta-analysis.Frontiers in oncology · 2026Pooled it
- Clinical Utility of Dual-Energy CT for Detection, Characterization, and Staging of Lung Tumors: A Rapid Review.Diagnostics (Basel, Switzerland) · 2026Review
- Application of dual-energy computed tomography combined with radiomics in the clinical diagnosis of lung cancer: a systematic review and meta-analysis.Journal of thoracic disease · 2026Article
- Construction of a postoperative disease-free survival prediction model for non-small cell lung cancer patients based on dual-energy computed tomography parameters and blood inflammatory indicators.Translational cancer research · 2026Article
- Radiomics analysis of dual-energy CT-derived iodine maps for predicting histopathologic grading of pancreatic ductal adenocarcinoma: a two-center study.Frontiers in medicine · 2026Article
- Computed tomography radiomics of intratumoral and peritumoral microenvironments for identifying the invasiveness of subcentimeter lung adenocarcinomas.BMC medical imaging · 2025Article
- Predicting lung cancer bone metastasis using CT and pathological imaging with a Swin Transformer model.Journal of bone oncology · 2025Article
- Review of the application of dual-energy CT combined with radiomics in the diagnosis and analysis of lung cancer.Journal of applied clinical medical physics · 2025Review
- Dual-layer spectral detector computed tomography multiparameter machine learning model for prediction of invasive lung adenocarcinoma.Translational lung cancer research · 2025Article
- Spectral dual-layer detector CT-based radiomics-deep learning for predicting pathological aggressiveness of stage I lung adenocarcinoma: discrimination of precursor glandular lesions and invasive adenocarcinomas.Translational lung cancer research · 2025Article
- Combinations of Clinical Factors, CT Signs, and Radiomics for Differentiating High-Density Areas after Mechanical Thrombectomy in Patients with Acute Ischemic Stroke.AJNR. American journal of neuroradiology · 2025Article
- Automatic knee osteoarthritis severity grading based on X-ray images using a hierarchical classification method.Arthritis research & therapy · 2024Article
- Radiomics Based on Multimodal magnetic resonance imaging for the Differential Diagnosis of Benign and Malignant Vertebral Compression Fractures.Orthopaedic surgery · 2024Article
- Predicting invasion in early-stage ground-glass opacity pulmonary adenocarcinoma: a radiomics-based machine learning approach.BMC medical imaging · 2024Article
- What to Expect (and What Not) from Dual-Energy CT Imaging Now and in the Future?Journal of imaging · 2024Review
- MISD-IR: material-image subspace decomposition-based iterative reconstruction with spectrum estimation for dual-energy computed tomography.Quantitative imaging in medicine and surgery · 2024Article
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10 authors.
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Abstract
Objectives: To explore the value of radiomics based on Dual-energy CT (DECT) for discriminating preinvasive or MIA from IA appearing as GGNs before surgery. Methods: The retrospective study included 92 patients with lung adenocarcinoma comprising 30 IA and 62 preinvasive-MIA, which were further divided into a training (n=64) and a test set (n=28). Clinical and radiographic features along with quantitative parameters were recorded. Radiomics features were derived from virtual monoenergetic images (VMI), including 50kev and 150kev images. Intraclass correlation coefficients (ICCs), Pearson's correlation analysis and least absolute shrinkage and selection operator (LASSO) penalized logistic regression were conducted to eliminate unstable and redundant features. The performance of the models was evaluated by area under the curve (AUC) and the clinical utility was assessed using decision curve analysis (DCA). Results: The DECT-based radiomics model performed well with an AUC of 0.957 and 0.865 in the training and test set. The clinical-DECT model, comprising sex, age, tumor size, density, smoking, alcohol, effective atomic number, and normalized iodine concentration, had an AUC of 0.929 in the training and 0.719 in the test set. In addition, the radiomics model revealed a higher AUC value and a greater net benefit to patients than the clinical-DECT model. Conclusion: DECT-based radiomics features were valuable in predicting the invasiveness of GGNs, yielding a better predictive performance than the clinical-DECT model.
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