2019
DOI: 10.1002/acm2.12795
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Impact of image preprocessing methods on reproducibility of radiomic features in multimodal magnetic resonance imaging in glioblastoma

Abstract: To investigate the effect of image preprocessing, in respect to intensity inhomogeneity correction and noise filtering, on the robustness and reproducibility of the radiomics features extracted from the Glioblastoma (GBM) tumor in multimodal MR images (mMRI).In this study, for each patient 1461 radiomics features were extracted from GBM subregions (i.e., edema, necrosis, enhancement, and tumor) of mMRI (i.e., FLAIR, T1, T1C, and T2) volumes for five preprocessing combinations (in total 116 880 radiomics featur… Show more

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Cited by 111 publications
(85 citation statements)
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“…Radiomics relies on the extraction of features from multimodal imaging, aiming to improve patient care. Although acquisition parameters strongly affect the content of MR images, only some recent studies have specifically focused on the impact of MRI pre-processing methods on radiomics features 10,40,41 . Here, we investigated the impact of three different intensity normalization approaches combined with two grey-level discretization methods on brain MR-based radiomics.…”
Section: Discussionmentioning
confidence: 99%
See 2 more Smart Citations
“…Radiomics relies on the extraction of features from multimodal imaging, aiming to improve patient care. Although acquisition parameters strongly affect the content of MR images, only some recent studies have specifically focused on the impact of MRI pre-processing methods on radiomics features 10,40,41 . Here, we investigated the impact of three different intensity normalization approaches combined with two grey-level discretization methods on brain MR-based radiomics.…”
Section: Discussionmentioning
confidence: 99%
“…Most recently, 2 publications focused on the image pre-processing steps and their impact on radiomics feature reproducibility in brain patients. Moradmand et al 10 evaluated the impact of 5 combinations of image pre-processing on the reproducibility of 1461 radiomic features (i.e., spatial resampling, skull stripping, noise reduction, bias field correction and intensity normalization) extracted from different glioblastoma (GBM) subregions (i.e., oedema, necrosis, enhanced tumour). They showed that radiomics features extracted from necrotic regions were the most reproducible and recommended that, after the bias field correction step, noise filtering should be applied.…”
Section: Discussionmentioning
confidence: 99%
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“…Image preprocessing was mainly performed through the FMRIB software library ( http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSL ) and with Python's SimpleITK package. To increase the robustness of features as much as possible through preprocessing [ 24 ], the following steps were adopted in this study: use of FLIRT in FMRIB to coregister the same T1WI image [ 25 ] as the template. After skull stripping, the isotropic voxel was resampled [ 26 ] to 1 × 1 × 1 mm 3 .…”
Section: Methodsmentioning
confidence: 99%
“…In feature selection, we selected 2 CTR features, Skewness and Kurtosis (6) based on histogram, and 2 PETR features, SUVmean and SUVmax 7, with high reproducibility for slice thickness condition changes. The study of stability and reproducibility of the radiomics features (6,7,(24)(25)(26)(27)(28)(29)(30)(31) shows multiple parameter changes (e.g., slice thickness) in general produces greater measurement errors. In this case, the selected 4 features only have good reproducibility against slice thickness.…”
Section: Discussionmentioning
confidence: 99%