ArticleTomography (Ann Arbor, Mich.)2019
Reliability of Radiomic Features Across Multiple Abdominal CT Image Acquisition Settings: A Pilot Study Using ACR CT Phantom.
Article in Tomography (Ann Arbor, Mich.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
28 citing papers in PubMed, 33 citations in OpenAlex.
- Hunting for signal in the noise: identifying robust CT radiomic features across variable scanning settings.European radiology · 2026Article
- Influence of CT parameters upon the quantification of sarcopenia and myosteatosis: a study with human bodies.Scientific reports · 2026Article
- Annotated test-retest dataset of lung cancer CT scan images reconstructed at multiple imaging parameters.Scientific data · 2024Article
- Adjusting for the effect of IV contrast on automated CT body composition measures during the portal venous phase.Abdominal radiology (New York) · 2024Article
- Technical and clinical considerations of a physical liver phantom for CT radiomics analysis.Journal of applied clinical medical physics · 2024Article
- Comparing fully automated AI body composition measures derived from thin and thick slice CT image data.Abdominal radiology (New York) · 2024Article
- Protocol selection formalism for minimizing detectable differences in morphological radiomics features of lung lesions in repeated CT acquisitions.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Reproducibility and location-stability of radiomic features derived from cone-beam computed tomography: a phantom study.Dento maxillo facial radiology · 2023Article
- Evaluation of radiomics feature stability in abdominal monoenergetic photon counting CT reconstructions.Scientific reports · 2022Article
- Two-Stage Deep Learning Model for Automated Segmentation and Classification of Splenomegaly.Cancers · 2022Article
- Radiomics: a primer on high-throughput image phenotyping.Abdominal radiology (New York) · 2022Review
- The Potential and Emerging Role of Quantitative Imaging Biomarkers for Cancer Characterization.Cancers · 2022Review
- Malignancy risk of gastrointestinal stromal tumors evaluated with noninvasive radiomics: A multi-center study.Frontiers in oncology · 2022Article
- Stability of Liver Radiomics across Different 3D ROI Sizes-An MRI In Vivo Study.Tomography (Ann Arbor, Mich.) · 2021Article
- Convolutional Neural Network Addresses the Confounding Impact of CT Reconstruction Kernels on Radiomics Studies.Tomography (Ann Arbor, Mich.) · 2021Article
- Stability of Radiomic Features across Different Region of Interest Sizes-A CT and MR Phantom Study.Tomography (Ann Arbor, Mich.) · 2021Article
- Identifying Robust Radiomics Features for Lung Cancer by Using In-Vivo and Phantom Lung Lesions.Tomography (Ann Arbor, Mich.) · 2021Article
- Identification of robust and reproducible CT-texture metrics using a customized 3D-printed texture phantom.Journal of applied clinical medical physics · 2021Article
- Reproducibility and Repeatability of CBCT-Derived Radiomics Features.Frontiers in oncology · 2021Article
- Understanding Sources of Variation to Improve the Reproducibility of Radiomics.Frontiers in oncology · 2021Review
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
We studied the reliability of radiomic features on abdominal computed tomography (CT) images reconstructed with multiple CT image acquisition settings using the ACR (American College of Radiology) CT Phantom. Twenty-four sets of CT images of the ACR CT phantom were attained from a GE Discovery 750HD scanner using 24 different image acquisition settings, combinations of 4 tube currents (25, 50, 100, 200 Effective mAs), 3 slice thicknesses (1.25, 2.5, 5 mm), and 2 convolution kernels (STANDARD and SOFT). Polyethylene (-95 HU) and acrylic (120 HU) of the phantom model were selected for calculating real feature value; a noise-free, computer-generated phantom image series that reproduced the 2 objects and the background was used for calculating reference feature value. Feature reliability was defined as the degree of predicting reference feature value from real feature value. Radiomic features
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What OpenQuestion holds
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.