Isaac Shiri1, Ghasem Hajianfar2, Ahmad Sohrabi3, Hamid Abdollahi4, Sajad P Shayesteh5, Parham Geramifar6, Habib Zaidi1,7,8,9, Mehrdad Oveisi2,10, Arman Rahmim11,12. 1. Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva 4, CH-1211, Switzerland. 2. Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran. 3. Cancer Control Research Center, Cancer Control Foundation, Iran University of Medical Sciences, Tehran, Iran. 4. Department of Radiologic Sciences and Medical Physics, Faculty of Allied Medicine, Kerman University of Medical Science, Kerman, Iran. 5. Department of Physiology, Pharmacology and Medical Physics, Faculty of Medicine, Alborz University of Medical Sciences, Karaj, Iran. 6. Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran. 7. Geneva University Neurocenter, Geneva University, Geneva, Switzerland. 8. Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands. 9. Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark. 10. Department of Computer Science, University of British Columbia, Vancouver, BC, Canada. 11. Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada. 12. Department of Integrative Oncology, BC Cancer Research Centre, Vancouver, BC, Canada.
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.
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.
Authors: Natalia Saltybaeva; Stephanie Tanadini-Lang; Diem Vuong; Simon Burgermeister; Michael Mayinger; Andrea Bink; Nicolaus Andratschke; Matthias Guckenberger; Marta Bogowicz Journal: Phys Imaging Radiat Oncol Date: 2022-05-14
Authors: Sarthak Pati; Ruchika Verma; Hamed Akbari; Michel Bilello; Virginia B Hill; Chiharu Sako; Ramon Correa; Niha Beig; Ludovic Venet; Siddhesh Thakur; Prashant Serai; Sung Min Ha; Geri D Blake; Russell Taki Shinohara; Pallavi Tiwari; Spyridon Bakas Journal: Med Phys Date: 2020-12-04 Impact factor: 4.071