ArticleComputational and mathematical methods in medicine2019
The Effect of CT Scan Parameters on the Measurement of CT Radiomic Features: A Lung Nodule Phantom Study.
Article in Computational and mathematical methods in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Evaluating consistency of radiomic features derived from CT images: A cross-center phantom study.Journal of applied clinical medical physics · 2026Article
- Effect of slice thickness on quantitative analysis of interstitial lung disease: a retrospective volumetric chest CT study.La Radiologia medica · 2025Article
- A Clinical-Radiomics Nomogram for the Preoperative Prediction of Aggressive Micropapillary and a Solid Pattern in Lung Adenocarcinoma.Current oncology (Toronto, Ont.) · 2025Article
- Reproducibility of the CT radiomic features of pulmonary nodules: the effects of the CT reconstruction algorithm, radiation dose, and contrast agent.Quantitative imaging in medicine and surgery · 2025Article
- A comparative analysis of image harmonization techniques in mitigating differences in CT acquisition and reconstruction.Physics in medicine and biology · 2025Article
- Finding Reproducible and Prognostic Radiomic Features in Variable Slice Thickness Contrast Enhanced CT of Colorectal Liver Metastases.The journal of machine learning for biomedical imaging · 2025Article
- Radiomics-based prediction of nonalcoholic fatty liver disease following pancreatoduodenectomy.Surgery today · 2024Article
- Rank acquisition impact on radiomics estimation (AсquIRE) in chest CT imaging: A retrospective multi-site, multi-use-case study.Computer methods and programs in biomedicine · 2024Article
- Radiomics of metastatic brain tumor as a predictive image biomarker of progression-free survival in patients with non-small-cell lung cancer with brain metastasis receiving tyrosine kinase inhibitors.Translational oncology · 2024Article
- Article
- Impact of CT scan parameters on deformable image registration accuracy using deformable thorax phantom.Journal of applied clinical medical physics · 2023Article
- Radiomic and quantitative-semantic models of low-dose computed tomography for predicting the poorly differentiated invasive non-mucinous pulmonary adenocarcinoma.La Radiologia medica · 2023Article
- Predicting PD-L1 expression status in patients with non-small cell lung cancer using [EJNMMI research · 2023Article
- Comparison of MRI radiomics-based machine learning survival models in predicting prognosis of glioblastoma multiforme.Frontiers in medicine · 2023Article
- Applying a CT texture analysis model trained with deep-learning reconstruction images to iterative reconstruction images in pulmonary nodule diagnosis.Journal of applied clinical medical physics · 2022Article
- A Review of Radiomics in Predicting Therapeutic Response in Colorectal Liver Metastases: From Traditional to Artificial Intelligence Techniques.Healthcare (Basel, Switzerland) · 2022Review
- Reproducibility of radiomic features of pulmonary nodules between low-dose CT and conventional-dose CT.Quantitative imaging in medicine and surgery · 2022Article
- Vulnerabilities of radiomic features to respiratory motion on four-dimensional computed tomography-based average intensity projection images: A phantom study.Journal of applied clinical medical physics · 2022Article
- The role of deep learning-based survival model in improving survival prediction of patients with glioblastoma.Cancer medicine · 2021Article
- Effect of adaptive statistical iterative reconstruction-V (ASiR-V) levels on ultra-low-dose CT radiomics quantification in pulmonary nodules.Quantitative imaging in medicine and surgery · 2021Article
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4 authors.
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Abstract
The purpose of this study was to explore the effects of CT slice thickness, reconstruction algorithm, and radiation dose on quantification of CT features to characterize lung nodules using a chest phantom. Spherical lung nodule phantoms of known densities (-630 and + 100 HU) were inserted into an anthropomorphic thorax phantom. CT scan was performed ten times with relocations. CT data were reconstructed using 12 different imaging settings; three different slice thicknesses of 1.25, 2.5, and 5.0 mm, two reconstruction kernels of sharp and standard, and two radiation dose of 30 mAs and 12 mAs. Lesions were segmented using a semiautomated method. Twenty representative CT quantitative features representing CT density and texture were compared using multiple regression analysis. In 100 HU nodule phantoms, 18 and 19 among 20 computer features showed significant difference between different mAs and reconstruction algorithms, respectively (
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