Repeatability of Radiomic Features in Magnetic Resonance Imaging of Glioblastoma: Test–Retest and Image Registration Analyses Publisher Pubmed



Shiri I¹ ; Hajianfar G² ; Sohrabi A³ ; Abdollahi H⁴ ; P Shayesteh S⁵ ; Geramifar P⁶ ; Zaidi H^{1, 7, 8, 9} ; Oveisi M^{2, 10} ; Rahmim A^{11, 12} Show Affiliations
Source: Medical Physics Published:2020

Abstract

Purpose: To assess the repeatability of radiomic features in magnetic resonance (MR) imaging of glioblastoma (GBM) tumors with respect to test–retest, different image registration approaches and inhomogeneity bias field correction. Methods: We analyzed MR images of 17 GBM patients including T1- and T2-weighted images (performed within the same imaging unit on two consecutive days). For image segmentation, we used a comprehensive segmentation approach including entire tumor, active area of tumor, necrotic regions in T1-weighted images, and edema regions in T2-weighted images (test studies only; registration to retest studies is discussed next). Analysis included N3, N4 as well as no bias correction performed on raw MR images. We evaluated 20 image registration approaches, generated by cross-combination of four transformation and five cost function methods. In total, 714 images (17 patients × 2 images × ((4 transformations × 5 cost functions) + 1 test image) and 2856 segmentations (714 images × 4 segmentations) were prepared for feature extraction. Various radiomic features were extracted, including the use of preprocessing filters, specifically wavelet (WAV) and Laplacian of Gaussian (LOG), as well as discretization into fixed bin width and fixed bin count (16, 32, 64, 128, and 256), Exponential, Gradient, Logarithm, Square and Square Root scales. Intraclass correlation coefficients (ICC) were calculated to assess the repeatability of MRI radiomic features (high repeatability defined as ICC ≥ 95%). Results: In our ICC results, we observed high repeatability (ICC ≥ 95%) with respect to image preprocessing, different image registration algorithms, and test–retest analysis, for example: RLNU and GLNU from GLRLM, GLNU and DNU from GLDM, Coarseness and Busyness from NGTDM, GLNU and ZP from GLSZM, and Energy and RMS from first order. Highest fraction (percent) of repeatable features was observed, among registration techniques, for the method Full Affine transformation with 12 degrees of freedom using Mutual Information cost function (mean 32.4%), and among image processing methods, for the method Laplacian of Gaussian (LOG) with Sigma (2.5–4.5 mm) (mean 78.9%). The trends were relatively consistent for N4, N3, or no bias correction. Conclusion: Our results showed varying performances in repeatability of MR radiomic features for GBM tumors due to test–retest and image registration. The findings have implications for appropriate usage in diagnostic and predictive models. © 2020 American Association of Physicists in Medicine