Table 1 Summary of literature for magnetic resonance imaging radiomics feature robustness
From: Robustness of radiomic features in magnetic resonance imaging: review and a phantom study
Reference | Disease / phantom | MR sequences | # features | Feature classes | Parameters evaluated | Statistical analysis | Robustness evaluation |
|---|---|---|---|---|---|---|---|
Baessler et al. [26], 2019 | Vegetable/fruit phantom | FLAIR, T1w, T2w | 45 | Intensity, shape, texture | MR sequence, resolution | CCC, DR, Bland-Altman analyses, ICC | Test-retest robustness, intraobserver and interobserver reproducibility |
Traverso et al. [50], 2019 | Locally advanced rectal cancer | DWI (ADC map) | 70 | Intensity, shape, texture | Pre-processing filter, re-binning and resampling | CCC, ICC, Spearman correlation | Inter-observer dependence |
Duron et al. [39], 2019 | Lacrymal gland tumor and breast lesion | T1w, DWI (ADC map), DIXON, DISCO | 69/57 (2 softwares) | Texture | Discretization method, bin width and bin number | CCC, ICC(2,1) | Intra- and inter-observer reproducibility |
Lecler et al. [37], 2019 | Lacrimal gland tumor | T1w, DWI (ADC map), DIXON | 85 | Intensity, shape, texture | MR sequence, metric threshold | CCC, ICC(2,1), Spearman correlation | Intra- and inter-observer reproducibility, non-redundancy |
Um et al. [51], 2019 | Glioblastoma multiforme | FLAIR, T1w, post-contrast T1w | 420 | Intensity, shape, texture, filter-based | Preprocessing technique on multi-scanner datasets, bin number | Two-sided Wilcoxon tests | Feature variability |
Schwier et al. [24], 2019 | Prostate cancer | T2w, DWI (ADC map) | NA | Intensity, shape, texture, filter-based | Image normalization, 2D/3D texture computation, bin widths, and image pre-filtering | ICC(1,1) | Test-retest repeatability |
Fiset et al. [38], 2019 | Cervical cancer | T2w | 1761 | Intensity, shape, texture, filter-based | Quantization method, LoG kernel sizes, | ICC(1,1), ICC(2,1), Pearson correlation, Krippendorff’s alpha | Test-retest repeatability, cross-scanner reproducibility, inter-observer reproducibility |
Peerlings et al. [33], 2019 | Ovarian, lung and colorectal liver metastasis cancer | DWI (ADC map) | 1322 | Intensity, shape, texture, filter-based | Center and vendor | CCC | Feature stability |
Buch et al. [52], 2018 | Nonanatomic Gd-DTPA phantom | T1w | 41 | Intensity, texture, filter-based (Laws) | Magnet strength, flip-angle, number of excitations, scanner platform | Q values | Feature variability |
Yang et al. [53], 2018 | Simulated data from digital phantom and glioma | T1w, T2w | 26 | Texture | Noise level, acceleration factor, and image reconstruction algorithm | Student’s t-test, CV | Feature variance |
Bologna et al. [32], 2018 | Soft tissue sarcoma and oropharyngeal cancer | DWI (ADC map) | 69 | Intensity, texture | ROI transformation and bin number | Absolute percentage variation, two-way mixed effect ICC | Feature stability and discrimination |
Chirra et al. [40], 2018 | Prostate cancer | T2w | 406 | Intensity, texture, filter-based | Different sites | Multivariate CV and Instability Score | Cross-site reproducibility |
Saha et al. [31], 2018 | Breast cancer | DCE-MRI (first postcontrast, PE, SER, washing rate maps) | 529 | Intensity, shape, texture | Scanner, contrast agent | ICC(3,1), Pearson correlation, average DSC | Inter-reader stability, inter-relations within feature groups, pairwise reader variability |
Molina et al. [27], 2017 | Glioblastoma | T1w | 16 | Texture | Spatial resolution and bin number | CV | Feature variation |
Brynolfsson et al. [54], 2017 | Glioma and prostate cancer | DWI (ADC map) | 19 | Texture | noise level, resolution, ADC map construction, quantization method, and bin number | Two-sample Kolmogorov-Smirnov tests | Feature distribution variation |
Gourtsoyianni et al. [41], 2017 | Primary rectal cancer | T2w | 46 | Intensity, texture, filter-based | 2 baseline examinations | wCV | Test-retest repeatability |
Guan et al. [55], 2016 | Cervical cancer | DWI (ADC map) | 8 | Intensity, texture | GLCM direction | ICC, Wilcoxon test, Kruskal-Wallis test, and ROC curve | Inter- and intra-observer agreement |
Molina et al. [56], 2016 | Glioblastoma | T1w | 16 | Texture | Matrix size and bin number | CV | Feature variation |
Savio et al. [57], 2010 | Multiple sclerosis | T1w | 264 | Intensity, texture, filter-based | Global, regional and local features | Wilcoxon’s signed ranks test | Feature variation |
Mayerhoefer et al. [58], 2009 | PSAG phantom | T2w | NA | Texture, filter-based | Spatial resolution, NAs, TR, TE, and SBW | LDA and k-NN classifier | Ability to distinguish between different patterns |
Collewet et al. [59], 2004 | Cheese phantom | T2w, PDW | 90 | Texture, filter-based | MRI acquisition protocol and quantization method | POE, ACC, 1-NN classifier | Classification |