ArticleBMC medical imaging2022
The value of radiomics based on dual-energy CT for differentiating benign from malignant solitary pulmonary nodules.
Article in BMC medical imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Noninvasive differentiation of benign and malignant solid pulmonary nodules using multiparameter dual-layer spectral CT radiomics.Insights into imaging · 2026Article
- 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
- Evaluation of a dual-energy computed tomography parameter -radiomics combined model for solitary pulmonary nodules.Frontiers in oncology · 2026Article
- 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
- New Perspectives on Lung Cancer Screening and Artificial Intelligence.Life (Basel, Switzerland) · 2025Review
- 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
- Development of a clinical prediction model for benign and malignant pulmonary nodules with a CTR ≥ 50% utilizing artificial intelligence-driven radiomics analysis.BMC medical imaging · 2025Article
- Computed tomography-derived radiomics models for distinguishing difficult-to-diagnose inflammatory and malignant pulmonary nodules.Biomedical engineering and computational biology · 2025Article
- Deep cross entropy fusion for pulmonary nodule classification based on ultrasound Imagery.Frontiers in oncology · 2025Article
- Prediction of Ki-67 expression in gastric gastrointestinal stromal tumors using histogram analysis of monochromatic and iodine images derived from spectral CT.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Prediction of the Benign or Malignant Nature of Pulmonary Pure Ground-Glass Nodules Based on Radiomics Analysis of High-Resolution Computed Tomography Images.Tomography (Ann Arbor, Mich.) · 2024Article
- What to Expect (and What Not) from Dual-Energy CT Imaging Now and in the Future?Journal of imaging · 2024Review
- Quantitative energy spectrum CT in differential diagnosis of aldosterone-producing adenoma and cortisol-producing adenoma.Quantitative imaging in medicine and surgery · 2023Article
- Construction of U-Net++ pulmonary nodule intelligent analysis model based on feature weighted aggregation.Technology and health care : official journal of the European Society for Engineering and Medicine · 2023Article
- Prediction of VEGF and EGFR Expression in Peripheral Lung Cancer Based on the Radiomics Model of Spectral CT Enhanced Images.International journal of general medicine · 2022Article
- Quantitative parameters of enhanced dual-energy computed tomography for differentiating lung cancers from benign lesions in solid pulmonary nodules.Frontiers in oncology · 2022Article
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5 authors.
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Abstract
objectiveTo investigate the value of monochromatic dual-energy CT (DECT) images based on radiomics in differentiating benign from malignant solitary pulmonary nodules. MATERIALS AND
methodsThis retrospective study was approved by the institutional review board, and informed consent was waived. Pathologically confirmed lung nodules smaller than 3 cm with integrated arterial phase and venous phase (AP and VP) gemstone spectral imaging were retrospectively identified. After extracting the radiomic features of each case, principal component analysis (PCA) was used for feature selection, and after training with the logistic regression method, three classification models (Model
resultsA total of 153 patients were included and divided into a training cohort (n = 107) and a validation cohort (n = 46). A total of 1130 radiomic features were extracted from each case. The PCA method selected 22, 25 and 35 principal components to construct the three models. The diagnostic accuracy of Model
conclusionsWe developed a radiomics model based on monochromatic DECT images to identify solitary pulmonary nodules. This model could serve as an effective tool for discriminating benign from malignant pulmonary nodules in patients. The combination of arterial phase and venous phase imaging could significantly improve the model performance.
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