Abstract
PurposeThe purpose of the paper was to use a virtual phantom to identify a set of radiomic features from T1‐weighted and T2‐weighted magnetic resonance imaging (MRI) of the brain which is stable to variations in image acquisition parameters and to evaluate the effect of image preprocessing on radiomic features stability.MethodsStability to different sources of variability (time of repetition and echo, voxel size, random noise and intensity non‐uniformity) was evaluated for both T1‐weighted and T2‐weighted MRI images. A set of 107 radiomic features, accounting for shape and size, first order statistics, and textural features was used. Feature stability was quantified using intraclass correlation coefficient (ICC). For each source of variability, stability was evaluated before and after preprocessing (Z‐score normalization, resampling, gaussian filtering and bias field correction). Features that have ICC > 0.75 in all the analysis of variability are selected as stable features. Last, the robust feature sets were tested on images acquired with random simulation parameters to assess their generalizability to unseen conditions.ResultsPreprocessing significantly increased the robustness of radiomic features to the different sources of variability. When preprocessing is applied, a set of 67 and 61 features resulted as stable for T1‐weighted and T2‐wieghted images respectively, over 80% of which were confirmed by the analysis on the images acquired with random simulation parameters.ConclusionA set of MRI‐radiomic features, robust to changes in TR/TE/PS/ST, was identified. This set of features may be used in radiomic analyses based on T1‐weighted and T2‐weighted MRI images.
Highlights
Radiomics is one of the most recent fields in medical image analysis and it consists in the extraction of a large number of features from radiological images such as computed tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET) with the final aim of providing “imaging biomarkers” that can be acquired in an inexpensive and non-invasive way.[1]
Research on radiomics has already been performed in oncology for different purposes like tumor prognosis,[2] staging,[3] and prediction of response to treatment,[4] often with success
This is due to the high number of possible confounding factors whose exhaustive analysis is not trivial
Summary
Radiomics is one of the most recent fields in medical image analysis and it consists in the extraction of a large number of features from radiological images such as computed tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET) with the final aim of providing “imaging biomarkers” that can be acquired in an inexpensive and non-invasive way.[1]. It is well known that differences in protocols may reduce the reliability of radiomic features,[5,6,7,8,9,10,11,12,13] research on association between differences in imaging conditions and radiomic features variability is still ongoing. This is due to the high number of possible confounding factors (which may be dependent on the specific imaging technique that is considered) whose exhaustive analysis is not trivial
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