ArticleJournal of computer assisted tomography
Investigating the Robustness Neighborhood Gray Tone Difference Matrix and Gray Level Co-occurrence Matrix Radiomic Features on Clinical Computed Tomography Systems Using Anthropomorphic Phantoms: Evidence From a Multivendor Study.
Article in Journal of computer assisted tomography. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed, 18 citations in OpenAlex.
- Assessment of Influencing Factors and Robustness of Computable Image Texture Features in Digital Images.Tomography (Ann Arbor, Mich.) · 2025Article
- Development of a deep learning radiomics model combining lumbar CT, multi-sequence MRI, and clinical data to predict high-risk cage subsidence after lumbar fusion: a retrospective multicenter study.Biomedical engineering online · 2025Article
- Noninvasive Imaging Evaluation Based on Computed Tomography of the Efficacy of Initial Transarterial Chemoembolization to Predict Outcome in Patients with Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2022Article
- Application of Radiomics Model of CT Images in the Identification of Ureteral Calculus and Phlebolith.International journal of clinical practice · 2022Article
- Analysing the possibility of utilizing CBCT radiomics as an independent modality: a phantom study.Asian Pacific journal of cancer prevention : APJCP · 2021Article
- Quality control of radiomic features using 3D-printed CT phantoms.Journal of medical imaging (Bellingham, Wash.) · 2021Article
- Radiomics for Identification and Prediction in Metastatic Prostate Cancer: A Review of Studies.Frontiers in oncology · 2021Review
- Understanding Sources of Variation to Improve the Reproducibility of Radiomics.Frontiers in oncology · 2021Review
- Novel radiomics features from CCTA images for the functional evaluation of significant ischaemic lesions based on the coronary fractional flow reserve score.The international journal of cardiovascular imaging · 2020Article
- Artificial Intelligence and Texture Analysis in Cardiac Imaging.Current cardiology reports · 2020Review
- Radiomics as a personalized medicine tool in lung cancer: Separating the hope from the hype.Lung cancer (Amsterdam, Netherlands) · 2020Review
- CT Texture Analysis Challenges: Influence of Acquisition and Reconstruction Parameters: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2020Review
- Effect of tube current on computed tomography radiomic features.Scientific reports · 2018Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
objectiveThe aim of this study was to determine if optimized imaging protocols across multiple computed tomography (CT) vendors could result in reproducible radiomic features calculated from an anthropomorphic phantom.
methodsMaterials with varying degrees of heterogeneity were placed throughout the lungs of the phantom. Twenty scans of the phantom were acquired on 3 CT manufacturers with chest CT protocols that had optimized protocol parameters. Scans were reconstructed using vendor-specific standards and lung kernels. The concordance correlation coefficient (CCC) was used to calculate reproducibility between features. For features with high CCC values, Bland-Altman analysis was also used to quantify agreement.
resultsThe mean Hounsfield unit (HU) was 32.93 HU (141.7 to -26.5 HU) for the rubber insert and 347.2 HU (-320.9 to -347.7 HU) for the wood insert. Low CCC values of less than 0.9 were calculated for all features across all scans.
conclusionsRadiomic features that are derived from the spatial distribution of voxel intensities should be particularly scrutinized for reproducibility in a multivendor environment.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.