ReviewDiagnostics (Basel, Switzerland)2020
CT Texture Analysis Challenges: Influence of Acquisition and Reconstruction Parameters: A Comprehensive Review.
Review in Diagnostics (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic Accuracy of CT Texture Analysis in Adrenal Masses: A Systematic Review.International journal of molecular sciences · 2022Pooled it
- Explainable machine learning-based mortality prediction in critically ill patients with rheumatoid arthritis-associated lung disease: a radiomics and clinical data integration study.BMC medical imaging · 2026Article
- Expanding the role of radiomics and artificial intelligence in the management of inflammatory bowel disease: Insights, opportunities, and challenges.World journal of gastroenterology · 2025Article
- A narrative review of imaging intratumor heterogeneity in non-small cell lung cancer: current advances.Translational lung cancer research · 2025Review
- Impact of a deep learning image reconstruction algorithm on the robustness of abdominal computed tomography radiomics features using standard and low radiation doses.Quantitative imaging in medicine and surgery · 2025Article
- Photon-Counting CT Scan Phantom Study: Stability of Radiomics Features.Diagnostics (Basel, Switzerland) · 2025Article
- Advances in the mechanisms, imaging characteristics and management strategies for immune checkpoint inhibitor-related pneumonitis.Frontiers in immunology · 2025Review
- Radiomics Feature Stability in True and Virtual Non-Contrast Reconstructions from Cardiac Photon-Counting Detector CT Datasets.Diagnostics (Basel, Switzerland) · 2024Article
- Impact of Preprocessing Parameters in Medical Imaging-Based Radiomic Studies: A Systematic Review.Cancers · 2024Review
- Impacts of Adaptive Statistical Iterative Reconstruction-V and Deep Learning Image Reconstruction Algorithms on Robustness of CT Radiomics Features: Opportunity for Minimizing Radiomics Variability Among Scans of Different Dose Levels.Journal of imaging informatics in medicine · 2024Article
- Surface-based anthropomorphic bone structures for use in high-resolution simulated medical imaging.Physics in medicine and biology · 2023Article
- Deep learning approach for differentiating indeterminate adrenal masses using CT imaging.Abdominal radiology (New York) · 2023Article
- Impact of deep learning image reconstruction algorithms on CT radiomic features in patients with liver tumors.Frontiers in oncology · 2023Article
- Impact of slice thickness on reproducibility of CT radiomic features of lung tumors.F1000Research · 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
- Application of machine learning model to predict osteoporosis based on abdominal computed tomography images of the psoas muscle: a retrospective study.BMC geriatrics · 2022Article
- CT Reconstruction Kernels and the Effect of Pre- and Post-Processing on the Reproducibility of Handcrafted Radiomic Features.Journal of personalized medicine · 2022Article
- Cutaneous and local radiation injuries.Journal of radiological protection : official journal of the Society for Radiological Protection · 2022Review
- Influence of the Computer-Aided Decision Support System Design on Ultrasound-Based Breast Cancer Classification.Cancers · 2022Article
- Response to Induction Therapy in Pediatric Hodgkin's Lymphoma: Performance of First-Order Texture Parameters of CT Images.Journal of the Belgian Society of Radiology · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Texture analysis in medical imaging is a promising tool that is designed to improve the characterization of abnormal images from patients, to ultimately serve as a predictive or prognostic biomarker. However, the nature of image acquisition itself implies variability in each pixel/voxel value that could jeopardize the usefulness of texture analysis in the medical field. In this review, a search was performed to identify current published data for computed tomography (CT) texture reproducibility and variability. On the basis of this analysis, the critical steps were identified with a view of using texture analysis as a reliable tool in medical imaging. The need to specify the CT scanners used and the associated parameters in published studies is highlighted. Harmonizing acquisition parameters between studies is a crucial step for future texture analysis.
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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.