CROP - The Clinico-Radiologico-Ophthalmological Paradox in Multiple Sclerosis: Are Patterns of Retinal and MRI Changes Heterogeneous and Thus Not Predictable?

Predictive MRI Biomarkers in MS—A Critical Review

Background and Objectives: In this critical review, we explore the potential use of MRI measurements as prognostic biomarkers in multiple sclerosis (MS) patients, for both conventional measurements and more novel techniques such as magnetization transfer, diffusion tensor, and proton spectroscopy MRI. Materials and Methods: All authors individually and comprehensively reviewed each of the aspects listed below in PubMed, Medline, and Google Scholar. Results: There are numerous MRI metrics that have been proven by clinical studies to hold important prognostic value for MS patients, most of which can be readily obtained from standard 1.5T MRI scans. Conclusions: While some of these parameters have passed the test of time and seem to be associated with a reliable predictive power, some are still better interpreted with caution. We hope this will serve as a reminder of how vast a resource we have on our hands in this versatile tool—it is up to us to make use of it.

Design and Validation of an Expanded Disability Status Scale Model in Multiple Sclerosis

INTRODUCTION

We aimed to develop and validate an Expanded Disability Status Scale (EDSS) model through clinical, optical coherence tomography (OCT), and magnetic resonance imaging (MRI) measures.

METHODS

Sixty-four multiple sclerosis (MS) patients underwent peripapillary retinal nerve fiber layer and segmented macular layers evaluation through OCT (Spectralis, Heidelberg Engineering). Brain parenchymal fraction was quantified through Freesurfer, while cervical spinal cord (SC) volume was assessed manually guided by Spinal Cord Toolbox software analysis. EDSS, neuroradiological, and OCT assessment were carried out within 3 months. OCT parameters were calculated as the average of both nonoptic neuritis (ON) eyes, and in case the patient had previous ON, the value of the fellow non-ON eye was taken. Brain lesion volume, sex, age, disease duration, and history of disease-modifying treatment (1st or 2nd line disease-modifying treatments) were tested as covariables of the EDSS score.

RESULTS

EDSS values correlated with patient's age (r = 0.543, p = 0.001), SC volume (r = -0.301, p = 0.034), and ganglion cell layer (GCL, r = -0.354, p = 0.012). Using these correlations, an ordinal regression model to express probability of diverse EDSS scores were designed, the highest of which was the most probable (Nagelkerke R2 = 43.3%). Using EDSS cutoff point of 4.0 in a dichotomous model, compared to a cutoff of 2.0, permits the inclusion of GCL as a disability predictor, in addition to age and SC.

CONCLUSIONS

MS disability measured through EDSS is an age-dependent magnitude that is partly conditioned by SC and GCL. Further studies assessing paraclinical disability predictors are needed.

Magnetic resonance imaging and optical coherence tomography correlations in multiple sclerosis beyond anatomical landmarks

OBJECTIVE

To investigate multiple sclerosis (MS) optical coherence tomography (OCT) cross-sectional correlations with central nervous system (CNS) magnetic resonance imaging (MRI).

MATERIAL AND METHODS

Peripapillary retinal nerve fiber layer (pRNFL), ganglion cell layer (GCL), inner plexiform layer (IPL), inner (INL) and outer nuclear layer (ONL) of 54 relapsing remitting (RRMS) and 38 progressive (PMS, 9 primary and 29 secondary) patients were measured. With less than 3 months brain parenchymal fraction (BPF), spinal cord (SC), total gray matter (GM) and white matter volumes were calculated. Demographical and clinical data was compared according to the history of optic neuritis (HON). Relationships between OCT and MRI data were assessed using multivariable linear regression models, adjusting for age, gender and disease duration, taking into account HON and disease subtype.

RESULTS

Cerebellum (p = 0.008), pRNFL (p = 0.001), GCL (p = 0.001) and IPL (p = 0.001) were thinner, while INL was thicker (p = 0.02) if HON. SC correlated better with nasal pRNFL sectors in eyes with HON (all eyes: average pRNFL p = 0.035 η² = 0.213; N-pRNFL p = 0.04 η² = 0.36, NI-pRNFL p = 0.0001 η² = 0.484. RRMS eyes: N-pRNFL p = 0.034 η² = 0.348; NI-pRNFL p = 0.013 η² = 0.441), while it correlates with PMB (p = 0.032 η² = 0.144), GCL (p = 0.03 η² = 0.147) and IPL (p = 0.028 η² = 0.151) in eyes without HON regardless of the disease subtype. INL presented no microcystic macular oedema and was inversely associated with BPF (p = 0.029 η² = 0.363) and cerebellum (p = 0.015 η² = 0.428) in PMS eyes without HON.

CONCLUSIONS

OCT data correlates with different CNS compartments, even with no anatomical or functional linkage, serving as useful neurodegeneration and inflammation surrogate marker.

Quantifying the Metabolic Signature of Multiple Sclerosis by in vivo Proton Magnetic Resonance Spectroscopy: Current Challenges and Future Outlook in the Translation From Proton Signal to Diagnostic Biomarker

Proton magnetic resonance spectroscopy (¹H-MRS) offers a growing variety of methods for querying potential diagnostic biomarkers of multiple sclerosis in living central nervous system tissue. For the past three decades, ¹H-MRS has enabled the acquisition of a rich dataset suggestive of numerous metabolic alterations in lesions, normal-appearing white matter, gray matter, and spinal cord of individuals with multiple sclerosis, but this body of information is not free of seeming internal contradiction. The use of ¹H-MRS signals as diagnostic biomarkers depends on reproducible and generalizable sensitivity and specificity to disease state that can be confounded by a multitude of influences, including experiment group classification and demographics; acquisition sequence; spectral quality and quantifiability; the contribution of macromolecules and lipids to the spectroscopic baseline; spectral quantification pipeline; voxel tissue and lesion composition; T₁ and T₂ relaxation; B₁ field characteristics; and other features of study design, spectral acquisition and processing, and metabolite quantification about which the experimenter may possess imperfect or incomplete information. The direct comparison of ¹H-MRS data from individuals with and without multiple sclerosis poses a special challenge in this regard, as several lines of evidence suggest that experimental cohorts may differ significantly in some of these parameters. We review the existing findings of in vivo¹H-MRS on central nervous system metabolic abnormalities in multiple sclerosis and its subtypes within the context of study design, spectral acquisition and processing, and metabolite quantification and offer an outlook on technical considerations, including the growing use of machine learning, by future investigations into diagnostic biomarkers of multiple sclerosis measurable by ¹H-MRS.