Patterns of inflammation, microstructural alterations, and sodium accumulation define multiple sclerosis subtypes after 15 years from onset
| Autor: | Antonio Ricciardi, Francesco Grussu, Baris Kanber, Ferran Prados, Marios C. Yiannakas, Bhavana S. Solanky, Frank Riemer, Xavier Golay, Wallace Brownlee, Olga Ciccarelli, Daniel C. Alexander, Claudia A. M. Gandini Wheeler-Kingshott |
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| Přispěvatelé: | Institut Català de la Salut, [Ricciardi A, Yiannakas MC, Solanky BS] NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom. [Grussu F] NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom. Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain. [Kanber B] NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom. Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom. [Prados F] NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom. Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom. eHealth Center, Universitat Oberta de Catalunya, Barcelona, Spain, Vall d'Hebron Barcelona Hospital Campus |
| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: |
Mathematical Concepts::Algorithms::Artificial Intelligence::Machine Learning [PHENOMENA AND PROCESSES]
Diagnosis::Diagnostic Techniques and Procedures::Diagnostic Imaging::Tomography::Magnetic Resonance Imaging [ANALYTICAL DIAGNOSTIC AND THERAPEUTIC TECHNIQUES AND EQUIPMENT] Aprenentatge automàtic enfermedades del sistema nervioso::enfermedades autoinmunitarias del sistema nervioso::enfermedades autoinmunes desmielinizantes del SNC::esclerosis múltiple [ENFERMEDADES] Biomedical Engineering Neuroscience (miscellaneous) conceptos matemáticos::algoritmos::inteligencia artificial::aprendizaje automático [FENÓMENOS Y PROCESOS] Nervous System Diseases::Autoimmune Diseases of the Nervous System::Demyelinating Autoimmune Diseases CNS::Multiple Sclerosis [DISEASES] diagnóstico::técnicas y procedimientos diagnósticos::diagnóstico por imagen::tomografía::imagen por resonancia magnética [TÉCNICAS Y EQUIPOS ANALÍTICOS DIAGNÓSTICOS Y TERAPÉUTICOS] Esclerosi múltiple - Imatgeria per ressonància magnètica Computer Science Applications |
| Zdroj: | Scientia |
| Popis: | MRI; Machine learning; Multiple sclerosis Ressonància magnètica; Aprenentatge automàtic; Esclerosi múltiple Resonancia magnética; Aprendizaje automático; Esclerosis múltiple Introduction: Conventional MRI is routinely used for the characterization of pathological changes in multiple sclerosis (MS), but due to its lack of specificity is unable to provide accurate prognoses, explain disease heterogeneity and reconcile the gap between observed clinical symptoms and radiological evidence. Quantitative MRI provides measures of physiological abnormalities, otherwise invisible to conventional MRI, that correlate with MS severity. Analyzing quantitative MRI measures through machine learning techniques has been shown to improve the understanding of the underlying disease by better delineating its alteration patterns. Methods: In this retrospective study, a cohort of healthy controls (HC) and MS patients with different subtypes, followed up 15 years from clinically isolated syndrome (CIS), was analyzed to produce a multi-modal set of quantitative MRI features encompassing relaxometry, microstructure, sodium ion concentration, and tissue volumetry. Random forest classifiers were used to train a model able to discriminate between HC, CIS, relapsing remitting (RR) and secondary progressive (SP) MS patients based on these features and, for each classification task, to identify the relative contribution of each MRI-derived tissue property to the classification task itself. Results and discussion: Average classification accuracy scores of 99 and 95% were obtained when discriminating HC and CIS vs. SP, respectively; 82 and 83% for HC and CIS vs. RR; 76% for RR vs. SP, and 79% for HC vs. CIS. Different patterns of alterations were observed for each classification task, offering key insights in the understanding of MS phenotypes pathophysiology: atrophy and relaxometry emerged particularly in the classification of HC and CIS vs. MS, relaxometry within lesions in RR vs. SP, sodium ion concentration in HC vs. CIS, and microstructural alterations were involved across all tasks. This project has received funding under the European Union's Horizon 2020 Research and Innovation Programme under grant agreement No. 634541. FG received the support of a fellowship from “la Caixa” Foundation (ID 100010434). The fellowship code is “LCF/BQ/PR22/11920010”. FG has also received support from the Beatriu de Pinós (2020 BP 00117) programme, funded by the Secretary of Universities and Research (Government of Catalonia). BK, FP, and OC are supported by the National Institute of Health Research Biomedical Research Centre at UCL and UCLH. EPSRC grants EP/M020533/1 and EP/J020990/01, MRC MR/T046422/1 and MR/T046473/1, Wellcome Trust award 221915/Z/20/Z, and the NIHR UCLH BRC support DCA's work in this area. CGWK also receives funding from Horizon 2020 [Research and Innovation Action Grants Human Brain Project 945539 (SGA3)], BRC (#BRC704/CAP/CGW), MRC (#MR/S026088/1), and Ataxia UK. |
| Databáze: | OpenAIRE |
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