Multicenter evaluation of MRI‐based radiomic features: A phantom study
| Autor: | Michael Barton, Lois Holloway, Gary P Liney, Rasmus Lübeck Christiansen, Carsten Brink, Robba Rai, Yu Sun, Matthew Field |
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| Rok vydání: | 2020 |
| Předmět: |
Reproducibility
medicine.diagnostic_test Phantoms Imaging Computer science MRI radiomics Reproducibility of Results Magnetic resonance imaging 3D printing General Medicine Magnetic Resonance Imaging Imaging phantom 030218 nuclear medicine & medical imaging image features 03 medical and health sciences 0302 clinical medicine Radiomics Image texture Feature (computer vision) 030220 oncology & carcinogenesis Printing Three-Dimensional Image Processing Computer-Assisted medicine image texture MRI phantoms Biomedical engineering |
| Zdroj: | Rai, R, Holloway, L C, Brink, C, Field, M, Christiansen, R L, Sun, Y, Barton, M B & Liney, G P 2020, ' Multicenter evaluation of MRI-based radiomic features : A Phantom Study ', Medical Physics, vol. 47, no. 7, pp. 3054-3063 . https://doi.org/10.1002/mp.14173 |
| ISSN: | 2473-4209 0094-2405 |
| Popis: | INTRODUCTION: This work describes the development of a novel radiomics phantom designed for magnetic resonance imaging (MRI) that can be used in a multicenter setting. The purpose of this study is to assess the stability and reproducibility of MRI-based radiomic features using this phantom across different MRI scanners.METHODS & MATERIALS: A set of phantoms were three-dimensional (3D) printed using MRI visible materials. One set of phantoms were imaged on seven MRI scanners and one was imaged on one MRI scanner. Radiomics analysis of the phantoms, which included first-order features, shape and texture features was performed. Intraclass correlation coefficient (ICC) was used to assess the stability of radiomic features across eight scanners and the reproducibility of two printed models on one scanner. Coefficient of variation (COV) was used to assess the reproducibility of radiomics measurements in the phantom on a single scanner.RESULTS: The phantom models provide sufficient signal-to-noise and contrast in all the tumor models permitting robust automatic segmentation. During a 12-month period of monitoring, the phantom material was stable with T1 and T2 of 150.7 ± 6.7 ms and 56.1 ± 3.9 ms, respectively. Of all the radiomic features computed, 34 of 69 had COV < 10%. Features from first-order statistics were the most robust in stability across the eight scanners with eight of 12 (67%) having high stability. About 29 of 50 (58%) texture features had high stability and no shape features had high stability features across the eight scanners.CONCLUSION: A novel MRI radiomics phantom has been developed to assess the reproducibility and stability of MRI-based radiomic features across multiple institutions. The variation in radiomic feature stability demonstrates the need for caution when interpreting these features for clinical studies. |
| Databáze: | OpenAIRE |
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