Samy Ammari1,2, Stephanie Pitre-Champagnat2, Laurent Dercle1,3,4, Emilie Chouzenoux5, Salma Moalla1, Sylvain Reuze6, Hugues Talbot5, Tite Mokoyoko1, Joya Hadchiti1, Sebastien Diffetocq1, Andreas Volk2, Mickeal El Haik1, Sara Lakiss1, Corinne Balleyguier1,2, Nathalie Lassau1,2, Francois Bidault1,2. 1. Department of Radiology, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif, France. 2. BioMaps (UMR1281), Université Paris-Saclay, CNRS, INSERM, CEA, Orsay and Gustave Roussy, Villejuif, France. 3. Immunology of Tumours and Immunotherapy INSERM U1015, Gustave Roussy Cancer Campus, Université Paris Saclay, Villejuif, France. 4. Radiology Department, Columbia University Medical Center, New York Presbyterian Hospital, New York, NY, United States. 5. Center for Visual Computing, CentraleSupelec, Inria, Université Paris-Saclay, Gif-sur-Yvette, France. 6. Department of Radiotherapy - Medical Physics, Gustave Roussy, Université ParisSaclay, Villejuif, France.
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.
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.
Authors: R A Lerski; L R Schad; R Luypaert; A Amorison; R N Muller; L Mascaro; P Ring; A Spisni; X Zhu; A Bruno Journal: Magn Reson Imaging Date: 1999-09 Impact factor: 2.546
Authors: Marius E Mayerhoefer; Pavol Szomolanyi; Daniel Jirak; Andrzej Materka; Siegfried Trattnig Journal: Med Phys Date: 2009-04 Impact factor: 4.071
Authors: Roger Sun; Fanny Orlhac; Charlotte Robert; Sylvain Reuzé; Antoine Schernberg; Irène Buvat; Eric Deutsch; Charles Ferté Journal: Int J Radiat Oncol Biol Phys Date: 2016-08-01 Impact factor: 7.038
Authors: R Sun; E J Limkin; L Dercle; S Reuzé; E I Zacharaki; C Chargari; A Schernberg; A S Dirand; A Alexis; N Paragios; É Deutsch; C Ferté; C Robert Journal: Cancer Radiother Date: 2017-08-31 Impact factor: 1.018
Authors: Stefan Leger; Alex Zwanenburg; Karoline Pilz; Fabian Lohaus; Annett Linge; Klaus Zöphel; Jörg Kotzerke; Andreas Schreiber; Inge Tinhofer; Volker Budach; Ali Sak; Martin Stuschke; Panagiotis Balermpas; Claus Rödel; Ute Ganswindt; Claus Belka; Steffi Pigorsch; Stephanie E Combs; David Mönnich; Daniel Zips; Mechthild Krause; Michael Baumann; Esther G C Troost; Steffen Löck; Christian Richter Journal: Sci Rep Date: 2017-10-16 Impact factor: 4.379
Authors: Chintan Parmar; Ralph T H Leijenaar; Patrick Grossmann; Emmanuel Rios Velazquez; Johan Bussink; Derek Rietveld; Michelle M Rietbergen; Benjamin Haibe-Kains; Philippe Lambin; Hugo J W L Aerts Journal: Sci Rep Date: 2015-06-05 Impact factor: 4.379
Authors: Violeta Pina; Víctor M Campello; Karim Lekadir; Santi Seguí; Jose M García-Santos; Luis J Fuentes Journal: Front Neurosci Date: 2022-04-14 Impact factor: 4.677
Authors: Marianna Inglese; Neva Patel; Kristofer Linton-Reid; Flavia Loreto; Zarni Win; Richard J Perry; Christopher Carswell; Matthew Grech-Sollars; William R Crum; Haonan Lu; Paresh A Malhotra; Eric O Aboagye Journal: Commun Med (Lond) Date: 2022-06-20
Authors: Andrew Hope; Maikel Verduin; Thomas J Dilling; Ananya Choudhury; Rianne Fijten; Leonard Wee; Hugo Jwl Aerts; Issam El Naqa; Ross Mitchell; Marc Vooijs; Andre Dekker; Dirk de Ruysscher; Alberto Traverso Journal: Cancers (Basel) Date: 2021-05-14 Impact factor: 6.639