Abstract
ObjectivesTo assess the influence of gray-level discretization on inter- and intra-observer reproducibility of texture radiomics features on clinical MR images.Materials and methodsWe studied two independent MRI datasets of 74 lacrymal gland tumors and 30 breast lesions from two different centers. Two pairs of readers performed three two-dimensional delineations for each dataset. Texture features were extracted using two radiomics softwares (Pyradiomics and an in-house software). Reproducible features were selected using a combination of intra-class correlation coefficient (ICC) and concordance and coherence coefficient (CCC) with 0.8 and 0.9 as thresholds, respectively. We tested six absolute and eight relative gray-level discretization methods and analyzed the distribution and highest number of reproducible features obtained for each discretization. We also analyzed the number of reproducible features extracted from computer simulated delineations representative of inter-observer variability.ResultsThe gray-level discretization method had a direct impact on texture feature reproducibility, independent of observers, software or method of delineation (simulated vs. human). The absolute discretization consistently provided statistically significantly more reproducible features than the relative discretization. Varying the bin number of relative discretization led to statistically significantly more variable results than varying the bin size of absolute discretization.ConclusionsWhen considering inter-observer reproducible results of MRI texture radiomics features, an absolute discretization should be favored to allow the extraction of the highest number of potential candidates for new imaging biomarkers. Whichever the chosen method, it should be systematically documented to allow replicability of results.
Highlights
Medical imaging is progressively shifting from conventional visual image analysis to quantitative personalized medicine thanks to the recent development of data-driven analysis methods like radiomics [1]
When considering inter-observer reproducible results of MRI texture radiomics features, an absolute discretization should be favored to allow the extraction of the highest number of potential candidates for new imaging biomarkers
The potential of radiomics-based phenotyping in precision medicine is encouraging [2,3,4] but the diversity of the implementation methods of the radiomics pipeline and the absence of widespread standards result in a high variability of the possible approaches that may lead to non-replicable results
Summary
Medical imaging is progressively shifting from conventional visual image analysis to quantitative personalized medicine thanks to the recent development of data-driven analysis methods like radiomics [1]. Feature reduction can be performed in a number of ways, but a frequently-used method is based on the selection of the most reproducible features, based on the hypothesis that reproducibility is a mandatory quality for an imaging biomarker derived from the radiomics process. This feature-reduction step will impact the results of a radiomics study, and may lead to potentially discard a highly informative feature because it is not reproducible enough to be used in clinical routine. There are no current recommendations or standardization of this step
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