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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...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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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. |
format | Online Article Text |
id | pubmed-8892169 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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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