1
|
Ojha A, Tommasin S, Piervincenzi C, Baione V, Gangemi E, Gallo A, d'Ambrosio A, Altieri M, De Stefano N, Cortese R, Valsasina P, Tedone N, Pozzilli C, Rocca MA, Filippi M, Pantano P. Clinical and MRI features contributing to the clinico-radiological dissociation in a large cohort of people with multiple sclerosis. J Neurol 2025; 272:327. [PMID: 40204954 PMCID: PMC11982092 DOI: 10.1007/s00415-025-12977-6] [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: 12/04/2024] [Revised: 02/11/2025] [Accepted: 02/14/2025] [Indexed: 04/11/2025]
Abstract
BACKGROUND People with Multiple Sclerosis (PwMS) often show a mismatch between disability and T2-hyperintense white matter (WM) lesion volume (LV), that in general is referred to as the clinico-radiological paradox. OBJECTIVES This study aimed to understand how an extensive clinical, neuropsychological, and MRI analysis could better elucidate the clinico-radiological dissociation in a large cohort of PwMS. METHODS Clinical scores, such as Expanded Disability Status Scale (EDSS), 9 Hole Peg Test (9HPT), 25-foot Walking Test (25-FWT), Paced Auditory Serial Addition Test at 3 s (PASAT3), Symbol digit Modalities Test (SDMT), demographics, and 3 T-MRI of 717 PwMS and 284 healthy subjects (HS) were downloaded from the INNI database. Considering medians of LV and EDSS scores, PwMS were divided into four groups: low LV and disability (LL/LD); high LV and low disability (HL/LD); low LV and high disability (LL/HD); high LV and disability (HL/HD). MRI measures included: volumes of gray matter (GM), WM, cerebellum, basal ganglia and thalamus, spinal cord (SC) area, and functional connectivity of resting-state networks. RESULTS The clinico-radiological dissociation involved 36% of our sample. HL/LD showed worse SDMT scores and lower global and deep GM volumes than HS and LL/LD. LL/HD showed lower GM, thalamus, and cerebellum volumes, and SC area than HS, and lower SC area than LL/LD. CONCLUSIONS A more extensive clinical assessment, including cognitive tests, and MRI evaluation including deep GM and SC, could better describe the real status of the disease and help clinicians in early and tailored treatment in PwMS.
Collapse
Affiliation(s)
- Abhineet Ojha
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
| | - Silvia Tommasin
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy.
- Unicamillus-Saint Camillus International University of Health Sciences, Rome, Italy.
| | | | - Viola Baione
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Emma Gangemi
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
| | - Antonio Gallo
- Department of Advanced Medical and Surgical Sciences, 3t MRI‑Center, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Alessandro d'Ambrosio
- Department of Advanced Medical and Surgical Sciences, 3t MRI‑Center, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Manuela Altieri
- Department of Advanced Medical and Surgical Sciences, 3t MRI‑Center, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Nicola De Stefano
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Rosa Cortese
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Paola Valsasina
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Nicolò Tedone
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Carlo Pozzilli
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
| | - Maria A Rocca
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
- Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Patrizia Pantano
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
- IRCSS NEUROMED, Pozzilli, Italy
| |
Collapse
|
2
|
Schmidt P, Pongratz V, Küster P, Meier D, Wuerfel J, Lukas C, Bellenberg B, Zipp F, Groppa S, Sämann PG, Weber F, Gaser C, Franke T, Bussas M, Kirschke J, Zimmer C, Hemmer B, Mühlau M. Automated segmentation of changes in FLAIR-hyperintense white matter lesions in multiple sclerosis on serial magnetic resonance imaging. NEUROIMAGE-CLINICAL 2019; 23:101849. [PMID: 31085465 PMCID: PMC6517532 DOI: 10.1016/j.nicl.2019.101849] [Citation(s) in RCA: 67] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Accepted: 05/01/2019] [Indexed: 11/30/2022]
Abstract
Longitudinal analysis of white matter lesion changes on serial MRI has become an important parameter to study diseases with white-matter lesions. Here, we build on earlier work on cross-sectional lesion segmentation; we present a fully automatic pipeline for serial analysis of FLAIR-hyperintense white matter lesions. Our algorithm requires three-dimensional gradient echo T1- and FLAIR- weighted images at 3 Tesla as well as available cross-sectional lesion segmentations of both time points. Preprocessing steps include lesion filling and intrasubject registration. For segmentation of lesion changes, initial lesion maps of different time points are fused; herein changes in intensity are analyzed at the voxel level. Significance of lesion change is estimated by comparison with the difference distribution of FLAIR intensities within normal appearing white matter. The method is validated on MRI data of two time points from 40 subjects with multiple sclerosis derived from two different scanners (20 subjects per scanner). Manual segmentation of lesion increases served as gold standard. Across all lesion increases, voxel-wise Dice coefficient (0.7) as well as lesion-wise detection rate (0.8) and false-discovery rate (0.2) indicate good overall performance. Analysis of scans from a repositioning experiment in a single patient with multiple sclerosis did not yield a single false positive lesion. We also introduce the lesion change plot as a descriptive tool for the lesion change of individual patients with regard to both number and volume. An open source implementation of the algorithm is available at http://www.statistical-modeling.de/lst.html. Quantification of white matter lesion changes is important in multiple sclerosis. We developed and validated an algorithm for automated detection of lesion changes. Our algorithm requires T1-weighted and FLAIR images derived at 3 T as well as available cross-sectional lesion segmentations. With data from 2 different scanners, the tool showed good agreement with manual tracing. An open-source application is available.
Collapse
Affiliation(s)
- Paul Schmidt
- Neurology, Technische Universität München, Ismaninger Str. 22, 81541 Munich, Germany; TUM-Neuroimaging Center, Technische Universität München, Ismaninger Str. 22, 81541 Munich, Germany
| | - Viola Pongratz
- Neurology, Technische Universität München, Ismaninger Str. 22, 81541 Munich, Germany; TUM-Neuroimaging Center, Technische Universität München, Ismaninger Str. 22, 81541 Munich, Germany
| | - Pascal Küster
- Medical Image Analysis Center, MIAC AG, Mittlere Strasse 83, CH-4031 Basel, Switzerland; Biomedical Engineering, University Basel, Switzerland
| | - Dominik Meier
- Medical Image Analysis Center, MIAC AG, Mittlere Strasse 83, CH-4031 Basel, Switzerland
| | - Jens Wuerfel
- Medical Image Analysis Center, MIAC AG, Mittlere Strasse 83, CH-4031 Basel, Switzerland; Biomedical Engineering, University Basel, Switzerland
| | - Carsten Lukas
- Diagnostic and Interventional Radiology, St. Josef Hospital, Ruhr-University of Bochum, Gudrunstr. 56, 44791 Bochum, Germany
| | - Barbara Bellenberg
- Diagnostic and Interventional Radiology, St. Josef Hospital, Ruhr-University of Bochum, Gudrunstr. 56, 44791 Bochum, Germany
| | - Frauke Zipp
- Neurology, University Medical Centre of the Johannes Gutenberg University Mainz and Neuroimaging Center of the Focus Program Translational Neuroscience (FTN-NIC), Langenbeckstr. 1, 55131 Mainz, Germany
| | - Sergiu Groppa
- Neurology, University Medical Centre of the Johannes Gutenberg University Mainz and Neuroimaging Center of the Focus Program Translational Neuroscience (FTN-NIC), Langenbeckstr. 1, 55131 Mainz, Germany
| | - Philipp G Sämann
- Max Planck Institute of Psychiatry, Kraepelinstr. 2-10, 80804 Munich, Germany
| | - Frank Weber
- Max Planck Institute of Psychiatry, Kraepelinstr. 2-10, 80804 Munich, Germany; Neurology, Sana Kliniken des Landkreises Cham, August-Holz-Straße 1, 93413 Cham, Germany
| | - Christian Gaser
- Department of Psychiatry and Department of Neurology, Jena University Hospital, Jena, Germany
| | - Thomas Franke
- Medical Informatics, University Medical Center Göttingen, Germany
| | - Matthias Bussas
- Neurology, Technische Universität München, Ismaninger Str. 22, 81541 Munich, Germany; TUM-Neuroimaging Center, Technische Universität München, Ismaninger Str. 22, 81541 Munich, Germany
| | - Jan Kirschke
- Neuroradiology, Technische Universität München, Ismaninger Str. 22, 81541 Munich, Germany
| | - Claus Zimmer
- Neuroradiology, Technische Universität München, Ismaninger Str. 22, 81541 Munich, Germany
| | - Bernhard Hemmer
- Neurology, Technische Universität München, Ismaninger Str. 22, 81541 Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Feodor-Lynen-Str. 17, 81377 Munich, Germany
| | - Mark Mühlau
- Neurology, Technische Universität München, Ismaninger Str. 22, 81541 Munich, Germany; TUM-Neuroimaging Center, Technische Universität München, Ismaninger Str. 22, 81541 Munich, Germany.
| |
Collapse
|
3
|
Rocca MA, Comi G, Filippi M. The Role of T1-Weighted Derived Measures of Neurodegeneration for Assessing Disability Progression in Multiple Sclerosis. Front Neurol 2017; 8:433. [PMID: 28928705 PMCID: PMC5591328 DOI: 10.3389/fneur.2017.00433] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2017] [Accepted: 08/08/2017] [Indexed: 12/26/2022] Open
Abstract
Introduction Multiple sclerosis (MS) is characterised by the accumulation of permanent neurological disability secondary to irreversible tissue loss (neurodegeneration) in the brain and spinal cord. MRI measures derived from T1-weighted image analysis (i.e., black holes and atrophy) are correlated with pathological measures of irreversible tissue loss. Quantifying the degree of neurodegeneration in vivo using MRI may offer a surrogate marker with which to predict disability progression and the effect of treatment. This review evaluates the literature examining the association between MRI measures of neurodegeneration derived from T1-weighted images and disability in MS patients. Methods A systematic PubMed search was conducted in January 2017 to identify MRI studies in MS patients investigating the relationship between “black holes” and/or atrophy in the brain and spinal cord, and disability. Results were limited to human studies published in English in the previous 10 years. Results A large number of studies have evaluated the association between the previous MRI measures and disability. These vary considerably in terms of study design, duration of follow-up, size, and phenotype of the patient population. Most, although not all, have shown that there is a significant correlation between disability and black holes in the brain, as well as atrophy of the whole brain and grey matter. The results for brain white matter atrophy are less consistently positive, whereas studies evaluating spinal cord atrophy consistently showed a significant correlation with disability. Newer ways of measuring atrophy, thanks to the development of segmentation and voxel-wise methods, have allowed us to assess the involvement of strategic regions of the CNS (e.g., thalamus) and to map the regional distribution of damage. This has resulted in better correlations between MRI measures and disability and in the identification of the critical role played by some CNS structures for MS clinical manifestations. Conclusion The evaluation of MRI measures of atrophy as predictive markers of disability in MS is a highly active area of research. At present, measurement of atrophy remains within the realm of clinical studies, but its utility in clinical practice has been recognized and barriers to its implementation are starting to be addressed.
Collapse
Affiliation(s)
- Maria A Rocca
- Neuroimaging Research Unit, Institute of Experimental Neurology, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy.,Department of Neurology, Institute of Experimental Neurology, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Giancarlo Comi
- Department of Neurology, Institute of Experimental Neurology, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy.,Department of Neurology, Institute of Experimental Neurology, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| |
Collapse
|
4
|
Vågberg M, Granåsen G, Svenningsson A. Brain Parenchymal Fraction in Healthy Adults-A Systematic Review of the Literature. PLoS One 2017; 12:e0170018. [PMID: 28095463 PMCID: PMC5240949 DOI: 10.1371/journal.pone.0170018] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2016] [Accepted: 12/26/2016] [Indexed: 01/18/2023] Open
Abstract
Brain atrophy is an important feature of many neurodegenerative disorders. It can be described in terms of change in the brain parenchymal fraction (BPF). In order to interpret the BPF in disease, knowledge on the BPF in healthy individuals is required. The aim of this study was to establish a normal range of values for the BPF of healthy individuals via a systematic review of the literature. The databases PubMed and Scopus were searched and 95 articles, including a total of 9269 individuals, were identified including the required data. We present values of BPF from healthy individuals stratified by age and post-processing method. The mean BPF correlated with mean age and there were significant differences in age-adjusted mean BPF between methods. This study contributes to increased knowledge about BPF in healthy individuals, which may assist in the interpretation of BPF in the setting of disease. We highlight the differences between post-processing methods and the need for a consensus gold standard.
Collapse
Affiliation(s)
- Mattias Vågberg
- Department of Pharmacology and Clinical Neuroscience, Umeå University, Umeå, Sweden
| | - Gabriel Granåsen
- Epidemiology and Global Health Unit, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Anders Svenningsson
- Department of Pharmacology and Clinical Neuroscience, Umeå University, Umeå, Sweden
- Department of Clinical Sciences, Karolinska Institutet, Danderyd Hospital, Stockholm, Sweden
| |
Collapse
|