Abstract
Objectives Radiomic features extracted from diverse MRI modalities have been investigated regarding their predictive and/or prognostic value in a variety of cancers. With the aid of a 3D realistic digital MRI phantom of the brain, the aim of this study was to examine the impact of pulse sequence parameter selection on MRI-based textural parameters of the brain. Methods MR images of the employed digital phantom were realized with SimuBloch, a simulation package made for fast generation of image sequences based on the Bloch equations. Pulse sequences being investigated consisted of spin echo (SE), gradient echo (GRE), spoiled gradient echo (SP-GRE), inversion recovery spin echo (IR-SE), and inversion recovery gradient echo (IR-GRE). Twenty-nine radiomic textural features related, respectively, to gray-level intensity histograms (GLIH), cooccurrence matrices (GLCOM), zone size matrices (GLZSM), and neighborhood difference matrices (GLNDM) were evaluated for the obtained MR realizations, and differences were identified. Results It was found that radiomic features vary considerably among images generated by the five different T1-weighted pulse sequences, and the deviations from those measured on the T1 map vary among features, from a few percent to over 100%. Radiomic features extracted from T1-weighted spin-echo images with TR varying from 360 ms to 620 ms and TE = 3.4 ms showed coefficients of variation (CV) up to 45%, while up to 70%, for T2-weighted spin-echo images with TE varying over the range 60–120 ms and TR = 6400 ms. Conclusion Variability of radiologic textural appearance on MR realizations with respect to the choice of pulse sequence and imaging parameters is feature-dependent and can be substantial. It calls for caution in employing MRI-derived radiomic features especially when pooling imaging data from multiple institutions with intention of correlating with clinical endpoints.
Highlights
Given the noninvasive nature of medical imaging along with its ready availability, radiomic features, that is, radiographic cancer imaging traits, have recently been sought after actively as prognostic and/or predictive indicators under the hypothesis that tumoral radiologic appearance conveys underlying phenotypic and/or genetic diversity [1,2,3,4,5,6]
Pulse sequences being considered consisted of spin echo (SE), gradient echo (GRE), spoiled gradient echo (SP-GRE), inversion recovery spin echo (IRSE), and inversion recovery gradient echo (IR-GRE)
Examples of axial slices of T1-weighted images generated from the 3D digital brain phantom utilizing various pulse sequences are shown in Figure 2 with panel (b) for spin echo (SE), (c) for gradient echo (GRE), (d) for spoiled gradient echo (SP-GRE), (e) for inversion recovery spin-echo (IR-SE), and (f ) for inversion recovery gradient-echo (IRGRE)
Summary
Given the noninvasive nature of medical imaging along with its ready availability, radiomic features, that is, radiographic cancer imaging traits, have recently been sought after actively as prognostic and/or predictive indicators under the hypothesis that tumoral radiologic appearance conveys underlying phenotypic and/or genetic diversity [1,2,3,4,5,6]. Successful translation of radiomics research into clinic will greatly depend on the repeatability, reproducibility, and validity of the radiomic features being investigated. By repeatability, it means that given the same subject and imaging protocol, the same results will consistently occur while reproducibility is a measure of consistency from one scanner or one institution to another. Validity relates to the extent to which radiomic features measure the underlying construct they purport to measure [19]. ese concerns have been
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