ArticleJournal of magnetic resonance imaging : JMRI2021
Prospective Evaluation of Repeatability and Robustness of Radiomic Descriptors in Healthy Brain Tissue Regions In Vivo Across Systematic Variations in T2-Weighted Magnetic Resonance Imaging Acquisition Parameters.
Article in Journal of magnetic resonance imaging : JMRI, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Article
- Assessment of Robustness of MRI Radiomic Features in the Abdomen: Impact of Deep Learning Reconstruction and Accelerated Acquisition.Journal of imaging informatics in medicine · 2026Article
- Variability Regularized Feature Selection (VaRFS) for optimal identification of robust and discriminable features from medical imaging.Npj imaging · 2026Article
- 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
- Robustness of radiomics features on 0.35 T magnetic resonance imaging for magnetic resonance-guided radiotherapy.Physics and imaging in radiation oncology · 2024Article
- Quantifying 3D MR fingerprinting (3D-MRF) reproducibility across subjects, sessions, and scanners automatically using MNI atlases.Magnetic resonance in medicine · 2024Article
- 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
- Test-Retest Data for the Assessment of Breast MRI Radiomic Feature Repeatability.Journal of magnetic resonance imaging : JMRI · 2022Article
- Response to: Correspondence on 'Novel imaging biomarkers predict outcomes in stage III unresectable non-small cell lung cancer treated with chemoradiation and durvalumab' by ZhengJournal for immunotherapy of cancer · 2022Article
- Challenges in ensuring the generalizability of image quantitation methods for MRI.Medical physics · 2022Review
- A Comprehensive Evaluation of Radiomic Features in Normal Brain Magnetic Resonance Imaging: Investigating Robustness and Region Variations.Journal of medical physicsArticle
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9 authors.
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Abstract
backgroundRadiomic descriptors from magnetic resonance imaging (MRI) are promising for disease diagnosis and characterization but may be sensitive to differences in imaging parameters.
objectiveTo evaluate the repeatability and robustness of radiomic descriptors within healthy brain tissue regions on prospectively acquired MRI scans; in a test-retest setting, under controlled systematic variations of MRI acquisition parameters, and after postprocessing. STUDY TYPE: Prospective. SUBJECTS: Fifteen healthy participants. FIELD STRENGTH/SEQUENCE: A 3.0 T, axial T ASSESSMENT: One hundred and forty-six radiomic descriptors were extracted from a contiguous 2D region of white matter in each scan, before and after postprocessing. STATISTICAL TESTS: Repeatability was assessed in a test/retest setting and between manual and automated annotations for the reference scan. Robustness was evaluated between the reference scan and each group of variant scans (contrast weighting, resolution, and acceleration). Both repeatability and robustness were quantified as the proportion of radiomic descriptors that fell into distinct ranges of the concordance correlation coefficient (CCC): excellent (CCC > 0.85), good (0.7 ≤ CCC ≤ 0.85), moderate (0.5 ≤ CCC < 0.7), and poor (CCC < 0.5); for unprocessed and postprocessed scans separately.
resultsGood to excellent repeatability was observed for 52% of radiomic descriptors between test/retest scans and 48% of descriptors between automated vs. manual annotations, respectively. Contrast weighting (TR/TE) changes were associated with the largest proportion of highly robust radiomic descriptors (21%, after processing). Image resolution changes resulted in the largest proportion of poorly robust radiomic descriptors (97%, before postprocessing). Postprocessing of images with only resolution/acceleration differences resulted in 73% of radiomic descriptors showing poor robustness. DATA
conclusionsMany radiomic descriptors appear to be nonrobust across variations in MR contrast weighting, resolution, and acceleration, as well in test-retest settings, depending on feature formulation and postprocessing. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.
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