Brain atrophy assessment in multiple sclerosis: technical- and subject-related barriers for translation to real-world application in individual subjects

• Published in: Expert review of neurotherapeutics Vol. 24; no. 11; p. 1081
• Main Authors: Zivadinov, Robert, Tranquille, Ashley, Reeves, Jack A, Dwyer, Michael G, Bergsland, Niels
• Format: Journal Article
• Language: English
• Published: England 01.11.2024
• Subjects: Atrophy; Brain - diagnostic imaging; Brain - pathology; Disease Progression; Humans; Magnetic Resonance Imaging - methods; Multiple Sclerosis - diagnostic imaging; Multiple Sclerosis - pathology; real-world; Multiple sclerosis; technical barriers; clinical routine; biological factors; measurement; subject-related barriers; methodological pitfalls
• ISSN: 1744-8360
• DOI: 10.1080/14737175.2024.2398484

Brain atrophy is a well-established MRI outcome for predicting clinical progression and monitoring treatment response in persons with multiple sclerosis (pwMS) at the group level. Despite the important progress made, the translation of brain atrophy assessment into clinical practice faces several challenges. In this review, the authors discuss technical- and subject-related barriers for implementing brain atrophy assessment as part of the clinical routine at the individual level. Substantial progress has been made to understand and mitigate technical barriers behind MRI acquisition. Numerous research and commercial segmentation techniques for volume estimation are available and technically validated, but their clinical value has not been fully established. A systematic assessment of subject-related barriers, which include genetic, environmental, biological, lifestyle, comorbidity, and aging confounders, is critical for the interpretation of brain atrophy measures at the individual subject level. Educating both medical providers and pwMS will help better clarify the benefits and limitations of assessing brain atrophy for disease monitoring and prognosis. Integrating brain atrophy assessment into clinical practice for pwMS requires overcoming technical and subject-related challenges. Advances in MRI standardization, artificial intelligence, and clinician education will facilitate this process, improving disease management and potentially reducing long-term healthcare costs.