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Comi G, Dalla Costa G, Stankoff B, Hartung HP, Soelberg Sørensen P, Vermersch P, Leocani L. Assessing disease progression and treatment response in progressive multiple sclerosis. Nat Rev Neurol 2024; 20:573-586. [PMID: 39251843 DOI: 10.1038/s41582-024-01006-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/30/2024] [Indexed: 09/11/2024]
Abstract
Progressive multiple sclerosis poses a considerable challenge in the evaluation of disease progression and treatment response owing to its multifaceted pathophysiology. Traditional clinical measures such as the Expanded Disability Status Scale are limited in capturing the full scope of disease and treatment effects. Advanced imaging techniques, including MRI and PET scans, have emerged as valuable tools for the assessment of neurodegenerative processes, including the respective role of adaptive and innate immunity, detailed insights into brain and spinal cord atrophy, lesion dynamics and grey matter damage. The potential of cerebrospinal fluid and blood biomarkers is increasingly recognized, with neurofilament light chain levels being a notable indicator of neuro-axonal damage. Moreover, patient-reported outcomes are crucial for reflecting the subjective experience of disease progression and treatment efficacy, covering aspects such as fatigue, cognitive function and overall quality of life. The future incorporation of digital technologies and wearable devices in research and clinical practice promises to enhance our understanding of functional impairments and disease progression. This Review offers a comprehensive examination of these diverse evaluation tools, highlighting their combined use in accurately assessing disease progression and treatment efficacy in progressive multiple sclerosis, thereby guiding more effective therapeutic strategies.
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Affiliation(s)
- Giancarlo Comi
- Department of Neurorehabilitation Sciences, Casa di Cura Igea, Milan, Italy.
| | | | - Bruno Stankoff
- Sorbonne Université, Paris Brain Institute, Institut du Cerveau et de la Moelle Épinière, Centre National de la Recherche Scientifique, Inserm, Paris, France
| | - Hans-Peter Hartung
- Brain and Mind Center, University of Sydney, Sydney, Australia
- Department of Neurology, Palacky University Olomouc, Olomouc, Czech Republic
- Department of Neurology, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany
| | - Per Soelberg Sørensen
- Department of Neurology, Danish Multiple Sclerosis Center, University of Copenhagen, Rigshospitalet, Copenhagen, Denmark
| | - Patrick Vermersch
- University of Lille, Inserm U1172, Lille Neuroscience & Cognition, Centre Hospitalier Universitaire de Lille, Fédération Hospitalo-Universitaire Precision Medicine in Psychiatry, Lille, France
| | - Letizia Leocani
- Vita-Salute San Raffaele University, Milan, Italy
- Multiple Sclerosis Center, Casa di Cura Igea, Milan, Italy
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Kreiter D, Postma AA, Hupperts R, Gerlach O. Hallmarks of spinal cord pathology in multiple sclerosis. J Neurol Sci 2024; 456:122846. [PMID: 38142540 DOI: 10.1016/j.jns.2023.122846] [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: 10/19/2023] [Accepted: 12/13/2023] [Indexed: 12/26/2023]
Abstract
A disparity exists between spinal cord and brain involvement in multiple sclerosis (MS), each independently contributing to disability. Underlying differences between brain and cord are not just anatomical in nature (volume, white/grey matter organization, vascularization), but also in barrier functions (differences in function and composition of the blood-spinal cord barrier compared to blood-brain barrier) and possibly in repair mechanisms. Also, immunological phenotypes seem to influence localization of inflammatory activity. Whereas the brain has gained a lot of attention in MS research, the spinal cord lags behind. Advanced imaging techniques and biomarkers are improving and providing us with tools to uncover the mechanisms of spinal cord pathology in MS. In the present review, we elaborate on the underlying anatomical and physiological factors driving differences between brain and cord involvement in MS and review current literature on pathophysiology of spinal cord involvement in MS and the observed differences to brain involvement.
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Affiliation(s)
- Daniel Kreiter
- Academic MS Center Zuyd, Department of Neurology, Zuyderland MC, Sittard-Geleen, the Netherlands; School for Mental Health and Neuroscience, Department of Neurology, Maastricht University Medical Center, Maastricht, the Netherlands.
| | - Alida A Postma
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands; School for Mental Health and Neuroscience, Department of Neurology, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Raymond Hupperts
- Academic MS Center Zuyd, Department of Neurology, Zuyderland MC, Sittard-Geleen, the Netherlands; School for Mental Health and Neuroscience, Department of Neurology, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Oliver Gerlach
- Academic MS Center Zuyd, Department of Neurology, Zuyderland MC, Sittard-Geleen, the Netherlands; School for Mental Health and Neuroscience, Department of Neurology, Maastricht University Medical Center, Maastricht, the Netherlands
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Kreiter D, Spee R, Merry A, Hupperts R, Gerlach O. Effect of disease-modifying treatment on spinal cord lesion formation in multiple sclerosis: A retrospective observational study. Mult Scler Relat Disord 2023; 79:104994. [PMID: 37683557 DOI: 10.1016/j.msard.2023.104994] [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: 05/25/2023] [Revised: 07/12/2023] [Accepted: 09/03/2023] [Indexed: 09/10/2023]
Abstract
BACKGROUND Spinal cord lesions in multiple sclerosis (MS) are an important contributor to disability. Knowledge on the effect of disease-modifying treatment (DMT) on spinal lesion formation in MS is sparse, as cord outcome measures are seldom included in MS treatment trials. We aim to investigate whether intermediate- or high-efficacy DMTs (i/hDMT) can reduce spinal lesion formation, compared with low-efficacy DMTs (lDMT) and/or no treatment. METHODS Relapse-onset MS patients with ≥2 spinal MRIs (interval >3 months and <10 years) were retrospectively identified. The i/hDMT-group was defined as patients who were treated with i/hDMTs during ≥90% of spinal MRI follow-up time. Controls received lDMTs and/or no treatment ≥90% of follow-up duration. In a secondary analysis, only patients using lDMT for ≥90% of follow-up were considered controls. Patients were matched using propensity-scores. Cox proportional hazards models were used to estimate the risk of new spinal lesions. RESULTS 323 patients had ≥2 spinal cord MRIs. 49 satisfied i/hDMT and 168 control group criteria. 34 i/hDMT patients were matched to 83 controls. Patients in the i/hDMT-group were significantly less likely to develop new cord lesions at follow-up (HR 0.29 [0.12-0.75], p = 0.01). When the i/hDMT-group was matched to only controls using lDMT ≥90% of follow-up time (n = 17 and n = 25, respectively), there was no statistically significant difference (HR 1.01 [0.19-5.24], p = 0.99). CONCLUSION Treatment with intermediate- or high-efficacy DMTs reduces the risk of new spinal cord lesions compared with matched patients receiving no treatment and/or lDMTs. No conclusions could be drawn on whether i/hDMTs provide a larger risk reduction compared to only lDMTs (control group receiving lDMTs ≥90% of follow-up time).
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Affiliation(s)
- Daniel Kreiter
- Department of Neurology, Academic MS center Zuyd, Zuyderland MC, Sittard-Geleen, The Netherlands; Department of Neurology, School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, The Netherlands.
| | - Romy Spee
- Faculty of Health, Medicine & Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Audrey Merry
- Zuyderland Academy, Zuyderland Medical Center, Sittard-Geleen & Heerlen, The Netherlands
| | - Raymond Hupperts
- Department of Neurology, Academic MS center Zuyd, Zuyderland MC, Sittard-Geleen, The Netherlands; Department of Neurology, School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Oliver Gerlach
- Department of Neurology, Academic MS center Zuyd, Zuyderland MC, Sittard-Geleen, The Netherlands; Department of Neurology, School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, The Netherlands
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Matusche B, Litvin L, Schneider R, Bellenberg B, Mühlau M, Pongratz V, Berthele A, Groppa S, Muthuraman M, Zipp F, Paul F, Wiendl H, Meuth SG, Sämann P, Weber F, Linker RA, Kümpfel T, Gold R, Lukas C. Early spinal cord pseudoatrophy in interferon-beta-treated multiple sclerosis. Eur J Neurol 2023; 30:453-462. [PMID: 36318271 DOI: 10.1111/ene.15620] [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: 10/10/2022] [Accepted: 10/26/2022] [Indexed: 11/16/2022]
Abstract
BACKGROUND AND PURPOSE Brain pseudoatrophy has been shown to play a pivotal role in the interpretation of brain atrophy measures during the first year of disease-modifying therapy in multiple sclerosis. Whether pseudoatrophy also affects the spinal cord remains unclear. The aim of this study was to analyze the extent of pseudoatrophy in the upper spinal cord during the first 2 years after therapy initiation and compare this to the brain. METHODS A total of 129 patients from a prospective longitudinal multicentric national cohort study for whom magnetic resonance imaging scans at baseline, 12 months, and 24 months were available were selected for brain and spinal cord volume quantification. Annual percentage brain volume and cord area change were calculated using SIENA (Structural Image Evaluation of Normalized Atrophy) and NeuroQLab, respectively. Linear mixed model analyses were performed to compare patients on interferon-beta therapy (n = 84) and untreated patients (n = 45). RESULTS Patients treated with interferon-beta demonstrated accelerated annual percentage brain volume and cervical cord area change in the first year after treatment initiation, whereas atrophy rates stabilized to a similar and not significantly different level compared to untreated patients during the second year. CONCLUSIONS These results suggest that pseudoatrophy occurs not only in the brain, but also in the spinal cord during the first year of interferon-beta treatment.
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Affiliation(s)
- Britta Matusche
- Institute for Neuroradiology, St Josef Hospital, Ruhr University Bochum, Bochum, Germany
| | - Ludmila Litvin
- Institute for Neuroradiology, St Josef Hospital, Ruhr University Bochum, Bochum, Germany
| | - Ruth Schneider
- Department of Neurology, St Josef Hospital, Ruhr University Bochum, Bochum, Germany
| | - Barbara Bellenberg
- Institute for Neuroradiology, St Josef Hospital, Ruhr University Bochum, Bochum, Germany
| | - Mark Mühlau
- Department of Neurology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Viola Pongratz
- Department of Neurology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Achim Berthele
- Department of Neurology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Sergiu Groppa
- Department of Neurology, Focus Program Translational Neuroscience and Immunotherapy, Rhine-Main Neuroscience Network, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Muthuraman Muthuraman
- Department of Neurology, Focus Program Translational Neuroscience and Immunotherapy, Rhine-Main Neuroscience Network, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Frauke Zipp
- Department of Neurology, Focus Program Translational Neuroscience and Immunotherapy, Rhine-Main Neuroscience Network, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Friedemann Paul
- Experimental and Clinical Research Center, Max Delbrueck Center for Molecular Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Heinz Wiendl
- Department of Neurology, Institute of Translational Neurology, University of Münster, Münster, Germany
| | - Sven G Meuth
- Department of Neurology, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | | | - Frank Weber
- Neurological Clinic, Sana Clinic Cham, Cham, Germany
| | - Ralf A Linker
- Department of Neurology, University of Regensburg, Regensburg, Germany
| | - Tania Kümpfel
- Institute of Clinical Neuroimmunology, Biomedical Center and University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
| | - Ralf Gold
- Department of Neurology, St Josef Hospital, Ruhr University Bochum, Bochum, Germany
| | - Carsten Lukas
- Institute for Neuroradiology, St Josef Hospital, Ruhr University Bochum, Bochum, Germany
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Bischof A, Papinutto N, Keshavan A, Rajesh A, Kirkish G, Zhang X, Mallott JM, Asteggiano C, Sacco S, Gundel TJ, Zhao C, Stern WA, Caverzasi E, Zhou Y, Gomez R, Ragan NR, Santaniello A, Zhu AH, Juwono J, Bevan CJ, Bove RM, Crabtree E, Gelfand JM, Goodin DS, Graves JS, Green AJ, Oksenberg JR, Waubant E, Wilson MR, Zamvil SS, Cree BA, Hauser SL, Henry RG. Spinal cord atrophy predicts progressive disease in relapsing multiple sclerosis. Ann Neurol 2021; 91:268-281. [PMID: 34878197 PMCID: PMC8916838 DOI: 10.1002/ana.26281] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 12/04/2021] [Accepted: 12/06/2021] [Indexed: 11/06/2022]
Abstract
Objective A major challenge in multiple sclerosis (MS) research is the understanding of silent progression and Progressive MS. Using a novel method to accurately capture upper cervical cord area from legacy brain MRI scans we aimed to study the role of spinal cord and brain atrophy for silent progression and conversion to secondary progressive disease (SPMS). Methods From a single‐center observational study, all RRMS (n = 360) and SPMS (n = 47) patients and 80 matched controls were evaluated. RRMS patient subsets who converted to SPMS (n = 54) or silently progressed (n = 159), respectively, during the 12‐year observation period were compared to clinically matched RRMS patients remaining RRMS (n = 54) or stable (n = 147), respectively. From brain MRI, we assessed the value of brain and spinal cord measures to predict silent progression and SPMS conversion. Results Patients who developed SPMS showed faster cord atrophy rates (−2.19%/yr) at least 4 years before conversion compared to their RRMS matches (−0.88%/yr, p < 0.001). Spinal cord atrophy rates decelerated after conversion (−1.63%/yr, p = 0.010) towards those of SPMS patients from study entry (−1.04%). Each 1% faster spinal cord atrophy rate was associated with 69% (p < 0.0001) and 53% (p < 0.0001) shorter time to silent progression and SPMS conversion, respectively. Interpretation Silent progression and conversion to secondary progressive disease are predominantly related to cervical cord atrophy. This atrophy is often present from the earliest disease stages and predicts the speed of silent progression and conversion to Progressive MS. Diagnosis of SPMS is rather a late recognition of this neurodegenerative process than a distinct disease phase. ANN NEUROL 2022;91:268–281
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Affiliation(s)
- Antje Bischof
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Nico Papinutto
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Anisha Keshavan
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Anand Rajesh
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Gina Kirkish
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Xinheng Zhang
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Jacob M Mallott
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Carlo Asteggiano
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Simone Sacco
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Tristan J Gundel
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Chao Zhao
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - William A Stern
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Eduardo Caverzasi
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Yifan Zhou
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Refujia Gomez
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Nicholas R Ragan
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Adam Santaniello
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Alyssa H Zhu
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Jeremy Juwono
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Carolyn J Bevan
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Riley M Bove
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Elizabeth Crabtree
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Jeffrey M Gelfand
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Douglas S Goodin
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Jennifer S Graves
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Ari J Green
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Jorge R Oksenberg
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Emmanuelle Waubant
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Michael R Wilson
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Scott S Zamvil
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
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- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Bruce A Cree
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Stephen L Hauser
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Roland G Henry
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
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Rose DR, Amin M, Ontaneda D. Prediction in treatment outcomes in multiple sclerosis: challenges and recent advances. Expert Rev Clin Immunol 2021; 17:1187-1198. [PMID: 34570656 DOI: 10.1080/1744666x.2021.1986005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
INTRODUCTION Multiple Sclerosis (MS) is a chronic autoimmune and neurodegenerative disease of the central nervous system with a course dependent on early treatment response. Increasing evidence also suggests that despite eliminating disease activity (relapses and lesions), many patients continue to accrue disability, highlighting the need for a more comprehensive definition of treatment success. Optimizing disability outcome measures, as well as continuously improving our understanding of neuroinflammatory and neurodegenerative biomarkers is required. AREAS COVERED This review describes the challenges inherent in classifying and monitoring disease phenotype in MS. The review also provides an assessment of clinical, radiological, and blood biomarker tools for current and future practice. EXPERT OPINION Emerging MRI techniques and standardized patient outcome assessments will increase the accuracy of initial diagnosis and understanding of disease progression.
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Affiliation(s)
- Deja R Rose
- Cleveland Clinic, Mellen Center for Multiple Sclerosis, Cleveland Ohio, United States
| | - Moein Amin
- Cleveland Clinic, Mellen Center for Multiple Sclerosis, Cleveland Ohio, United States.,Department of Neurology, Cleveland Clinic, Cleveland Ohio, United States
| | - Daniel Ontaneda
- Cleveland Clinic, Mellen Center for Multiple Sclerosis, Cleveland Ohio, United States.,Department of Neurology, Cleveland Clinic, Cleveland Ohio, United States
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Imaging of the Spinal Cord in Multiple Sclerosis: Past, Present, Future. Brain Sci 2020; 10:brainsci10110857. [PMID: 33202821 PMCID: PMC7696997 DOI: 10.3390/brainsci10110857] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2020] [Revised: 10/30/2020] [Accepted: 11/11/2020] [Indexed: 11/17/2022] Open
Abstract
Spinal cord imaging in multiple sclerosis (MS) plays a significant role in diagnosing and tracking disease progression. The spinal cord is one of four key areas of the central nervous system where documenting the dissemination in space in the McDonald criteria for diagnosing MS. Spinal cord lesion load and the severity of cord atrophy are believed to be more relevant to disability than white matter lesions in the brain in different phenotypes of MS. Axonal loss contributes to spinal cord atrophy in MS and its degree correlates with disease severity and prognosis. Therefore, measures of axonal loss are often reliable biomarkers for monitoring disease progression. With recent technical advances, more and more qualitative and quantitative MRI techniques have been investigated in an attempt to provide objective and reliable diagnostic and monitoring biomarkers in MS. In this article, we discuss the role of spinal cord imaging in the diagnosis and prognosis of MS and, additionally, we review various techniques that may improve our understanding of the disease.
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Tavazzi E, Zivadinov R, Dwyer MG, Jakimovski D, Singhal T, Weinstock-Guttman B, Bergsland N. MRI biomarkers of disease progression and conversion to secondary-progressive multiple sclerosis. Expert Rev Neurother 2020; 20:821-834. [PMID: 32306772 DOI: 10.1080/14737175.2020.1757435] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
INTRODUCTION Conventional imaging measures remain a key clinical tool for the diagnosis multiple sclerosis (MS) and monitoring of patients. However, most measures used in the clinic show unsatisfactory performance in predicting disease progression and conversion to secondary progressive MS. AREAS COVERED Sophisticated imaging techniques have facilitated the identification of imaging biomarkers associated with disease progression, such as global and regional brain volume measures, and with conversion to secondary progressive MS, such as leptomeningeal contrast enhancement and chronic inflammation. The relevance of emerging imaging approaches partially overcoming intrinsic limitations of traditional techniques is also discussed. EXPERT OPINION Imaging biomarkers capable of detecting tissue damage early on in the disease, with the potential to be applied in multicenter trials and at an individual level in clinical settings, are strongly needed. Several measures have been proposed, which exploit advanced imaging acquisitions and/or incorporate sophisticated post-processing, can quantify irreversible tissue damage. The progressively wider use of high-strength field MRI and the development of more advanced imaging techniques will help capture the missing pieces of the MS puzzle. The ability to more reliably identify those at risk for disability progression will allow for earlier intervention with the aim to favorably alter the disease course.
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Affiliation(s)
- Eleonora Tavazzi
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York , Buffalo, NY, USA
| | - Robert Zivadinov
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York , Buffalo, NY, USA.,Translational Imaging Center, Clinical and Translational Science Institute, University at Buffalo, The State University of New York , Buffalo, NY, USA
| | - Michael G Dwyer
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York , Buffalo, NY, USA
| | - Dejan Jakimovski
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York , Buffalo, NY, USA
| | - Tarun Singhal
- PET Imaging Program in Neurologic Diseases and Partners Multiple Sclerosis Center, Ann Romney Center for Neurologic Disease, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School , Boston, MA, USA
| | - Bianca Weinstock-Guttman
- Jacobs Comprehensive MS Treatment and Research Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York , Buffalo, NY, USA
| | - Niels Bergsland
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York , Buffalo, NY, USA.,IRCCS, Fondazione Don Carlo Gnocchi , Milan, Italy
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Rocca MA, Preziosa P, Filippi M. What role should spinal cord MRI take in the future of multiple sclerosis surveillance? Expert Rev Neurother 2020; 20:783-797. [PMID: 32133874 DOI: 10.1080/14737175.2020.1739524] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
INTRODUCTION In multiple sclerosis (MS), inflammatory, demyelinating, and neurodegenerative phenomena affect the spinal cord, with detrimental effects on patients' clinical disability. Although spinal cord imaging may be challenging, improvements in MRI technologies have contributed to better evaluate spinal cord involvement in MS. AREAS COVERED This review summarizes the current state-of-art of the application of conventional and advanced MRI techniques to evaluate spinal cord damage in MS. Typical features of spinal cord lesions, their role in the diagnostic work-up of suspected MS, their predictive role for subsequent disease course and clinical worsening, and their utility to define treatment response are discussed. The role of spinal cord atrophy and of other advanced MRI techniques to better evaluate the associations between spinal cord abnormalities and the accumulation of clinical disability are also evaluated. Finally, how spinal cord assessment could evolve in the future to improve monitoring of disease progression and treatment effects is examined. EXPERT OPINION Spinal cord MRI provides relevant additional information to brain MRI in understanding MS pathophysiology, in allowing an earlier and more accurate diagnosis of MS, and in identifying MS patients at higher risk to develop more severe disability. A future role in monitoring the effects of treatments is also foreseen.
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Affiliation(s)
- Maria A Rocca
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute , Milan, Italy.,Neurology Unit, IRCCS San Raffaele Scientific Institute , Milan, Italy
| | - Paolo Preziosa
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute , Milan, Italy.,Neurology Unit, IRCCS San Raffaele Scientific Institute , Milan, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute , Milan, Italy.,Neurology Unit, IRCCS San Raffaele Scientific Institute , Milan, Italy.,Neurophysiology Unit, IRCCS San Raffaele Scientific Institute , Milan, Italy.,Vita-Salute San Raffaele University , Milan, Italy
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10
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Song X, Li D, Qiu Z, Su S, Wu Y, Wang J, Liu Z, Dong H. Correlation between EDSS scores and cervical spinal cord atrophy at 3T MRI in multiple sclerosis: A systematic review and meta-analysis. Mult Scler Relat Disord 2019; 37:101426. [PMID: 32172997 DOI: 10.1016/j.msard.2019.101426] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Revised: 08/28/2019] [Accepted: 09/30/2019] [Indexed: 12/14/2022]
Abstract
BACKGROUND Cervical spinal cord atrophy (CSCA), which partly reflects the axonal loss in the spinal cord, is increasingly recognized as a valuable predictor of disease outcome. However, inconsistent results have been reported regarding the correlation of CSCA and clinical disability in multiple sclerosis (MS). The aim of this meta-analysis was to synthesize the available data obtained from 3.0-Tesla (3T) MRI scanners and to explore the relationship between CSCA and scores on the Expanded Disability Status Scale (EDSS). METHODS We searched PubMed, Embase, and Web of Science for articles published from the database inception to February 1, 2019. The quality of the articles was assessed according to a quality evaluation checklist which was created based on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. We conducted a meta-analysis of the correlation between EDSS scores and CSCA at 3T MRI in MS. RESULTS Twenty-two eligible studies involving 1933 participants were incorporated into our meta-analysis. Our results demonstrated that CSCA was negatively and moderately correlated with EDSS scores (rs = -0.42, 95% CI: -0.51 to -0.32; p < 0.0001). Subgroup analyses revealed a weaker correlation in the group of relapsing-remitting multiple sclerosis (RRMS) and clinically isolated syndrome (CIS) (rs = -0.19, 95% CI: -0.31 to -0.07; p = 0.0029). CONCLUSIONS The correlation between CSCA and EDSS scores was significant but moderate. We encourage more studies using reliable and consistent methods to explore whether CSCA is suitable as a predictor for MS progression.
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Affiliation(s)
- Xiaodong Song
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, PR China
| | - Dawei Li
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, PR China
| | - Zhandong Qiu
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, PR China
| | - Shengyao Su
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, PR China
| | - Yan Wu
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, PR China
| | - Jingsi Wang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, PR China
| | - Zheng Liu
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, PR China.
| | - Huiqing Dong
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, PR China.
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11
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Moccia M, Ruggieri S, Ianniello A, Toosy A, Pozzilli C, Ciccarelli O. Advances in spinal cord imaging in multiple sclerosis. Ther Adv Neurol Disord 2019; 12:1756286419840593. [PMID: 31040881 PMCID: PMC6477770 DOI: 10.1177/1756286419840593] [Citation(s) in RCA: 63] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Accepted: 03/03/2019] [Indexed: 11/18/2022] Open
Abstract
The spinal cord is frequently affected in multiple sclerosis (MS), causing motor, sensory and autonomic dysfunction. A number of pathological abnormalities, including demyelination and neuroaxonal loss, occur in the MS spinal cord and are studied in vivo with magnetic resonance imaging (MRI). The aim of this review is to summarise and discuss recent advances in spinal cord MRI. Advances in conventional spinal cord MRI include improved identification of MS lesions, recommended spinal cord MRI protocols, enhanced recognition of MRI lesion characteristics that allow MS to be distinguished from other myelopathies, evidence for the role of spinal cord lesions in predicting prognosis and monitoring disease course, and novel post-processing methods to obtain lesion probability maps. The rate of spinal cord atrophy is greater than that of brain atrophy (-1.78% versus -0.5% per year), and reflects neuroaxonal loss in an eloquent site of the central nervous system, suggesting that it can become an important outcome measure in clinical trials, especially in progressive MS. Recent developments allow the calculation of spinal cord atrophy from brain volumetric scans and evaluation of its progression over time with registration-based techniques. Fully automated analysis methods, including segmentation of grey matter and intramedullary lesions, will facilitate the use of spinal cord atrophy in trial designs and observational studies. Advances in quantitative imaging techniques to evaluate neuroaxonal integrity, myelin content, metabolic changes, and functional connectivity, have provided new insights into the mechanisms of damage in MS. Future directions of research and the possible impact of 7T scanners on spinal cord imaging will be discussed.
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Affiliation(s)
- Marcello Moccia
- Queen Square MS Centre, NMR Research Unit, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Multiple Sclerosis Clinical Care and Research Centre, Department of Neurosciences, Federico II University of Naples, via Sergio Pansini, 5, Edificio 17 - piano terra, Napoli, 80131 Naples, Italy
| | - Serena Ruggieri
- Department of Human Neuroscience, Sapienza University of Rome, Italy
| | - Antonio Ianniello
- Department of Human Neuroscience, Sapienza University of Rome, Italy
| | - Ahmed Toosy
- Queen Square MS Centre, NMR Research Unit, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Carlo Pozzilli
- Department of Human Neuroscience, Sapienza University of Rome, Italy
| | - Olga Ciccarelli
- Queen Square MS Centre, NMR Research Unit, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- National Institute for Health Research, University College London Hospitals Biomedical Research Centre, London, UK
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12
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Gros C, De Leener B, Badji A, Maranzano J, Eden D, Dupont SM, Talbott J, Zhuoquiong R, Liu Y, Granberg T, Ouellette R, Tachibana Y, Hori M, Kamiya K, Chougar L, Stawiarz L, Hillert J, Bannier E, Kerbrat A, Edan G, Labauge P, Callot V, Pelletier J, Audoin B, Rasoanandrianina H, Brisset JC, Valsasina P, Rocca MA, Filippi M, Bakshi R, Tauhid S, Prados F, Yiannakas M, Kearney H, Ciccarelli O, Smith S, Treaba CA, Mainero C, Lefeuvre J, Reich DS, Nair G, Auclair V, McLaren DG, Martin AR, Fehlings MG, Vahdat S, Khatibi A, Doyon J, Shepherd T, Charlson E, Narayanan S, Cohen-Adad J. Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks. Neuroimage 2019; 184:901-915. [PMID: 30300751 PMCID: PMC6759925 DOI: 10.1016/j.neuroimage.2018.09.081] [Citation(s) in RCA: 130] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Revised: 09/05/2018] [Accepted: 09/28/2018] [Indexed: 12/12/2022] Open
Abstract
The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS) patients. Segmentation of the spinal cord and lesions from MRI data provides measures of damage, which are key criteria for the diagnosis, prognosis, and longitudinal monitoring in MS. Automating this operation eliminates inter-rater variability and increases the efficiency of large-throughput analysis pipelines. Robust and reliable segmentation across multi-site spinal cord data is challenging because of the large variability related to acquisition parameters and image artifacts. In particular, a precise delineation of lesions is hindered by a broad heterogeneity of lesion contrast, size, location, and shape. The goal of this study was to develop a fully-automatic framework - robust to variability in both image parameters and clinical condition - for segmentation of the spinal cord and intramedullary MS lesions from conventional MRI data of MS and non-MS cases. Scans of 1042 subjects (459 healthy controls, 471 MS patients, and 112 with other spinal pathologies) were included in this multi-site study (n = 30). Data spanned three contrasts (T1-, T2-, and T2∗-weighted) for a total of 1943 vol and featured large heterogeneity in terms of resolution, orientation, coverage, and clinical conditions. The proposed cord and lesion automatic segmentation approach is based on a sequence of two Convolutional Neural Networks (CNNs). To deal with the very small proportion of spinal cord and/or lesion voxels compared to the rest of the volume, a first CNN with 2D dilated convolutions detects the spinal cord centerline, followed by a second CNN with 3D convolutions that segments the spinal cord and/or lesions. CNNs were trained independently with the Dice loss. When compared against manual segmentation, our CNN-based approach showed a median Dice of 95% vs. 88% for PropSeg (p ≤ 0.05), a state-of-the-art spinal cord segmentation method. Regarding lesion segmentation on MS data, our framework provided a Dice of 60%, a relative volume difference of -15%, and a lesion-wise detection sensitivity and precision of 83% and 77%, respectively. In this study, we introduce a robust method to segment the spinal cord and intramedullary MS lesions on a variety of MRI contrasts. The proposed framework is open-source and readily available in the Spinal Cord Toolbox.
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Affiliation(s)
- Charley Gros
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Benjamin De Leener
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Atef Badji
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
- Department of Neuroscience, Faculty of Medicine, University of Montreal, Montreal, QC, Canada
| | - Josefina Maranzano
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Canada
| | - Dominique Eden
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Sara M. Dupont
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
- Department of Radiology and Biomedical Imaging, Zuckerberg San Francisco General Hospital, University of California, San Francisco, CA, USA
| | - Jason Talbott
- Department of Radiology and Biomedical Imaging, Zuckerberg San Francisco General Hospital, University of California, San Francisco, CA, USA
| | - Ren Zhuoquiong
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing 100053, P. R. China
| | - Yaou Liu
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing 100053, P. R. China
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, P. R. China
| | - Tobias Granberg
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, USA
| | - Russell Ouellette
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, USA
| | | | | | | | - Lydia Chougar
- Juntendo University Hospital, Tokyo, Japan
- Hospital Cochin, Paris, France
| | - Leszek Stawiarz
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Jan Hillert
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Elise Bannier
- CHU Rennes, Radiology Department
- Univ Rennes, Inria, CNRS, Inserm, IRISA UMR 6074, Visages U1128, France
| | - Anne Kerbrat
- Univ Rennes, Inria, CNRS, Inserm, IRISA UMR 6074, Visages U1128, France
- CHU Rennes, Neurology Department
| | - Gilles Edan
- Univ Rennes, Inria, CNRS, Inserm, IRISA UMR 6074, Visages U1128, France
- CHU Rennes, Neurology Department
| | - Pierre Labauge
- MS Unit. DPT of Neurology. University Hospital of Montpellier
| | - Virginie Callot
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, CHU Timone, CEMEREM, Marseille, France
| | - Jean Pelletier
- APHM, CHU Timone, CEMEREM, Marseille, France
- APHM, Department of Neurology, CHU Timone, APHM, Marseille
| | - Bertrand Audoin
- APHM, CHU Timone, CEMEREM, Marseille, France
- APHM, Department of Neurology, CHU Timone, APHM, Marseille
| | | | - Jean-Christophe Brisset
- Observatoire Français de la Sclérose en Plaques (OFSEP) ; Univ Lyon, Université Claude Bernard Lyon 1 ; Hospices Civils de Lyon ; CREATIS-LRMN, UMR 5220 CNRS & U 1044 INSERM ; Lyon, France
| | - Paola Valsasina
- Neuroimaging Research Unit, INSPE, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Maria A. Rocca
- Neuroimaging Research Unit, INSPE, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, INSPE, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Rohit Bakshi
- Brigham and Women’s Hospital, Harvard Medical School, Boston, USA
| | - Shahamat Tauhid
- Brigham and Women’s Hospital, Harvard Medical School, Boston, USA
| | - Ferran Prados
- Queen Square MS Centre, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London (UK)
- Center for Medical Image Computing (CMIC), Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Marios Yiannakas
- Queen Square MS Centre, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London (UK)
| | - Hugh Kearney
- Queen Square MS Centre, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London (UK)
| | - Olga Ciccarelli
- Queen Square MS Centre, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London (UK)
| | | | | | - Caterina Mainero
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, USA
| | - Jennifer Lefeuvre
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Maryland, USA
| | - Daniel S. Reich
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Maryland, USA
| | - Govind Nair
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Maryland, USA
| | | | | | - Allan R. Martin
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Michael G. Fehlings
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Shahabeddin Vahdat
- Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada
- Neurology Department, Stanford University, US
| | - Ali Khatibi
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Canada
- Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada
| | - Julien Doyon
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Canada
- Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada
| | | | | | - Sridar Narayanan
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Canada
| | - Julien Cohen-Adad
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
- Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada
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13
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Rocca MA, Preziosa P, Filippi M. Application of advanced MRI techniques to monitor pharmacologic and rehabilitative treatment in multiple sclerosis: current status and future perspectives. Expert Rev Neurother 2018; 19:835-866. [PMID: 30500303 DOI: 10.1080/14737175.2019.1555038] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Introduction: Advances in magnetic resonance imaging (MRI) technology and analyses are improving our understanding of the pathophysiology of multiple sclerosis (MS). Due to their ability to grade the presence of irreversible tissue loss, microstructural tissue abnormalities, metabolic changes and functional plasticity, the application of these techniques is also expanding our knowledge on the efficacy and mechanisms of action of different pharmacological and rehabilitative treatments. Areas covered: This review discusses recent findings derived from the application of advanced MRI techniques to evaluate the structural and functional substrates underlying the effects of pharmacologic and rehabilitative treatments in patients with MS. Current applications as outcome in clinical trials and observational studies, their interpretation and possible pitfalls in their use are discussed. Finally, how these techniques could evolve in the future to improve monitoring of disease progression and treatment response is examined. Expert commentary: The number of treatments currently available for MS is increasing. The application of advanced MRI techniques is providing reliable and specific measures to better understand the targets of different treatments, including neuroprotection, tissue repair, and brain plasticity. This is a fundamental progress to move toward personalized medicine and individual treatment selection.
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Affiliation(s)
- Maria A Rocca
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University , Milan , Italy.,Department of Neurology, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University , Milan , Italy
| | - Paolo Preziosa
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University , Milan , Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University , Milan , Italy.,Department of Neurology, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University , Milan , Italy
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14
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Casserly C, Seyman EE, Alcaide-Leon P, Guenette M, Lyons C, Sankar S, Svendrovski A, Baral S, Oh J. Spinal Cord Atrophy in Multiple Sclerosis: A Systematic Review and Meta-Analysis. J Neuroimaging 2018; 28:556-586. [PMID: 30102003 DOI: 10.1111/jon.12553] [Citation(s) in RCA: 71] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Revised: 07/12/2018] [Accepted: 07/16/2018] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND AND PURPOSE Spinal cord atrophy (SCA) is an important emerging outcome measure in multiple sclerosis (MS); however, there is limited consensus on the magnitude and rate of atrophy. The objective of this study was to synthesize the available data on measures of SCA in MS. METHODS Using published guidelines, relevant literature databases were searched between 1977 and 2017 for case-control or cohort studies reporting a quantitative measure of SCA in MS patients. Random-effects models pooled cross-sectional measures and longitudinal rates of SCA in MS and healthy controls (HCs). Student's t-test assessed differences between pooled measures in patient subgroups. Heterogeneity was assessed using DerSimonian and Laird's Q-test and the I 2 -index. RESULTS A total of 1,465 studies were retrieved including 94 that met inclusion and exclusion criteria. Pooled estimates of mean cervical spinal cord (SC) cross-sectional area (CSA) in all MS patients, relapsing-remitting MS (RRMS), all progressive MS, secondary progressive MS (SPMS), primary-progressive MS (PPMS), and HC were: 73.07 mm2 (95% CI [71.52-74.62]), 78.88 mm2 (95% CI [76.92-80.85]), 69.72 mm2 (95% CI [67.96-71.48]), 68.55 mm2 (95% CI [65.43-71.66]), 70.98 mm2 (95% CI [68.78-73.19]), and 80.87 mm2 (95% C I [78.70-83.04]), respectively. Pooled SC-CSA was greater in HC versus MS (P < .001) and RRMS versus progressive MS (P < .001). SCA showed moderate correlations with global disability in cross-sectional studies (r-value with disability score range [-.75 to -.22]). In longitudinal studies, the pooled annual rate of SCA was 1.78%/year (95%CI [1.28-2.27]). CONCLUSIONS The SC is atrophied in MS. The magnitude of SCA is greater in progressive versus relapsing forms and correlates with clinical disability. The pooled estimate of annual rate of SCA is greater than reported rates of brain atrophy in MS. These results demonstrate that SCA is highly relevant as an imaging outcome in MS clinical trials.
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Affiliation(s)
- Courtney Casserly
- Division of Neurology, Department of Medicine, St Michael's Hospital, University of Toronto, Toronto, Ontario, Canada.,Department of Neurology, London Health Sciences Centre, Western University, London, Ontario, Canada
| | - Estelle E Seyman
- Division of Neurology, Department of Medicine, St Michael's Hospital, University of Toronto, Toronto, Ontario, Canada
| | - Paula Alcaide-Leon
- Division of Neuroradiology, Department of Medical Imaging, St. Michael's Hospital, University of Toronto, Toronto, Ontario, Canada
| | - Melanie Guenette
- Division of Neurology, Department of Medicine, St Michael's Hospital, University of Toronto, Toronto, Ontario, Canada
| | - Carrie Lyons
- Division of Neurology, Department of Medicine, St Michael's Hospital, University of Toronto, Toronto, Ontario, Canada
| | - Stephanie Sankar
- Division of Neurology, Department of Medicine, St Michael's Hospital, University of Toronto, Toronto, Ontario, Canada
| | - Anton Svendrovski
- Division of Neurology, Department of Medicine, St Michael's Hospital, University of Toronto, Toronto, Ontario, Canada
| | - Stefan Baral
- Department of Epidemiology, Johns Hopkins School of Public Health, Baltimore, MD
| | - Jiwon Oh
- Division of Neurology, Department of Medicine, St Michael's Hospital, University of Toronto, Toronto, Ontario, Canada.,Department of Neurology, Johns Hopkins University, Baltimore, MD
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15
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Sinnecker T, Granziera C, Wuerfel J, Schlaeger R. Future Brain and Spinal Cord Volumetric Imaging in the Clinic for Monitoring Treatment Response in MS. Curr Treat Options Neurol 2018; 20:17. [PMID: 29679165 DOI: 10.1007/s11940-018-0504-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
PURPOSE OF REVIEW Volumetric analysis of brain imaging has emerged as a standard approach used in clinical research, e.g., in the field of multiple sclerosis (MS), but its application in individual disease course monitoring is still hampered by biological and technical limitations. This review summarizes novel developments in volumetric imaging on the road towards clinical application to eventually monitor treatment response in patients with MS. RECENT FINDINGS In addition to the assessment of whole-brain volume changes, recent work was focused on the volumetry of specific compartments and substructures of the central nervous system (CNS) in MS. This included volumetric imaging of the deep brain structures and of the spinal cord white and gray matter. Volume changes of the latter indeed independently correlate with clinical outcome measures especially in progressive MS. Ultrahigh field MRI and quantitative MRI added to this trend by providing a better visualization of small compartments on highly resolving MR images as well as microstructural information. New developments in volumetric imaging have the potential to improve sensitivity as well as specificity in detecting and hence monitoring disease-related CNS volume changes in MS.
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Affiliation(s)
- Tim Sinnecker
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Petersgraben 4, 4031, Basel, Switzerland
- Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Medical Image Analysis Center Basel AG, Basel, Switzerland
- NeuroCure Clinical Research Center, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Cristina Granziera
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Petersgraben 4, 4031, Basel, Switzerland
- Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Jens Wuerfel
- Medical Image Analysis Center Basel AG, Basel, Switzerland
- NeuroCure Clinical Research Center, Charité Universitätsmedizin Berlin, Berlin, Germany
- Berlin Ultrahigh Field Facility, Max Delbrück Center for Molecular Medicine, Berlin, Germany
| | - Regina Schlaeger
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Petersgraben 4, 4031, Basel, Switzerland.
- Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.
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