ArticleEuropean radiology2017
The impact of image reconstruction settings on 18F-FDG PET radiomic features: multi-scanner phantom and patient studies.
Article in European radiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 110 papers, 3 of them syntheses that pooled it.
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Who cites it
110 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- From center to edge: Prognostic implications of spatial metabolic heterogeneity, normalized hotspot-to-centroid and perimeter distance, on PET imaging. A systematic review and meta-analysis.European journal of nuclear medicine and molecular imaging · 2026Pooled it
- Image Quality Standardization in Radiomics: A Systematic Review of Artifacts, Variability, and Feature Stability.Sensors (Basel, Switzerland) · 2026Pooled it
- Joint EANM/SNMMI guideline on radiomics in nuclear medicine : Jointly supported by the EANM Physics Committee and the SNMMI Physics, Instrumentation and Data Sciences Council.European journal of nuclear medicine and molecular imaging · 2023Guideline
- FUO-PETMamba: a pet maximum-intensity-projection-based artificial intelligence framework for aetiological classification of fever of unknown origin with multicentre validation.European journal of nuclear medicine and molecular imaging · 2026Article
- Impact of image reconstruction algorithms on histogram analysis of iodine concentration for assessing chemotherapy response in pancreatic ductal adenocarcinoma.Abdominal radiology (New York) · 2026Article
- Multiparametric PET/CT Tensor Radiomics for Stability-Aware Machine Learning in Lung Cancer Survival Prediction.Journal of imaging informatics in medicine · 2026Article
- Prognostic value of TMTV and Dmax calculated fromEuropean journal of nuclear medicine and molecular imaging · 2026Article
- Radiomics-based diagnosis of carotid artery stenosis using non-contrast CT: model development and validation.European journal of medical research · 2025Article
- The Role of Artificial Intelligence in Theranostics.Journal of nuclear medicine technology · 2025Review
- Impact of image preprocessing methods on MRI radiomics feature variability and classification performance in Parkinson's disease motor subtype analysis.Scientific reports · 2025Article
- Reducing acquisition time in O-(2-Quantitative imaging in medicine and surgery · 2025Article
- Insights into radiomics: a comprehensive review for beginners.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Review
- Effect of reconstruction settings on radiomics feature robustness in positron emission tomography images: a clinical study.Quantitative imaging in medicine and surgery · 2025Article
- A stacking ensemble framework integrating radiomics and deep learning for prognostic prediction in head and neck cancer.Radiation oncology (London, England) · 2025Article
- Enhancing radiomics robustness using bayesian penalized likelihood PET reconstruction: application to Phantom and non-small cell lung cancer patient studies.BMC medical imaging · 2025Article
- Deep learning image enhancement algorithms in PET/CT imaging: a phantom and sarcoma patient radiomic evaluation.European journal of nuclear medicine and molecular imaging · 2025Article
- PET/CT acquisition and processing protocols in the Netherlands.EJNMMI physics · 2025Article
- Classification of carotid artery plaques: promising alternative methods to computed tomography angiography through radiomics approach using neck non-contrast computed tomography.Quantitative imaging in medicine and surgery · 2025Article
- Robust vs. Non-robust radiomic features: the quest for optimal machine learning models using phantom and clinical studies.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- The Predictive Role of BaselineDiagnostics (Basel, Switzerland) · 2025Article
50 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors.
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Abstract
objectivesThe purpose of this study was to investigate the robustness of different PET/CT image radiomic features over a wide range of different reconstruction settings.
methodsPhantom and patient studies were conducted, including two PET/CT scanners. Different reconstruction algorithms and parameters including number of sub-iterations, number of subsets, full width at half maximum (FWHM) of Gaussian filter, scan time per bed position and matrix size were studied. Lesions were delineated and one hundred radiomic features were extracted. All radiomics features were categorized based on coefficient of variation (COV).
resultsForty seven percent features showed COV ≤ 5% and 10% of which showed COV > 20%. All geometry based, 44% and 41% of intensity based and texture based features were found as robust respectively. In regard to matrix size, 56% and 6% of all features were found non-robust (COV > 20%) and robust (COV ≤ 5%) respectively.
conclusionsVariability and robustness of PET/CT image radiomics in advanced reconstruction settings is feature-dependent, and different settings have different effects on different features. Radiomic features with low COV can be considered as good candidates for reproducible tumour quantification in multi-center studies. KEY POINTS: • PET/CT image radiomics is a quantitative approach assessing different aspects of tumour uptake. • Radiomic features robustness is an important issue over different image reconstruction settings. • Variability and robustness of PET/CT image radiomics in advanced reconstruction settings is feature-dependent. • Robust radiomic features can be considered as good candidates for tumour quantification.
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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.