ArticleEuropean radiology experimental2021
MRI texture feature repeatability and image acquisition factor robustness, a phantom study and in silico study.
Article in European radiology experimental, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- How deep resolve affects the stability of texture features for different MR image acceleration methods - a phantom and a human brain study.BMC medical imaging · 2026Article
- Multi-institutional MRI-based radiomic pilot study to measure the variations between scanner vendors and imaging sessions.Frontiers in oncology · 2026Article
- Article
- 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
- Robust radiomics: a review of guidelines for radiomics in medical imaging.Frontiers in radiology · 2025Review
- Machine learning model for predicting epidermal growth factor receptor expression status in breast cancer using ultrasound radiomics.Frontiers in oncology · 2025Article
- Impact of Preprocessing Parameters in Medical Imaging-Based Radiomic Studies: A Systematic Review.Cancers · 2024Review
- CT and MRI radiomic features of lung cancer (NSCLC): comparison and software consistency.European radiology experimental · 2024Article
- Assessment of chemotherapy resistance changes in human colorectal cancer xenografts in rats based on MRI histogram features.Frontiers in oncology · 2024Article
- Enhancing the stability of CT radiomics across different volume of interest sizes using parametric feature maps: a phantom study.European radiology experimental · 2022Article
- Radiomics quality score in renal masses: a systematic assessment on current literature.The British journal of radiology · 2022Review
- Test-Retest Data for the Assessment of Breast MRI Radiomic Feature Repeatability.Journal of magnetic resonance imaging : JMRI · 2022Article
- Tissue volume estimation and age prediction using rapid structural brain scans.Scientific reports · 2022Article
- Machine Learning for Prediction of Recurrence in Parasagittal and Parafalcine Meningiomas: Combined Clinical and MRI Texture Features.Journal of personalized medicine · 2022Article
- Article
- Radiomics for Identification and Prediction in Metastatic Prostate Cancer: A Review of Studies.Frontiers in oncology · 2021Review
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7 authors.
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Abstract
purposeTo evaluate robustness and repeatability of magnetic resonance imaging (MRI) texture features in water and tissue phantom test-retest study. MATERIALS AND
methodsSeparate water and tissue phantoms were imaged twice with the same protocol in a test-retest experiment using a 1.5-T scanner. Protocols were acquired to favour signal-to-noise ratio and resolution. Forty-six features including first order statistics and second-order texture features were extracted, and repeatability was assessed by calculating the concordance correlation coefficient. Separately, base image noise and resolution were manipulated in an in silico experiment, and robustness of features was calculated by assessing percentage coefficient of variation and linear correlation of features with noise and resolution. These simulation data were compared with the acquired data. Features were classified by their degree (high, intermediate, or low) of robustness and repeatability.
resultsEighty percent of the MRI features were repeatable (concordance correlation coefficient > 0.9) in the phantom test-retest experiment. The majority (approximately 90%) demonstrated a strong or intermediate correlation with image acquisition parameter, and 19/46 (41%) and 13/46 (28%) of features were highly robust to noise and resolution, respectively (coefficient of variation < 5%). Agreement between the acquired and simulation data varied, with the range of agreement within feature classes between 11 and 92%.
conclusionMost MRI features were repeatable in a phantom test-retest study. This phantom data may serve as a lower limit of feature MRI repeatability. Robustness of features varies with acquisition parameter, and appropriate features can be selected for clinical validation studies.
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