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Gavoille A, Rollot F, Casey R, Kerbrat A, Le Page E, Bigaut K, Mathey G, Michel L, Ciron J, Ruet A, Maillart E, Labauge P, Zephir H, Papeix C, Defer G, Lebrun-Frenay C, Moreau T, Berger E, Stankoff B, Clavelou P, Thouvenot E, Heinzlef O, Pelletier J, Al-Khedr A, Casez O, Bourre B, Cabre P, Wahab A, Magy L, Camdessanché JP, Doghri I, Moulin S, Ben-Nasr H, Labeyrie C, Hankiewicz K, Neau JP, Pottier C, Nifle C, Manchon E, Lapergue B, Wiertlewski S, De Sèze J, Vukusic S, Laplaud DA. Acute Clinical Events Identified as Relapses With Stable Magnetic Resonance Imaging in Multiple Sclerosis. JAMA Neurol 2024; 81:814-823. [PMID: 38949816 PMCID: PMC11217890 DOI: 10.1001/jamaneurol.2024.1961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 05/03/2024] [Indexed: 07/02/2024]
Abstract
Importance Understanding the association between clinically defined relapses and radiological activity in multiple sclerosis (MS) is essential for patient treatment and therapeutic development. Objective To investigate clinical events identified as relapses but not associated with new T2 lesions or gadolinium-enhanced T1 lesions on brain and spinal cord magnetic resonance imaging (MRI). Design, Setting, and Participants This multicenter observational cohort study was conducted between January 2015 and June 2023. Data were extracted on June 8, 2023, from the French MS registry. All clinical events reported as relapses in patients with relapsing-remitting MS were included if brain and spinal cord MRI was performed within 12 and 24 months before the event, respectively, and 50 days thereafter with gadolinium injection. Exposures Events were classified as relapses with active MRI (RAM) if a new T2 lesion or gadolinium-enhanced T1 lesion appeared on brain or spinal cord MRI or as acute clinical events with stable MRI (ACES) otherwise. Main Outcomes and Measures Factors associated with ACES were investigated; patients with ACES and RAM were compared regarding Expanded Disability Status Scale (EDSS) course, relapse rate, confirmed disability accrual (CDA), relapse-associated worsening (RAW), progression independent of relapse activity (PIRA), and transition to secondary progressive (SP) MS, and ACES and RAM rates under each disease-modifying therapy (DMT) were estimated. Results Among 31 885 clinical events, 637 in 608 patients (493 [77.4%] female; mean [SD] age, 35.8 [10.7] years) were included. ACES accounted for 166 (26.1%) events and were more likely in patients receiving highly effective DMTs, those with longer disease duration (odds ratio [OR], 1.04; 95% CI, 1.01-1.07), or those presenting with fatigue (OR, 2.14; 95% CI, 1.15-3.96). ACES were associated with significant EDSS score increases, lower than those found for RAM. Before the index event, patients with ACES experienced significantly higher rates of relapse (relative rate [RR], 1.21; 95% CI, 1.01-1.46), CDA (hazard ratio [HR], 1.54; 95% CI, 1.13-2.11), and RAW (HR, 1.72; 95% CI, 1.20-2.45). Patients with ACES were at significantly greater risk of SP transition (HR, 2.58; 95% CI, 1.02-6.51). Although RAM rate decreased with DMTs according to their expected efficacy, ACES rate was stable across DMTs. Conclusions and Relevance The findings in this study introduce the concept of ACES in MS, which accounted for one-fourth of clinical events identified as relapses.
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Affiliation(s)
- Antoine Gavoille
- Hospices Civils de Lyon, Service de Neurologie, sclérose en plaques, pathologies de la myéline et neuro-inflammation, Bron, France
- Université de Lyon, Université Lyon 1, CNRS, Laboratoire de Biométrie et Biologie Évolutive UMR 5558, Villeurbanne, France
- Service de Biostatistique-Bioinformatique, Hospices Civils de Lyon, Lyon, France
| | - Fabien Rollot
- Observatoire Français de la Sclérose en Plaques, Centre de Recherche en Neurosciences de Lyon, l'Institut national de la santé et de la recherche médicale 1028 et CNRS UMR 5292, Lyon, France
- Université de Lyon, Université Claude Bernard Lyon 1, Lyon, France
| | - Romain Casey
- Observatoire Français de la Sclérose en Plaques, Centre de Recherche en Neurosciences de Lyon, l'Institut national de la santé et de la recherche médicale 1028 et CNRS UMR 5292, Lyon, France
- Université de Lyon, Université Claude Bernard Lyon 1, Lyon, France
| | - Anne Kerbrat
- CHU Pontchaillou, CIC1414 l'Institut national de la santé et de la recherche médicale, Rennes, France
| | - Emmanuelle Le Page
- CHU Pontchaillou, CIC1414 l'Institut national de la santé et de la recherche médicale, Rennes, France
| | - Kevin Bigaut
- Department of Neurology and Clinical Investigation Center, CHU de Strasbourg, CIC1434, l'Institut national de la santé et de la recherche médicale 1434, Strasbourg, France
| | - Guillaume Mathey
- Department of Neurology, Nancy University Hospital, Nancy, France. Université de Lorraine, APEMAC, Nancy, France
| | - Laure Michel
- CHU Pontchaillou, CIC1414 l'Institut national de la santé et de la recherche médicale, Rennes, France
| | - Jonathan Ciron
- Department of Neurology, CHU de Toulouse, CRC-SEP, Toulouse Cedex 9, France
- Université Toulouse III, Infinity, l'Institut national de la santé et de la recherche médicale UMR1291 - CNRS UMR 5051, Toulouse Cedex 3, France
| | - Aurelie Ruet
- Department of Neurology, University Hospital of Bordeaux, Bordeaux, France
- Neurocentre Magendie, Bordeaux University, l'Institut national de la santé et de la recherche médicale U1215, Bordeaux, France
| | - Elisabeth Maillart
- Département de neurologie, Hôpital Pitié-Salpêtrière, APHP, Paris, Centre de Ressources et de Compétences SEP, Paris, France
| | - Pierre Labauge
- CHU de Montpellier, MS Unit, Montpellier Cedex 5, France
- University of Montpellier, Montpellier, France
| | - Hélène Zephir
- CHU Lille, CRC-SEP Lille, Univ Lille, U1172, Lille, France
| | - Caroline Papeix
- Department of Neurology, Fondation Rothschild, Paris, France
| | - Gilles Defer
- Department of Neurology, CHU de Caen, MS expert centre, avenue de la Côte-de-Nacre, Normandy University, Caen, France
| | - Christine Lebrun-Frenay
- Neurology, UR2CA_URRIS, Centre Hospitalier Universitaire Pasteur2, Université Nice Côte d'Azur, Nice, France
| | - Thibault Moreau
- Department of Neurology, CHU de Dijon, EA4184, Dijon, France
| | - Eric Berger
- CHU de Besançon, Service de Neurologie Besançon, France
| | - Bruno Stankoff
- Sorbonne Universités, UPMC Paris 06, Brain and Spine Institute, ICM, Hôpital de la Pitié Salpêtrière, l'Institut national de la santé et de la recherche médicale UMR S 1127, CNRS UMR 7225, and Department of Neurology, AP-HP, Saint-Antoine hospital, Paris, France
| | - Pierre Clavelou
- Department of Neurology, CHU Clermont-Ferrand, Clermont-Ferrand, France
- Université Clermont Auvergne, l'Institut national de la santé et de la recherche médicale, Neuro-Dol, Clermont-Ferrand, France
| | - Eric Thouvenot
- Université Clermont Auvergne, l'Institut national de la santé et de la recherche médicale, Neuro-Dol, Clermont-Ferrand, France
- Department of Neurology, Nimes University Hospital, Nimes Cedex 9, France
| | | | - Jean Pelletier
- Aix Marseille Univ, APHM, Hôpital de la Timone, Pôle de Neurosciences Cliniques, Service de Neurologie, Marseille, France
| | | | - Olivier Casez
- Department of Neurology, CHU Grenoble Alpes, Neurology MS Clinic Grenoble, Grenoble Alpes university hospital, Grenoble, La Tronche, France
- T-RAIG, TIMC-IMAG, Grenoble Alpes University, France
| | | | - Philippe Cabre
- Department of Neurology, CHU de la Martinique, Fort-de-France, France
| | - Abir Wahab
- Department of Neurology, APHP, Hôpital Henri Mondor, Créteil, France
| | - Laurent Magy
- Department of Neurology, CHU de Limoges, Hôpital Dupuytren, Limoges, France
| | | | - Inès Doghri
- Department of Neurology, and CHU de Tours, Hôpital Bretonneau, CRC-SEP Tours, France
| | - Solène Moulin
- Department of Neurology, CHU de Reims, CRC-SEP, F-51092 Reims Cedex, France
| | - Haifa Ben-Nasr
- Department of Neurology, Hôpital Sud Francilien, Corbeil Essonnes, France
| | - Céline Labeyrie
- Department of Neurology, CHU Bicêtre, Le Kremlin Bicêtre, France
| | - Karolina Hankiewicz
- Department of Neurology, Hôpital Pierre Delafontaine, Centre Hospitalier de Saint-Denis, Saint-Denis, France
| | - Jean-Philippe Neau
- Department of Neurology, CHU la Milétrie, Hôpital Jean Bernard, Poitiers, France
| | - Corinne Pottier
- Department of Neurology, CH de Pontoise, Hôpital René Dubos, Pontoise, France
| | - Chantal Nifle
- Department of Neurology, Centre Hospitalier de Versailles, Le Chesnay, France
| | - Eric Manchon
- Department of Neurology, CH de Gonesse, Gonesse, France
| | | | - Sandrine Wiertlewski
- Nantes Université, l'Institut national de la santé et de la recherche médicale, CHU de Nantes, Center for Research in Transplantation and Translational Immunology, UMR 1064, CIC l'Institut national de la santé et de la recherche médicale 1413, Service de Neurologie, Nantes, France
| | - Jérôme De Sèze
- Department of Neurology and Clinical Investigation Center, CHU de Strasbourg, CIC1434, l'Institut national de la santé et de la recherche médicale 1434, Strasbourg, France
| | - Sandra Vukusic
- Hospices Civils de Lyon, Service de Neurologie, sclérose en plaques, pathologies de la myéline et neuro-inflammation, Bron, France
- Observatoire Français de la Sclérose en Plaques, Centre de Recherche en Neurosciences de Lyon, l'Institut national de la santé et de la recherche médicale 1028 et CNRS UMR 5292, Lyon, France
- Université de Lyon, Université Claude Bernard Lyon 1, Lyon, France
- EDMUS Foundation Against Multiple Sclerosis Bron Cedex, France
| | - David Axel Laplaud
- Nantes Université, l'Institut national de la santé et de la recherche médicale, CHU de Nantes, Center for Research in Transplantation and Translational Immunology, UMR 1064, CIC l'Institut national de la santé et de la recherche médicale 1413, Service de Neurologie, Nantes, France
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Karakachoff M, Goronflot T, Coudol S, Toublant D, Bazoge A, Constant Dit Beaufils P, Varey E, Leux C, Mauduit N, Wargny M, Gourraud PA. Implementing a Biomedical Data Warehouse From Blueprint to Bedside in a Regional French University Hospital Setting: Unveiling Processes, Overcoming Challenges, and Extracting Clinical Insight. JMIR Med Inform 2024; 12:e50194. [PMID: 38915177 PMCID: PMC11217163 DOI: 10.2196/50194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 04/08/2024] [Accepted: 04/17/2024] [Indexed: 06/26/2024] Open
Abstract
Background Biomedical data warehouses (BDWs) have become an essential tool to facilitate the reuse of health data for both research and decisional applications. Beyond technical issues, the implementation of BDWs requires strong institutional data governance and operational knowledge of the European and national legal framework for the management of research data access and use. Objective In this paper, we describe the compound process of implementation and the contents of a regional university hospital BDW. Methods We present the actions and challenges regarding organizational changes, technical architecture, and shared governance that took place to develop the Nantes BDW. We describe the process to access clinical contents, give details about patient data protection, and use examples to illustrate merging clinical insights. Unlabelled More than 68 million textual documents and 543 million pieces of coded information concerning approximately 1.5 million patients admitted to CHUN between 2002 and 2022 can be queried and transformed to be made available to investigators. Since its creation in 2018, 269 projects have benefited from the Nantes BDW. Access to data is organized according to data use and regulatory requirements. Conclusions Data use is entirely determined by the scientific question posed. It is the vector of legitimacy of data access for secondary use. Enabling access to a BDW is a game changer for research and all operational situations in need of data. Finally, data governance must prevail over technical issues in institution data strategy vis-à-vis care professionals and patients alike.
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Affiliation(s)
- Matilde Karakachoff
- Centre d'Investigation Clinique 1413, INSERM, Clinique des données, Pôle Hospitalo-Universitaire 11: Santé Publique, Centre Hospitalier Universitaire Nantes, Nantes Université, Nantes, France
| | - Thomas Goronflot
- Centre d'Investigation Clinique 1413, INSERM, Clinique des données, Pôle Hospitalo-Universitaire 11: Santé Publique, Centre Hospitalier Universitaire Nantes, Nantes Université, Nantes, France
| | - Sandrine Coudol
- Centre d'Investigation Clinique 1413, INSERM, Clinique des données, Pôle Hospitalo-Universitaire 11: Santé Publique, Centre Hospitalier Universitaire Nantes, Nantes Université, Nantes, France
| | - Delphine Toublant
- Centre d'Investigation Clinique 1413, INSERM, Clinique des données, Pôle Hospitalo-Universitaire 11: Santé Publique, Centre Hospitalier Universitaire Nantes, Nantes Université, Nantes, France
- IT Services, Centre Hospitalier Universitaire Nantes, Nantes Université, Nantes, France
| | - Adrien Bazoge
- Centre d'Investigation Clinique 1413, INSERM, Clinique des données, Pôle Hospitalo-Universitaire 11: Santé Publique, Centre Hospitalier Universitaire Nantes, Nantes Université, Nantes, France
- Unité Mixte de Recherche 6004, Laboratoire des Sciences du Numérique de Nantes, Centre National de Recherche Scientifique, École Centrale Nantes, Nantes Université, Nantes, France
| | - Pacôme Constant Dit Beaufils
- Centre d'Investigation Clinique 1413, INSERM, Clinique des données, Pôle Hospitalo-Universitaire 11: Santé Publique, Centre Hospitalier Universitaire Nantes, Nantes Université, Nantes, France
- l’institut du thorax, Service de neuroradiologie diagnostique et interventionnelle, Centre Hospitalier Universitaire Nantes, Nantes Université, Nantes, France
| | - Emilie Varey
- Centre d'Investigation Clinique 1413, INSERM, Clinique des données, Pôle Hospitalo-Universitaire 11: Santé Publique, Centre Hospitalier Universitaire Nantes, Nantes Université, Nantes, France
- Direction de la Recherche et de l’Innovation, Centre Hospitalier Universitaire Nantes, Nantes Université, Nantes, France
| | - Christophe Leux
- Service d'information médicale, Centre Hospitalier Universitaire Nantes, Nantes Université, Nantes, France
| | - Nicolas Mauduit
- Service d'information médicale, Centre Hospitalier Universitaire Nantes, Nantes Université, Nantes, France
| | - Matthieu Wargny
- Centre d'Investigation Clinique 1413, INSERM, Clinique des données, Pôle Hospitalo-Universitaire 11: Santé Publique, Centre Hospitalier Universitaire Nantes, Nantes Université, Nantes, France
| | - Pierre-Antoine Gourraud
- Centre d'Investigation Clinique 1413, INSERM, Clinique des données, Pôle Hospitalo-Universitaire 11: Santé Publique, Centre Hospitalier Universitaire Nantes, Nantes Université, Nantes, France
- INSERM Center for Research in Transplantation and Translational Immunology, Nantes Université, Nantes, France
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Koubiyr I, Yamamoto T, Blyau S, Kamroui RA, Mansencal B, Planche V, Petit L, Saranathan M, Casey R, Ruet A, Brochet B, Manjón JV, Dousset V, Coupé P, Tourdias T. Vulnerability of Thalamic Nuclei at CSF Interface During the Entire Course of Multiple Sclerosis. NEUROLOGY(R) NEUROIMMUNOLOGY & NEUROINFLAMMATION 2024; 11:e200222. [PMID: 38635941 PMCID: PMC11087027 DOI: 10.1212/nxi.0000000000200222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 01/19/2024] [Indexed: 04/20/2024]
Abstract
BACKGROUND AND OBJECTIVES Thalamic atrophy can be used as a proxy for neurodegeneration in multiple sclerosis (MS). Some data point toward thalamic nuclei that could be affected more than others. However, the dynamic of their changes during MS evolution and the mechanisms driving their differential alterations are still uncertain. METHODS We paired a large cohort of 1,123 patients with MS with the same number of healthy controls, all scanned with conventional 3D-T1 MRI. To highlight the main atrophic regions at the thalamic nuclei level, we validated a segmentation strategy consisting of deep learning-based synthesis of sequences, which were used for automatic multiatlas segmentation. Then, through a lifespan-based approach, we could model the dynamics of the 4 main thalamic nuclei groups. RESULTS All analyses converged toward a higher rate of atrophy for the posterior and medial groups compared with the anterior and lateral groups. We also demonstrated that focal MS white matter lesions were associated with atrophy of groups of nuclei when specifically located within the associated thalamocortical projections. The volumes of the most affected posterior group, but also of the anterior group, were better associated with clinical disability than the volume of the whole thalamus. DISCUSSION These findings point toward the thalamic nuclei adjacent to the third ventricle as more susceptible to neurodegeneration during the entire course of MS through potentiation of disconnection effects by regional factors. Because this information can be obtained even from standard T1-weighted MRI, this paves the way toward such an approach for future monitoring of patients with MS.
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Affiliation(s)
- Ismail Koubiyr
- From the University of Bordeaux (I.K., T.Y., A.R., B.B., V.D., T.T.), INSERM, Neurocentre Magendie, U1215; Neuroimagerie diagnostique et thérapeutique (S.B.), CHU de Bordeaux; University of Bordeaux (R.A.K., B.M., P.C.), CNRS, Bordeaux INP, LABRI, UMR5800, Talence; Univ. Bordeaux (V.P.), CNRS, IMN, UMR 5293; Groupe d'Imagerie Neurofonctionnelle (L.P.), Institut des Maladies Neurodégénératives CNRS UMR 5293, Bordeaux, France; Department of Medical Imaging (M.S.), The University of Arizona, Tucson; Université de Lyon (R.C.), Université Claude Bernard Lyon 1, France; and Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA) (J.V.M.), Universitat Politècnica de València, Spain
| | - Takayuki Yamamoto
- From the University of Bordeaux (I.K., T.Y., A.R., B.B., V.D., T.T.), INSERM, Neurocentre Magendie, U1215; Neuroimagerie diagnostique et thérapeutique (S.B.), CHU de Bordeaux; University of Bordeaux (R.A.K., B.M., P.C.), CNRS, Bordeaux INP, LABRI, UMR5800, Talence; Univ. Bordeaux (V.P.), CNRS, IMN, UMR 5293; Groupe d'Imagerie Neurofonctionnelle (L.P.), Institut des Maladies Neurodégénératives CNRS UMR 5293, Bordeaux, France; Department of Medical Imaging (M.S.), The University of Arizona, Tucson; Université de Lyon (R.C.), Université Claude Bernard Lyon 1, France; and Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA) (J.V.M.), Universitat Politècnica de València, Spain
| | - Simon Blyau
- From the University of Bordeaux (I.K., T.Y., A.R., B.B., V.D., T.T.), INSERM, Neurocentre Magendie, U1215; Neuroimagerie diagnostique et thérapeutique (S.B.), CHU de Bordeaux; University of Bordeaux (R.A.K., B.M., P.C.), CNRS, Bordeaux INP, LABRI, UMR5800, Talence; Univ. Bordeaux (V.P.), CNRS, IMN, UMR 5293; Groupe d'Imagerie Neurofonctionnelle (L.P.), Institut des Maladies Neurodégénératives CNRS UMR 5293, Bordeaux, France; Department of Medical Imaging (M.S.), The University of Arizona, Tucson; Université de Lyon (R.C.), Université Claude Bernard Lyon 1, France; and Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA) (J.V.M.), Universitat Politècnica de València, Spain
| | - Reda A Kamroui
- From the University of Bordeaux (I.K., T.Y., A.R., B.B., V.D., T.T.), INSERM, Neurocentre Magendie, U1215; Neuroimagerie diagnostique et thérapeutique (S.B.), CHU de Bordeaux; University of Bordeaux (R.A.K., B.M., P.C.), CNRS, Bordeaux INP, LABRI, UMR5800, Talence; Univ. Bordeaux (V.P.), CNRS, IMN, UMR 5293; Groupe d'Imagerie Neurofonctionnelle (L.P.), Institut des Maladies Neurodégénératives CNRS UMR 5293, Bordeaux, France; Department of Medical Imaging (M.S.), The University of Arizona, Tucson; Université de Lyon (R.C.), Université Claude Bernard Lyon 1, France; and Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA) (J.V.M.), Universitat Politècnica de València, Spain
| | - Boris Mansencal
- From the University of Bordeaux (I.K., T.Y., A.R., B.B., V.D., T.T.), INSERM, Neurocentre Magendie, U1215; Neuroimagerie diagnostique et thérapeutique (S.B.), CHU de Bordeaux; University of Bordeaux (R.A.K., B.M., P.C.), CNRS, Bordeaux INP, LABRI, UMR5800, Talence; Univ. Bordeaux (V.P.), CNRS, IMN, UMR 5293; Groupe d'Imagerie Neurofonctionnelle (L.P.), Institut des Maladies Neurodégénératives CNRS UMR 5293, Bordeaux, France; Department of Medical Imaging (M.S.), The University of Arizona, Tucson; Université de Lyon (R.C.), Université Claude Bernard Lyon 1, France; and Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA) (J.V.M.), Universitat Politècnica de València, Spain
| | - Vincent Planche
- From the University of Bordeaux (I.K., T.Y., A.R., B.B., V.D., T.T.), INSERM, Neurocentre Magendie, U1215; Neuroimagerie diagnostique et thérapeutique (S.B.), CHU de Bordeaux; University of Bordeaux (R.A.K., B.M., P.C.), CNRS, Bordeaux INP, LABRI, UMR5800, Talence; Univ. Bordeaux (V.P.), CNRS, IMN, UMR 5293; Groupe d'Imagerie Neurofonctionnelle (L.P.), Institut des Maladies Neurodégénératives CNRS UMR 5293, Bordeaux, France; Department of Medical Imaging (M.S.), The University of Arizona, Tucson; Université de Lyon (R.C.), Université Claude Bernard Lyon 1, France; and Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA) (J.V.M.), Universitat Politècnica de València, Spain
| | - Laurent Petit
- From the University of Bordeaux (I.K., T.Y., A.R., B.B., V.D., T.T.), INSERM, Neurocentre Magendie, U1215; Neuroimagerie diagnostique et thérapeutique (S.B.), CHU de Bordeaux; University of Bordeaux (R.A.K., B.M., P.C.), CNRS, Bordeaux INP, LABRI, UMR5800, Talence; Univ. Bordeaux (V.P.), CNRS, IMN, UMR 5293; Groupe d'Imagerie Neurofonctionnelle (L.P.), Institut des Maladies Neurodégénératives CNRS UMR 5293, Bordeaux, France; Department of Medical Imaging (M.S.), The University of Arizona, Tucson; Université de Lyon (R.C.), Université Claude Bernard Lyon 1, France; and Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA) (J.V.M.), Universitat Politècnica de València, Spain
| | - Manojkumar Saranathan
- From the University of Bordeaux (I.K., T.Y., A.R., B.B., V.D., T.T.), INSERM, Neurocentre Magendie, U1215; Neuroimagerie diagnostique et thérapeutique (S.B.), CHU de Bordeaux; University of Bordeaux (R.A.K., B.M., P.C.), CNRS, Bordeaux INP, LABRI, UMR5800, Talence; Univ. Bordeaux (V.P.), CNRS, IMN, UMR 5293; Groupe d'Imagerie Neurofonctionnelle (L.P.), Institut des Maladies Neurodégénératives CNRS UMR 5293, Bordeaux, France; Department of Medical Imaging (M.S.), The University of Arizona, Tucson; Université de Lyon (R.C.), Université Claude Bernard Lyon 1, France; and Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA) (J.V.M.), Universitat Politècnica de València, Spain
| | - Romain Casey
- From the University of Bordeaux (I.K., T.Y., A.R., B.B., V.D., T.T.), INSERM, Neurocentre Magendie, U1215; Neuroimagerie diagnostique et thérapeutique (S.B.), CHU de Bordeaux; University of Bordeaux (R.A.K., B.M., P.C.), CNRS, Bordeaux INP, LABRI, UMR5800, Talence; Univ. Bordeaux (V.P.), CNRS, IMN, UMR 5293; Groupe d'Imagerie Neurofonctionnelle (L.P.), Institut des Maladies Neurodégénératives CNRS UMR 5293, Bordeaux, France; Department of Medical Imaging (M.S.), The University of Arizona, Tucson; Université de Lyon (R.C.), Université Claude Bernard Lyon 1, France; and Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA) (J.V.M.), Universitat Politècnica de València, Spain
| | - Aurélie Ruet
- From the University of Bordeaux (I.K., T.Y., A.R., B.B., V.D., T.T.), INSERM, Neurocentre Magendie, U1215; Neuroimagerie diagnostique et thérapeutique (S.B.), CHU de Bordeaux; University of Bordeaux (R.A.K., B.M., P.C.), CNRS, Bordeaux INP, LABRI, UMR5800, Talence; Univ. Bordeaux (V.P.), CNRS, IMN, UMR 5293; Groupe d'Imagerie Neurofonctionnelle (L.P.), Institut des Maladies Neurodégénératives CNRS UMR 5293, Bordeaux, France; Department of Medical Imaging (M.S.), The University of Arizona, Tucson; Université de Lyon (R.C.), Université Claude Bernard Lyon 1, France; and Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA) (J.V.M.), Universitat Politècnica de València, Spain
| | - Bruno Brochet
- From the University of Bordeaux (I.K., T.Y., A.R., B.B., V.D., T.T.), INSERM, Neurocentre Magendie, U1215; Neuroimagerie diagnostique et thérapeutique (S.B.), CHU de Bordeaux; University of Bordeaux (R.A.K., B.M., P.C.), CNRS, Bordeaux INP, LABRI, UMR5800, Talence; Univ. Bordeaux (V.P.), CNRS, IMN, UMR 5293; Groupe d'Imagerie Neurofonctionnelle (L.P.), Institut des Maladies Neurodégénératives CNRS UMR 5293, Bordeaux, France; Department of Medical Imaging (M.S.), The University of Arizona, Tucson; Université de Lyon (R.C.), Université Claude Bernard Lyon 1, France; and Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA) (J.V.M.), Universitat Politècnica de València, Spain
| | - José V Manjón
- From the University of Bordeaux (I.K., T.Y., A.R., B.B., V.D., T.T.), INSERM, Neurocentre Magendie, U1215; Neuroimagerie diagnostique et thérapeutique (S.B.), CHU de Bordeaux; University of Bordeaux (R.A.K., B.M., P.C.), CNRS, Bordeaux INP, LABRI, UMR5800, Talence; Univ. Bordeaux (V.P.), CNRS, IMN, UMR 5293; Groupe d'Imagerie Neurofonctionnelle (L.P.), Institut des Maladies Neurodégénératives CNRS UMR 5293, Bordeaux, France; Department of Medical Imaging (M.S.), The University of Arizona, Tucson; Université de Lyon (R.C.), Université Claude Bernard Lyon 1, France; and Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA) (J.V.M.), Universitat Politècnica de València, Spain
| | - Vincent Dousset
- From the University of Bordeaux (I.K., T.Y., A.R., B.B., V.D., T.T.), INSERM, Neurocentre Magendie, U1215; Neuroimagerie diagnostique et thérapeutique (S.B.), CHU de Bordeaux; University of Bordeaux (R.A.K., B.M., P.C.), CNRS, Bordeaux INP, LABRI, UMR5800, Talence; Univ. Bordeaux (V.P.), CNRS, IMN, UMR 5293; Groupe d'Imagerie Neurofonctionnelle (L.P.), Institut des Maladies Neurodégénératives CNRS UMR 5293, Bordeaux, France; Department of Medical Imaging (M.S.), The University of Arizona, Tucson; Université de Lyon (R.C.), Université Claude Bernard Lyon 1, France; and Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA) (J.V.M.), Universitat Politècnica de València, Spain
| | - Pierrick Coupé
- From the University of Bordeaux (I.K., T.Y., A.R., B.B., V.D., T.T.), INSERM, Neurocentre Magendie, U1215; Neuroimagerie diagnostique et thérapeutique (S.B.), CHU de Bordeaux; University of Bordeaux (R.A.K., B.M., P.C.), CNRS, Bordeaux INP, LABRI, UMR5800, Talence; Univ. Bordeaux (V.P.), CNRS, IMN, UMR 5293; Groupe d'Imagerie Neurofonctionnelle (L.P.), Institut des Maladies Neurodégénératives CNRS UMR 5293, Bordeaux, France; Department of Medical Imaging (M.S.), The University of Arizona, Tucson; Université de Lyon (R.C.), Université Claude Bernard Lyon 1, France; and Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA) (J.V.M.), Universitat Politècnica de València, Spain
| | - Thomas Tourdias
- From the University of Bordeaux (I.K., T.Y., A.R., B.B., V.D., T.T.), INSERM, Neurocentre Magendie, U1215; Neuroimagerie diagnostique et thérapeutique (S.B.), CHU de Bordeaux; University of Bordeaux (R.A.K., B.M., P.C.), CNRS, Bordeaux INP, LABRI, UMR5800, Talence; Univ. Bordeaux (V.P.), CNRS, IMN, UMR 5293; Groupe d'Imagerie Neurofonctionnelle (L.P.), Institut des Maladies Neurodégénératives CNRS UMR 5293, Bordeaux, France; Department of Medical Imaging (M.S.), The University of Arizona, Tucson; Université de Lyon (R.C.), Université Claude Bernard Lyon 1, France; and Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA) (J.V.M.), Universitat Politècnica de València, Spain
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Voigt I, Fischer S, Proschmann U, Konofalska U, Richter P, Schlieter H, Berger T, Meuth SG, Hartung HP, Akgün K, Ziemssen T. Consensus quality indicators for monitoring multiple sclerosis. THE LANCET REGIONAL HEALTH. EUROPE 2024; 40:100891. [PMID: 38585674 PMCID: PMC10998202 DOI: 10.1016/j.lanepe.2024.100891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 03/13/2024] [Accepted: 03/13/2024] [Indexed: 04/09/2024]
Abstract
Multiple sclerosis (MS) as a chronic, degenerative autoimmune disease of the central nervous system has a longitudinal and heterogeneous course with increasing treatment options and risk profiles requiring constant monitoring of a growing number of parameters. Despite treatment guidelines, there is a lack of strategic and individualised monitoring pathways, including respective quality indicators (QIs). To address this, we systematically developed transparent, traceable, and measurable QIs for MS monitoring. Through literature review, expert discussions, and consensus-building, existing QIs were identified and refined. In a two-stage online Delphi process involving MS specialists (on average 53 years old and with 25 years of professional experience), the QIs were evaluated for content, clarity, and intelligibility, resulting in a set of 24 QIs and checklists to assess the quality of care. The final QIs provide a structured approach to document, monitor, and enhance the quality of care for people with MS across their treatment journey.
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Affiliation(s)
- Isabel Voigt
- Center of Clinical Neuroscience, Department of Neurology, Medical Faculty and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Fetscherstraße 74, Dresden 01307, Germany
| | - Stefanie Fischer
- Center of Clinical Neuroscience, Department of Neurology, Medical Faculty and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Fetscherstraße 74, Dresden 01307, Germany
| | - Undine Proschmann
- Center of Clinical Neuroscience, Department of Neurology, Medical Faculty and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Fetscherstraße 74, Dresden 01307, Germany
| | - Urszula Konofalska
- Center of Clinical Neuroscience, Department of Neurology, Medical Faculty and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Fetscherstraße 74, Dresden 01307, Germany
| | - Peggy Richter
- Research Group Digital Health, Faculty of Business and Economics, TUD Dresden University of Technology, Dresden 01062, Germany
| | - Hannes Schlieter
- Research Group Digital Health, Faculty of Business and Economics, TUD Dresden University of Technology, Dresden 01062, Germany
| | - Thomas Berger
- Department of Neurology, Medical University of Vienna, Währinger Gürtel 18-20, Vienna 1090, Austria
- Comprehensive Center for Clinical Neurosciences & Mental Health, Medical University of Vienna, Währinger Gürtel 18-20, Vienna 1090, Austria
| | - Sven G. Meuth
- Department of Neurology, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-University Düsseldorf, Moorenstr. 5, Düsseldorf 40225, Germany
| | - Hans-Peter Hartung
- Department of Neurology, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-University Düsseldorf, Moorenstr. 5, Düsseldorf 40225, Germany
| | - Katja Akgün
- Center of Clinical Neuroscience, Department of Neurology, Medical Faculty and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Fetscherstraße 74, Dresden 01307, Germany
| | - Tjalf Ziemssen
- Center of Clinical Neuroscience, Department of Neurology, Medical Faculty and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Fetscherstraße 74, Dresden 01307, Germany
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Levraut M, Gavoille A, Landes-Chateau C, Cohen M, Bresch S, Seitz-Polski B, Mondot L, Lebrun-Frenay C. Kappa Free Light Chain Index Predicts Disease Course in Clinically and Radiologically Isolated Syndromes. NEUROLOGY(R) NEUROIMMUNOLOGY & NEUROINFLAMMATION 2023; 10:e200156. [PMID: 37640543 PMCID: PMC10462056 DOI: 10.1212/nxi.0000000000200156] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 07/19/2023] [Indexed: 08/31/2023]
Abstract
BACKGROUND AND OBJECTIVES To evaluate whether the kappa free light chain index (K-index) can predict the occurrence of new T2-weighted MRI lesions (T2L) and clinical events in clinically isolated syndrome (CIS) and radiologically isolated syndrome (RIS). METHODS All consecutive patients presenting for the diagnostic workup, including CSF analysis, of clinical and/or MRI suspicion of multiple sclerosis (MS) since May 1, 2018, were evaluated. All patients diagnosed with CIS and RIS with at least 1-year follow-up were included. Clinical events and new T2L were collected during follow-up. The K-index performances in predicting new T2L and a clinical event were evaluated using time-dependent ROC analyses. The time to clinical event or new T2L was estimated using survival analysis according to the binarized K-index using an independent cutoff of 8.9, and the ability of each variable to predict outcomes was compared using the Harrell c-index. RESULTS One hundred and eighty two patients (146 CIS and 36 RIS, median age 39 [30; 48] y-o, 70% females) were included with a median follow-up of 21 [13, 33] months. One hundred five (58%) patients (85 CIS and 20 RIS) experienced new T2L, and 28 (15%; 21 CIS and 7 RIS) experienced a clinical event. The K-index could predict new T2L over time in CIS (area under the curve [AUC] ranging from 0.86 to 0.96) and in RIS (AUC ranging from 0.84 to 0.54) but also a clinical event in CIS (AUC ranging from 0.75 to 0.87). Compared with oligoclonal bands (OCBs), the K-index had a better sensitivity and a slight lower specificity in predicting new T2L and clinical events in both populations. In the predictive model, the K-index was the variable that best predict new T2L in both CIS and RIS but also clinical events in CIS (c-index ranging from 0.70 to 0.77), better than the other variables, including OCB. DISCUSSION This study provides evidence that the K-index predicts new T2L in CIS and RIS but also clinical attack in patients with CIS. We suggest adding the K-index in the further MS diagnosis criteria revisions as a dissemination-in-time biomarker.
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Affiliation(s)
- Michael Levraut
- From the URRIS (M.L., C.L.-C., M.C., L.M., C.L.-F.), Unité Mixte de Recherche Clinique Côte d'Azur (UMR2CA); Service de Médecine Interne (M.L.), Hôpital l'Archet 1, Centre Hospitalier Universitaire de Nice; Service de Biostatistique-Bioinformatique (A.G.), Hospices Civils de Lyon; Service de Neurologie (A.G.), Sclérose en Plaques, Pathologies de La Myéline et Neuro-inflammation, Hôpital Neurologique Pierre-Wertheimer, Hospices Civils de Lyon, Bron; Service de Neurologie (M.C., S.B., C.L.-F.), Centre de Ressource et Compétence - Sclérose En Plaques, Hôpital Pasteur 2; ImmunoPredict (B.S.-P.), Unité Mixte de Recherche Clinique Côte d'Azur (UMR2CA); Laboratoire d'Immunologie (B.S.-P.), Hôpital l'Archet 1; and Service de Radiologie (L.M.), Hôpital Pasteur 2, Centre Hospitalier Universitaire de Nice, France.
| | - Antoine Gavoille
- From the URRIS (M.L., C.L.-C., M.C., L.M., C.L.-F.), Unité Mixte de Recherche Clinique Côte d'Azur (UMR2CA); Service de Médecine Interne (M.L.), Hôpital l'Archet 1, Centre Hospitalier Universitaire de Nice; Service de Biostatistique-Bioinformatique (A.G.), Hospices Civils de Lyon; Service de Neurologie (A.G.), Sclérose en Plaques, Pathologies de La Myéline et Neuro-inflammation, Hôpital Neurologique Pierre-Wertheimer, Hospices Civils de Lyon, Bron; Service de Neurologie (M.C., S.B., C.L.-F.), Centre de Ressource et Compétence - Sclérose En Plaques, Hôpital Pasteur 2; ImmunoPredict (B.S.-P.), Unité Mixte de Recherche Clinique Côte d'Azur (UMR2CA); Laboratoire d'Immunologie (B.S.-P.), Hôpital l'Archet 1; and Service de Radiologie (L.M.), Hôpital Pasteur 2, Centre Hospitalier Universitaire de Nice, France
| | - Cassandre Landes-Chateau
- From the URRIS (M.L., C.L.-C., M.C., L.M., C.L.-F.), Unité Mixte de Recherche Clinique Côte d'Azur (UMR2CA); Service de Médecine Interne (M.L.), Hôpital l'Archet 1, Centre Hospitalier Universitaire de Nice; Service de Biostatistique-Bioinformatique (A.G.), Hospices Civils de Lyon; Service de Neurologie (A.G.), Sclérose en Plaques, Pathologies de La Myéline et Neuro-inflammation, Hôpital Neurologique Pierre-Wertheimer, Hospices Civils de Lyon, Bron; Service de Neurologie (M.C., S.B., C.L.-F.), Centre de Ressource et Compétence - Sclérose En Plaques, Hôpital Pasteur 2; ImmunoPredict (B.S.-P.), Unité Mixte de Recherche Clinique Côte d'Azur (UMR2CA); Laboratoire d'Immunologie (B.S.-P.), Hôpital l'Archet 1; and Service de Radiologie (L.M.), Hôpital Pasteur 2, Centre Hospitalier Universitaire de Nice, France
| | - Mikael Cohen
- From the URRIS (M.L., C.L.-C., M.C., L.M., C.L.-F.), Unité Mixte de Recherche Clinique Côte d'Azur (UMR2CA); Service de Médecine Interne (M.L.), Hôpital l'Archet 1, Centre Hospitalier Universitaire de Nice; Service de Biostatistique-Bioinformatique (A.G.), Hospices Civils de Lyon; Service de Neurologie (A.G.), Sclérose en Plaques, Pathologies de La Myéline et Neuro-inflammation, Hôpital Neurologique Pierre-Wertheimer, Hospices Civils de Lyon, Bron; Service de Neurologie (M.C., S.B., C.L.-F.), Centre de Ressource et Compétence - Sclérose En Plaques, Hôpital Pasteur 2; ImmunoPredict (B.S.-P.), Unité Mixte de Recherche Clinique Côte d'Azur (UMR2CA); Laboratoire d'Immunologie (B.S.-P.), Hôpital l'Archet 1; and Service de Radiologie (L.M.), Hôpital Pasteur 2, Centre Hospitalier Universitaire de Nice, France
| | - Saskia Bresch
- From the URRIS (M.L., C.L.-C., M.C., L.M., C.L.-F.), Unité Mixte de Recherche Clinique Côte d'Azur (UMR2CA); Service de Médecine Interne (M.L.), Hôpital l'Archet 1, Centre Hospitalier Universitaire de Nice; Service de Biostatistique-Bioinformatique (A.G.), Hospices Civils de Lyon; Service de Neurologie (A.G.), Sclérose en Plaques, Pathologies de La Myéline et Neuro-inflammation, Hôpital Neurologique Pierre-Wertheimer, Hospices Civils de Lyon, Bron; Service de Neurologie (M.C., S.B., C.L.-F.), Centre de Ressource et Compétence - Sclérose En Plaques, Hôpital Pasteur 2; ImmunoPredict (B.S.-P.), Unité Mixte de Recherche Clinique Côte d'Azur (UMR2CA); Laboratoire d'Immunologie (B.S.-P.), Hôpital l'Archet 1; and Service de Radiologie (L.M.), Hôpital Pasteur 2, Centre Hospitalier Universitaire de Nice, France
| | - Barbara Seitz-Polski
- From the URRIS (M.L., C.L.-C., M.C., L.M., C.L.-F.), Unité Mixte de Recherche Clinique Côte d'Azur (UMR2CA); Service de Médecine Interne (M.L.), Hôpital l'Archet 1, Centre Hospitalier Universitaire de Nice; Service de Biostatistique-Bioinformatique (A.G.), Hospices Civils de Lyon; Service de Neurologie (A.G.), Sclérose en Plaques, Pathologies de La Myéline et Neuro-inflammation, Hôpital Neurologique Pierre-Wertheimer, Hospices Civils de Lyon, Bron; Service de Neurologie (M.C., S.B., C.L.-F.), Centre de Ressource et Compétence - Sclérose En Plaques, Hôpital Pasteur 2; ImmunoPredict (B.S.-P.), Unité Mixte de Recherche Clinique Côte d'Azur (UMR2CA); Laboratoire d'Immunologie (B.S.-P.), Hôpital l'Archet 1; and Service de Radiologie (L.M.), Hôpital Pasteur 2, Centre Hospitalier Universitaire de Nice, France
| | - Lydiane Mondot
- From the URRIS (M.L., C.L.-C., M.C., L.M., C.L.-F.), Unité Mixte de Recherche Clinique Côte d'Azur (UMR2CA); Service de Médecine Interne (M.L.), Hôpital l'Archet 1, Centre Hospitalier Universitaire de Nice; Service de Biostatistique-Bioinformatique (A.G.), Hospices Civils de Lyon; Service de Neurologie (A.G.), Sclérose en Plaques, Pathologies de La Myéline et Neuro-inflammation, Hôpital Neurologique Pierre-Wertheimer, Hospices Civils de Lyon, Bron; Service de Neurologie (M.C., S.B., C.L.-F.), Centre de Ressource et Compétence - Sclérose En Plaques, Hôpital Pasteur 2; ImmunoPredict (B.S.-P.), Unité Mixte de Recherche Clinique Côte d'Azur (UMR2CA); Laboratoire d'Immunologie (B.S.-P.), Hôpital l'Archet 1; and Service de Radiologie (L.M.), Hôpital Pasteur 2, Centre Hospitalier Universitaire de Nice, France
| | - Christine Lebrun-Frenay
- From the URRIS (M.L., C.L.-C., M.C., L.M., C.L.-F.), Unité Mixte de Recherche Clinique Côte d'Azur (UMR2CA); Service de Médecine Interne (M.L.), Hôpital l'Archet 1, Centre Hospitalier Universitaire de Nice; Service de Biostatistique-Bioinformatique (A.G.), Hospices Civils de Lyon; Service de Neurologie (A.G.), Sclérose en Plaques, Pathologies de La Myéline et Neuro-inflammation, Hôpital Neurologique Pierre-Wertheimer, Hospices Civils de Lyon, Bron; Service de Neurologie (M.C., S.B., C.L.-F.), Centre de Ressource et Compétence - Sclérose En Plaques, Hôpital Pasteur 2; ImmunoPredict (B.S.-P.), Unité Mixte de Recherche Clinique Côte d'Azur (UMR2CA); Laboratoire d'Immunologie (B.S.-P.), Hôpital l'Archet 1; and Service de Radiologie (L.M.), Hôpital Pasteur 2, Centre Hospitalier Universitaire de Nice, France
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6
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Hodel J, Vernooij MW, Beyer MK, Severino M, Leclerc X, Créange A, Wahab A, Badat N, Tolédano S, van den Hauwe L, Ramos A, Castellano A, Krainik A, Yousry T, Rovira À. Multiple sclerosis imaging in clinical practice: a European-wide survey of 428 centers and conclusions by the ESNR Working Group. Eur Radiol 2023; 33:7025-7033. [PMID: 37199796 DOI: 10.1007/s00330-023-09701-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 01/23/2023] [Accepted: 03/09/2023] [Indexed: 05/19/2023]
Abstract
OBJECTIVES To evaluate compliance with the available recommendations, we assessed the current clinical practice of imaging in the evaluation of multiple sclerosis (MS). METHODS An online questionnaire was emailed to all members and affiliates. Information was gathered on applied MR imaging protocols, gadolinium-based contrast agents (GBCA) use and image analysis. We compared the survey results with the Magnetic Resonance Imaging in MS (MAGNIMS) recommendations considered as the reference standard. RESULTS A total of 428 entries were received from 44 countries. Of these, 82% of responders were neuroradiologists. 55% performed more than ten scans per week for MS imaging. The systematic use of 3 T is rare (18%). Over 90% follow specific protocol recommendations with 3D FLAIR, T2-weighted and DWI being the most frequently used sequences. Over 50% use SWI at initial diagnosis and 3D gradient-echo T1-weighted imaging is the most used MRI sequence for pre- and post-contrast imaging. Mismatches with recommendations were identified including the use of only one sagittal T2-weighted sequence for spinal cord imaging, the systematic use of GBCA at follow-up (over 30% of institutions), a delay time shorter than 5 min after GBCA administration (25%) and an inadequate follow-up duration in pediatric acute disseminated encephalomyelitis (80%). There is scarce use of automated software to compare images or to assess atrophy (13% and 7%). The proportions do not differ significantly between academic and non-academic institutions. CONCLUSIONS While current practice in MS imaging is rather homogeneous across Europe, our survey suggests that recommendations are only partially followed. CLINICAL RELEVANCE STATEMENT Hurdles were identified, mainly in the areas of GBCA use, spinal cord imaging, underuse of specific MRI sequences and monitoring strategies. This work will help radiologists to identify the mismatches between their own practices and the recommendations and act upon them. KEY POINTS • While current practice in MS imaging is rather homogeneous across Europe, our survey suggests that available recommendations are only partially followed. • Several hurdles have been identified through the survey that mainly lies in the areas of GBCA use, spinal cord imaging, underuse of specific MRI sequences and monitoring strategies.
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Affiliation(s)
- Jérôme Hodel
- Department of Radiology, Groupe Hospitalier Paris-Saint Joseph, Paris, France.
| | - Meike W Vernooij
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Mona K Beyer
- Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | | | - Xavier Leclerc
- Department of Neuroradiology, Lille University Hospital, Lille, France
| | - Alain Créange
- Department of Neurology, AP-HP, Henri Mondor University Hospital, Université Paris Est Créteil, 4391, Creteil, EA, France
| | - Abir Wahab
- Department of Neurology, AP-HP, Henri Mondor University Hospital, Université Paris Est Créteil, 4391, Creteil, EA, France
| | - Neesmah Badat
- Department of Radiology, Groupe Hospitalier Paris-Saint Joseph, Paris, France
| | - Sarah Tolédano
- Department of Radiology, Groupe Hospitalier Paris-Saint Joseph, Paris, France
| | - Luc van den Hauwe
- Department of Radiology, Antwerp University Hospital, Antwerp, Belgium
| | - Ana Ramos
- Neuroradiology, Department of Radiology, University Hospital, 12 de Octubre, Madrid, Spain
| | - Antonella Castellano
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele and Vita-Salute San Raffaele University, 20132, Milan, Italy
| | - Alexandre Krainik
- Department of Neuroradiology, University Hospital of Grenoble, Grenoble, France
| | - Tarek Yousry
- Lysholm Department of Neuroradiology, UCLH National Hospital for Neurology and Neurosurgery, London, UK
- Neuroradiological Academic Unit, University College London Queen Square Institute of Neurology, University College London, London, UK
| | - Àlex Rovira
- Section of Neuroradiology, Department of Radiology, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
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7
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Abou Mrad T, Naja K, Khoury SJ, Hannoun S. Central vein sign and paramagnetic rim sign: From radiologically isolated syndrome to multiple sclerosis. Eur J Neurol 2023; 30:2912-2918. [PMID: 37350369 DOI: 10.1111/ene.15922] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Revised: 06/02/2023] [Accepted: 06/07/2023] [Indexed: 06/24/2023]
Abstract
The widespread use of magnetic resonance imaging (MRI) has led to an increase in incidental findings in the central nervous system. Radiologically isolated syndrome (RIS) is a condition where imaging reveals lesions suggestive of demyelinating disease without any clinical episodes consistent with multiple sclerosis (MS). The prognosis for RIS patients is uncertain, with some remaining asymptomatic while others progress to MS. Several risk factors for disease progression have been identified, including male sex, younger age at diagnosis, and spinal cord lesions. This article reviews two promising biomarkers, the central vein sign (CVS) and the paramagnetic rim sign (PRS), and their potential role in the diagnosis and prognosis of MS and RIS. Both CVS and PRS have been shown to be accurate diagnostic markers in MS, with high sensitivity and specificity, and have been useful in distinguishing MS from other disorders. Further research is needed to validate these findings and determine the clinical utility of these biomarkers in routine practice.
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Affiliation(s)
- Tatiana Abou Mrad
- Faculty of Medicine, American University of Beirut Medical Center, Beirut, Lebanon
| | - Kim Naja
- Faculty of Medicine, American University of Beirut Medical Center, Beirut, Lebanon
| | - Samia J Khoury
- Nehme and Therese Tohme Multiple Sclerosis Center, Faculty of Medicine, American University of Beirut Medical Center, Beirut, Lebanon
| | - Salem Hannoun
- Medical Imaging Sciences Program, Division of Health Professions, Faculty of Health Sciences, American University of Beirut, Beirut, Lebanon
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8
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Schlaeger S, Li HB, Baum T, Zimmer C, Moosbauer J, Byas S, Mühlau M, Wiestler B, Finck T. Longitudinal Assessment of Multiple Sclerosis Lesion Load With Synthetic Magnetic Resonance Imaging-A Multicenter Validation Study. Invest Radiol 2023; 58:320-326. [PMID: 36730638 DOI: 10.1097/rli.0000000000000938] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
INTRODUCTION Double inversion recovery (DIR) has been validated as a sensitive magnetic resonance imaging (MRI) contrast in multiple sclerosis (MS). Deep learning techniques can use basic input data to generate synthetic DIR (synthDIR) images that are on par with their acquired counterparts. As assessment of longitudinal MRI data is paramount in MS diagnostics, our study's purpose is to evaluate the utility of synthDIR longitudinal subtraction imaging for detection of disease progression in a multicenter data set of MS patients. METHODS We implemented a previously established generative adversarial network to synthesize DIR from input T1-weighted and fluid-attenuated inversion recovery (FLAIR) sequences for 214 MRI data sets from 74 patients and 5 different centers. One hundred and forty longitudinal subtraction maps of consecutive scans (follow-up scan-preceding scan) were generated for both acquired FLAIR and synthDIR. Two readers, blinded to the image origin, independently quantified newly formed lesions on the FLAIR and synthDIR subtraction maps, grouped into specific locations as outlined in the McDonald criteria. RESULTS Both readers detected significantly more newly formed MS-specific lesions in the longitudinal subtractions of synthDIR compared with acquired FLAIR (R1: 3.27 ± 0.60 vs 2.50 ± 0.69 [ P = 0.0016]; R2: 3.31 ± 0.81 vs 2.53 ± 0.72 [ P < 0.0001]). Relative gains in detectability were most pronounced in juxtacortical lesions (36% relative gain in lesion counts-pooled for both readers). In 5% of the scans, synthDIR subtraction maps helped to identify a disease progression missed on FLAIR subtraction maps. CONCLUSIONS Generative adversarial networks can generate high-contrast DIR images that may improve the longitudinal follow-up assessment in MS patients compared with standard sequences. By detecting more newly formed MS lesions and increasing the rates of detected disease activity, our methodology promises to improve clinical decision-making.
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Affiliation(s)
- Sarah Schlaeger
- From the Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum Rechts der Isar
| | | | - Thomas Baum
- From the Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum Rechts der Isar
| | - Claus Zimmer
- From the Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum Rechts der Isar
| | | | | | - Mark Mühlau
- Department of Neurology, School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Benedikt Wiestler
- From the Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum Rechts der Isar
| | - Tom Finck
- From the Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum Rechts der Isar
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Commowick O, Combès B, Cervenansky F, Dojat M. Editorial: Automatic methods for multiple sclerosis new lesions detection and segmentation. Front Neurosci 2023; 17:1176625. [PMID: 36998735 PMCID: PMC10043498 DOI: 10.3389/fnins.2023.1176625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 03/01/2023] [Indexed: 03/15/2023] Open
Affiliation(s)
- Olivier Commowick
- Empenn INSERM U1228, CNRS UMR6074, Inria, University of Rennes I, Rennes, France
| | - Benoît Combès
- Empenn INSERM U1228, CNRS UMR6074, Inria, University of Rennes I, Rennes, France
| | - Frédéric Cervenansky
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, Lyon, France
| | - Michel Dojat
- Univ Grenoble Alpes, Inserm, U1216, CHU Grenoble Alpes, Grenoble Institut Neurosciences, GIN, Grenoble, France
- *Correspondence: Michel Dojat
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10
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Facial emotion impairment in multiple sclerosis is linked to modifying observation strategies of emotional faces. Mult Scler Relat Disord 2023; 69:104439. [PMID: 36525898 DOI: 10.1016/j.msard.2022.104439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 11/18/2022] [Accepted: 12/01/2022] [Indexed: 12/12/2022]
Abstract
BACKGROUND Facial emotion recognition (FER) may be impaired in patients with multiple sclerosis (MS). Nevertheless, the literature is heterogeneous, with studies not highlighting this kind of impairment. Moreover, most studies have not explored differences between MS spectrum disorders (radiologically isolated syndrome (RIS), clinically-isolated syndrome (CIS), relapsing-remitting (RRMS), and progressive (primary - (PPMS) and secondary - (SPMS)). One hypothesis would be that FER impairment results from an alteration of eye-gaze strategies while observing emotional faces. Consequently, a FER deficit would be found in MS patients for whom these observation strategies would be disturbed and more frequent in the progressive forms. METHODS We prospectively enroled 52 patients (10 RIS, 10 CIS, 12RRMS, 10 SPMS, 10 PPMS) and 23 healthy controls (HC) to assess FER using Ekman Faces Test. Eye movements (number and duration of fixations) were recorded with an eye-tracking device. RESULTS 21% of the MS participants had significant FER impairment. This impairment was observed in all phenotypes. In progressive forms, FER impairment was more frequent, more severe, and associated with modified emotional face observation strategies. MS participants with significant FER impairment had significantly more modification of eye-gaze strategies during observation of expressive faces than MS participants without FER impairment. CONCLUSION FER impairment seems to be linked to a deficit of attention orientation in MS. Remediation of eye-gaze strategies during observation of emotional faces could be beneficial, as observed in other neurological diseases.
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11
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El Ayoubi NK, Sabbagh HM, Bou Rjeily N, Hannoun S, Khoury SJ. Rate of Retinal Layer Thinning as a Biomarker for Conversion to Progressive Disease in Multiple Sclerosis. NEUROLOGY(R) NEUROIMMUNOLOGY & NEUROINFLAMMATION 2022; 9:9/6/e200030. [PMID: 36229190 PMCID: PMC9562042 DOI: 10.1212/nxi.0000000000200030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 08/01/2022] [Indexed: 11/05/2022]
Abstract
Background and Objectives The diagnosis of secondary progressive multiple sclerosis (SPMS) is often delayed because of the lack of objective clinical tools, which increases the diagnostic uncertainty and hampers the therapeutic development in progressive multiple sclerosis (MS). Optical coherence tomography (OCT) has been proposed as a promising biomarker of progressive neurodegeneration. To explore longitudinal changes in the thicknesses of retinal layers on OCT in individuals with relapsing-remitting MS (RRMS) who converted to SPMS vs matched patients with RRMS who did not convert to SPMS. Our hypothesis is that the 2 cohorts exhibit different rates of retinal thinning. Methods From our prospective observational cohort of patients with MS at the American University of Beirut, we selected patients with RRMS who converted to SPMS during the observation period and patients with RRMS, matched by age, disease duration, and Expanded Disability Status Scale (EDSS) at the first visit. Baseline retinal measurements were obtained using spectral domain OCT, and all patients underwent clinical and OCT evaluation every 6–12 months on average throughout the study period (mean = 4 years). Mixed-effect regression models were used to assess the annualized rates of retinal changes and the differences between the 2 groups and between converters to SPMS before and after their conversion. Results A total of 61 participants were selected (21 SPMS and 40 RRMS). There were no differences in baseline characteristics and retinal measurements between the 2 groups. The annualized rates of thinning of all retinal layers, except for macular volume, were greater in converters before conversion compared with nonconverters by 112% for peripapillary retinal nerve fiber layer (p = 0.008), 344% for tRNFL (p < 0.0001), and 82% for cell-inner plexiform layer (GCIPL) (p = 0.002). When comparing the annualized rate of thinning for the same patients with SPMS before and after conversion, no significant differences were found except for tRNFL and GCIPL with slower thinning rates postconversion (46% and 68%, respectively). Discussion Patients who converted to SPMS exhibited faster retinal thinning as reflected on OCT. Longitudinal assessment of retinal thinning could confirm the transition to SPMS and help with the therapeutic decision making for patients with MS with clinical suspicion of disease progression.
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12
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Dorcet G, Migné H, Biotti D, Bost C, Lerebours F, Ciron J, Treiner E. Early B cells repopulation in multiple sclerosis patients treated with rituximab is not predictive of a risk of relapse or clinical progression. J Neurol 2022; 269:5443-5453. [PMID: 35652942 PMCID: PMC9159933 DOI: 10.1007/s00415-022-11197-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 05/12/2022] [Accepted: 05/15/2022] [Indexed: 11/29/2022]
Abstract
BACKGROUND It is currently unknown whether early B cell reconstitution (EBR) in MS patients under rituximab is associated with a risk of relapse or progression. OBJECTIVES Analyzing EBR in rituximab-treated patients and its putative association with clinical findings. METHODS Prospective lymphocytes immunophenotyping was performed in a monocentric cohort of MS patients treated by rituximab for 2 years. EBR was defined when B cells concentration was > 5 cells/mm3. B cell subsets were retrospectively associated with clinical data. Clinical and radiological monitoring included relapses, EDSS (Expanded Disability Status Scale), SDMT (Symbol Digit Modalities Test), and MRI. RESULTS 182 patients were analyzed (61 remitting-relapsing and 121 progressive-active). 38.5% experienced EBR at least once, but very few (7/182) showed systematic reconstitution. Most patients remained stable upon treatment, regardless of the occurrence of EBR. Dynamics of B cell reconstitution featured increased naïve/transitional B cells, and decreased memory subsets. Homeostasis of the B cell compartment differed at baseline between patients experiencing or not EBR upon treatment. In patients with EBR, reciprocal dynamics of transitional and pro-inflammatory double-negative B cell subsets was associated with better response to rituximab treatment. CONCLUSION EBR is common in rituximab-treated MS patients and is not associated with clinical disease activity. EBR in the peripheral blood may reflect regulatory immunological phenomena in subgroup of patients.
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Affiliation(s)
- Guillaume Dorcet
- Department of Neurology, CRC-SEP, University Hospital of Toulouse, Toulouse, France.,INSERM U1291-CNRS 5051, INFINITy, Toulouse, France
| | - Hugo Migné
- Immunology Laboratory, Biology Department, University Hospital of Toulouse, Toulouse, France
| | - Damien Biotti
- Department of Neurology, CRC-SEP, University Hospital of Toulouse, Toulouse, France.,INSERM U1291-CNRS 5051, INFINITy, Toulouse, France
| | - Chloé Bost
- INSERM U1291-CNRS 5051, INFINITy, Toulouse, France.,Immunology Laboratory, Biology Department, University Hospital of Toulouse, Toulouse, France
| | - Fleur Lerebours
- Department of Neurology, CRC-SEP, University Hospital of Toulouse, Toulouse, France
| | - Jonathan Ciron
- Department of Neurology, CRC-SEP, University Hospital of Toulouse, Toulouse, France.,INSERM U1291-CNRS 5051, INFINITy, Toulouse, France
| | - Emmanuel Treiner
- INSERM U1291-CNRS 5051, INFINITy, Toulouse, France. .,Immunology Laboratory, Biology Department, University Hospital of Toulouse, Toulouse, France.
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13
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Tran P, Thoprakarn U, Gourieux E, Dos Santos CL, Cavedo E, Guizard N, Cotton F, Krolak-Salmon P, Delmaire C, Heidelberg D, Pyatigorskaya N, Ströer S, Dormont D, Martini JB, Chupin M. Automatic segmentation of white matter hyperintensities: validation and comparison with state-of-the-art methods on both Multiple Sclerosis and elderly subjects. Neuroimage Clin 2022; 33:102940. [PMID: 35051744 PMCID: PMC8896108 DOI: 10.1016/j.nicl.2022.102940] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 12/15/2021] [Accepted: 01/06/2022] [Indexed: 11/27/2022]
Abstract
Automatic segmentation of MS lesions and age-related WMH from 3D T1 and T2-FLAIR. Comparison to consensus show improved performance of WHASA-3D compared to WHASA. WHASA-3D outperforms available state-of-the-art methods with their default settings. WHASA-3D could be a useful tool for clinical practice and clinical trials.
Different types of white matter hyperintensities (WMH) can be observed through MRI in the brain and spinal cord, especially Multiple Sclerosis (MS) lesions for patients suffering from MS and age-related WMH for subjects with cognitive disorders and/or elderly people. To better diagnose and monitor the disease progression, the quantitative evaluation of WMH load has proven to be useful for clinical routine and trials. Since manual delineation for WMH segmentation is highly time-consuming and suffers from intra and inter observer variability, several methods have been proposed to automatically segment either MS lesions or age-related WMH, but none is validated on both WMH types. Here, we aim at proposing the White matter Hyperintensities Automatic Segmentation Algorithm adapted to 3D T2-FLAIR datasets (WHASA-3D), a fast and robust automatic segmentation tool designed to be implemented in clinical practice for the detection of both MS lesions and age-related WMH in the brain, using both 3D T1-weighted and T2-FLAIR images. In order to increase its robustness for MS lesions, WHASA-3D expands the original WHASA method, which relies on the coupling of non-linear diffusion framework and watershed parcellation, where regions considered as WMH are selected based on intensity and location characteristics, and finally refined with geodesic dilation. The previous validation was performed on 2D T2-FLAIR and subjects with cognitive disorders and elderly subjects. 60 subjects from a heterogeneous database of dementia patients, multiple sclerosis patients and elderly subjects with multiple MRI scanners and a wide range of lesion loads were used to evaluate WHASA and WHASA-3D through volume and spatial agreement in comparison with consensus reference segmentations. In addition, a direct comparison on the MS database with six available supervised and unsupervised state-of-the-art WMH segmentation methods (LST-LGA and LPA, Lesion-TOADS, lesionBrain, BIANCA and nicMSlesions) with default and optimised settings (when feasible) was conducted. WHASA-3D confirmed an improved performance with respect to WHASA, achieving a better spatial overlap (Dice) (0.67 vs 0.63), a reduced absolute volume error (AVE) (3.11 vs 6.2 mL) and an increased volume agreement (intraclass correlation coefficient, ICC) (0.96 vs 0.78). Compared to available state-of-the-art algorithms on the MS database, WHASA-3D outperformed both unsupervised and supervised methods when used with their default settings, showing the highest volume agreement (ICC = 0.95) as well as the highest average Dice (0.58). Optimising and/or retraining LST-LGA, BIANCA and nicMSlesions, using a subset of the MS database as training set, resulted in improved performances on the remaining testing set (average Dice: LST-LGA default/optimized = 0.41/0.51, BIANCA default/optimized = 0.22/0.39, nicMSlesions default/optimized = 0.17/0.63, WHASA-3D = 0.58). Evaluation and comparison results suggest that WHASA-3D is a reliable and easy-to-use method for the automated segmentation of white matter hyperintensities, for both MS lesions and age-related WMH. Further validation on larger datasets would be useful to confirm these first findings.
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Affiliation(s)
- Philippe Tran
- Qynapse, Paris, France; Equipe-projet ARAMIS, ICM, CNRS UMR 7225, Inserm U1117, Sorbonne Université UMR_S 1127, Centre Inria de Paris, Groupe Hospitalier Pitié-Salpêtrière Charles Foix, Faculté de Médecine Sorbonne Université, Paris, France.
| | | | - Emmanuelle Gourieux
- CATI, ICM, CNRS UMR 7225, Inserm U1117, Sorbonne Université UMR_S 1127, Paris, France; NeuroSpin, CEA, Saclay, France
| | | | | | | | - François Cotton
- Service de Radiologie, Centre Hospitalier Lyon-Sud, Hospices Civils de Lyon, Pierre-Bénite, France; Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, F-69495, Pierre-Bénite, France
| | - Pierre Krolak-Salmon
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, F-69495, Pierre-Bénite, France; Clinical and Research Memory Centre of Lyon, Hospices Civils de Lyon, Lyon, France; INSERM, U1028, UMR CNRS 5292, Lyon Neuroscience Research Center, Lyon, France
| | | | - Damien Heidelberg
- Service de Radiologie, Centre Hospitalier Lyon-Sud, Hospices Civils de Lyon, Pierre-Bénite, France
| | - Nadya Pyatigorskaya
- Department of Neuroradiology, Groupe Hospitalier Pitié-Salpêtrière, AP-HP, Sorbonne Université UMR_S 1127, Paris, France
| | - Sébastian Ströer
- Department of Neuroradiology, Groupe Hospitalier Pitié-Salpêtrière, AP-HP, Sorbonne Université UMR_S 1127, Paris, France
| | - Didier Dormont
- Equipe-projet ARAMIS, ICM, CNRS UMR 7225, Inserm U1117, Sorbonne Université UMR_S 1127, Centre Inria de Paris, Groupe Hospitalier Pitié-Salpêtrière Charles Foix, Faculté de Médecine Sorbonne Université, Paris, France; Department of Neuroradiology, Groupe Hospitalier Pitié-Salpêtrière, AP-HP, Sorbonne Université UMR_S 1127, Paris, France
| | | | - Marie Chupin
- CATI, ICM, CNRS UMR 7225, Inserm U1117, Sorbonne Université UMR_S 1127, Paris, France
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Chaaban L, Safwan N, Moussa H, El‐Sammak S, Khoury S, Hannoun S. Central vein sign: A putative diagnostic marker for multiple sclerosis. Acta Neurol Scand 2022; 145:279-287. [PMID: 34796472 DOI: 10.1111/ane.13553] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 10/04/2021] [Accepted: 11/03/2021] [Indexed: 11/29/2022]
Abstract
The presence of a "central vein sign" (CVS) has been introduced as a biomarker for the diagnosis of multiple sclerosis (MS) and shown to have the ability to accurately differentiate MS from other white matter diseases (MS mimics). Following the development of susceptibility-based magnetic resonance venography that allowed the in vivo detection of CVS, a standard CVS definition was established by introducing the "40% rule" that assesses the number of MS lesions with CVS as a fraction of the total number of lesions to differentiate MS lesions from other types of lesions. The "50% rule," the "three-lesion criteria," and the "six-lesion criteria" were later introduced and defined. Each of these rules had high levels of sensitivity, specificity, and accuracy in differentiating MS from other diseases, which has been recognized by the Magnetic Resonance Imaging in MS (MAGNIMS) group and the Consortium of MS Centers task force. The North American Imaging in Multiple Sclerosis Cooperative even provided statements and recommendations aiming to refine, standardize and evaluate the CVS in MS. Herein, we review the existing literature on CVS and evaluate its added value in the diagnosis of MS and usefulness in differentiating it from other vasculopathies. We also review the histopathology of CVS and identify available automated CVS assessment methods as well as define the role of vascular comorbidities in the diagnosis of MS.
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Affiliation(s)
- Lara Chaaban
- Department of Agriculture and Food Sciences American University of Beirut Beirut Lebanon
| | - Nancy Safwan
- Department of Agriculture and Food Sciences American University of Beirut Beirut Lebanon
| | - Hussein Moussa
- Nehme and Therese Tohme Multiple Sclerosis Center American University of Beirut Medical Center Beirut Lebanon
| | - Sally El‐Sammak
- Nehme and Therese Tohme Multiple Sclerosis Center American University of Beirut Medical Center Beirut Lebanon
| | - Samia J. Khoury
- Nehme and Therese Tohme Multiple Sclerosis Center American University of Beirut Medical Center Beirut Lebanon
- Faculty of Medicine Abu‐Haidar Neuroscience Institute American University of Beirut Medical Center Beirut Lebanon
| | - Salem Hannoun
- Nehme and Therese Tohme Multiple Sclerosis Center American University of Beirut Medical Center Beirut Lebanon
- Medical Imaging Sciences Program Division of Health Professions Faculty of Health Sciences American University of Beirut Beirut Lebanon
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15
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Multiple sclerosis lesions segmentation from multiple experts: The MICCAI 2016 challenge dataset. Neuroimage 2021; 244:118589. [PMID: 34563682 DOI: 10.1016/j.neuroimage.2021.118589] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 09/03/2021] [Accepted: 09/16/2021] [Indexed: 11/23/2022] Open
Abstract
MRI plays a crucial role in multiple sclerosis diagnostic and patient follow-up. In particular, the delineation of T2-FLAIR hyperintense lesions is crucial although mostly performed manually - a tedious task. Many methods have thus been proposed to automate this task. However, sufficiently large datasets with a thorough expert manual segmentation are still lacking to evaluate these methods. We present a unique dataset for MS lesions segmentation evaluation. It consists of 53 patients acquired on 4 different scanners with a harmonized protocol. Hyperintense lesions on FLAIR were manually delineated on each patient by 7 experts with control on T2 sequence, and gathered in a consensus segmentation for evaluation. We provide raw and preprocessed data and a split of the dataset into training and testing data, the latter including data from a scanner not present in the training dataset. We strongly believe that this dataset will become a reference in MS lesions segmentation evaluation, allowing to evaluate many aspects: evaluation of performance on unseen scanner, comparison to individual experts performance, comparison to other challengers who already used this dataset, etc.
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16
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Hannoun S, Kocevar G, Codjia P, Barile B, Cotton F, Durand-Dubief F, Sappey-Marinier D. T1/T2 ratio: A quantitative sensitive marker of brain tissue integrity in multiple sclerosis. J Neuroimaging 2021; 32:328-336. [PMID: 34752685 DOI: 10.1111/jon.12943] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 09/30/2021] [Accepted: 10/14/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND AND PURPOSE The aim of this study is to determine whether cerebral white matter (WM) microstructural damage, defined by decreased fractional anisotropy (FA) and increased axial (AD) and radial (RD) diffusivities, could be detected as accurately by measuring the T1/T2 ratio, in relapsing-remitting multiple sclerosis (RRMS) patients compared to healthy control (HC) subjects. METHODS Twenty-eight RRMS patients and 24 HC subjects were included in this study. Region-based analysis based on the ICBM-81 diffusion tensor imaging (DTI) atlas WM labels was performed to compare T1/T2 ratio to DTI values in normal-appearing WM (NAWM) regions of interest. Lesions segmentation was also performed and compared to the HC global WM. RESULTS A significant 19.65% decrease of T1/T2 ratio values was observed in NAWM regions of RRMS patients compared to HC. A significant 6.30% decrease of FA, as well as significant 4.76% and 10.27% increases of AD and RD, respectively, were observed in RRMS compared to the HC group in various NAWM regions. Compared to the global WM HC mask, lesions have significantly decreased T1/T2 ratio and FA and increased AD and RD (p < . 001). CONCLUSIONS Results showed significant differences between RRMS and HC in both DTI and T1/T2 ratio measurements. T1/T2 ratio even demonstrated extensive WM abnormalities when compared to DTI, thereby highlighting the ratio's sensitivity to subtle differences in cerebral WM structural integrity using only conventional MRI sequences.
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Affiliation(s)
- Salem Hannoun
- Medical Imaging Sciences Program, Division of Health Professions, Faculty of Health Sciences, American University of Beirut, Beirut, Lebanon
| | - Gabriel Kocevar
- CREATIS, UMR 5220 CNRS & U1294 INSERM, Université Claude Bernard - Lyon1, Université de Lyon, Villeurbanne, France.,Seenovate, Datascience pole, Lyon, France
| | - Pekes Codjia
- CREATIS, UMR 5220 CNRS & U1294 INSERM, Université Claude Bernard - Lyon1, Université de Lyon, Villeurbanne, France.,Service de Radiologie, Centre Hospitalier Lyon-Sud, Hospices Civils de Lyon, Pierre Bénite, France
| | - Berardino Barile
- CREATIS, UMR 5220 CNRS & U1294 INSERM, Université Claude Bernard - Lyon1, Université de Lyon, Villeurbanne, France
| | - Francois Cotton
- CREATIS, UMR 5220 CNRS & U1294 INSERM, Université Claude Bernard - Lyon1, Université de Lyon, Villeurbanne, France.,Service de Radiologie, Centre Hospitalier Lyon-Sud, Hospices Civils de Lyon, Pierre Bénite, France
| | - Francoise Durand-Dubief
- CREATIS, UMR 5220 CNRS & U1294 INSERM, Université Claude Bernard - Lyon1, Université de Lyon, Villeurbanne, France.,Service de Neurologie A, Hôpital Neurologique Pierre Wertheimer, Groupement Hospitalier Est, Hospices Civils de Lyon, Bron, France
| | - Dominique Sappey-Marinier
- CREATIS, UMR 5220 CNRS & U1294 INSERM, Université Claude Bernard - Lyon1, Université de Lyon, Villeurbanne, France.,Département IRM, CERMEP-Imagerie du Vivant, Université de Lyon, Bron, France
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17
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Lebrun-Frénay C, Rollot F, Mondot L, Zephir H, Louapre C, Le Page E, Durand-Dubief F, Labauge P, Bensa C, Thouvenot E, Laplaud D, de Seze J, Ciron J, Bourre B, Cabre P, Casez O, Ruet A, Mathey G, Berger E, Moreau T, Al Khedr A, Derache N, Clavelou P, Guennoc AM, Créange A, Neau JP, Tourbah A, Camdessanché JP, Maarouf A, Callier C, Vermersch P, Kantarci O, Siva A, Azevedo C, Makhani N, Cohen M, Pelletier D, Okuda D, Vukusic S. Risk Factors and Time to Clinical Symptoms of Multiple Sclerosis Among Patients With Radiologically Isolated Syndrome. JAMA Netw Open 2021; 4:e2128271. [PMID: 34633424 PMCID: PMC8506228 DOI: 10.1001/jamanetworkopen.2021.28271] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
IMPORTANCE Younger age, oligoclonal bands, and infratentorial and spinal cord lesions are factors associated with an increased 10-year risk of clinical conversion from radiologically isolated syndrome (RIS) to multiple sclerosis (MS). Whether disease-modifying therapy is beneficial for individuals with RIS is currently unknown. OBJECTIVES To evaluate the 2-year risk of a clinical event (onset of clinical symptoms of MS) prospectively, identify factors associated with developing an early clinical event, and simulate the sample size needed for a phase III clinical trial of individuals with RIS meeting 2009 RIS criteria. DESIGN, SETTING, AND PARTICIPANTS This cohort study used data on prospectively followed-up individuals with RIS identified at 1 of 26 tertiary centers for MS care in France that collect data for the Observatoire Français de la Sclérose en Plaques database. Participants were aged 10 to 80 years with 2 or more magnetic resonance imaging (MRI) scans after study entry and an index scan after 2000. All diagnoses were validated by an expert group, whose review included a double centralized MRI reading. Data were analyzed from July 2020 to January 2021. EXPOSURE Diagnosis of RIS. MAIN OUTCOMES AND MEASURES Risk of clinical event and associated covariates at index scan were analyzed among all individuals with RIS. Time to the first clinical event was compared by covariates, and sample size estimates were modeled based on identified risk factors. RESULTS Among 372 individuals with RIS (mean [SD] age at index MRI scan, 38.6 [12.1] years), 354 individuals were included in the analysis (264 [74.6%] women). A clinical event was identified among 49 patients (13.8%) within 2 years, which was associated with an estimated risk of conversion of 19.2% (95% CI, 14.1%-24.0%). In multivariate analysis, age younger than 37 years (hazard ratio [HR], 4.04 [95% CI, 2.00-8.15]; P < .001), spinal cord lesions (HR, 5.11 [95% CI, 1.99-13.13]; P = .001), and gadolinium-enhancing lesions on index scan (HR, 2.09 [95% CI, 1.13-3.87]; P = .02) were independently associated with an increased risk of conversion to MS. Having 2 factors at the time of the index MRI scan was associated with a risk of 27.9% (95% CI, 13.5%-39.9%) of a seminal event within 2 years, increasing to 90.9% (95% CI, 41.1%-98.6%) for individuals with all 3 factors (3 risk factors vs none: HR, 23.34 [95% CI, 9.08-59.96]; P < .001). Overall, with 80% power to detect an effect size of 60% within 24 months, a total of 160 individuals with RIS were needed assuming an event rate of 20%. CONCLUSIONS AND RELEVANCE This study found that age younger than age 37 years, spinal cord involvement, and gadolinium-enhancing lesions on index MRI scan were associated with earlier clinical disease and relevant to the number of enrolled patients needed to detect a potential treatment effect.
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Affiliation(s)
- Christine Lebrun-Frénay
- Centre de Resssource et Competence Sclérose En Plaques Nice, Unité Recherche Clinique Cote d'Azur Unité de Recherche sur le Syndrome Radiologique Isolé, Université Nice Côte d’Azur, Neurologie Centre Hospitalier Universitaire Pasteur 2, Nice, France
| | - Fabien Rollot
- Centre des Neurosciences de Lyon, Observatoire Français de la Sclérose en Plaques, Institut National de la Santé et de la Recherche Médicale 1028 et Centre National de Recherche Scientifique Unité Mixte de Recherche 5292, Lyon, France Université Claude Bernard Lyon 1, Lyon, France
- European Database for Multiple Sclerosis Foundation, Lyon, France
- Université Claude Bernard Lyon 1, Faculté de Médecine Lyon Est, Lyon, France
| | - Lydiane Mondot
- Centre de Resssource et Competence Sclérose En Plaques Nice, Unité Recherche Clinique Cote d'Azur Unité de Recherche sur le Syndrome Radiologique Isolé, Université Nice Côte d’Azur, Neurologie Centre Hospitalier Universitaire Pasteur 2, Nice, France
| | - Helene Zephir
- Université de Lille, Inserm Unité Mixte de Recherche-S 1172 LilNcog, Centre Hospitalier Universitaire Lille, Fédération Hospitalo-Universitaire Precise, Lille, France
| | - Celine Louapre
- Sorbonne University, Department of Neurology, Assistance Publique des Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Paris, France
| | - Emmanuelle Le Page
- Centre Hospitalier Universitaire Pontchaillou, Centre d'Investigation Clinique 1414 Institut National de la Santé et de la Recherche Médicale, Rennes, France
| | - Françoise Durand-Dubief
- Service de Neurologie, Sclérose en Plaques, Pathologies de la Myéline et Neuro-Inflammation, Hôpital Neurologique Pierre Wertheimer, Hospices Civils de Lyon, Lyon, France
| | - Pierre Labauge
- Centre de Ressources et Competences Sclerose En Plaques, Centre Hospitalier Universitaire de Montpellier, Montpellier, France
- University of Montpellier, Montpellier, France
| | - Caroline Bensa
- Department of Neurology, Fondation Rothschild, Paris, France
| | - Eric Thouvenot
- Department of Neurology, Centre Hospitalier Universitaire de Nîmes, Nîmes, France; Institut de Génomique Fonctionnelle, Université de Montpellier, Centre National de Recherche Scientifique, Institut National de la Santé et de la Recherche Médicale, Montpellier, France
| | - David Laplaud
- Service de Neurologie, Centre d'Investigation Clinique 015 Institut National de la Santé et de la Recherche Médicale, Nantes, France; Institut National de la Santé et de la Recherche Médicale 1064, Nantes, France
| | - Jerome de Seze
- Department Clinical Investigation Center, Department of Neurology, Centre Hospitalier Universitaire de Strasbourg, Institut National de la Santé et de la Recherche Médicale 1434, Strasbourg, France
| | - Jonathan Ciron
- Department of Neurology, Centre de Resssource et Competence Sclérose En Plaques, Centre Hospitalier Universitaire de Toulouse; Institut Toulousain des Maladies Infectieuses et Inflammatoires (Infinity), Institut National de la Santé et de la Recherche Médicale Unité Mixte de Recherche1291, Centre National de Recherche Scientifique Unité Mixte de Recherche 5051, Université Toulouse III Toulouse, France
| | - Bertrand Bourre
- Department of Neurology, Centre Hospitalier Universitaire de Rouen, Rouen, France
| | - Philippe Cabre
- Department of Neurology, Centre Hospitalier Universitaire de la Martinique, Fort-de-France, France
| | - Olivier Casez
- Department of Neurology, Centre Hospitalier Universitaire Grenoble Alpes, Grenoble, France
| | - Aurélie Ruet
- Centre de Resssource et Competence Sclérose En Plaques, Neurology Department, Centre Hospitalier Universitaire of Bordeaux, Bordeaux, France; Université Bordeaux, Institut National de la Santé et de la Recherche Médicale, Neurocentre Magendie, U1215, Bordeaux, France
| | - Guillaume Mathey
- Department of Neurology, Nancy University Hospital, Nancy, France; Université de Lorraine, Equipe Avenir 4360 Adaptation, Mesure et Evaluation en Sante Approches Interdisciplinaires, Vandoeuvre-Lès-Nancy, Nancy, France
| | - Eric Berger
- Department of Neurology, Centre Hospitalier Universitaire de Besançon, Besançon, France
| | - Thibault Moreau
- Department of Neurology, Centre Hospitalier Universitaire de Dijon, EA4184, Dijon, France
| | - Abdulatif Al Khedr
- Department of Neurology, Centre Hospitalier Universitaire d’Amiens, Amiens, France
| | - Nathalie Derache
- Department of Neurology, Centre Hospitalier Universitaire de Caen Normandie, Caen, France
| | - Pierre Clavelou
- Department of Neurology, Neuro-Dol, Centre Hospitalier Universitaire Clermont-Ferrand, Université Clermont Auvergne, Institut National de la Santé et de la Recherche Médicale U1107, Clermont-Ferrand, France
| | - Anne-Marie Guennoc
- Department of Neurology, Centre Hospitalier Universitaire de Tours, Hôpital Bretonneau, Centre de Resssource et Competence Sclérose En Plaques, Tours, France
| | - Alain Créange
- Department of Neurology, Assistance Publique des Hôpitaux de Paris, Hôpital Henri Mondor, Université Paris Est, Créteil, France
| | - Jean-Philippe Neau
- Department of Neurology, Centre Hospitalier Universitaire de Poitiers, Poitiers, France
| | - Ayman Tourbah
- Department of Neurology, Hôpital Raymond Poincaré, Garches, Unité de Formation de Recherche Simone Veil, Institut National de la Santé et de la Recherche Médicale U1195, Assistance Publique Hopitaux de Paris, Université Paris Saclay, France
| | - Jean-Philippe Camdessanché
- Department of Neurology, Centre Hospitalier Universitaire de Saint-Étienne, Hôpital Nord, Saint-Étienne, France
| | - Adil Maarouf
- Department of Neurology, Centre Hospitalier Universitaire Timone, Marseille, France
| | - Celine Callier
- Centre de Resssource et Competence Sclérose En Plaques Nice, Unité Recherche Clinique Cote d'Azur Unité de Recherche sur le Syndrome Radiologique Isolé, Université Nice Côte d’Azur, Neurologie Centre Hospitalier Universitaire Pasteur 2, Nice, France
| | - Patrick Vermersch
- Université de Lille, Inserm Unité Mixte de Recherche-S 1172 LilNcog, Centre Hospitalier Universitaire Lille, Fédération Hospitalo-Universitaire Precise, Lille, France
| | | | - Aksel Siva
- Department of Neurology, Istanbul University Cerrahpasa School of Medicine, Turkey
| | | | - Naila Makhani
- Departments of Pediatrics and Neurology, Yale School of Medicine, New Haven, Connecticut
| | - Mikael Cohen
- Centre de Resssource et Competence Sclérose En Plaques Nice, Unité Recherche Clinique Cote d'Azur Unité de Recherche sur le Syndrome Radiologique Isolé, Université Nice Côte d’Azur, Neurologie Centre Hospitalier Universitaire Pasteur 2, Nice, France
| | | | - Darin Okuda
- University of Texas Southwestern Medical Center, Dallas
| | - Sandra Vukusic
- Université Claude Bernard Lyon 1, Faculté de Médecine Lyon Est, Lyon, France
- Service de Neurologie, Sclérose en Plaques, Pathologies de la Myéline et Neuro-Inflammation, Hôpital Neurologique Pierre Wertheimer, Hospices Civils de Lyon, Lyon, France
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Lavrova E, Lommers E, Woodruff HC, Chatterjee A, Maquet P, Salmon E, Lambin P, Phillips C. Exploratory Radiomic Analysis of Conventional vs. Quantitative Brain MRI: Toward Automatic Diagnosis of Early Multiple Sclerosis. Front Neurosci 2021; 15:679941. [PMID: 34421515 PMCID: PMC8374240 DOI: 10.3389/fnins.2021.679941] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 06/14/2021] [Indexed: 12/23/2022] Open
Abstract
Conventional magnetic resonance imaging (cMRI) is poorly sensitive to pathological changes related to multiple sclerosis (MS) in normal-appearing white matter (NAWM) and gray matter (GM), with the added difficulty of not being very reproducible. Quantitative MRI (qMRI), on the other hand, attempts to represent the physical properties of tissues, making it an ideal candidate for quantitative medical image analysis or radiomics. We therefore hypothesized that qMRI-based radiomic features have added diagnostic value in MS compared to cMRI. This study investigated the ability of cMRI (T1w) and qMRI features extracted from white matter (WM), NAWM, and GM to distinguish between MS patients (MSP) and healthy control subjects (HCS). We developed exploratory radiomic classification models on a dataset comprising 36 MSP and 36 HCS recruited in CHU Liege, Belgium, acquired with cMRI and qMRI. For each image type and region of interest, qMRI radiomic models for MS diagnosis were developed on a training subset and validated on a testing subset. Radiomic models based on cMRI were developed on the entire training dataset and externally validated on open-source datasets with 167 HCS and 10 MSP. Ranked by region of interest, the best diagnostic performance was achieved in the whole WM. Here the model based on magnetization transfer imaging (a type of qMRI) features yielded a median area under the receiver operating characteristic curve (AUC) of 1.00 in the testing sub-cohort. Ranked by image type, the best performance was achieved by the magnetization transfer models, with median AUCs of 0.79 (0.69-0.90, 90% CI) in NAWM and 0.81 (0.71-0.90) in GM. The external validation of the T1w models yielded an AUC of 0.78 (0.47-1.00) in the whole WM, demonstrating a large 95% CI and a low sensitivity of 0.30 (0.10-0.70). This exploratory study indicates that qMRI radiomics could provide efficient diagnostic information using NAWM and GM analysis in MSP. T1w radiomics could be useful for a fast and automated check of conventional MRI for WM abnormalities once acquisition and reconstruction heterogeneities have been overcome. Further prospective validation is needed, involving more data for better interpretation and generalization of the results.
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Affiliation(s)
- Elizaveta Lavrova
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, Maastricht, Netherlands
- GIGA Cyclotron Research Centre In Vivo Imaging, University of Liège, Liège, Belgium
| | - Emilie Lommers
- GIGA Cyclotron Research Centre In Vivo Imaging, University of Liège, Liège, Belgium
- Clinical Neuroimmunology Unit, Neurology Department, CHU Liège, Liège, Belgium
| | - Henry C. Woodruff
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, Maastricht, Netherlands
- Department of Radiology and Nuclear Imaging, GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, Netherlands
| | - Avishek Chatterjee
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, Maastricht, Netherlands
| | - Pierre Maquet
- GIGA Cyclotron Research Centre In Vivo Imaging, University of Liège, Liège, Belgium
- Clinical Neuroimmunology Unit, Neurology Department, CHU Liège, Liège, Belgium
| | - Eric Salmon
- GIGA Cyclotron Research Centre In Vivo Imaging, University of Liège, Liège, Belgium
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, Maastricht, Netherlands
- Department of Radiology and Nuclear Imaging, GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, Netherlands
| | - Christophe Phillips
- GIGA Cyclotron Research Centre In Vivo Imaging, University of Liège, Liège, Belgium
- GIGA In Silico Medicine, University of Liège, Liège, Belgium
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Guery D, Marignier R, Durand-Dubief F, Lavie C, Pique J, Guerrier O, Vukusic S. Clinical failure of natalizumab in multiple sclerosis: Specific causes and strategy. Rev Neurol (Paris) 2021; 177:1241-1249. [PMID: 34176658 DOI: 10.1016/j.neurol.2021.02.393] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 02/08/2021] [Accepted: 02/09/2021] [Indexed: 11/17/2022]
Abstract
BACKGROUND Natalizumab is a very effective treatment of multiple sclerosis (MS). Failure is rare and should lead to consider some specific etiologies. The purpose of our study was to describe causes of subacute neurological events under natalizumab. METHODS Observational single-center retrospective study in the MS expert center of Lyon, France. INCLUSION CRITERIA any patient with definite MS who received at least three infusions of natalizumab between April 2007 and February 2017. Clinical data were extracted from the Lyon EDMUS/OFSEP database. Events of interest: occurrence of a subacute neurological deficit, characterized by new clinical symptoms. We excluded pseudo-relapses and progression. FINDINGS A subacute neurological deficit occurred in 35 cases, for 607 patients treated with natalizumab. Ten patients presented natalizumab antibodies, nine had progressive multifocal leukoencephalopathy (PML), five presented an isolated subacute neurological deficit and two had AQP4 antibodies. No myelin oligodendrocyte glycoprotein (MOG) antibodies were found. INTERPRETATION The occurrence of an acute or subacute neurological deficit with natalizumab is rarely a MS relapse and should lead systematically to explore some important alternate etiologies, eliminating PML first.
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Affiliation(s)
- D Guery
- Service de neurologie, sclérose en plaques, pathologies de la myéline et neuro-inflammation, hôpital Neurologique Pierre Wertheimer, hospices civils de Lyon, 69677 Lyon/Bron, France
| | - R Marignier
- Service de neurologie, sclérose en plaques, pathologies de la myéline et neuro-inflammation, hôpital Neurologique Pierre Wertheimer, hospices civils de Lyon, 69677 Lyon/Bron, France; Inserm 1028 et CNRS UMR5292, Centre des neurosciences de Lyon, FLUID Team, 69003 Lyon, France; Université Claude Bernard Lyon 1, faculté de médecine Lyon Est, 69000 Lyon, France
| | - F Durand-Dubief
- Service de neurologie, sclérose en plaques, pathologies de la myéline et neuro-inflammation, hôpital Neurologique Pierre Wertheimer, hospices civils de Lyon, 69677 Lyon/Bron, France; CREATIS, Lyon, France
| | - C Lavie
- Service de neurologie, sclérose en plaques, pathologies de la myéline et neuro-inflammation, hôpital Neurologique Pierre Wertheimer, hospices civils de Lyon, 69677 Lyon/Bron, France
| | - J Pique
- Service de neurologie, sclérose en plaques, pathologies de la myéline et neuro-inflammation, hôpital Neurologique Pierre Wertheimer, hospices civils de Lyon, 69677 Lyon/Bron, France
| | - O Guerrier
- Service de neurologie, sclérose en plaques, pathologies de la myéline et neuro-inflammation, hôpital Neurologique Pierre Wertheimer, hospices civils de Lyon, 69677 Lyon/Bron, France
| | - S Vukusic
- Service de neurologie, sclérose en plaques, pathologies de la myéline et neuro-inflammation, hôpital Neurologique Pierre Wertheimer, hospices civils de Lyon, 69677 Lyon/Bron, France; Université Claude Bernard Lyon 1, faculté de médecine Lyon Est, 69000 Lyon, France; Inserm 1028 et CNRS UMR5292, Centre des neurosciences de Lyon, Observatoire français de la sclérose en plaques, 69003 Lyon, France.
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Brisset JC, Vukusic S, Cotton F. Update on brain MRI for the diagnosis and follow-up of MS patients. Presse Med 2021; 50:104067. [PMID: 33989722 DOI: 10.1016/j.lpm.2021.104067] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Accepted: 05/06/2021] [Indexed: 10/21/2022] Open
Abstract
Over the past decades, MRI has become a major tool in the diagnosis and the follow-up of patients with multiple sclerosis (MS), especially for monitoring the effectiveness of therapy. The recent international recommendations issued for the standardization of neurological and radiological clinical practices converge on many points. In this setting, recommendations made by the "Observatoire français de la sclérose en plaques", the French MS registry, can be distinguished by its interdisciplinary complementarity, its longevity, its size, and its positions in direct connection with the clinic. Hence, after suspicions of gadolinium deposition in the brain, with multiple warning from the American and European health authorities, a national consultation took place and resulted in limitation to useful injections. The precautionary principle prevailing, the patient receives a limited quantity of contrast product even if no clinically harmful manifestation has been detected to date. The result of this round table bringing together neurologists and neuroradiologists from specialized centers was published in the form of a recommendation in early 2020. The interest of this project also lies in the constant improvement of the management of patients with MS and the possibility of developing advanced techniques to assist the clinician. The aim of this review is to explain to the neurologist, the interest of following this imaging protocol both in his/her clinical practice and in the possibilities that this opens up.
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Affiliation(s)
- Jean-Christophe Brisset
- Observatoire Français de la Sclérose en Plaques, Centre de Recherche en Neurosciences de Lyon, INSERM 1028 et CNRS UMR 5292, 69003 Lyon, France
| | - Sandra Vukusic
- Observatoire Français de la Sclérose en Plaques, Centre de Recherche en Neurosciences de Lyon, INSERM 1028 et CNRS UMR 5292, 69003 Lyon, France; Hospices Civils de Lyon, Service de Neurologie, sclérose en plaques, pathologies de la myéline et neuro-inflammation, 69677 Bron, France; Université de Lyon, Université Claude Bernard Lyon 1, 69000 Lyon, France
| | - Francois Cotton
- Observatoire Français de la Sclérose en Plaques, Centre de Recherche en Neurosciences de Lyon, INSERM 1028 et CNRS UMR 5292, 69003 Lyon, France; Eugène Devic EDMUS Foundation Against Multiple Sclerosis (a government approved foundation), 69677 Bron, France; Inserm, UJM-Saint-Étienne, CNRS, CREATIS UMR 5220, U1206, INSA-Lyon, University Lyon, Université Claude-Bernard Lyon 1, 69495 Pierre-Bénite, France.
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Farges V, Hannoun S, Benini T, Marignier R, Cotton F. MRI to detect and localize the area postrema in multiple sclerosis: The role of 3D-DIR and 3D-FLAIR. J Neuroimaging 2021; 31:701-705. [PMID: 33930239 DOI: 10.1111/jon.12867] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 03/29/2021] [Accepted: 03/31/2021] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND AND PURPOSE Area postrema (AP) is a highly vascularized paired 2 mm-long anatomical structure, localized on the dorsal inferior surface of the medulla oblongata, at the caudal end of the fourth-ventricle. AP is principally affected in AP syndrome, which is commonly associated with autoimmune inflammatory diseases, including essentially neuromyelitis optica spectrum disorder (NMOSD). The aim of this study is to assess the best cerebral MRI sequences and planes for AP detection in order to assist or aid in the diagnosis of difficult NMOSD cases. METHODS 3DT1, 2DT2, 3D-fluid-attenuated inversion recovery (3DFLAIR), and 3D-double inversion recuperation (3DDIR), routinely used in inflammatory diseases, were analyzed and scored based on quality (0-2), and ability to detect AP in each plane (0 = no detection, 1 = probable detection, 2 = obvious detection). Based on image availability, subjects were divided into three groups: Group-1, including 100 randomly selected subjects with 3DT1 and 3DFLAIR, Group-2, including 30 multiple sclerosis (MS) patients from the "Observatoire Français de la Sclérose En Plaques" (OFSEP) with 3DT1, 3DFLAIR, and 3DDIR, and Group-3, including 164 OFSEP MS patients with 3DFLAIR and 2DT2. RESULTS AP was undetectable on 3DT1 and 2DT2. AP was detected in 87% of 3DFLAIR in Group-1, 90% in Group-2, and 90% in Group-3. AP was also detected in 100% of 3DDIR images in the axial plane. CONCLUSIONS As evidenced, AP was easily assessed on 3DDIR and 3DFLAIR emphasizing the importance of adding these sequences to NMOSD MRI-protocols. Moreover, the most effective imaging plane in identifying AP was the axial plane.
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Affiliation(s)
- Valentine Farges
- Faculty of Medicine, Claude Bernard Lyon 1 University, Lyon, France
| | - Salem Hannoun
- Medical Imaging Sciences Program, Division of Health Professions, Faculty of Health Sciences, American University of Beirut, Beirut, Lebanon
| | - Théo Benini
- Faculty of Medicine, Claude Bernard Lyon 1 University, Lyon, France
| | - Romain Marignier
- Service de Neurologie, Sclérose en Plaques, Pathologies de la Myéline et Neuro-inflammation, Hôpital Neurologique Pierre Wertheimer, Hospices Civils de Lyon, Lyon, France
| | - François Cotton
- Radiology Department, Centre Hospitalier Lyon Sud, Hospices Civils de Lyon, Pierre-Bénite, France.,CREATIS - CNRS UMR 5220 - INSERM U1294, Université Lyon 1, Villeurbanne, France
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22
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Goujon A, Mirafzal S, Zuber K, Deschamps R, Sadik JC, Gout O, Savatovsky J, Lecler A. 3D-Fast Gray Matter Acquisition with Phase Sensitive Inversion Recovery Magnetic Resonance Imaging at 3 Tesla: Application for detection of spinal cord lesions in patients with multiple sclerosis. PLoS One 2021; 16:e0247813. [PMID: 33886586 PMCID: PMC8061976 DOI: 10.1371/journal.pone.0247813] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Accepted: 02/16/2021] [Indexed: 12/03/2022] Open
Abstract
Background and purpose To compare 3D-Fast Gray Matter Acquisition with Phase Sensitive Inversion Recovery (3D-FGAPSIR) with conventional 3D-Short-Tau Inversion Recovery (3D-STIR) and sagittal T1-and T2-weighted MRI dataset at 3 Tesla when detecting MS spinal cord lesions. Material and methods This prospective single-center study was approved by an institutional review board and enrolled participants from December 2016 to August 2018. Two neuroradiologists blinded to all data, individually analyzed the 3D-FGAPSIR and the conventional datasets separately and in random order. Discrepancies were resolved by consensus by a third neuroradiologist. The primary judgment criterion was the number of MS spinal cord lesions. Secondary judgment criteria included lesion enhancement, lesion delineation, reader-reported confidence and lesion-to-cord-contrast-ratio. A Wilcoxon’s test was used to compare the two datasets. Results 51 participants were included. 3D-FGAPSIR detected significantly more lesions than the conventional dataset (344 versus 171 respectively, p<0.001). Two participants had no detected lesion on the conventional dataset, whereas 3D-FGAPSIR detected at least one lesion. 3/51 participants had a single enhancing lesion detected by both datasets. Lesion delineation and reader-reported confidence were significantly higher with 3D-FGAPSIR: 4.5 (IQR 1) versus 2 (IQR 0.5), p<0.0001 and 4.5 (IQR 1) versus 2.5 (IQR 0.5), p<0.0001. Lesion-to-cord-contrast-ratio was significantly higher using 3D-FGAPSIR as opposed to 3D-STIR and T2: 1.4 (IQR 0,3) versus 0.4 (IQR 0,1) and 0.3 (IQR 0,1)(p = 0.04). Correlations with clinical data and inter- and intra-observer agreements were higher with 3D-FGAPSIR. Conclusion 3D-FGAPSIR improved overall MS spinal cord lesion detection as compared to conventional set and detected all enhancing lesions.
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Affiliation(s)
- Adrien Goujon
- Department of Neuroradiology, Foundation Adolphe de Rothschild Hospital, Paris, France
- * E-mail:
| | - Sonia Mirafzal
- Department of Neuroradiology, Foundation Adolphe de Rothschild Hospital, Paris, France
| | - Kevin Zuber
- Department of Clinical Research, Foundation Adolphe de Rothschild Hospital, Paris, France
| | - Romain Deschamps
- Department of Neurology, Foundation Adolphe de Rothschild Hospital, Paris, France
| | - Jean-Claude Sadik
- Department of Neuroradiology, Foundation Adolphe de Rothschild Hospital, Paris, France
| | - Olivier Gout
- Department of Neurology, Foundation Adolphe de Rothschild Hospital, Paris, France
| | - Julien Savatovsky
- Department of Neuroradiology, Foundation Adolphe de Rothschild Hospital, Paris, France
| | - Augustin Lecler
- Department of Neuroradiology, Foundation Adolphe de Rothschild Hospital, Paris, France
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Zhang Q, Dai X, Zhang H, Zeng Y, Luo K, Li W. Recent advances in development of nanomedicines for multiple sclerosis diagnosis. Biomed Mater 2021; 16:024101. [PMID: 33472182 DOI: 10.1088/1748-605x/abddf4] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Multiple sclerosis (MS) is a neurodegenerative disease with a high morbidity and disease burden. It is characterized by the loss of the myelin sheath, resulting in the disruption of neuron electrical signal transmissions and sensory and motor ability deficits. The diagnosis of MS is crucial to its management, but the diagnostic sensitivity and specificity are always a challenge. To overcome this challenge, nanomedicines have recently been employed to aid the diagnosis of MS with an improved diagnostic efficacy. Advances in nanomedicine-based contrast agents in magnetic resonance imaging scanning of MS lesions, and nanomedicine-derived sensors for detecting biomarkers in the cerebrospinal fluid biopsy, or analyzing the composition of exhaled breath gas, have demonstrated the potential of using nanomedicines in the accurate diagnosis of MS. This review aims to provide an overview of recent advances in the application of nanomedicines for the diagnosis of MS and concludes with perspectives of using nanomedicines for the development of safe and effective MS diagnostic nanotools.
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Affiliation(s)
- Qin Zhang
- Department of Radiology, Department of Postgraduate Students, and Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu 610041, People's Republic of China. West China School of Medicine, Sichuan University, Chengdu 610041, People's Republic of China. These authors contributed equally to this work
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McKinley R, Wepfer R, Aschwanden F, Grunder L, Muri R, Rummel C, Verma R, Weisstanner C, Reyes M, Salmen A, Chan A, Wagner F, Wiest R. Simultaneous lesion and brain segmentation in multiple sclerosis using deep neural networks. Sci Rep 2021; 11:1087. [PMID: 33441684 PMCID: PMC7806997 DOI: 10.1038/s41598-020-79925-4] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Accepted: 12/04/2020] [Indexed: 12/12/2022] Open
Abstract
Segmentation of white matter lesions and deep grey matter structures is an important task in the quantification of magnetic resonance imaging in multiple sclerosis. In this paper we explore segmentation solutions based on convolutional neural networks (CNNs) for providing fast, reliable segmentations of lesions and grey-matter structures in multi-modal MR imaging, and the performance of these methods when applied to out-of-centre data. We trained two state-of-the-art fully convolutional CNN architectures on the 2016 MSSEG training dataset, which was annotated by seven independent human raters: a reference implementation of a 3D Unet, and a more recently proposed 3D-to-2D architecture (DeepSCAN). We then retrained those methods on a larger dataset from a single centre, with and without labels for other brain structures. We quantified changes in performance owing to dataset shift, and changes in performance by adding the additional brain-structure labels. We also compared performance with freely available reference methods. Both fully-convolutional CNN methods substantially outperform other approaches in the literature when trained and evaluated in cross-validation on the MSSEG dataset, showing agreement with human raters in the range of human inter-rater variability. Both architectures showed drops in performance when trained on single-centre data and tested on the MSSEG dataset. When trained with the addition of weak anatomical labels derived from Freesurfer, the performance of the 3D Unet degraded, while the performance of the DeepSCAN net improved. Overall, the DeepSCAN network predicting both lesion and anatomical labels was the best-performing network examined.
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Affiliation(s)
- Richard McKinley
- Support Center for Advanced Neuroimaging, University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, Bern, Switzerland.
| | - Rik Wepfer
- Support Center for Advanced Neuroimaging, University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Fabian Aschwanden
- Support Center for Advanced Neuroimaging, University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Lorenz Grunder
- Support Center for Advanced Neuroimaging, University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Raphaela Muri
- Support Center for Advanced Neuroimaging, University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Christian Rummel
- Support Center for Advanced Neuroimaging, University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, Bern, Switzerland
| | | | | | - Mauricio Reyes
- ARTORG Centre for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Anke Salmen
- University Clinic for Neurology, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Andrew Chan
- University Clinic for Neurology, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Franca Wagner
- Support Center for Advanced Neuroimaging, University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging, University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, Bern, Switzerland
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Progressive multifocal leukoencephalopathy: MRI findings in HIV-infected patients are closer to rituximab- than natalizumab-associated PML. Eur Radiol 2020; 31:2944-2955. [PMID: 33155106 PMCID: PMC7644389 DOI: 10.1007/s00330-020-07362-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2020] [Revised: 08/26/2020] [Accepted: 09/29/2020] [Indexed: 12/22/2022]
Abstract
Objectives To compare brain MRI findings in progressive multifocal leukoencephalopathy (PML) associated to rituximab and natalizumab treatments and HIV infection. Materials and methods In this retrospective, multicentric study, we analyzed brain MRI exams from 72 patients diagnosed with definite PML: 32 after natalizumab treatment, 20 after rituximab treatment, and 20 HIV patients. We compared T2- or FLAIR-weighted images, diffusion-weighted images, T2*-weighted images, and contrast enhancement features, as well as lesion distribution, especially gray matter involvement. Results The three PML entities affect U-fibers associated with low signal intensities on T2*-weighted sequences. Natalizumab-associated PML showed a punctuate microcystic appearance in or in the vicinity of the main PML lesions, a potential involvement of the cortex, and contrast enhancement. HIV and rituximab-associated PML showed only mild contrast enhancement, punctuate appearance, and cortical involvement. The CD4/CD8 ratio showed a trend to be higher in the natalizumab group, possibly mirroring a more efficient immune response. Conclusion Imaging features of rituximab-associated PML are different from those of natalizumab-associated PML and are closer to those observed in HIV-associated PML. Key Points • Nowadays, PML is emerging as a complication of new effective therapies based on monoclonal antibodies. • Natalizumab-associated PML shows more inflammatory signs, a perivascular distribution “the milky way,” and more cortex involvement than rituximab- and HIV-associated PML. • MRI differences are probably related to higher levels of immunosuppression in HIV patients and those under rituximab therapy.
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A contrast-adaptive method for simultaneous whole-brain and lesion segmentation in multiple sclerosis. Neuroimage 2020; 225:117471. [PMID: 33099007 PMCID: PMC7856304 DOI: 10.1016/j.neuroimage.2020.117471] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Revised: 10/12/2020] [Accepted: 10/16/2020] [Indexed: 12/24/2022] Open
Abstract
Here we present a method for the simultaneous segmentation of white matter lesions and normal-appearing neuroanatomical structures from multi-contrast brain MRI scans of multiple sclerosis patients. The method integrates a novel model for white matter lesions into a previously validated generative model for whole-brain segmentation. By using separate models for the shape of anatomical structures and their appearance in MRI, the algorithm can adapt to data acquired with different scanners and imaging protocols without retraining. We validate the method using four disparate datasets, showing robust performance in white matter lesion segmentation while simultaneously segmenting dozens of other brain structures. We further demonstrate that the contrast-adaptive method can also be safely applied to MRI scans of healthy controls, and replicate previously documented atrophy patterns in deep gray matter structures in MS. The algorithm is publicly available as part of the open-source neuroimaging package FreeSurfer.
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Cohen M, Mondot L, Fakir S, Landes C, Lebrun C. Digital biomarkers can highlight subtle clinical differences in radiologically isolated syndrome compared to healthy controls. J Neurol 2020; 268:1316-1322. [PMID: 33078309 DOI: 10.1007/s00415-020-10276-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 10/12/2020] [Accepted: 10/13/2020] [Indexed: 11/25/2022]
Abstract
OBJECTIVE To explore the use of digital biomarkers to distinguish healthy controls (HC) from subjects with a radiologically isolated syndrome (RIS). METHODS We developed a smartphone application called MS Screen Test (MSST) to explore several dimensions of the neurological exam such as finger tapping speed, agility, hand synchronization, low contrast vision and cognition during a short evaluation. This app was tested on a cohort of healthy volunteers including a subset of subjects who underwent two evaluations on the same day to assess reproducibility. In a second step, the app was tested on a cohort of RIS subjects. Performances of RIS subjects were compared with age and genre-matched HC. RESULTS HC underwent two consecutive evaluations on MSST. The analysis showed good reproducibility for all measures. Then 21 RIS subjects were compared to 32 matched HC. Compared to HC, we found that RIS subjects had a lower finger tapping speed on the dominant hand (5.6 versus 6.5 taps per second; p = 0.005), a longer inter hand interval during the hand synchronization task (14.4 versus 11.3 ms; p = 0.03) and significantly poorer scores on the low contrast vision and cognition tests. CONCLUSION MSST only requires a smartphone to obtain digital biomarkers relative to several dimensions of the neurological examination. Our results highlighted subtle differences between HC and RIS subjects. We plan to evaluate this tool in MS patients, which will allow us to get a much larger sample of subjects, to determine whether digital biomarkers can predict disease course.
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Affiliation(s)
- Mikael Cohen
- Service de Neurologie, CRC SEP, Unité de Recherche Clinique Cote D'Azur (UR2CA-URRIS), Centre Hospitalier Universitaire Pasteur 2, 30 Voie Romaine, 06002, Nice, Cedex, France.
| | - Lydiane Mondot
- Service de Radiologie, Unité de Recherche Clinique Cote D'Azur (UR2CA - URRIS), Centre Hospitalier Universitaire Pasteur 2, 30 Voie Romaine, 06002, Nice, Cedex, France
| | - Salim Fakir
- Service de Neurologie, CRC SEP, Unité de Recherche Clinique Cote D'Azur (UR2CA-URRIS), Centre Hospitalier Universitaire Pasteur 2, 30 Voie Romaine, 06002, Nice, Cedex, France
| | - Cassandre Landes
- Service de Neurologie, CRC SEP, Unité de Recherche Clinique Cote D'Azur (UR2CA-URRIS), Centre Hospitalier Universitaire Pasteur 2, 30 Voie Romaine, 06002, Nice, Cedex, France
| | - Christine Lebrun
- Service de Neurologie, CRC SEP, Unité de Recherche Clinique Cote D'Azur (UR2CA-URRIS), Centre Hospitalier Universitaire Pasteur 2, 30 Voie Romaine, 06002, Nice, Cedex, France
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Durand-Dubief F. Should spinal cord MRI be systematically performed for diagnosis and follow-up of multiple sclerosis? Synthesis. Rev Neurol (Paris) 2020; 176:490-493. [DOI: 10.1016/j.neurol.2020.03.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Accepted: 03/23/2020] [Indexed: 11/25/2022]
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Brisset JC, Kremer S, Hannoun S, Bonneville F, Durand-Dubief F, Tourdias T, Barillot C, Guttmann C, Vukusic S, Dousset V, Cotton F, Ameli R, Anxionnat R, Audoin B, Attye A, Bannier E, Barillot C, Ben Salem D, Boncoeur-Martel MP, Bonhomme G, Bonneville F, Boutet C, Brisset J, Cervenanski F, Claise B, Commowick O, Constans JM, Cotton F, Dardel P, Desal H, Dousset V, Durand-Dubief F, Ferre JC, Gaultier A, Gerardin E, Glattard T, Grand S, Grenier T, Guillevin R, Guttmann C, Krainik A, Kremer S, Lion S, Champfleur NMD, Mondot L, Outteryck O, Pyatigorskaya N, Pruvo JP, Rabaste S, Ranjeva JP, Roch JA, Sadik JC, Sappey-Marinier D, Savatovsky J, Stankoff B, Tanguy JY, Tourbah A, Tourdias T, Brochet B, Casey R, Cotton F, De Sèze J, Douek P, Guillemin F, Laplaud D, Lebrun-Frenay C, Mansuy L, Moreau T, Olaiz J, Pelletier J, Rigaud-Bully C, Stankoff B, Vukusic S, Debouverie M, Edan G, Ciron J, Lubetzki C, Vermersch P, Labauge P, Defer G, Berger E, Clavelou P, Gout O, Thouvenot E, Heinzlef O, Al-Khedr A, Bourre B, Casez O, Cabre P, Montcuquet A, Créange A, Camdessanché JP, Bakchine S, Maurousset A, Patry I, De Broucker T, Pottier C, Neau JP, Labeyrie C, Nifle C. New OFSEP recommendations for MRI assessment of multiple sclerosis patients: Special consideration for gadolinium deposition and frequent acquisitions. J Neuroradiol 2020; 47:250-258. [DOI: 10.1016/j.neurad.2020.01.083] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 01/22/2020] [Accepted: 01/22/2020] [Indexed: 01/04/2023]
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Calocer F, Dejardin O, Kwiatkowski A, Bourre B, Vermersch P, Hautecoeur P, Launoy G, Defer G. Socioeconomic deprivation increases the risk of disability in multiple sclerosis patients. Mult Scler Relat Disord 2020; 40:101930. [DOI: 10.1016/j.msard.2020.101930] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2019] [Revised: 11/25/2019] [Accepted: 01/02/2020] [Indexed: 11/16/2022]
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Maillart E, Durand-Dubief F, Louapre C, Audoin B, Bourre B, Derache N, Ciron J, Collongues N, de Sèze J, Cohen M, Lebrun-Frenay C, Hadhoum N, Zéphir H, Deschamps R, Carra-Dallière C, Labauge P, Kerschen P, Montcuquet A, Wiertlewski S, Laplaud D, Runavot G, Vukusic S, Papeix C, Marignier R. Outcome and risk of recurrence in a large cohort of idiopathic longitudinally extensive transverse myelitis without AQP4/MOG antibodies. J Neuroinflammation 2020; 17:128. [PMID: 32326965 PMCID: PMC7178729 DOI: 10.1186/s12974-020-01773-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Accepted: 03/16/2020] [Indexed: 12/23/2022] Open
Abstract
Background Longitudinally extensive transverse myelitis (LETM) is classically related to aquaporin (AQP4)-antibodies (Ab) neuromyelitis optica spectrum disorders (NMOSD) or more recently to myelin oligodendrocyte glycoprotein (MOG)-Ab associated disease. However, some patients remain negative for any diagnosis, despite a large work-up including AQP4-Ab and MOG-Ab. Data about natural history, disability outcome, and treatment are limited in this group of patients. We aimed to (1) describe clinical, biological, and radiological features of double seronegative LETM patients; (2) assess the clinical course and identify prognostic factors; and (3) assess the risk of recurrence, according to maintenance immunosuppressive therapy. Methods Retrospective evaluation of patients with a first episode of LETM, tested negative for AQP-Ab and MOG-Ab, from the French nationwide observatory study NOMADMUS. Results Fifty-three patients (median age 38 years (range 16–80)) with double seronegative LETM were included. Median nadir EDSS at onset was 6.0 (1–8.5), associated to a median EDSS at last follow-up of 4.0 (0–8). Recurrence was observed in 24.5% of patients in the 18 following months, with a median time to first relapse of 5.7 months. The risk of recurrence was lower in the group of patients treated early with an immunosuppressive drug (2/22, 9%), in comparison with untreated patients (10/31, 32%). Conclusions A first episode of a double seronegative LETM is associated to a severe outcome and a high rate of relapse in the following 18 months, suggesting that an early immunosuppressive treatment may be beneficial in that condition.
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Affiliation(s)
- Elisabeth Maillart
- Department of Neurology; Centre de Référence des Maladies Inflammatoires Rares du Cerveau et de la Moelle, AP-HP, Hôpital Pitié-Salpêtrière, Paris, France.
| | - Françoise Durand-Dubief
- Service de neurologie, sclérose en plaques, pathologies de la myéline et neuro-inflammation, and Centre de Référence des Maladies Inflammatoires Rares du Cerveau et de la Moelle, Hôpital Neurologique Pierre Wertheimer, Hospices Civils de Lyon, 69677, Lyon/Bron, France
| | - Céline Louapre
- Department of Neurology; Centre de Référence des Maladies Inflammatoires Rares du Cerveau et de la Moelle, AP-HP, Hôpital Pitié-Salpêtrière, Paris, France
| | - Bertrand Audoin
- APHM, Hôpital de la Timone, Pôle de Neurosciences Cliniques, Service de Neurologie, Marseille, France
| | - Bertrand Bourre
- Department of Neurology, University Hospital of Rouen, Rouen, France
| | - Nathalie Derache
- Department of Neurology, University Hospital of Caen, Caen, France
| | - Jonathan Ciron
- Department of Neurology, University Hospital of Toulouse, Toulouse, France
| | - Nicolas Collongues
- Department of Neurology, University Hospital of Strasbourg, Strasbourg, France
| | - Jérome de Sèze
- Department of Neurology, University Hospital of Strasbourg, Strasbourg, France
| | - Mikael Cohen
- Centre de Ressources et Compétence Sclerose en plaques (CRCSEP); Unité de Recherche Clinique Côte d'azur (UR2CA), CHU Pasteur 2, Nice, France
| | - Christine Lebrun-Frenay
- Centre de Ressources et Compétence Sclerose en plaques (CRCSEP); Unité de Recherche Clinique Côte d'azur (UR2CA), CHU Pasteur 2, Nice, France
| | - Nawel Hadhoum
- Department of Neurology, University Hospital of Lille, Lille, France
| | - Hélène Zéphir
- Department of Neurology, University Hospital of Lille, Lille, France
| | - Romain Deschamps
- Department of Neurology, Fondation Ophtalmologique Adolphe de Rothschild, 25-29, rue Manin, 75940, Paris cedex 19, France
| | | | - Pierre Labauge
- Department of Neurology, University Hospital of Montpellier, Montpellier, France
| | - Philippe Kerschen
- Department of Neurology, University Hospital of Luxembourg, Luxembourg, Luxembourg
| | - Alexis Montcuquet
- Department of Neurology, University Hospital of Limoges, Limoges, France
| | | | - David Laplaud
- Department of Neurology, University Hospital of Nantes, Nantes, France
| | - Gwenaëlle Runavot
- Department of Neurology, University Hospital of Saint-Pierre, Saint-Pierre, La Réunion, France
| | - Sandra Vukusic
- Service de neurologie, sclérose en plaques, pathologies de la myéline et neuro-inflammation, and Centre de Référence des Maladies Inflammatoires Rares du Cerveau et de la Moelle, Hôpital Neurologique Pierre Wertheimer, Hospices Civils de Lyon, 69677, Lyon/Bron, France
| | - Caroline Papeix
- Department of Neurology; Centre de Référence des Maladies Inflammatoires Rares du Cerveau et de la Moelle, AP-HP, Hôpital Pitié-Salpêtrière, Paris, France
| | - Romain Marignier
- Service de neurologie, sclérose en plaques, pathologies de la myéline et neuro-inflammation, and Centre de Référence des Maladies Inflammatoires Rares du Cerveau et de la Moelle, Hôpital Neurologique Pierre Wertheimer, Hospices Civils de Lyon, 69677, Lyon/Bron, France
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Mayssam EN, Eid C, Khoury SJ, Hannoun S. "No evidence of disease activity": Is it an aspirational therapeutic goal in multiple sclerosis? Mult Scler Relat Disord 2020; 40:101935. [PMID: 31951861 DOI: 10.1016/j.msard.2020.101935] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Revised: 01/02/2020] [Accepted: 01/04/2020] [Indexed: 01/01/2023]
Abstract
'No evidence of disease activity' (NEDA) that has been identified as a potential outcome measure for the evaluation of DMTs effects. The concept has been adopted from other diseases such as cancer where treatment is intended to free the patient from the disease. Disease-free status has been substituted by NEDA in MS, since we are limited when it comes to fully evaluating the underlying disease. In general, NEDA, otherwise termed as NEDA-3, is defined by the lack of disease activity based on the absence of clinical relapses, disability progression with the expanded disability status score (EDSS), and radiological activity. Recently, brain atrophy, a highly predictive marker of disability progression, has been added as a fourth component (NEDA-4). The use of this composite allowed a more comprehensive assessment of the disease activity. Indeed, it has an important role in clinical trials as a secondary outcome in addition to primary endpoints. However, the evidence is insufficient regarding the ability of NEDA to predict future disability and treatment response. Moreover, combining different composites does not eliminate the limitation of each, therefore the use of NEDA in clinical routine is still not implemented. The aim of this review is first to report from the literature the available definitions of NEDA and its different variants, and second, evaluate the importance of its use as a surrogate marker to assess the efficacy of different DMTs.
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Affiliation(s)
- El Najjar Mayssam
- Nehme and Therese Tohme Multiple Sclerosis Center, American University of Beirut Medical Center, Riad El Solh 1107 2020. P.O.Box: 11-0236, Beirut, Lebanon
| | - Cynthia Eid
- Nehme and Therese Tohme Multiple Sclerosis Center, American University of Beirut Medical Center, Riad El Solh 1107 2020. P.O.Box: 11-0236, Beirut, Lebanon
| | - Samia J Khoury
- Nehme and Therese Tohme Multiple Sclerosis Center, American University of Beirut Medical Center, Riad El Solh 1107 2020. P.O.Box: 11-0236, Beirut, Lebanon; Abu-Haidar Neuroscience Institute, American University of Beirut Medical Center, Beirut, Lebanon
| | - Salem Hannoun
- Nehme and Therese Tohme Multiple Sclerosis Center, American University of Beirut Medical Center, Riad El Solh 1107 2020. P.O.Box: 11-0236, Beirut, Lebanon; Abu-Haidar Neuroscience Institute, American University of Beirut Medical Center, Beirut, Lebanon.
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McKinley R, Wepfer R, Grunder L, Aschwanden F, Fischer T, Friedli C, Muri R, Rummel C, Verma R, Weisstanner C, Wiestler B, Berger C, Eichinger P, Muhlau M, Reyes M, Salmen A, Chan A, Wiest R, Wagner F. Automatic detection of lesion load change in Multiple Sclerosis using convolutional neural networks with segmentation confidence. Neuroimage Clin 2019; 25:102104. [PMID: 31927500 PMCID: PMC6953959 DOI: 10.1016/j.nicl.2019.102104] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Revised: 09/27/2019] [Accepted: 11/18/2019] [Indexed: 12/19/2022]
Abstract
The detection of new or enlarged white-matter lesions is a vital task in the monitoring of patients undergoing disease-modifying treatment for multiple sclerosis. However, the definition of 'new or enlarged' is not fixed, and it is known that lesion-counting is highly subjective, with high degree of inter- and intra-rater variability. Automated methods for lesion quantification, if accurate enough, hold the potential to make the detection of new and enlarged lesions consistent and repeatable. However, the majority of lesion segmentation algorithms are not evaluated for their ability to separate radiologically progressive from radiologically stable patients, despite this being a pressing clinical use-case. In this paper, we explore the ability of a deep learning segmentation classifier to separate stable from progressive patients by lesion volume and lesion count, and find that neither measure provides a good separation. Instead, we propose a method for identifying lesion changes of high certainty, and establish on an internal dataset of longitudinal multiple sclerosis cases that this method is able to separate progressive from stable time-points with a very high level of discrimination (AUC = 0.999), while changes in lesion volume are much less able to perform this separation (AUC = 0.71). Validation of the method on two external datasets confirms that the method is able to generalize beyond the setting in which it was trained, achieving an accuracies of 75 % and 85 % in separating stable and progressive time-points.
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Affiliation(s)
- Richard McKinley
- Support Center for Advanced Neuroimaging, University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Switzerland.
| | - Rik Wepfer
- Support Center for Advanced Neuroimaging, University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - Lorenz Grunder
- Support Center for Advanced Neuroimaging, University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - Fabian Aschwanden
- Support Center for Advanced Neuroimaging, University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - Tim Fischer
- Universitätsklinik Balgrist, Zurich, Switzerland
| | - Christoph Friedli
- Univeristy Clinic for Neurology, Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - Raphaela Muri
- Support Center for Advanced Neuroimaging, University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - Christian Rummel
- Support Center for Advanced Neuroimaging, University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - Rajeev Verma
- Department of Neuroradiology, Spital Tiefenau, Switzerland
| | | | - Benedikt Wiestler
- Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar der TU München, Munich, Germany
| | - Christoph Berger
- Center for Translational Cancer Research (TranslaTUM), TU München, Munich, Germany
| | - Paul Eichinger
- Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar der TU München, Munich, Germany
| | - Mark Muhlau
- Department of Neurology, Klinikum rechts der Isar der TU München, Munich, Germany
| | - Mauricio Reyes
- Insel Data Science Centre, Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - Anke Salmen
- Univeristy Clinic for Neurology, Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - Andrew Chan
- Univeristy Clinic for Neurology, Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging, University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - Franca Wagner
- Support Center for Advanced Neuroimaging, University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Switzerland
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Lecler A, El Sanharawi I, El Methni J, Gout O, Koskas P, Savatovsky J. Improving Detection of Multiple Sclerosis Lesions in the Posterior Fossa Using an Optimized 3D-FLAIR Sequence at 3T. AJNR Am J Neuroradiol 2019; 40:1170-1176. [PMID: 31248862 DOI: 10.3174/ajnr.a6107] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Accepted: 05/14/2019] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE There is no consensus regarding the best MR imaging sequence for detecting MS lesions. The aim of our study was to assess the diagnostic value of optimized 3D-FLAIR in the detection of infratentorial MS lesions compared with an axial T2-weighted imaging, a 3D-FLAIR with factory settings, and a 3D double inversion recovery sequence. MATERIALS AND METHODS In this prospective study, 27 patients with confirmed MS were included. Two radiologists blinded to clinical data independently read the following sequences: axial T2WI, 3D double inversion recovery, standard 3D-FLAIR with factory settings, and optimized 3D-FLAIR. The main judgment criterion was the overall number of high-signal-intensity lesions in the posterior fossa; secondary objectives were the assessment of the reading confidence and the measurement of the contrast. A nonparametric Wilcoxon test was used to compare the MR images. RESULTS Twenty-two patients had at least 1 lesion in the posterior fossa. The optimized FLAIR sequence detected significantly more posterior fossa lesions than any other sequence: 7.5 versus 5.8, 4.8, and 4.1 (P values of .04, .03, and .03) with the T2WI, the double inversion recovery, and the standard FLAIR, respectively. The reading confidence index was significantly higher with the optimized FLAIR, and the contrast was significantly higher with the optimized FLAIR than with the standard FLAIR and the double inversion recovery. CONCLUSIONS An optimized 3D-FLAIR sequence improved posterior fossa lesion detection in patients with MS.
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Affiliation(s)
- A Lecler
- From the Departments of Neuroradiology (A.L., I.E.S., P.K., J.S.)
| | - I El Sanharawi
- From the Departments of Neuroradiology (A.L., I.E.S., P.K., J.S.)
| | - J El Methni
- Department of Biostatistics (J.E.M.), MAP5 Laboratory, Unité Mixte de Recherche Centre National de la Recherche Scientifique 8145, Paris Descartes University, Sorbonne Paris Cité, Paris, France
| | - O Gout
- Neurology (O.G.), Fondation Ophtalmologique Adolphe de Rothschild, Paris, France
| | - P Koskas
- From the Departments of Neuroradiology (A.L., I.E.S., P.K., J.S.)
| | - J Savatovsky
- From the Departments of Neuroradiology (A.L., I.E.S., P.K., J.S.)
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Bellanger G, Biotti D, Patsoura S, Ciron J, Ferrier M, Gramada R, Meluchova Z, Lerebours F, Catalaa I, Dumas H, Cognard C, Brassat D, Bonneville F. What is the Relevance of the Systematic Use of Gadolinium During the MRI Follow-Up of Multiple Sclerosis Patients Under Natalizumab? Clin Neuroradiol 2019; 30:553-558. [PMID: 31143968 DOI: 10.1007/s00062-019-00794-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Accepted: 05/08/2019] [Indexed: 10/26/2022]
Abstract
BACKGROUND Multiple sclerosis (MS) patients represent a population potentially affected by the intracerebral accumulation of gadolinium-based contrast agents (GBCA) due to repeated magnetic resonance imaging (MRI) performed during their lifetime; however, MRI is still the best tool to monitor MS inflammatory activity. OBJECTIVE This study aimed to evaluate the relevance of GBCA injections during the MRI follow-up of MS patients under natalizumab (Tysabri) treatment. METHODS The MRI data results were retrospectively reviewed in a monocentric study (University Hospital of Toulouse, France) from all consecutive patients treated with natalizumab from January 2014 to January 2017. For each examination during the whole MRI follow-up, new lesions (enhancing and non-enhancing) were analyzed. RESULTS A total of 129 patients were included in this study (65% female, mean age = 41 years, mean treatment duration 6.5 years, 50% positive for John Cunningham virus) and benefited from 735 MRIs with GBCA. Only 3 MRIs showed a new enhancing lesion, systematically encountered after treatment discontinuation. CONCLUSION According to this study based on the clinical and radiological practice, the systematic use of GBCA seems of limited relevance in the MRI follow-up of asymptomatic patients treated continuously with natalizumab.
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Affiliation(s)
- Guillaume Bellanger
- Department of Neuroradiology, CHU Purpan, Place du Docteur Baylac, 31059, Toulouse, France.
| | - Damien Biotti
- Department of Neurology, CHU Purpan, Toulouse, France
| | - Sofia Patsoura
- Department of Neuroradiology, CHU Purpan, Place du Docteur Baylac, 31059, Toulouse, France
| | | | - Marine Ferrier
- Department of Neuroradiology, CHU Purpan, Place du Docteur Baylac, 31059, Toulouse, France
| | - Raluca Gramada
- Department of Neuroradiology, CHU Purpan, Place du Docteur Baylac, 31059, Toulouse, France
| | - Zuzana Meluchova
- Department of Neuroradiology, CHU Purpan, Place du Docteur Baylac, 31059, Toulouse, France
| | | | - Isabelle Catalaa
- Department of Neuroradiology, CHU Purpan, Place du Docteur Baylac, 31059, Toulouse, France
| | - Hervé Dumas
- Department of Neuroradiology, CHU Purpan, Place du Docteur Baylac, 31059, Toulouse, France
| | - Christophe Cognard
- Department of Neuroradiology, CHU Purpan, Place du Docteur Baylac, 31059, Toulouse, France
| | - David Brassat
- Department of Neurology, CHU Purpan, Toulouse, France
| | - Fabrice Bonneville
- Department of Neuroradiology, CHU Purpan, Place du Docteur Baylac, 31059, Toulouse, France
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Trojano M, Bergamaschi R, Amato MP, Comi G, Ghezzi A, Lepore V, Marrosu MG, Mosconi P, Patti F, Ponzio M, Zaratin P, Battaglia MA. The Italian multiple sclerosis register. Neurol Sci 2019; 40:155-165. [PMID: 30426289 PMCID: PMC6329744 DOI: 10.1007/s10072-018-3610-0] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Accepted: 10/16/2018] [Indexed: 11/26/2022]
Abstract
The past decade has seen extraordinary increase in worldwide availability of and access to several large multiple sclerosis (MS) databases and registries. MS registries represent powerful tools to provide meaningful information on the burden, natural history, and long-term safety and effectiveness of treatments. Moreover, patients, physicians, industry, and policy makers have an active interest in real-world observational studies based on register data, as they have the potential to answer the questions that are most relevant to daily treatment decision-making. In 2014, the Italian MS Foundation, in collaboration with the Italian MS clinical centers, promoted and funded the creation of the Italian MS Register, a project in continuity with the existing Italian MS Database Network set up from 2001. Main objective of the Italian MS Register is to create an organized multicenter structure to collect data of all MS patients for better defining the disease epidemiology, improving quality of care, and promoting research projects in high-priority areas. The aim of this article is to present the current framework and network of the Italian MS register, including the methodology used to improve the quality of data collection and to facilitate the exchange of data and the collaboration among national and international groups.
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Affiliation(s)
- Maria Trojano
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari "Aldo Moro" Policlinico, Italy Piazza Umberto I, Bari, Bari, Italy.
| | | | - Maria Pia Amato
- Department NEUROFARBA, MS Center AOU Careggi, University of Florence, Florence, Italy
| | - Giancarlo Comi
- Neurology Department and INSPE-Institute of Experimental Neurology, Vita-Salute San Raffaele University, Milan, Italy
| | - Angelo Ghezzi
- Centro Studi Sclerosi Multipla, Ospedale di Gallarate, Gallarate, Va, Italy
| | - Vito Lepore
- Coreserach Center for Outcomes Research and Clinical Epidemiology, Pescara, Italy
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | | | - Paola Mosconi
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Francesco Patti
- Department of Neurosciences G.F. Ingrassia, University of Catania, Catania, Italy
| | - Michela Ponzio
- Italian Multiple Sclerosis Foundation, Via Operai 40, Genoa, Italy
| | - Paola Zaratin
- Italian Multiple Sclerosis Foundation, Via Operai 40, Genoa, Italy
| | - Mario Alberto Battaglia
- Italian Multiple Sclerosis Foundation, Via Operai 40, Genoa, Italy.
- Department of Life Sciences, University of Siena, Siena, Italy.
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Vukusic S, Casey R, Rollot F, Brochet B, Pelletier J, Laplaud DA, De Sèze J, Cotton F, Moreau T, Stankoff B, Fontaine B, Guillemin F, Debouverie M, Clanet M. Observatoire Français de la Sclérose en Plaques (OFSEP): A unique multimodal nationwide MS registry in France. Mult Scler 2018; 26:118-122. [PMID: 30541380 DOI: 10.1177/1352458518815602] [Citation(s) in RCA: 63] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The care of multiple sclerosis (MS) in France is based on two complementary interlinked networks: MS expert centers in university hospitals and regional networks of neurologists. The routine use of European database for multiple sclerosis (EDMUS) in all those centers has paved the way for the constitution of a national registry, designated as Observatoire Français de la Sclérose En Plaques (OFSEP). It promotes a prospective, standardized, high-quality, and multimodal collection of data. On June 2018, there were 68.097 files, with 71.1% females, representing 761,185 person-years. This huge database is open to the scientific community and might contribute exploring unresolved issues and unmet needs in MS.
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Affiliation(s)
- Sandra Vukusic
- Service de neurologie-sclérose en plaques, pathologies de la myéline et neuro-inflammation, Hôpital Pierre Wertheimer, Hospices Civils de Lyon, Bron, France/Observatoire Français de la Sclérose en Plaques, Centre de Recherche en Neurosciences de Lyon, INSERM 1028 et CNRS UMR 5292, F-69003 Lyon, France/Université Claude Bernard Lyon 1, Université de Lyon, Lyon, France/ Eugène Devic EDMUS Foundation against Multiple Sclerosis, Bron, France
| | - Romain Casey
- Service de neurologie-sclérose en plaques, pathologies de la myéline et neuro-inflammation, Hôpital Pierre Wertheimer, Hospices Civils de Lyon, Bron, France/Observatoire Français de la Sclérose en Plaques, Centre de Recherche en Neurosciences de Lyon, INSERM 1028 et CNRS UMR 5292, F-69003 Lyon, France/Université Claude Bernard Lyon 1, Université de Lyon, Lyon, France/ Eugène Devic EDMUS Foundation against Multiple Sclerosis, Bron, France
| | - Fabien Rollot
- Service de neurologie-sclérose en plaques, pathologies de la myéline et neuro-inflammation, Hôpital Pierre Wertheimer, Hospices Civils de Lyon, Bron, France/Observatoire Français de la Sclérose en Plaques, Centre de Recherche en Neurosciences de Lyon, INSERM 1028 et CNRS UMR 5292, F-69003 Lyon, France/Université Claude Bernard Lyon 1, Université de Lyon, Lyon, France/ Eugène Devic EDMUS Foundation against Multiple Sclerosis, Bron, France
| | - Bruno Brochet
- Service de Neurologie, CHU de Bordeaux, Bordeaux, France/Université de Bordeaux, Bordeaux, France/INSERM U 1215, Neurocentre Magendie, Bordeaux, France
| | - Jean Pelletier
- Service de Neurologie, Pôle de Neurosciences Cliniques, Hôpital de la Timone, APHM and CNRS, CRMBM UMR 7339, Aix-Marseille Université, Marseille, France
| | - David-Axel Laplaud
- Service de Neurologie, Centre Hospitalier Universitaire de Nantes and CIC015 INSERM, Nantes, France/INSERM CR1064, Nantes, France
| | - Jérôme De Sèze
- Department of Neurology and Clinical Investigation Center, CHU de Strasbourg, INSERM 1434, Strasbourg, France
| | - François Cotton
- Department of Radiology, Centre Hospitalier Lyon-Sud, Hospices Civils de Lyon, Pierre-Bénite, France/ CREATIS, CNRS UMR 5220, INSERM U1044, Villeurbanne, France/ Université Lyon 1, Lyon, France
| | - Thibault Moreau
- Department of Neurology, CHU de Dijon, EA7270, University Bourgogne Franche-Comté, Dijon, France
| | - Bruno Stankoff
- Neurology Department, Hôpital Saint-Antoine, APHP, Paris, France
| | - Bertrand Fontaine
- INSERM UMR S 1127, CNRS UMR 7225, Institut du Cerveau et de la Moelle épinière (ICM), Hôpital de la Pitié Salpêtrière, Sorbonne Universités, UPMC Paris 06, Paris, France
| | - Francis Guillemin
- EA 4360 APEMAC, Université de Lorraine, Nancy, France/INSERM CIC 1433 Clinical Epidemiology, Nancy University Hospital, Nancy, France
| | - Marc Debouverie
- EA 4360 APEMAC, Université de Lorraine, Nancy, France/Department of Neurology, Nancy University Hospital, Nancy, France
| | - Michel Clanet
- Department of Neurology, CHU de Toulouse, Toulouse, France
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Said M, El Ayoubi NK, Hannoun S, Haddad R, Saba L, Jalkh Y, Yamout BI, Khoury SJ. The Bayesian risk estimate at onset (BREMSO) correlates with cognitive and physical disability in patients with early multiple sclerosis. Mult Scler Relat Disord 2018; 26:96-102. [PMID: 30243236 DOI: 10.1016/j.msard.2018.09.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2018] [Revised: 09/03/2018] [Accepted: 09/06/2018] [Indexed: 11/18/2022]
Abstract
BACKGROUND Prevention of long-term disability is the goal of therapeutic intervention in Relapsing Remitting MS (RRMS). The Bayesian Risk Estimate for MS at Onset (BREMSO) gives an individual risk score predicting disease evolution into Secondary Progressive MS (SPMS). We investigated whether BREMSO correlates with physical disability, cognitive dysfunction, and regional brain atrophy early in MS. METHODS One hundred RRMS patients with at least two years of follow-up were enrolled. BREMSO score as well as Symbol Digit Modalities Test (SDMT) and Multiple Sclerosis Severity Score (MSSS), Timed 25-Foot Walk Test (T25-FW) and 9-Hole Peg Test (9-HPT), were assessed. Intracranial volume (ICV), subcortical gray matter structures and corpus callosum (CC) were automatically segmented on MRI images and their volumes measured. RESULTS BREMSO score correlated negatively with SDMT at visit1 (β = -0.33, p = 0.019), visit2 (β = -0.34, p = 0.017) and visit3 (β = -0.34, p = 0.014), and positively with MSSS at visit1 (r = 0.38, p = 0.006), visit2 (r = 0.47, p < 0.0001) and visit3 (r = 0.42, p = 0.002), but not with T25-FW and 9-HPT. BREMSO negatively correlated with CC volume at baseline (p < 0.03). No correlations were found with ICV and subcortical gray matter. CONCLUSIONS BREMSO score at onset correlated with physical disability (MSSS), cognitive function (SDMT) and CC volume measurements in patients with early MS.
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Affiliation(s)
- Marianne Said
- Nehme and Therese Tohme Multiple Sclerosis Center, American University of Beirut Medical Center, PO Box: 11-0236, Riad El Solh, Beirut 1107 2020, Lebanon
| | - Nabil K El Ayoubi
- Nehme and Therese Tohme Multiple Sclerosis Center, American University of Beirut Medical Center, PO Box: 11-0236, Riad El Solh, Beirut 1107 2020, Lebanon
| | - Salem Hannoun
- Nehme and Therese Tohme Multiple Sclerosis Center, American University of Beirut Medical Center, PO Box: 11-0236, Riad El Solh, Beirut 1107 2020, Lebanon; Abu-Haidar Neuroscience Institute, American University of Beirut Medical Center, PO Box: 11-0236, Riad El Solh, Beirut 1107 2020, Lebanon
| | - Ribal Haddad
- Nehme and Therese Tohme Multiple Sclerosis Center, American University of Beirut Medical Center, PO Box: 11-0236, Riad El Solh, Beirut 1107 2020, Lebanon
| | - Leslie Saba
- UCD School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland
| | - Youmna Jalkh
- Nehme and Therese Tohme Multiple Sclerosis Center, American University of Beirut Medical Center, PO Box: 11-0236, Riad El Solh, Beirut 1107 2020, Lebanon
| | - Bassem I Yamout
- Nehme and Therese Tohme Multiple Sclerosis Center, American University of Beirut Medical Center, PO Box: 11-0236, Riad El Solh, Beirut 1107 2020, Lebanon
| | - Samia J Khoury
- Nehme and Therese Tohme Multiple Sclerosis Center, American University of Beirut Medical Center, PO Box: 11-0236, Riad El Solh, Beirut 1107 2020, Lebanon; Abu-Haidar Neuroscience Institute, American University of Beirut Medical Center, PO Box: 11-0236, Riad El Solh, Beirut 1107 2020, Lebanon.
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39
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Commowick O, Istace A, Kain M, Laurent B, Leray F, Simon M, Pop SC, Girard P, Améli R, Ferré JC, Kerbrat A, Tourdias T, Cervenansky F, Glatard T, Beaumont J, Doyle S, Forbes F, Knight J, Khademi A, Mahbod A, Wang C, McKinley R, Wagner F, Muschelli J, Sweeney E, Roura E, Lladó X, Santos MM, Santos WP, Silva-Filho AG, Tomas-Fernandez X, Urien H, Bloch I, Valverde S, Cabezas M, Vera-Olmos FJ, Malpica N, Guttmann C, Vukusic S, Edan G, Dojat M, Styner M, Warfield SK, Cotton F, Barillot C. Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure. Sci Rep 2018; 8:13650. [PMID: 30209345 PMCID: PMC6135867 DOI: 10.1038/s41598-018-31911-7] [Citation(s) in RCA: 114] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Accepted: 08/06/2018] [Indexed: 11/09/2022] Open
Abstract
We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner. This computing infrastructure was used to evaluate thirteen methods of MS lesions segmentation, exploring a broad range of state-of-theart algorithms, against a high-quality database of 53 MS cases coming from four centers following a common definition of the acquisition protocol. Each case was annotated manually by an unprecedented number of seven different experts. Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods (random forests, deep learning, …), are still trailing human expertise on both detection and delineation criteria. In addition, we demonstrate that computing a statistically robust consensus of the algorithms performs closer to human expertise on one score (segmentation) although still trailing on detection scores.
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Affiliation(s)
- Olivier Commowick
- VISAGES: INSERM U1228 - CNRS UMR6074 - Inria, University of Rennes I, Rennes, France.
| | - Audrey Istace
- Department of Radiology, Lyon Sud Hospital, Hospices Civils de Lyon, Lyon, France
| | - Michaël Kain
- VISAGES: INSERM U1228 - CNRS UMR6074 - Inria, University of Rennes I, Rennes, France
| | - Baptiste Laurent
- LaTIM, INSERM, UMR 1101, University of Brest, IBSAM, Brest, France
| | - Florent Leray
- VISAGES: INSERM U1228 - CNRS UMR6074 - Inria, University of Rennes I, Rennes, France
| | - Mathieu Simon
- VISAGES: INSERM U1228 - CNRS UMR6074 - Inria, University of Rennes I, Rennes, France
| | - Sorina Camarasu Pop
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, F-69621, Lyon, France
| | - Pascal Girard
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, F-69621, Lyon, France
| | - Roxana Améli
- Department of Radiology, Lyon Sud Hospital, Hospices Civils de Lyon, Lyon, France
| | - Jean-Christophe Ferré
- VISAGES: INSERM U1228 - CNRS UMR6074 - Inria, University of Rennes I, Rennes, France.,CHU Rennes, Department of Neuroradiology, F-35033, Rennes, France
| | - Anne Kerbrat
- VISAGES: INSERM U1228 - CNRS UMR6074 - Inria, University of Rennes I, Rennes, France.,CHU Rennes, Department of Neurology, F-35033, Rennes, France
| | - Thomas Tourdias
- CHU de Bordeaux, Service de Neuro-Imagerie, Bordeaux, France
| | - Frédéric Cervenansky
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, F-69621, Lyon, France
| | - Tristan Glatard
- Department of Computer Science and Software Engineering, Concordia University, Montreal, Canada
| | - Jérémy Beaumont
- VISAGES: INSERM U1228 - CNRS UMR6074 - Inria, University of Rennes I, Rennes, France
| | | | - Florence Forbes
- Pixyl Medical, Grenoble, France.,Inria Grenoble Rhône-Alpes, Grenoble, France
| | - Jesse Knight
- Image Analysis in Medicine Lab, School of Engineering, University of Guelph, Guelph, Canada
| | - April Khademi
- Image Analysis in Medicine Lab (IAMLAB), Ryerson University, Toronto, Canada
| | - Amirreza Mahbod
- School of Technology and Health, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Chunliang Wang
- School of Technology and Health, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Richard McKinley
- Department of Diagnostic and Interventional Neuroradiology, Inselspital, University of Bern, Bern, Switzerland
| | - Franca Wagner
- Department of Diagnostic and Interventional Neuroradiology, Inselspital, University of Bern, Bern, Switzerland
| | - John Muschelli
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | | | - Eloy Roura
- Research institute of Computer Vision and Robotics (VICOROB), University of Girona, Girona, Spain
| | - Xavier Lladó
- Research institute of Computer Vision and Robotics (VICOROB), University of Girona, Girona, Spain
| | - Michel M Santos
- Centro de Informática, Universidade Federal de Pernambuco, Pernambuco, Brazil
| | - Wellington P Santos
- Depto. de Eng. Biomédica, Universidade Federal de Pernambuco, Pernambuco, Brazil
| | - Abel G Silva-Filho
- Centro de Informática, Universidade Federal de Pernambuco, Pernambuco, Brazil
| | - Xavier Tomas-Fernandez
- Computational Radiology Laboratory, Department of Radiology, Children's Hospital, 300 Longwood Avenue, Boston, MA, USA
| | - Hélène Urien
- LTCI, Télécom ParisTech, Université Paris-Saclay, Paris, France
| | - Isabelle Bloch
- LTCI, Télécom ParisTech, Université Paris-Saclay, Paris, France
| | - Sergi Valverde
- Research institute of Computer Vision and Robotics (VICOROB), University of Girona, Girona, Spain
| | - Mariano Cabezas
- Research institute of Computer Vision and Robotics (VICOROB), University of Girona, Girona, Spain
| | | | - Norberto Malpica
- Medical Image Analysis Lab, Universidad Rey Juan Carlos, Madrid, Spain
| | - Charles Guttmann
- Center for Neurological Imaging, Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA
| | - Sandra Vukusic
- Department of Radiology, Lyon Sud Hospital, Hospices Civils de Lyon, Lyon, France
| | - Gilles Edan
- VISAGES: INSERM U1228 - CNRS UMR6074 - Inria, University of Rennes I, Rennes, France.,CHU Rennes, Department of Neurology, F-35033, Rennes, France
| | - Michel Dojat
- Inserm U1216, University Grenoble Alpes, CHU Grenoble, GIN, Grenoble, France
| | - Martin Styner
- Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA
| | - Simon K Warfield
- Computational Radiology Laboratory, Department of Radiology, Children's Hospital, 300 Longwood Avenue, Boston, MA, USA
| | - François Cotton
- Department of Radiology, Lyon Sud Hospital, Hospices Civils de Lyon, Lyon, France
| | - Christian Barillot
- VISAGES: INSERM U1228 - CNRS UMR6074 - Inria, University of Rennes I, Rennes, France
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40
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Gadolinium effect on thalamus and whole brain tissue segmentation. Neuroradiology 2018; 60:1167-1173. [DOI: 10.1007/s00234-018-2082-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Accepted: 08/15/2018] [Indexed: 10/28/2022]
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41
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Conventional and advanced MRI in multiple sclerosis. Rev Neurol (Paris) 2018; 174:391-397. [DOI: 10.1016/j.neurol.2018.03.009] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2017] [Revised: 03/08/2018] [Accepted: 03/08/2018] [Indexed: 12/28/2022]
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42
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Hannoun S, Heidelberg D, Hourani R, Nguyen TTT, Brisset JC, Grand S, Kremer S, Bonneville F, Guttmann CR, Dousset V, Cotton F. Diagnostic value of 3DFLAIR in clinical practice for the detection of infratentorial lesions in multiple sclerosis in regard to dual echo T2 sequences. Eur J Radiol 2018; 102:146-151. [DOI: 10.1016/j.ejrad.2018.03.017] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2017] [Revised: 02/19/2018] [Accepted: 03/13/2018] [Indexed: 11/16/2022]
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43
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Calocer F, Dejardin O, Droulon K, Launoy G, Defer G. Socio-economic status influences access to second-line disease modifying treatment in Relapsing Remitting Multiple Sclerosis patients. PLoS One 2018; 13:e0191646. [PMID: 29390025 PMCID: PMC5794112 DOI: 10.1371/journal.pone.0191646] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2017] [Accepted: 01/09/2018] [Indexed: 11/21/2022] Open
Abstract
Objective In MS, Socio-Economic status (SES) may influence healthcare and access to disease-modifying treatments (DMTs). Optimising delays to switch patients to a second-line DMT may hamper disease progression most effectively and achieve long term disease control. The objective of this study is to identify the influence of SES on the delay between first and second line DMT in RRMS patients, in Western-Normandy, France. Methods The association between SES and the delay to access a second-line DMT were studied using data from the MS registry of Western-Normandy including 733 patients with a diagnosis of RRMS during the period in question [1982–2011]. We used the European Deprivation Index (EDI), a score with a rank level inversely related to SES. We performed multivariate adjusted Cox models for studying EDI effect on the delay between first and second line DMT. Results No significant influence of SES was observed on delay to access a second-line DMT if first-line DMT exposure time was less than 5 years. After 5 years from initiation of first-line treatment the risk of accessing a second-line DMT is 3 times higher for patients with lower deprivation indices (1st quintile of EDI) ([HR] 3.14 95%CI [1.72–5.72], p-value<0.001) compared to patients with higher values (EDI quintiles 2 to 5). Interpretation In RRMS, a high SES may facilitate access to a second-line DMT a few years after first-line DMT exposure. Greater consideration should also be given to the SES of MS patients as a risk factor in therapeutic healthcare issues throughout medical follow-up.
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Affiliation(s)
- Floriane Calocer
- CHU de Caen, Department of Neurology, Caen, FR
- Normandie Université, UNICAEN, INSERM 1237, Physiopathology and Imaging of Neurological Disorders, Caen, FR
- * E-mail:
| | - Olivier Dejardin
- CHU de Caen, Pôle de Recherche, Caen, FR
- Normandie Université, UNICAEN, INSERM 1086, ANTICIPE « Cancers et Préventions » Caen, FR
| | | | - Guy Launoy
- CHU de Caen, Pôle de Recherche, Caen, FR
- Normandie Université, UNICAEN, INSERM 1086, ANTICIPE « Cancers et Préventions » Caen, FR
| | - Gilles Defer
- CHU de Caen, Department of Neurology, Caen, FR
- Normandie Université, UNICAEN, INSERM 1237, Physiopathology and Imaging of Neurological Disorders, Caen, FR
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44
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A hybrid approach based on logistic classification and iterative contrast enhancement algorithm for hyperintense multiple sclerosis lesion segmentation. Med Biol Eng Comput 2017; 56:1063-1076. [DOI: 10.1007/s11517-017-1747-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2017] [Accepted: 10/25/2017] [Indexed: 01/05/2023]
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45
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Atypical intracranial artifacts caused by dreadlocks during brain Magnetic Resonance Imaging: Keep calm and recognize them. J Neuroradiol 2017; 44:57-62. [DOI: 10.1016/j.neurad.2016.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2016] [Revised: 06/27/2016] [Accepted: 06/30/2016] [Indexed: 11/17/2022]
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46
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Vågberg M, Axelsson M, Birgander R, Burman J, Cananau C, Forslin Y, Granberg T, Gunnarsson M, von Heijne A, Jönsson L, Karrenbauer VD, Larsson EM, Lindqvist T, Lycke J, Lönn L, Mentesidou E, Müller S, Nilsson P, Piehl F, Svenningsson A, Vrethem M, Wikström J. Guidelines for the use of magnetic resonance imaging in diagnosing and monitoring the treatment of multiple sclerosis: recommendations of the Swedish Multiple Sclerosis Association and the Swedish Neuroradiological Society. Acta Neurol Scand 2017; 135:17-24. [PMID: 27558404 PMCID: PMC5157754 DOI: 10.1111/ane.12667] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/05/2016] [Indexed: 01/28/2023]
Abstract
Multiple sclerosis (MS) is associated with inflammatory lesions in the brain and spinal cord. The detection of such inflammatory lesions using magnetic resonance imaging (MRI) is important in the consideration of the diagnosis and differential diagnoses of MS, as well as in the monitoring of disease activity and predicting treatment efficacy. Although there is strong evidence supporting the use of MRI for both the diagnosis and monitoring of disease activity, there is a lack of evidence regarding which MRI protocols to use, the frequency of examinations, and in what clinical situations to consider MRI examination. A national workshop to discuss these issues was held in Stockholm, Sweden, in August 2015, which resulted in a Swedish consensus statement regarding the use of MRI in the care of individuals with MS. The aim of this consensus statement is to provide practical advice for the use of MRI in this setting. The recommendations are based on a review of relevant literature and the clinical experience of workshop attendees. It is our hope that these recommendations will benefit individuals with MS and guide healthcare professionals responsible for their care.
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Affiliation(s)
- M. Vågberg
- Department of Pharmacology and Clinical Neuroscience, Section of Neuroscience; Umeå University; Umeå Sweden
| | - M. Axelsson
- Department of Clinical Neuroscience; Institute of Neuroscience and Physiology at Sahlgrenska Academy; University of Gothenburg; Gothenburg Sweden
| | - R. Birgander
- Department of Radiation Sciences; Umeå University; Umeå Sweden
| | - J. Burman
- Department of Neuroscience; Uppsala University; Uppsala Sweden
| | - C. Cananau
- Department of Clinical Science, Intervention and Technology; Department of Radiology; Karolinska Institutet; Karolinska University Hospital; Stockholm Sweden
| | - Y. Forslin
- Department of Clinical Science, Intervention and Technology; Department of Radiology; Karolinska Institutet; Karolinska University Hospital; Stockholm Sweden
| | - T. Granberg
- Department of Clinical Science, Intervention and Technology; Department of Radiology; Karolinska Institutet; Karolinska University Hospital; Stockholm Sweden
| | - M. Gunnarsson
- Department of Neurology; School of Medical Sciences; Örebro University; Örebro Sweden
| | - A. von Heijne
- Department of Clinical Sciences; Karolinska Institutet; Danderyd Hospital; Stockholm Sweden
| | - L. Jönsson
- Department of Neuroradiology; Sahlgrenska University Hospital; Gothenburg Sweden
| | - V. D. Karrenbauer
- Department of Clinical Neuroscience; Department of Neurology; Karolinska Institutet; Karolinska University Hospital; Stockholm Sweden
| | - E.-M. Larsson
- Department of Surgical Sciences, Radiology; Uppsala University; Uppsala Sweden
| | - T. Lindqvist
- Department of Radiation Sciences; Umeå University; Umeå Sweden
| | - J. Lycke
- Department of Clinical Neuroscience; Institute of Neuroscience and Physiology at Sahlgrenska Academy; University of Gothenburg; Gothenburg Sweden
| | - L. Lönn
- Department of Clinical Science, Intervention and Technology; Department of Radiology; Karolinska Institutet; Karolinska University Hospital; Stockholm Sweden
| | - E. Mentesidou
- Department of Clinical Neuroscience; Department of Neurology; Karolinska Institutet; Karolinska University Hospital; Stockholm Sweden
| | - S. Müller
- Department of Clinical Science, Intervention and Technology; Department of Radiology; Karolinska Institutet; Karolinska University Hospital; Stockholm Sweden
| | - P. Nilsson
- Department of Clinical Sciences Lund, Neurology; Faculty of Medicine; Lund University; Lund Sweden
| | - F. Piehl
- Department of Clinical Neuroscience; Department of Neurology; Karolinska Institutet; Karolinska University Hospital; Stockholm Sweden
| | - A. Svenningsson
- Department of Clinical Sciences; Karolinska Institutet; Danderyd Hospital; Stockholm Sweden
| | - M. Vrethem
- Department of Neurology and Department of Clinical and Experimental Medicine; Linköping University; Linköping Sweden
| | - J. Wikström
- Department of Surgical Sciences, Radiology; Uppsala University; Uppsala Sweden
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Kocevar G, Stamile C, Hannoun S, Cotton F, Vukusic S, Durand-Dubief F, Sappey-Marinier D. Graph Theory-Based Brain Connectivity for Automatic Classification of Multiple Sclerosis Clinical Courses. Front Neurosci 2016; 10:478. [PMID: 27826224 PMCID: PMC5078266 DOI: 10.3389/fnins.2016.00478] [Citation(s) in RCA: 62] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2016] [Accepted: 10/06/2016] [Indexed: 11/13/2022] Open
Abstract
Purpose: In this work, we introduce a method to classify Multiple Sclerosis (MS) patients into four clinical profiles using structural connectivity information. For the first time, we try to solve this question in a fully automated way using a computer-based method. The main goal is to show how the combination of graph-derived metrics with machine learning techniques constitutes a powerful tool for a better characterization and classification of MS clinical profiles. Materials and Methods: Sixty-four MS patients [12 Clinical Isolated Syndrome (CIS), 24 Relapsing Remitting (RR), 24 Secondary Progressive (SP), and 17 Primary Progressive (PP)] along with 26 healthy controls (HC) underwent MR examination. T1 and diffusion tensor imaging (DTI) were used to obtain structural connectivity matrices for each subject. Global graph metrics, such as density and modularity, were estimated and compared between subjects' groups. These metrics were further used to classify patients using tuned Support Vector Machine (SVM) combined with Radial Basic Function (RBF) kernel. Results: When comparing MS patients to HC subjects, a greater assortativity, transitivity, and characteristic path length as well as a lower global efficiency were found. Using all graph metrics, the best F-Measures (91.8, 91.8, 75.6, and 70.6%) were obtained for binary (HC-CIS, CIS-RR, RR-PP) and multi-class (CIS-RR-SP) classification tasks, respectively. When using only one graph metric, the best F-Measures (83.6, 88.9, and 70.7%) were achieved for modularity with previous binary classification tasks. Conclusion: Based on a simple DTI acquisition associated with structural brain connectivity analysis, this automatic method allowed an accurate classification of different MS patients' clinical profiles.
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Affiliation(s)
- Gabriel Kocevar
- CREATIS Centre National de la Recherche Scientifique UMR5220 and Institut National de la Santé et de la Recherche Médicale U1206, INSA-Lyon, Université de Lyon, Université Claude Bernard-Lyon 1Lyon, France
| | - Claudio Stamile
- CREATIS Centre National de la Recherche Scientifique UMR5220 and Institut National de la Santé et de la Recherche Médicale U1206, INSA-Lyon, Université de Lyon, Université Claude Bernard-Lyon 1Lyon, France
| | - Salem Hannoun
- CREATIS Centre National de la Recherche Scientifique UMR5220 and Institut National de la Santé et de la Recherche Médicale U1206, INSA-Lyon, Université de Lyon, Université Claude Bernard-Lyon 1Lyon, France
- Faculty of Medicine, Abu-Haidar Neuroscience Institute, American University of BeirutBeirut, Lebanon
| | - François Cotton
- CREATIS Centre National de la Recherche Scientifique UMR5220 and Institut National de la Santé et de la Recherche Médicale U1206, INSA-Lyon, Université de Lyon, Université Claude Bernard-Lyon 1Lyon, France
- Service de Radiologie, Centre Hospitalier Lyon-Sud, Hospices Civils de LyonLyon, France
| | - Sandra Vukusic
- Service de Neurologie A, Hôpital Neurologique, Hospices Civils de LyonLyon, France
| | - Françoise Durand-Dubief
- CREATIS Centre National de la Recherche Scientifique UMR5220 and Institut National de la Santé et de la Recherche Médicale U1206, INSA-Lyon, Université de Lyon, Université Claude Bernard-Lyon 1Lyon, France
- Service de Neurologie A, Hôpital Neurologique, Hospices Civils de LyonLyon, France
| | - Dominique Sappey-Marinier
- CREATIS Centre National de la Recherche Scientifique UMR5220 and Institut National de la Santé et de la Recherche Médicale U1206, INSA-Lyon, Université de Lyon, Université Claude Bernard-Lyon 1Lyon, France
- CERMEP—Imagerie du Vivant, Université de LyonLyon, France
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48
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Tourdias T, Dousset V. Faut-il injecter ? J Neuroradiol 2016. [DOI: 10.1016/j.neurad.2016.01.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Baltruschat SA, Ventura-Campos N, Cruz-Gómez ÁJ, Belenguer A, Forn C. Gray matter atrophy is associated with functional connectivity reorganization during the Paced Auditory Serial Addition Test (PASAT) execution in Multiple Sclerosis (MS). J Neuroradiol 2015; 42:141-9. [PMID: 25857687 DOI: 10.1016/j.neurad.2015.02.006] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2014] [Revised: 02/14/2015] [Accepted: 02/28/2015] [Indexed: 01/21/2023]
Abstract
BACKGROUND AND PURPOSE We explored the relationship between gray matter atrophy and reorganization of functional connectivity in multiple sclerosis patients during execution of the Paced Auditory Serial Addition Test (PASAT). MATERIALS AND METHODS Seventeen patients and 15 healthy controls were selected for the study. Atrophy was determined using voxel-based morphometry, and atrophy-related connectivity changes were assessed using psychophysiological interaction analysis. Group differences, and correlations with PASAT performance and radiological variables were also examined. RESULTS Gray matter atrophy in MS patients was circumscribed to the bilateral posterior cingulate gyrus/precuneus. Compared with controls, patients showed stronger connectivity between the left posterior cingulate gyrus/precuneus, and the left middle temporal gyrus and left cerebellum. A regression analysis in controls showed a negative correlation between PASAT scores and functional connectivity between: (1) the left posterior cingulate gyrus/precuneus, and left pre/postcentral gyri and left occipital gyrus, and (2) the right posterior cingulate gyrus/precuneus, and bilateral cerebellum and left pre/postcentral gyri. Patients showed a negative correlation between brain parenchymal fraction and functional connectivity between the left posterior cingulate gyrus/precuneus and left cerebellum. CONCLUSION Patients with early MS and little brain damage presented more connectivity during PASAT execution, which may be interpreted as compensatory processes that help preserve cognitive functions.
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Affiliation(s)
- Sabina Anna Baltruschat
- Universitat Jaume I, Campus Riu Sec, Fac. Ciències de la Salut, Departament de Psicología Bàsica, Clínica i Psicobiología, Avd. Sos Baynat s/n, 12071 Castelló de la Plana, Spain
| | - Noelia Ventura-Campos
- Universitat Jaume I, Campus Riu Sec, Fac. Ciències de la Salut, Departament de Psicología Bàsica, Clínica i Psicobiología, Avd. Sos Baynat s/n, 12071 Castelló de la Plana, Spain
| | - Álvaro Javier Cruz-Gómez
- Universitat Jaume I, Campus Riu Sec, Fac. Ciències de la Salut, Departament de Psicología Bàsica, Clínica i Psicobiología, Avd. Sos Baynat s/n, 12071 Castelló de la Plana, Spain
| | - Antonio Belenguer
- Hospital General de Castellón, Servicio de Neurología, Castelló de la Plana, Spain
| | - Cristina Forn
- Universitat Jaume I, Campus Riu Sec, Fac. Ciències de la Salut, Departament de Psicología Bàsica, Clínica i Psicobiología, Avd. Sos Baynat s/n, 12071 Castelló de la Plana, Spain.
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Abstract
Optic neuritis, myelitis and brainstem syndrome accompanied by a symptomatic MRI T2 or FLAIR hyperintensity and T1 hypointensity are highly suggestive of multiple sclerosis (MS) in young adults. They are called "clinically isolated syndrome" (CIS) and correspond to the typical first multiple sclerosis (MS) episode, especially when associated with other asymptomatic demyelinating lesions, without clinical, radiological and immunological sign of differential diagnosis. After a CIS, the delay of apparition of a relapse, which corresponds to the conversion to clinically definite MS (CDMS), varies from several months to more than 10 years (10-15% of cases, generally called benign RRMS). This delay is generally associated with the number and location of demyelinating lesions of the brain and spinal cord and the results of CSF analysis. Several studies comparing different MRI criteria for dissemination in space and dissemination in time of demyelinating lesions, two hallmarks of MS, provided enough substantial data to update diagnostic criteria for MS after a CIS. In the last revision of the McDonald's criteria in 2010, diagnostic criteria were simplified and now the diagnosis can be made by a single initial scan that proves the presence of active asymptomatic lesions (with gadolinium enhancement) and of unenhanced lesions. However, time to conversion remains highly unpredictable for a given patient and CIS can remain isolated, especially for idiopathic unilateral optic neuritis or myelitis. Univariate analyses of clinical, radiological, biological or electrophysiological characteristics of CIS patients in small series identified numerous risk factors of rapid conversion to MS. However, large series of CIS patients analyzing several characteristics of CIS patients and the influence of disease modifying therapies brought important information about the risk of CDMS or RRMS over up to 20 years of follow-up. They confirmed the importance of the initial MRI pattern of demyelinating lesions and of CSF oligoclonal bands. Available treatments of MS (immunomodulators or immunosuppressants) have also shown unequivocal efficacy to slow the conversion to RRMS after a CIS, but they could be unnecessary for patients with benign RRMS. Beyond diagnostic criteria, knowledge of established and potential risk factors of conversion to MS and of disability progression is essential for CIS patients' follow-up and initiation of disease modifying therapies.
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Affiliation(s)
- Éric Thouvenot
- Hôpital Carémeau, service de neurologie, 30029 Nîmes cedex 9, France; Université de Montpellier, institut de génomique fonctionnelle, équipe « neuroprotéomique et signalisation des maladies neurologiques et psychiatriques », UMR 5203, 34094 Montpellier cedex, France.
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