ArticleAJNR. American journal of neuroradiology2017
Quantitative Assessment of Variation in CT Parameters on Texture Features: Pilot Study Using a Nonanatomic Phantom.
Article in AJNR. American journal of neuroradiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it, 51 citations in OpenAlex.
- Repeatability and Reproducibility of Radiomic Features: A Systematic Review.International journal of radiation oncology, biology, physics · 2018Pooled it
- An open-source CT texture phantom for radiomic imaging studies: Design, manufacture, and initial evaluation.Medical physics · 2026Article
- Evaluating consistency of radiomic features derived from CT images: A cross-center phantom study.Journal of applied clinical medical physics · 2026Article
- Robustness of radiomics features on 0.35 T magnetic resonance imaging for magnetic resonance-guided radiotherapy.Physics and imaging in radiation oncology · 2024Article
- Reproducibility in Radiomics: A Comparison of Feature Extraction Methods and Two Independent Datasets.Applied sciences (Basel, Switzerland) · 2024Article
- A Novel Deep Learning Model to Distinguish Malignant Versus Benign Solid Lung Nodules.Medical science monitor : international medical journal of experimental and clinical research · 2022Article
- Radiomics analysis for differentiating of cervical lymphadenopathy between cancer of unknown primary and malignant lymphoma on unenhanced computed tomography.Nagoya journal of medical science · 2022Article
- Can the computed tomography texture analysis of colorectal liver metastases predict the response to first-line cytotoxic chemotherapy?World journal of hepatology · 2022Article
- Reproducibility of radiomic features in CT images of NSCLC patients: an integrative analysis on the impact of acquisition and reconstruction parameters.European radiology experimental · 2022Article
- Article
- Combined model of radiomics and clinical features for differentiating pneumonic-type mucinous adenocarcinoma from lobar pneumonia: An exploratory study.Frontiers in endocrinology · 2022Article
- Article
- 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
- Distinguishing granulomas from adenocarcinomas by integrating stable and discriminating radiomic features on non-contrast computed tomography scans.European journal of cancer (Oxford, England : 1990) · 2021Article
- Unenhanced CT texture analysis with machine learning for differentiating between nasopharyngeal cancer and nasopharyngeal malignant lymphoma.Nagoya journal of medical science · 2021Article
- Bone Structure Analysis of the Radius Using Ultrahigh Field (7T) MRI: Relevance of Technical Parameters and Comparison with 3T MRI and Radiography.Diagnostics (Basel, Switzerland) · 2021Article
- Magnetic resonance radiomics features and prognosticators in different molecular subtypes of pediatric Medulloblastoma.PloS one · 2021Article
- Radiomic features based on Hessian index for prediction of prognosis in head-and-neck cancer patients.Scientific reports · 2020Article
- Effect of an iterative reconstruction quantum noise reduction technique on computed tomography radiomic features.Journal of medical imaging (Bellingham, Wash.) · 2020Article
- Deep learning analysis using FDG-PET to predict treatment outcome in patients with oral cavity squamous cell carcinoma.European radiology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Our aim was to evaluate changes in texture features based on variations in CT parameters on a phantom. Scans were performed with varying milliampere, kilovolt, section thickness, pitch, and acquisition mode. Forty-two texture features were extracted by using an in-house-developed Matlab program. Two-tailed
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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.