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Published on: July 19, 2019
Brain MRI atrophy quantification in MS: From methods to clinical application
Maria A Rocca1, Marco Battaglini1, Ralph H B Benedict1
1From the Neuroimaging Research Unit (M.A.R., E.P., M.F.), Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan; Department of Medicine, Surgery and Neuroscience (M.B., N.D.S.), University of Siena, Italy; Department of Neurology (R.H.B.B.), Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York; Department of Anatomy and Neuroscience (J.J.G.G.), Section of Clinical Neuroscience, VUmc MS Center Amsterdam, VU University Medical Center, the Netherlands; Department of Neurology (R.G.H.), University of California, San Francisco; Xinapse Systems Ltd. (M.A.H.), Colchester, Essex, UK; and FMRIB Centre (M.J.), Nuffield Department of Clinical Neurosciences, University of Oxford, UK.
Abstract:
Patients with the main clinical phenotypes of multiple sclerosis (MS) manifest varying degrees of brain atrophy beyond that of normal aging. Assessment of atrophy helps to distinguish clinically and cognitively deteriorating patients and predicts those who will have a less-favorable clinical outcome over the long term. Atrophy can be measured from brain MRI scans, and many technological improvements have been made over the last few years. Several software tools, with differing requirements on technical ability and levels of operator intervention, are currently available and have already been applied in research or clinical trial settings. Despite this, the measurement of atrophy in routine clinical practice remains an unmet need. After a short summary of the pathologic substrates of brain atrophy in MS, this review attempts to guide the clinician towards a better understanding of the methods currently used for quantifying brain atrophy in this condition. Important physiologic factors that affect brain volume measures are also considered. Finally, the most recent research on brain atrophy in MS is summarized, including whole brain and various compartments thereof (i.e., white matter, gray matter, selected CNS structures). Current methods provide sufficient precision for cohort studies, but are not adequate for confidently assessing changes in individual patients over the scale of months or a few years.
Insights
Brain atrophy measurement in multiple sclerosis (MS) using MRI is crucial for predicting patient outcomes. Current methods are precise for research but not yet for routine clinical monitoring of individual patients.
Area of Science:
- Neurology
- Radiology
- Biomedical Engineering
Background:
- Multiple sclerosis (MS) patients exhibit brain atrophy exceeding normal aging, impacting clinical and cognitive status.
- Brain atrophy assessment aids in distinguishing deteriorating patients and predicting long-term outcomes.
- Advances in MRI technology have improved atrophy quantification methods.
Purpose of the Study:
- To review current methods for quantifying brain atrophy in MS.
- To guide clinicians in understanding atrophy measurement techniques.
- To discuss factors influencing brain volume measures and summarize recent research.
Main Methods:
- Review of pathological substrates of brain atrophy in MS.
- Analysis of available software tools for MRI-based atrophy quantification.
- Consideration of physiological factors affecting brain volume measurements.
Main Results:
- Current atrophy measurement methods offer sufficient precision for MS cohort studies.
- Existing techniques are inadequate for confidently assessing individual patient changes over short time scales (months to years).
- Research has advanced in quantifying whole brain and specific compartment atrophy in MS.
Conclusions:
- Despite technological progress, routine clinical measurement of brain atrophy in MS remains a challenge.
- While valuable for research, current methods require further refinement for reliable individual patient monitoring.
- Understanding atrophy progression is key to improving long-term management of multiple sclerosis.
