ArticleJournal of medical imaging (Bellingham, Wash.)2019
Systematic analysis of bias and variability of texture measurements in computed tomography.
Article in Journal of medical imaging (Bellingham, Wash.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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.
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
7 citing papers in PubMed.
- Robustness of radiomics within photon-counting detector CT: impact of acquisition and reconstruction factors.European radiology · 2025Article
- Assessment of Influencing Factors and Robustness of Computable Image Texture Features in Digital Images.Tomography (Ann Arbor, Mich.) · 2025Article
- Correction for Systematic Bias in Radiomics Measurements Due to Variation in Imaging Protocols.Academic radiology · 2022Article
- Development, validation, and relevance of in vivo low-contrast task transfer function to estimate detectability in clinical CT images.Medical physics · 2021Article
- Contrast-Enhanced CT-Based Radiomics Analysis in Predicting Lymphovascular Invasion in Esophageal Squamous Cell Carcinoma.Frontiers in oncology · 2021Article
- Adversarial radiomics: the rising of potential risks in medical imaging from adversarial learning.European journal of nuclear medicine and molecular imaging · 2020Article
- Radiomics in medical imaging-"how-to" guide and critical reflection.Insights into imaging · 2020Review
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.
Funding
Abstract
Texture is a key radiomics measurement for quantification of disease and disease progression. The sensitivity of the measurements to image acquisition, however, is uncertain. We assessed bias and variability of computed tomography (CT) texture feature measurements across many clinical image acquisition settings and reconstruction algorithms. Diverse, anatomically informed textures (texture A, B, and C) were simulated across 1188 clinically relevant CT imaging conditions representing four in-plane pixel sizes (0.4, 0.5, 0.7, and 0.9 mm), three slice thicknesses (0.625, 1.25, and 2.5 mm), three dose levels (
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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.