ArticleScientific reports2021
Radiomics feature robustness as measured using an MRI phantom.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers.
What it found
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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
64 citing papers in PubMed.
- A combined radiomics-clinical nomogram for assessing MRI-defined femoral Head collapse status in osteonecrosis of the femoral Head.Journal of applied clinical medical physics · 2026Article
- Quantitative magnetic resonance imaging for accurate delineation of radiotherapy: a narrative review.Translational cancer research · 2026Review
- Why Radiomics Rarely Reaches the Clinic: Reproducibility, Validation, and Evidence Gap-A Critical Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Radiomics analysis of restaging MRI for detection of pathological complete response in locally advanced rectal cancer.European journal of radiology open · 2026Article
- RadRepro CBCT: An Open-Access CBCT Phantom Dataset for Improved Standardization and Reproducibility of Radiomics Research.Scientific data · 2026Article
- Variability Regularized Feature Selection (VaRFS) for optimal identification of robust and discriminable features from medical imaging.Npj imaging · 2026Article
- Multi-institutional MRI-based radiomic pilot study to measure the variations between scanner vendors and imaging sessions.Frontiers in oncology · 2026Article
- Cross species reproducibility of MRI radiomics features enables intervertebral disc degeneration assessment in experimental monkeys.Scientific reports · 2025Article
- A systematic review of multiparametric magnetic resonance imaging radiomics in the prediction of brain invasion in meningioma.Translational cancer research · 2025Review
- A Radiogenomic Deep Ensemble Learning Model for Identifying Radionecrosis Following Brain Metastases (BM) Stereotactic Radiosurgery in Patients With Non-small Cell Lung Cancer BM.Advances in radiation oncology · 2025Article
- Anatomical zone and tissue type impacts the repeatability of quantitative MRI parameters and radiomic features for longitudinal monitoring of treatment response in the prostate.Magma (New York, N.Y.) · 2025Article
- Radiomic features of infrapatellar fat pad are associated with knee symptoms and radiographic post-traumatic osteoarthritis at 10+ years after anterior cruciate ligament reconstruction.Osteoarthritis imaging · 2025Article
- Radiogenomic explainable AI with neural ordinary differential equation for identifying post-SRS brain metastasis radionecrosis.Medical physics · 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 need for evidence-based, outcome-focused medical imaging research for cancer management.Npj imaging · 2025Article
- Advances in Glioblastoma Diagnosis: Integrating Genetics, Noninvasive Sampling, and Advanced Imaging.Cancers · 2025Review
- Radiomics score derived from T1-w/T2-w ratio image can predict motor symptom progression in Parkinson's disease.European radiology · 2024Article
- Radiomic study of antenatal prediction of severe placenta accreta spectrum from MRI.The British journal of radiology · 2024Article
- Comparison of Machine Learning Models Using Diffusion-Weighted Images for Pathological Grade of Intrahepatic Mass-Forming Cholangiocarcinoma.Journal of imaging informatics in medicine · 2024Article
- Dose-Incorporated Deep Ensemble Learning for Improving Brain Metastasis Stereotactic Radiosurgery Outcome Prediction.International journal of radiation oncology, biology, physics · 2024Article
4 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Radiomics involves high-throughput extraction of large numbers of quantitative features from medical images and analysis of these features to predict patients' outcome and support clinical decision-making. However, radiomics features are sensitive to several factors, including scanning protocols. The purpose of this study was to investigate the robustness of magnetic resonance imaging (MRI) radiomics features with various MRI scanning protocol parameters and scanners using an MRI radiomics phantom. The variability of the radiomics features with different scanning parameters and repeatability measured using a test-retest scheme were evaluated using the coefficient of variation and intraclass correlation coefficient (ICC) for both T1- and T2-weighted images. For variability measures, the features were categorized into three groups: large, intermediate, and small variation. For repeatability measures, the average T1- and T2-weighted image ICCs for the phantom (0.963 and 0.959, respectively) were higher than those for a healthy volunteer (0.856 and 0.849, respectively). Our results demonstrated that various radiomics features are dependent on different scanning parameters and scanners. The radiomics features with a low coefficient of variation and high ICC for both the phantom and volunteer can be considered good candidates for MRI radiomics studies. The results of this study will assist current and future MRI radiomics studies.
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Registered trials
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