Abstract
Our purpose was to analyze the robustness and reproducibility of magnetic resonance imaging (MRI) radiomic features. We constructed a multi-object fruit phantom to perform MRI acquisition as scan-rescan using a 3 Tesla MRI scanner. We applied T2-weighted (T2w) half-Fourier acquisition single-shot turbo spin-echo (HASTE), T2w turbo spin-echo (TSE), T2w fluid-attenuated inversion recovery (FLAIR), T2 map and T1-weighted (T1w) TSE. Images were resampled to isotropic voxels. Fruits were segmented. The workflow was repeated by a second reader and the first reader after a pause of one month. We applied PyRadiomics to extract 107 radiomic features per fruit and sequence from seven feature classes. We calculated concordance correlation coefficients (CCC) and dynamic range (DR) to obtain measurements of feature robustness. Intraclass correlation coefficient (ICC) was calculated to assess intra- and inter-observer reproducibility. We calculated Gini scores to test the pairwise discriminative power specific for the features and MRI sequences. We depict Bland Altmann plots of features with top discriminative power (Mann–Whitney U test). Shape features were the most robust feature class. T2 map was the most robust imaging technique (robust features (rf), n = 84). HASTE sequence led to the least amount of rf (n = 20). Intra-observer ICC was excellent (≥ 0.75) for nearly all features (max–min; 99.1–97.2%). Deterioration of ICC values was seen in the inter-observer analyses (max–min; 88.7–81.1%). Complete robustness across all sequences was found for 8 features. Shape features and T2 map yielded the highest pairwise discriminative performance. Radiomics validity depends on the MRI sequence and feature class. T2 map seems to be the most promising imaging technique with the highest feature robustness, high intra-/inter-observer reproducibility and most promising discriminative power.
Highlights
Our purpose was to analyze the robustness and reproducibility of magnetic resonance imaging (MRI) radiomic features
The fraction of robust features rapidly decreases for increasing levels of robustness from relaxed (CCC & dynamic range (DR) ≥ 0.85) to strict (CCC & DR ≥ 0.95)
Monocenter as well as multicenter studies dealing with the robustness and reproducibility of radiomic features obtained controversial r esults[5,9,16,17,18]
Summary
Our purpose was to analyze the robustness and reproducibility of magnetic resonance imaging (MRI) radiomic features. T2 map seems to be the most promising imaging technique with the highest feature robustness, high intra-/inter-observer reproducibility and most promising discriminative power. GLDM Gray level dependence matrix GLRLM Gray level run length matrix GLSZM Gray level size zone matrix HASTE Half-Fourier acquisition single-shot turbo spin-echo IBSI Image biomarker standardization initiative ICC Intraclass correlation coefficients NGTDM Neighboring gray tone difference matrix ROC Receiver operating characteristic rrf Robust and reproducible features T1w T1-weighted T2w T2-weighted TSE Turbo spin-echo VOI Volume of interest. Schwier et al have shown that the methods of image preprocessing and feature extraction highly influence the repeatability of radiomic features[8]. Baeßler et al have constructed a multi-object phantom to acquire test–retest data using three sequences and two matrix sizes to investigate the repeatability and robustness of MRI radiomic features[9]. We applied the supposed reference software package P yRadiomics[19] to extract the quantitative imaging features
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