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Influence of Magnetic Field Strength on Magnetic Resonance Imaging Radiomics Features in Brain Imaging, an In Vitro and In Vivo Study

Samy Ammari, Stéphanie Pitre-Champagnat, Laurent Dercle, Émilie Chouzenoux, Salma Moalla, Sylvain Reuzé, Hugues Talbot, T. Mokoyoko, Joya Hadchiti, Sébastien Diffetocq, Andreas Volk, Mickeal El Haik, Sara Lakiss, Corinne Balleyguier, Nathalie Lassau, François Bidault

2021Frontiers in Oncology67 citationsDOIOpen Access PDF

Abstract

BACKGROUND: The development and clinical adoption of quantitative imaging biomarkers (radiomics) has established the need for the identification of parameters altering radiomics reproducibility. The aim of this study was to assess the impact of magnetic field strength on magnetic resonance imaging (MRI) radiomics features in neuroradiology clinical practice. METHODS: T1 3D SPGR sequence was acquired on two phantoms and 10 healthy volunteers with two clinical MR devices from the same manufacturer using two different magnetic fields (1.5 and 3T). Phantoms varied in terms of gadolinium concentrations and textural heterogeneity. 27 regions of interest were segmented (phantom: 21, volunteers: 6) using the LIFEX software. 34 features were analyzed. RESULTS: In the phantom dataset, 10 (67%) out of 15 radiomics features were significantly different when measured at 1.5T or 3T (student's t-test, p < 0.05). Gray levels resampling, and pixel size also influence part of texture features. These findings were validated in healthy volunteers. CONCLUSIONS: According to daily used protocols for clinical examinations, radiomic features extracted on 1.5T should not be used interchangeably with 3T when evaluating texture features. Such confounding factor should be adjusted when adapting the results of a study to a different platform, or when designing a multicentric trial.

Topics & Concepts

Magnetic resonance imagingRadiomicsImaging phantomMedicineReproducibilityNeuroradiologyNuclear medicineRadiologyBiomedical engineeringMedical physicsMathematicsNeurologyStatisticsPsychiatryRadiomics and Machine Learning in Medical ImagingMRI in cancer diagnosisGlioma Diagnosis and Treatment