ArticleRadiology. Cardiothoracic imaging2020
Reproducibility of Segmentation-based Myocardial Radiomic Features with Cardiac MRI.
Article in Radiology. Cardiothoracic imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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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
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Quality of science and reporting for radiomics in cardiac magnetic resonance imaging studies: a systematic review.European radiology · 2022Pooled it
- Effects of parametric feature maps on the reproducibility of radiomics from different fields of view in cardiac magnetic resonance cine images- a clinical and experimental study setting.The international journal of cardiovascular imaging · 2025Article
- Auto-segmentation, radiomic reproducibility, and comparison of radiomics between manual and AI-derived segmentations for coronary arteries in cardiac [EJNMMI physics · 2025Article
- Repeatability of radiomic features in myocardial T1 and T2 mapping.European radiology · 2025Article
- Myocardial perfusion SPECT radiomic features reproducibility assessment: Impact of image reconstruction and harmonization.Medical physics · 2025Article
- Effect of magnetic field strength and segmentation variability on the reproducibility and repeatability of radiomic texture features in cardiovascular magnetic resonance parametric mapping.The international journal of cardiovascular imaging · 2025Article
- Achieving imaging and computational reproducibility on multiparametric MRI radiomics features in brain tumor diagnosis: phantom and clinical validation.European radiology · 2024Article
- Present and Future Innovations in AI and Cardiac MRI.Radiology · 2024Review
- Radiomics for the detection of diffusely impaired myocardial perfusion: A proof-of-concept study using 13N-ammonia positron emission tomography.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023Article
- Collinearity and Dimensionality Reduction in Radiomics: Effect of Preprocessing Parameters in Hypertrophic Cardiomyopathy Magnetic Resonance T1 and T2 Mapping.Bioengineering (Basel, Switzerland) · 2023Article
- Reproducibility assessment of a biomechanical model-based elasticity imaging method for identifying changes in left ventricular mechanical stiffness.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- Radiomics and deep learning for myocardial scar screening in hypertrophic cardiomyopathy.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2022Article
- Image resampling and discretization effect on the estimate of myocardial radiomic features from T1 and T2 mapping in hypertrophic cardiomyopathy.Scientific reports · 2022Article
- Acquisition repeatability of MRI radiomics features in the head and neck: a dual-3D-sequence multi-scan study.Visual computing for industry, biomedicine, and art · 2022Article
- Machine learning phenotyping of scarred myocardium from cine in hypertrophic cardiomyopathy.European heart journal. Cardiovascular Imaging · 2022Article
- Radiomics of Patients with Locally Advanced Rectal Cancer: Effect of Preprocessing on Features Estimation from Computed Tomography Imaging.BioMed research international · 2022Article
- Machine learning of native T1 mapping radiomics for classification of hypertrophic cardiomyopathy phenotypes.Scientific reports · 2021Article
- Radiomics feature reliability assessed by intraclass correlation coefficient: a systematic review.Quantitative imaging in medicine and surgery · 2021Review
- Sensitivity of Myocardial Radiomic Features to Imaging Parameters in Cardiac MR Imaging.Journal of magnetic resonance imaging : JMRI · 2021Article
- Stability of MRI radiomic features according to various imaging parameters in fast scanned T2-FLAIR for acute ischemic stroke patients.Scientific reports · 2021Article
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8 authors.
Funding
Abstract
purposeTo investigate reproducibility of myocardial radiomic features with cardiac MRI. MATERIALS AND
methodsTest-retest studies were performed with a 3-T MRI system using commonly used cardiac MRI sequences of cine balanced steady-state free precession (cine bSSFP), T1-weighted and T2-weighted imaging, and quantitative T1 and T2 mapping in phantom experiments and 10 healthy participants (mean ± standard deviation age, 29 years ± 13). In addition, this study assessed repeatability in 51 patients (56 years ± 14) who underwent imaging twice during the same session. Three readers independently delineated the myocardium to investigate inter- and intraobserver reproducibility of radiomic features. A total of 1023 radiomic features were extracted by using PyRadiomics (
resultsDifferent reproducibility patterns were observed among sequences in in vivo test-retest studies. In cine bSSFP, the gray-level run-length matrix was the most reproducible feature family, and the wavelet low-pass filter applied horizontally and vertically was the most reproducible image filter. In T1 and T2 maps, intensity-based statistics (first-order) and gray-level co-occurrence matrix features were the most reproducible feature families, without a dominant reproducible image filter. Across all sequences, gray-level nonuniformity was the most frequently identified reproducible feature name. In inter- and intraobserver reproducibility studies, respectively, only 32%-47% and 61%-73% of features were identified as reproducible.
conclusionOnly a small subset of myocardial radiomic features was reproducible, and these reproducible radiomic features varied among different sequences.
Identifiers
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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.