ArticleInsights into imaging2023
Robustness of radiomics features of virtual unenhanced and virtual monoenergetic images in dual-energy CT among different imaging platforms and potential role of CT number variability.
Article in Insights into imaging, 2023. 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.
- Radiomics: Current Applications and Future Directions.MedComm · 2026Review
- Development and validation of a machine learning-based radiomics model using 45-keV virtual monoenergetic images for differentiating fat-poor angiomyolipoma from clear cell renal cell carcinoma.Translational andrology and urology · 2026Article
- Hunting for signal in the noise: identifying robust CT radiomic features across variable scanning settings.European radiology · 2026Article
- Assessment of Robustness of MRI Radiomic Features in the Abdomen: Impact of Deep Learning Reconstruction and Accelerated Acquisition.Journal of imaging informatics in medicine · 2026Article
- CT data harmonization via learned virtual monoenergetic imaging for cross-kV scan translation and radiomics reproducibility.Medical physics · 2026Article
- Multicenter peritumoral radiomics in oncology: advances, challenges, and future directions.Radiation oncology (London, England) · 2026Review
- Machine learning survival prediction in esophageal cancer using radiomics and body composition from pretreatment and follow-up T12-level computed tomography.World journal of gastrointestinal oncology · 2025Article
- Impact of a deep learning image reconstruction algorithm on the robustness of abdominal computed tomography radiomics features using standard and low radiation doses.Quantitative imaging in medicine and surgery · 2025Article
- Robustness of radiomics within photon-counting detector CT: impact of acquisition and reconstruction factors.European radiology · 2025Article
- Photon-counting CT in cancer radiotherapy: technological advances and clinical benefits.Physics in medicine and biology · 2025Review
- Robustness of radiomics among photon-counting detector CT and dual-energy CT systems: a texture phantom study.European radiology · 2025Article
- Deep learning image reconstruction generates thinner slice iodine maps with improved image quality to increase diagnostic acceptance and lesion conspicuity: a prospective study on abdominal dual-energy CT.BMC medical imaging · 2024Article
- What to Expect (and What Not) from Dual-Energy CT Imaging Now and in the Future?Journal of imaging · 2024Review
- Identification of CT radiomic features robust to acquisition and segmentation variations for improved prediction of radiotherapy-treated lung cancer patient recurrence.Scientific reports · 2024Article
- Impacts of Adaptive Statistical Iterative Reconstruction-V and Deep Learning Image Reconstruction Algorithms on Robustness of CT Radiomics Features: Opportunity for Minimizing Radiomics Variability Among Scans of Different Dose Levels.Journal of imaging informatics in medicine · 2024Article
- Experimental study on the effects of different exposure conditions and contrast agent concentrations on spectral computed tomography virtual non-contrast images.Quantitative imaging in medicine and surgery · 2024Article
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Authors and funding
13 authors.
Funding
Abstract
objectivesTo evaluate robustness of dual-energy CT (DECT) radiomics features of virtual unenhanced (VUE) image and virtual monoenergetic image (VMI) among different imaging platforms.
methodsA phantom with sixteen clinical-relevant densities was scanned on ten DECT platforms with comparable scan parameters. Ninety-four radiomic features were extracted via Pyradiomics from VUE images and VMIs at energy level of 70 keV (VMI
results92.02% and 92.87% of features were repeatable between scan-rescans for VUE images and VMI
conclusionsThe majority of DECT radiomics features were non-reproducible. The differences in CT number were considered as an indicator of inter-platform DECT radiomics variation. Critical relevance statement: The majority of radiomics features extracted from the VUE images and the VMI70keV were non-reproducible among platforms, while synchronizing energy levels of VMI to reduce the CT number value variability may be a potential way to mitigate radiomics instability.
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