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MAGNIMS recommendations for harmonization of MRI data in MS multicenter studies

There is an increasing need of sharing harmonized data from large, cooperative studies as this is essential to develop new diagnostic and prognostic biomarkers. In the field of multiple sclerosis (MS), the issue has become of paramount importance due to the need to translate into the clinical settin...

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Autores principales: De Stefano, Nicola, Battaglini, Marco, Pareto, Deborah, Cortese, Rosa, Zhang, Jian, Oesingmann, Niels, Prados, Ferran, Rocca, Maria A., Valsasina, Paola, Vrenken, Hugo, Gandini Wheeler-Kingshott, Claudia A.M., Filippi, Massimo, Barkhof, Frederik, Rovira, Àlex
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8892169/
https://www.ncbi.nlm.nih.gov/pubmed/35245791
http://dx.doi.org/10.1016/j.nicl.2022.102972
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author De Stefano, Nicola
Battaglini, Marco
Pareto, Deborah
Cortese, Rosa
Zhang, Jian
Oesingmann, Niels
Prados, Ferran
Rocca, Maria A.
Valsasina, Paola
Vrenken, Hugo
Gandini Wheeler-Kingshott, Claudia A.M.
Filippi, Massimo
Barkhof, Frederik
Rovira, Àlex
author_facet De Stefano, Nicola
Battaglini, Marco
Pareto, Deborah
Cortese, Rosa
Zhang, Jian
Oesingmann, Niels
Prados, Ferran
Rocca, Maria A.
Valsasina, Paola
Vrenken, Hugo
Gandini Wheeler-Kingshott, Claudia A.M.
Filippi, Massimo
Barkhof, Frederik
Rovira, Àlex
author_sort De Stefano, Nicola
collection PubMed
description There is an increasing need of sharing harmonized data from large, cooperative studies as this is essential to develop new diagnostic and prognostic biomarkers. In the field of multiple sclerosis (MS), the issue has become of paramount importance due to the need to translate into the clinical setting some of the most recent MRI achievements. However, differences in MRI acquisition parameters, image analysis and data storage across sites, with their potential bias, represent a substantial constraint. This review focuses on the state of the art, recent technical advances, and desirable future developments of the harmonization of acquisition, analysis and storage of large-scale multicentre MRI data of MS cohorts. Huge efforts are currently being made to achieve all the requirements needed to provide harmonized MRI datasets in the MS field, as proper management of large imaging datasets is one of our greatest opportunities and challenges in the coming years. Recommendations based on these achievements will be provided here. Despite the advances that have been made, the complexity of these tasks requires further research by specialized academical centres, with dedicated technical and human resources. Such collective efforts involving different professional figures are of crucial importance to offer to MS patients a personalised management while minimizing consumption of resources.
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spelling pubmed-88921692022-03-04 MAGNIMS recommendations for harmonization of MRI data in MS multicenter studies De Stefano, Nicola Battaglini, Marco Pareto, Deborah Cortese, Rosa Zhang, Jian Oesingmann, Niels Prados, Ferran Rocca, Maria A. Valsasina, Paola Vrenken, Hugo Gandini Wheeler-Kingshott, Claudia A.M. Filippi, Massimo Barkhof, Frederik Rovira, Àlex Neuroimage Clin Regular Article There is an increasing need of sharing harmonized data from large, cooperative studies as this is essential to develop new diagnostic and prognostic biomarkers. In the field of multiple sclerosis (MS), the issue has become of paramount importance due to the need to translate into the clinical setting some of the most recent MRI achievements. However, differences in MRI acquisition parameters, image analysis and data storage across sites, with their potential bias, represent a substantial constraint. This review focuses on the state of the art, recent technical advances, and desirable future developments of the harmonization of acquisition, analysis and storage of large-scale multicentre MRI data of MS cohorts. Huge efforts are currently being made to achieve all the requirements needed to provide harmonized MRI datasets in the MS field, as proper management of large imaging datasets is one of our greatest opportunities and challenges in the coming years. Recommendations based on these achievements will be provided here. Despite the advances that have been made, the complexity of these tasks requires further research by specialized academical centres, with dedicated technical and human resources. Such collective efforts involving different professional figures are of crucial importance to offer to MS patients a personalised management while minimizing consumption of resources. Elsevier 2022-02-25 /pmc/articles/PMC8892169/ /pubmed/35245791 http://dx.doi.org/10.1016/j.nicl.2022.102972 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Regular Article
De Stefano, Nicola
Battaglini, Marco
Pareto, Deborah
Cortese, Rosa
Zhang, Jian
Oesingmann, Niels
Prados, Ferran
Rocca, Maria A.
Valsasina, Paola
Vrenken, Hugo
Gandini Wheeler-Kingshott, Claudia A.M.
Filippi, Massimo
Barkhof, Frederik
Rovira, Àlex
MAGNIMS recommendations for harmonization of MRI data in MS multicenter studies
title MAGNIMS recommendations for harmonization of MRI data in MS multicenter studies
title_full MAGNIMS recommendations for harmonization of MRI data in MS multicenter studies
title_fullStr MAGNIMS recommendations for harmonization of MRI data in MS multicenter studies
title_full_unstemmed MAGNIMS recommendations for harmonization of MRI data in MS multicenter studies
title_short MAGNIMS recommendations for harmonization of MRI data in MS multicenter studies
title_sort magnims recommendations for harmonization of mri data in ms multicenter studies
topic Regular Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8892169/
https://www.ncbi.nlm.nih.gov/pubmed/35245791
http://dx.doi.org/10.1016/j.nicl.2022.102972
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