An update on the role of magnetic resonance imaging in predicting and monitoring multiple sclerosis progression

ABSTRACT

Introduction

While magnetic resonance imaging (MRI) is established in diagnosing and monitoring disease activity in multiple sclerosis (MS), its utility in predicting and monitoring disease progression is less clear.

Areas covered

The authors consider changing concepts in the phenotypic classification of MS, including progression independent of relapses; pathological processes underpinning progression; advances in MRI measures to assess them; how well MRI features explain and predict clinical outcomes, including models that assess disease effects on neural networks, and the potential role for machine learning.

Expert opinion

Relapsing-remitting and progressive MS have evolved from being viewed as mutually exclusive to having considerable overlap. Progression is likely the consequence of several pathological elements, each important in building more holistic prognostic models beyond conventional phenotypes. MRI is well placed to assess pathogenic processes underpinning progression, but we need to bridge the gap between MRI measures and clinical outcomes. Mapping pathological effects on specific neural networks may help and machine learning methods may be able to optimize predictive markers while identifying new, or previously overlooked, clinically relevant features. The ever-increasing ability to measure features on MRI raises the dilemma of what to measure and when, and the challenge of translating research methods into clinically useable tools.

KEYWORDS:

• Multiple sclerosis
• progression independent of relapse
• PIRA
• disability progression
• compartmentalized inflammation
• chronic inflammation
• brain atrophy
• neurodegeneration

Article highlights

• Multiple sclerosis (MS) is a chronic, inflammatory demyelinating neurodegenerative disease of the central nervous system (CNS) affecting over 130,000 people in the UK.

• Rather than relapsing-remitting and progressive MS being considered distinct phenotypes, there is a growing recognition that there is considerable overlap between them, and that substantial progression independent of relapses can occur in relapsing-remitting MS.

• There is increasing interest in MRI measures that assess specific pathological processes that may underpin disease progression, for example, compartmentalized inflammation.

• Mapping the effects pathology has on neural networks may help bridge the gap between MRI measures and clinical outcomes, but to date studies have mainly considered effects on white matter and gray matter separately rather than together.

• There is an emerging role for machine learning in optimizing prognostic models and identifying new or previously overlooked clinically relevant predictive markers of disease progression.

Declaration of interest

N Sahi is a clinical research fellow funded by an MRC grant (Ref: MR/W019906/1) and was previously in a post supported by Merck & Co. (supervised by D Chard and SA Trip). P Ananthavarathan is a clinical research fellow funded by an MS Society grant and was previously in a post supported by Merck & Co. (supervised by D Chard and SA Trip). D Chard is a consultant for Hoffmann-La Roche. In the last three years he has been a consultant for Biogen Idec, has received research funding from Hoffmann-La Roche, the International Progressive MS Alliance, the MS Society, the Medical Research Council, and the National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre, and a speaker’s honorarium from Novartis. He co-supervises a clinical fellowship at the National Hospital for Neurology and Neurosurgery, London, which is supported by Merck & Co. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Additional information

Funding

This paper was not funded.