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Amin M, Martínez-Heras E, Ontaneda D, Prados Carrasco F. Artificial Intelligence and Multiple Sclerosis. Curr Neurol Neurosci Rep 2024; 24:233-243. [PMID: 38940994 PMCID: PMC11258192 DOI: 10.1007/s11910-024-01354-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/18/2024] [Indexed: 06/29/2024]
Abstract
In this paper, we analyse the different advances in artificial intelligence (AI) approaches in multiple sclerosis (MS). AI applications in MS range across investigation of disease pathogenesis, diagnosis, treatment, and prognosis. A subset of AI, Machine learning (ML) models analyse various data sources, including magnetic resonance imaging (MRI), genetic, and clinical data, to distinguish MS from other conditions, predict disease progression, and personalize treatment strategies. Additionally, AI models have been extensively applied to lesion segmentation, identification of biomarkers, and prediction of outcomes, disease monitoring, and management. Despite the big promises of AI solutions, model interpretability and transparency remain critical for gaining clinician and patient trust in these methods. The future of AI in MS holds potential for open data initiatives that could feed ML models and increasing generalizability, the implementation of federated learning solutions for training the models addressing data sharing issues, and generative AI approaches to address challenges in model interpretability, and transparency. In conclusion, AI presents an opportunity to advance our understanding and management of MS. AI promises to aid clinicians in MS diagnosis and prognosis improving patient outcomes and quality of life, however ensuring the interpretability and transparency of AI-generated results is going to be key for facilitating the integration of AI into clinical practice.
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Affiliation(s)
- Moein Amin
- Mellen Center for Multiple Sclerosis Treatment and Research, Cleveland Clinic, Cleveland, OH, USA
| | - Eloy Martínez-Heras
- Neuroimmunology and Multiple Sclerosis Unit, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
| | - Daniel Ontaneda
- Mellen Center for Multiple Sclerosis Treatment and Research, Cleveland Clinic, Cleveland, OH, USA
| | - Ferran Prados Carrasco
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain.
- Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK.
- Center for Medical Image Computing, University College London, London, UK.
- National Institute for Health Research Biomedical Research Centre at UCL and UCLH, London, UK.
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2
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Attyé A, Renard F, Anglade V, Krainik A, Kahane P, Mansencal B, Coupé P, Calamante F. Data-driven normative values based on generative manifold learning for quantitative MRI. Sci Rep 2024; 14:7563. [PMID: 38555415 PMCID: PMC10981723 DOI: 10.1038/s41598-024-58141-4] [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: 04/20/2023] [Accepted: 03/26/2024] [Indexed: 04/02/2024] Open
Abstract
In medicine, abnormalities in quantitative metrics such as the volume reduction of one brain region of an individual versus a control group are often provided as deviations from so-called normal values. These normative reference values are traditionally calculated based on the quantitative values from a control group, which can be adjusted for relevant clinical co-variables, such as age or sex. However, these average normative values do not take into account the globality of the available quantitative information. For example, quantitative analysis of T1-weighted magnetic resonance images based on anatomical structure segmentation frequently includes over 100 cerebral structures in the quantitative reports, and these tend to be analyzed separately. In this study, we propose a global approach to personalized normative values for each brain structure using an unsupervised Artificial Intelligence technique known as generative manifold learning. We test the potential benefit of these personalized normative values in comparison with the more traditional average normative values on a population of patients with drug-resistant epilepsy operated for focal cortical dysplasia, as well as on a supplementary healthy group and on patients with Alzheimer's disease.
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Affiliation(s)
| | | | - Vanina Anglade
- Department of Neuroradiology and MRI, SFR RMN Neurosciences, University Grenoble Alpes Hospital, Grenoble, France
| | - Alexandre Krainik
- Department of Neuroradiology and MRI, SFR RMN Neurosciences, University Grenoble Alpes Hospital, Grenoble, France
| | - Philippe Kahane
- Department of Neurology, University Grenoble Alpes Hospital, Grenoble, France
| | - Boris Mansencal
- CNRS, Univ. Bordeaux, Bordeaux INP, LABRI, UMR5800, 33400, Talence, France
| | - Pierrick Coupé
- CNRS, Univ. Bordeaux, Bordeaux INP, LABRI, UMR5800, 33400, Talence, France
| | - Fernando Calamante
- School of Biomedical Engineering, The University of Sydney, Sydney, NSW, 2006, Australia
- Sydney Imaging-The University of Sydney, Sydney, Australia
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3
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Yearley AG, Goedmakers CMW, Panahi A, Doucette J, Rana A, Ranganathan K, Smith TR. FDA-approved machine learning algorithms in neuroradiology: A systematic review of the current evidence for approval. Artif Intell Med 2023; 143:102607. [PMID: 37673576 DOI: 10.1016/j.artmed.2023.102607] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 05/30/2023] [Accepted: 06/05/2023] [Indexed: 09/08/2023]
Abstract
Over the past decade, machine learning (ML) and artificial intelligence (AI) have become increasingly prevalent in the medical field. In the United States, the Food and Drug Administration (FDA) is responsible for regulating AI algorithms as "medical devices" to ensure patient safety. However, recent work has shown that the FDA approval process may be deficient. In this study, we evaluate the evidence supporting FDA-approved neuroalgorithms, the subset of machine learning algorithms with applications in the central nervous system (CNS), through a systematic review of the primary literature. Articles covering the 53 FDA-approved algorithms with applications in the CNS published in PubMed, EMBASE, Google Scholar and Scopus between database inception and January 25, 2022 were queried. Initial searches identified 1505 studies, of which 92 articles met the criteria for extraction and inclusion. Studies were identified for 26 of the 53 neuroalgorithms, of which 10 algorithms had only a single peer-reviewed publication. Performance metrics were available for 15 algorithms, external validation studies were available for 24 algorithms, and studies exploring the use of algorithms in clinical practice were available for 7 algorithms. Papers studying the clinical utility of these algorithms focused on three domains: workflow efficiency, cost savings, and clinical outcomes. Our analysis suggests that there is a meaningful gap between the FDA approval of machine learning algorithms and their clinical utilization. There appears to be room for process improvement by implementation of the following recommendations: the provision of compelling evidence that algorithms perform as intended, mandating minimum sample sizes, reporting of a predefined set of performance metrics for all algorithms and clinical application of algorithms prior to widespread use. This work will serve as a baseline for future research into the ideal regulatory framework for AI applications worldwide.
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Affiliation(s)
- Alexander G Yearley
- Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA; Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA.
| | - Caroline M W Goedmakers
- Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA; Department of Neurosurgery, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, Netherlands
| | - Armon Panahi
- The George Washington University School of Medicine and Health Sciences, 2300 I St NW, Washington, DC 20052, USA
| | - Joanne Doucette
- Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA; School of Pharmacy, MCPHS University, 179 Longwood Ave, Boston, MA 02115, USA
| | - Aakanksha Rana
- Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA; Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA
| | - Kavitha Ranganathan
- Division of Plastic Surgery, Brigham and Women's Hospital, 75 Francis St, Boston, MA 02115, USA
| | - Timothy R Smith
- Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA; Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA
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Spagnolo F, Depeursinge A, Schädelin S, Akbulut A, Müller H, Barakovic M, Melie-Garcia L, Bach Cuadra M, Granziera C. How far MS lesion detection and segmentation are integrated into the clinical workflow? A systematic review. Neuroimage Clin 2023; 39:103491. [PMID: 37659189 PMCID: PMC10480555 DOI: 10.1016/j.nicl.2023.103491] [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: 08/02/2023] [Accepted: 08/04/2023] [Indexed: 09/04/2023]
Abstract
INTRODUCTION Over the past few years, the deep learning community has developed and validated a plethora of tools for lesion detection and segmentation in Multiple Sclerosis (MS). However, there is an important gap between validating models technically and clinically. To this end, a six-step framework necessary for the development, validation, and integration of quantitative tools in the clinic was recently proposed under the name of the Quantitative Neuroradiology Initiative (QNI). AIMS Investigate to what extent automatic tools in MS fulfill the QNI framework necessary to integrate automated detection and segmentation into the clinical neuroradiology workflow. METHODS Adopting the systematic Cochrane literature review methodology, we screened and summarised published scientific articles that perform automatic MS lesions detection and segmentation. We categorised the retrieved studies based on their degree of fulfillment of QNI's six-steps, which include a tool's technical assessment, clinical validation, and integration. RESULTS We found 156 studies; 146/156 (94%) fullfilled the first QNI step, 155/156 (99%) the second, 8/156 (5%) the third, 3/156 (2%) the fourth, 5/156 (3%) the fifth and only one the sixth. CONCLUSIONS To date, little has been done to evaluate the clinical performance and the integration in the clinical workflow of available methods for MS lesion detection/segmentation. In addition, the socio-economic effects and the impact on patients' management of such tools remain almost unexplored.
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Affiliation(s)
- Federico Spagnolo
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland; MedGIFT, Institute of Informatics, School of Management, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland
| | - Adrien Depeursinge
- MedGIFT, Institute of Informatics, School of Management, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland; Nuclear Medicine and Molecular Imaging Department, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Sabine Schädelin
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Clinical Trial Unit, Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Aysenur Akbulut
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Ankara University School of Medicine, Ankara, Turkey
| | - Henning Müller
- MedGIFT, Institute of Informatics, School of Management, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland; The Sense Research and Innovation Center, Lausanne and Sion, Switzerland
| | - Muhamed Barakovic
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Lester Melie-Garcia
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Meritxell Bach Cuadra
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland; Radiology Department, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Cristina Granziera
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland.
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Kress GT, Popa ES, Thompson PM, Bookheimer SY, Thomopoulos SI, Ching CRK, Zheng H, Hirsh DA, Merrill DA, Panos SE, Raji CA, Siddarth P, Bramen JE. Preliminary validation of a structural magnetic resonance imaging metric for tracking dementia-related neurodegeneration and future decline. Neuroimage Clin 2023; 39:103458. [PMID: 37421927 PMCID: PMC10338152 DOI: 10.1016/j.nicl.2023.103458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 06/20/2023] [Indexed: 07/10/2023]
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by cognitive decline and atrophy in the medial temporal lobe (MTL) and subsequent brain regions. Structural magnetic resonance imaging (sMRI) has been widely used in research and clinical care for diagnosis and monitoring AD progression. However, atrophy patterns are complex and vary by patient. To address this issue, researchers have made efforts to develop more concise metrics that can summarize AD-specific atrophy. Many of these methods can be difficult to interpret clinically, hampering adoption. In this study, we introduce a novel index which we call an "AD-NeuroScore," that uses a modified Euclidean-inspired distance function to calculate differences between regional brain volumes associated with cognitive decline. The index is adjusted for intracranial volume (ICV), age, sex, and scanner model. We validated AD-NeuroScore using 929 older adults from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, with a mean age of 72.7 years (SD = 6.3; 55.1-91.5) and cognitively normal (CN), mild cognitive impairment (MCI), or AD diagnoses. Our validation results showed that AD-NeuroScore was significantly associated with diagnosis and disease severity scores (measured by MMSE, CDR-SB, and ADAS-11) at baseline. Furthermore, baseline AD-NeuroScore was associated with both changes in diagnosis and disease severity scores at all time points with available data. The performance of AD-NeuroScore was equivalent or superior to adjusted hippocampal volume (AHV), a widely used metric in AD research. Further, AD-NeuroScore typically performed as well as or sometimes better when compared to other existing sMRI-based metrics. In conclusion, we have introduced a new metric, AD-NeuroScore, which shows promising results in detecting AD, benchmarking disease severity, and predicting disease progression. AD-NeuroScore differentiates itself from other metrics by being clinically practical and interpretable.
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Affiliation(s)
- Gavin T Kress
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA; Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Emily S Popa
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA 90292, USA
| | - Susan Y Bookheimer
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA; David Geffen School of Medicine, University of California, Los Angeles, Westwood, CA 90095, USA
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA 90292, USA
| | - Christopher R K Ching
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA 90292, USA
| | - Hong Zheng
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA 90292, USA
| | - Daniel A Hirsh
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA.
| | - David A Merrill
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA; Department of Translational Neurosciences and Neurotherapeutics, Providence Saint John's Cancer Institute, Santa Monica, CA 90404, USA; UCLA Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Westwood, CA 90095, USA
| | - Stella E Panos
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA
| | - Cyrus A Raji
- Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO, USA
| | - Prabha Siddarth
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA; UCLA Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Westwood, CA 90095, USA
| | - Jennifer E Bramen
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA.
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6
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Mendelsohn Z, Pemberton HG, Gray J, Goodkin O, Carrasco FP, Scheel M, Nawabi J, Barkhof F. Commercial volumetric MRI reporting tools in multiple sclerosis: a systematic review of the evidence. Neuroradiology 2023; 65:5-24. [PMID: 36331588 PMCID: PMC9816195 DOI: 10.1007/s00234-022-03074-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 09/29/2022] [Indexed: 11/06/2022]
Abstract
PURPOSE MRI is integral to the diagnosis of multiple sclerosis (MS) and is important for clinical prognostication. Quantitative volumetric reporting tools (QReports) can improve the accuracy and objectivity of MRI-based assessments. Several QReports are commercially available; however, validation can be difficult to establish and does not currently follow a common pathway. To aid evidence-based clinical decision-making, we performed a systematic review of commercial QReports for use in MS including technical details and published reports of validation and in-use evaluation. METHODS We categorized studies into three types of testing: technical validation, for example, comparison to manual segmentation, clinical validation by clinicians or interpretation of results alongside clinician-rated variables, and in-use evaluation, such as health economic assessment. RESULTS We identified 10 companies, which provide MS lesion and brain segmentation and volume quantification, and 38 relevant publications. Tools received regulatory approval between 2006 and 2020, contextualize results to normative reference populations, ranging from 620 to 8000 subjects, and require T1- and T2-FLAIR-weighted input sequences for longitudinal assessment of whole-brain volume and lesions. In MS, six QReports provided evidence of technical validation, four companies have conducted clinical validation by correlating results with clinical variables, only one has tested their QReport by clinician end-users, and one has performed a simulated in-use socioeconomic evaluation. CONCLUSION We conclude that there is limited evidence in the literature regarding clinical validation and in-use evaluation of commercial MS QReports with a particular lack of clinician end-user testing. Our systematic review provides clinicians and institutions with the available evidence when considering adopting a quantitative reporting tool for MS.
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Affiliation(s)
- Zoe Mendelsohn
- grid.83440.3b0000000121901201Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK ,grid.83440.3b0000000121901201Department of Medical Physics and Bioengineering, Centre for Medical Image Computing (CMIC), University College London, London, UK ,grid.83440.3b0000000121901201Department of Neuroinflammation, Queen Square Multiple Sclerosis Centre, UCL Institute of Neurology, University College London, London, UK ,grid.6363.00000 0001 2218 4662Department of Neuroradiology, Charité School of Medicine and University Hospital Berlin, Berlin, Germany ,grid.6363.00000 0001 2218 4662Department of Radiology, Charité School of Medicine and University Hospital Berlin, Berlin, Germany
| | - Hugh G. Pemberton
- grid.83440.3b0000000121901201Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK ,grid.83440.3b0000000121901201Department of Medical Physics and Bioengineering, Centre for Medical Image Computing (CMIC), University College London, London, UK ,grid.420685.d0000 0001 1940 6527GE Healthcare, Amersham, UK
| | - James Gray
- grid.416626.10000 0004 0391 2793Stepping Hill Hospital, NHS Foundation Trust, Stockport, UK
| | - Olivia Goodkin
- grid.83440.3b0000000121901201Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK ,grid.83440.3b0000000121901201Department of Medical Physics and Bioengineering, Centre for Medical Image Computing (CMIC), University College London, London, UK ,grid.83440.3b0000000121901201Department of Neuroinflammation, Queen Square Multiple Sclerosis Centre, UCL Institute of Neurology, University College London, London, UK
| | - Ferran Prados Carrasco
- grid.83440.3b0000000121901201Department of Medical Physics and Bioengineering, Centre for Medical Image Computing (CMIC), University College London, London, UK ,grid.83440.3b0000000121901201Department of Neuroinflammation, Queen Square Multiple Sclerosis Centre, UCL Institute of Neurology, University College London, London, UK ,grid.36083.3e0000 0001 2171 6620E-Health Centre, Universitat Oberta de Catalunya, Barcelona, Spain
| | - Michael Scheel
- grid.6363.00000 0001 2218 4662Department of Neuroradiology, Charité School of Medicine and University Hospital Berlin, Berlin, Germany
| | - Jawed Nawabi
- grid.6363.00000 0001 2218 4662Department of Radiology, Charité School of Medicine and University Hospital Berlin, Berlin, Germany ,grid.484013.a0000 0004 6879 971XBerlin Institute of Health at Charité – Universitätsmedizin Berlin, BIH Biomedical Innovation Academy, BIH Charité Digital Clinician Scientist Program, Berlin, Germany
| | - Frederik Barkhof
- grid.83440.3b0000000121901201Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK ,grid.83440.3b0000000121901201Department of Medical Physics and Bioengineering, Centre for Medical Image Computing (CMIC), University College London, London, UK ,grid.83440.3b0000000121901201Department of Neuroinflammation, Queen Square Multiple Sclerosis Centre, UCL Institute of Neurology, University College London, London, UK ,grid.12380.380000 0004 1754 9227Radiology & Nuclear Medicine, Amsterdam University Medical Centers, Vrije Universiteit, Amsterdam, The Netherlands
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Ziccardi S, Pizzini FB, Guandalini M, Tamanti A, Cristofori C, Calabrese M. Making Visible the Invisible: Automatically Measured Global and Regional Brain Volume Is Associated with Cognitive Impairment and Fatigue in Multiple Sclerosis. BIOENGINEERING (BASEL, SWITZERLAND) 2022; 10:bioengineering10010041. [PMID: 36671613 PMCID: PMC9855175 DOI: 10.3390/bioengineering10010041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 12/23/2022] [Accepted: 12/25/2022] [Indexed: 12/30/2022]
Abstract
In multiple sclerosis (MS), the transition from relapsing-remitting to the secondary-progressive phase is characterized by a progression independent of relapse activity (PIRA), resulting in physical disability accumulation and invisible symptoms, i.e., fatigue and cognitive impairment (CI). These symptoms are related to neurodegenerative processes and have been correlated with MRI measures of brain atrophy only at a group level; however, the application in clinical practice of atrophy-based measurements for single-patient evaluation is yet to be fully investigated. In the present study, we aimed to evaluate the association between brain atrophy, measured with easy-to-use automatic software, and the "invisible" MS symptoms of cognition and fatigue. A total of 69 MS patients were included in the study; cognitive impairment and fatigue (FSS) (in addition to neurological disability, EDSS) were assessed and correlated with brain volumes calculated using the automated software QyScore® which is validated for single-patient use in the clinical setting. Results showed that the cognitive status was accurately reflected by measures of atrophy, with a sensitivity of up to 90%. CI patients showed a lower volume compared to cognitively normal patients in the whole brain (p = 0.017), gray matter (p = 0.042), insula (p = 0.035), cerebellum (p = 0.008), and limbic lobe (p = 0.049). FSS was associated with temporal lobe (r = -0.37, p = 0.013) and insular (r = -0.36, p = 0.019) volumes. The volumes of the same regions were also associated with EDSS. The global/regional atrophy results, assessed with automatic and easy-to-use software, correlated with cognitive and fatigue symptoms, thus supporting the clinical application in routine patient management.
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Affiliation(s)
- Stefano Ziccardi
- Neurology Section, Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, 37134 Verona, Italy
| | - Francesca Benedetta Pizzini
- Department of Diagnostics and Public Health, University of Verona, 37134 Verona, Italy
- Correspondence: (F.B.P.); (M.C.)
| | - Maddalena Guandalini
- Neurology Section, Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, 37134 Verona, Italy
| | - Agnese Tamanti
- Neurology Section, Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, 37134 Verona, Italy
| | - Cecilia Cristofori
- Neurology Section, Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, 37134 Verona, Italy
| | - Massimiliano Calabrese
- Neurology Section, Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, 37134 Verona, Italy
- Correspondence: (F.B.P.); (M.C.)
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8
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Response to the 'Letter to the editor'-10.1007/s00234-022-02906-z. Neuroradiology 2022; 64:849-850. [PMID: 35303140 DOI: 10.1007/s00234-022-02923-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 02/18/2022] [Indexed: 10/18/2022]
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