Effective Utilization of MRI in the Diagnosis and Management of Multiple Sclerosis

Sustained Effectiveness and Safety Over Time of Teriflunomide in Chinese Patients with Relapsing Multiple Sclerosis in the Greater Bay Area of China: Insights from Real-World Data

INTRODUCTION

The real-world data on the medium- to long-term effectiveness and safety of teriflunomide in Chinese patients with relapsing multiple sclerosis (MS) is limited. Therefore, this study aims to assess the treatment outcomes of teriflunomide in Chinese patients with MS over a medium- to long-term period.

METHODS

This cohort study was carried out in three tertiary hospitals and regional MS centers located in the Greater Bay Area of China. We obtained the historical clinical data of patients who underwent teriflunomide treatment for at least 6 months. The primary objective was to evaluate the proportion of patients achieving no evidence of disease activity (NEDA)-3 status, which is characterized by the absence of relapses, confirmed disability worsening, and new or enlarging MRI lesions, over time. Secondary objectives included assessing the proportion of patients meeting each NEDA-3 criterion, changes in motor and cognitive function, as well as the incidence of adverse events and treatment discontinuations.

RESULTS

A total of 160 patients with MS were enrolled, including 125 patients treated with teriflunomide for at least 1 year (≥ 1-year completers) and 71 patients treated for at least 2 years (≥ 2-year completers). A total of 85.63% of the overall population achieved clinical NEDA-3 status at 6 months of teriflunomide treatment, and 71.20% of ≥ 1-year completers achieved NEDA-3 status at 12 months of teriflunomide treatment. The median timed 25-foot walk test (T25FW), nine-hole peg test (9-HPT), and paced auditory serial addition test (PASAT) results were relatively stable before and after treatment.

CONCLUSION

Medium- to long-term MS disease activity, as indicated by NEDA-3 status, is well controlled in patients treated with continuous teriflunomide treatment in real-world settings.

A real-world clinical validation for AI-based MRI monitoring in multiple sclerosis

Modern management of MS targets No Evidence of Disease Activity (NEDA): no clinical relapses, no magnetic resonance imaging (MRI) disease activity and no disability worsening. While MRI is the principal tool available to neurologists for monitoring clinically silent MS disease activity and, where appropriate, escalating treatment, standard radiology reports are qualitative and may be insensitive to the development of new or enlarging lesions. Existing quantitative neuroimaging tools lack adequate clinical validation. In 397 multi-center MRI scan pairs acquired in routine practice, we demonstrate superior case-level sensitivity of a clinically integrated AI-based tool over standard radiology reports (93.3% vs 58.3%), relative to a consensus ground truth, with minimal loss of specificity. We also demonstrate equivalence of the AI-tool with a core clinical trial imaging lab for lesion activity and quantitative brain volumetric measures, including percentage brain volume loss (PBVC), an accepted biomarker of neurodegeneration in MS (mean PBVC -0.32% vs -0.36%, respectively), whereas even severe atrophy (>0.8% loss) was not appreciated in radiology reports. Finally, the AI-tool additionally embeds a clinically meaningful, experiential comparator that returns a relevant MS patient centile for lesion burden, revealing, in our cohort, inconsistencies in qualitative descriptors used in radiology reports. AI-based image quantitation enhances the accuracy of, and value-adds to, qualitative radiology reporting. Scaled deployment of these tools will open a path to precision management for patients with MS.

Predictive value of brain atrophy, serum biomarkers and information processing speed for early disease progression in multiple sclerosis

INTRODUCTION

Multiple sclerosis (MS) is a chronic autoimmune-mediated demyelinating disease of the central nervous system (CNS). A clinical presentation of the disease is highly differentiated even from the earliest stages of the disease. The application of stratifying tests in clinical practice would allow for improving clinical decision-making including a proper assessment of treatment benefit/risk balance.

METHODS

This prospective study included patients with MS diagnosed up to 1 year before recruitment. We analyzed serum biomarkers such as CXCL13, CHI3L1, OPN, IL-6, and GFAP and neurofilament light chains (NfLs); brain MRI parameters of linear atrophy such as bicaudate ratio (BCR), third ventricle width (TVW); and information processing speed were measured using the Symbol Digit Modalities Test (SDMT) during the 2 years follow-up.

RESULTS

The study included a total of 50 patients recruited shortly after the diagnosis of MS diagnosis (median 0 months; range 0-11 months), and the mean time of observation was 28 months (SD = 4.75). We observed a statistically significant increase in the EDSS score (Wilcoxon test: Z = 3.06, p = 0.002), BCR (Wilcoxon test: Z = 4.66, p < 0.001), and TVW (Wilcoxon test: Z = 2.84, p = 0.005) after 2 years of disease. Patients who had a significantly higher baseline level of NfL suffered from a more severe disease course as per the EDSS score (Mann-Whitney U-test: U = 107, Z = -2,74, p = 0.006) and presence of relapse (Mann-Whitney U-test: U = 188, Z = -2.01, p = 0.044). In the logistic regression model, none of the parameters was a significant predictor for the achieving of no evidence of disease activity status (NEDA). In the model considering all assessed parameters, only the level of NfL had a significant impact on disease progression, measured as the increase in EDSS (logistic regression: β = 0.002, p = 0.017).

CONCLUSION

We confirmed that NfL levels in serum are associated with more active disease. Moreover, we found that TVW at the time of diagnosis was associated with an impairment in cognitive function measured by information processing speed at the end of the 2-year observation. The inclusion of serum NfL and TVW assessment early in the disease may be a good predictor of disease progression independent of NEDA.

Different Impact of Gadopentetate and Gadobutrol on Inflammation-Promoted Retention and Toxicity of Gadolinium Within the Mouse Brain

OBJECTIVES

Using a murine model of multiple sclerosis, we previously showed that repeated administration of gadopentetate dimeglumine led to retention of gadolinium (Gd) within cerebellar structures and that this process was enhanced with inflammation. This study aimed to compare the kinetics and retention profiles of Gd in inflamed and healthy brains after application of the macrocyclic Gd-based contrast agent (GBCA) gadobutrol or the linear GBCA gadopentetate. Moreover, potential Gd-induced neurotoxicity was investigated in living hippocampal slices ex vivo.

MATERIALS AND METHODS

Mice at peak of experimental autoimmune encephalomyelitis (EAE; n = 29) and healthy control mice (HC; n = 24) were exposed to a cumulative dose of 20 mmol/kg bodyweight of either gadopentetate dimeglumine or gadobutrol (8 injections of 2.5 mmol/kg over 10 days). Magnetic resonance imaging (7 T) was performed at baseline as well as at day 1, 10, and 40 post final injection (pfi) of GBCAs. Mice were sacrificed after magnetic resonance imaging and brain and blood Gd content was assessed by laser ablation-inductively coupled plasma (ICP)-mass spectrometry (MS) and ICP-MS, respectively. In addition, using chronic organotypic hippocampal slice cultures, Gd-induced neurotoxicity was addressed in living brain tissue ex vivo, both under control or inflammatory (tumor necrosis factor α [TNF-α] at 50 ng/μL) conditions.

RESULTS

Neuroinflammation promoted a significant decrease in T1 relaxation times after multiple injections of both GBCAs as shown by quantitative T1 mapping of EAE brains compared with HC. This corresponded to higher Gd retention within the EAE brains at 1, 10, and 40 days pfi as determined by laser ablation-ICP-MS. In inflamed cerebellum, in particular in the deep cerebellar nuclei (CN), elevated Gd retention was observed until day 40 after last gadopentetate application (CN: EAE vs HC, 55.06 ± 0.16 μM vs 30.44 ± 4.43 μM). In contrast, gadobutrol application led to a rather diffuse Gd content in the inflamed brains, which strongly diminished until day 40 (CN: EAE vs HC, 0.38 ± 0.08 μM vs 0.17 ± 0.03 μM). The analysis of cytotoxic effects of both GBCAs using living brain tissue revealed an elevated cell death rate after incubation with gadopentetate but not gadobutrol at 50 mM. The cytotoxic effect due to gadopentetate increased in the presence of the inflammatory mediator TNF-α (with vs without TNF-α, 3.15% ± 1.18% vs 2.17% ± 1.14%; P = 0.0345).

CONCLUSIONS

In the EAE model, neuroinflammation promoted increased Gd retention in the brain for both GBCAs. Whereas in the inflamed brains, efficient clearance of macrocyclic gadobutrol during the investigated time period was observed, the Gd retention after application of linear gadopentetate persisted over the entire observational period. Gadopentetate but not gadubutrol appeared to be neurotoxic in an ex vivo paradigm of neuronal inflammation.

Automatic segmentation of white matter hyperintensities: validation and comparison with state-of-the-art methods on both Multiple Sclerosis and elderly subjects

•

Automatic segmentation of MS lesions and age-related WMH from 3D T1 and T2-FLAIR.

•

Comparison to consensus show improved performance of WHASA-3D compared to WHASA.

•

WHASA-3D outperforms available state-of-the-art methods with their default settings.

•

WHASA-3D could be a useful tool for clinical practice and clinical trials.

Different types of white matter hyperintensities (WMH) can be observed through MRI in the brain and spinal cord, especially Multiple Sclerosis (MS) lesions for patients suffering from MS and age-related WMH for subjects with cognitive disorders and/or elderly people. To better diagnose and monitor the disease progression, the quantitative evaluation of WMH load has proven to be useful for clinical routine and trials. Since manual delineation for WMH segmentation is highly time-consuming and suffers from intra and inter observer variability, several methods have been proposed to automatically segment either MS lesions or age-related WMH, but none is validated on both WMH types. Here, we aim at proposing the White matter Hyperintensities Automatic Segmentation Algorithm adapted to 3D T2-FLAIR datasets (WHASA-3D), a fast and robust automatic segmentation tool designed to be implemented in clinical practice for the detection of both MS lesions and age-related WMH in the brain, using both 3D T1-weighted and T2-FLAIR images. In order to increase its robustness for MS lesions, WHASA-3D expands the original WHASA method, which relies on the coupling of non-linear diffusion framework and watershed parcellation, where regions considered as WMH are selected based on intensity and location characteristics, and finally refined with geodesic dilation. The previous validation was performed on 2D T2-FLAIR and subjects with cognitive disorders and elderly subjects. 60 subjects from a heterogeneous database of dementia patients, multiple sclerosis patients and elderly subjects with multiple MRI scanners and a wide range of lesion loads were used to evaluate WHASA and WHASA-3D through volume and spatial agreement in comparison with consensus reference segmentations. In addition, a direct comparison on the MS database with six available supervised and unsupervised state-of-the-art WMH segmentation methods (LST-LGA and LPA, Lesion-TOADS, lesionBrain, BIANCA and nicMSlesions) with default and optimised settings (when feasible) was conducted. WHASA-3D confirmed an improved performance with respect to WHASA, achieving a better spatial overlap (Dice) (0.67 vs 0.63), a reduced absolute volume error (AVE) (3.11 vs 6.2 mL) and an increased volume agreement (intraclass correlation coefficient, ICC) (0.96 vs 0.78). Compared to available state-of-the-art algorithms on the MS database, WHASA-3D outperformed both unsupervised and supervised methods when used with their default settings, showing the highest volume agreement (ICC = 0.95) as well as the highest average Dice (0.58). Optimising and/or retraining LST-LGA, BIANCA and nicMSlesions, using a subset of the MS database as training set, resulted in improved performances on the remaining testing set (average Dice: LST-LGA default/optimized = 0.41/0.51, BIANCA default/optimized = 0.22/0.39, nicMSlesions default/optimized = 0.17/0.63, WHASA-3D = 0.58). Evaluation and comparison results suggest that WHASA-3D is a reliable and easy-to-use method for the automated segmentation of white matter hyperintensities, for both MS lesions and age-related WMH. Further validation on larger datasets would be useful to confirm these first findings.

Multi-lesion radiomics model for discrimination of relapsing-remitting multiple sclerosis and neuropsychiatric systemic lupus erythematosus

OBJECTIVES

To develop an MRI-based multi-lesion radiomics model for discrimination of relapsing-remitting multiple sclerosis (RRMS) and its mimicker neuropsychiatric systemic lupus erythematosus (NPSLE).

METHODS

A total of 112 patients with RRMS (n = 63) or NPSLE (n = 49) were assigned to training and test sets with a ratio of 3:1. All lesions across the whole brain were manually segmented on T2-weighted fluid-attenuated inversion recovery images. For each single lesion, 371 radiomics features were extracted and trained using machine learning algorithms, producing Radiomics Index for Lesion (RIL) for each lesion and a single-lesion radiomics model. Then, for each subject, single lesions were assigned to one of two disease courts based on their distance to decision threshold, and a Radiomics Index for Subject (RIS) was calculated as the mean RIL value of lesions on the higher-weighted court. Accordingly, a subject-level discrimination model was constructed and compared with performances of two radiologists.

RESULTS

The subject-based discrimination model satisfactorily differentiated RRMS and NPSLE in both training (AUC = 0.967, accuracy = 0.892, sensitivity = 0.917, and specificity = 0.872) and test sets (AUC = 0.962, accuracy = 0.931, sensitivity = 1.000, and specificity = 0.875), significantly better than the single-lesion radiomics method (training: p < 0.001; test: p = 0.001) Besides, the discrimination model significantly outperformed the senior radiologist in the training set (training: p = 0.018; test: p = 0.077) and the junior radiologist in both the training and test sets (training: p = 0.008; test: p = 0.023).

CONCLUSIONS

The multi-lesion radiomics model could effectively discriminate between RRMS and NPSLE, providing a supplementary tool for accurate differential diagnosis of the two diseases.

KEY POINTS

• Radiomic features of brain lesions in RRMS and NPSLE were different. • The multi-lesion radiomics model constructed using a merging strategy was comprehensively superior to the single-lesion-based model for discrimination of RRMS and NPSLE. • The RRMS-NPSLE discrimination model showed a significantly better performance or a trend toward significance than the radiologists.

High-resolution quantitative MRI of multiple sclerosis spinal cord lesions

PURPOSE

Validation of quantitative MR measures for myelin imaging in the postmortem multiple sclerosis spinal cord.

METHODS

Four fixed spinal cord samples were imaged first with a 3T clinical MR scanner to identify areas of interest for scanning, and then with a 7T small bore scanner using a multicomponent-driven equilibrium single-pulse observation of T₁ and T₂ protocol to produce apparent proton density, T₁ , T₂ , myelin water, intracellular water, and free-water fraction maps. After imaging, the cords were sectioned and stained with histological markers (hematoxylin and eosin, myelin basic protein, and neurofilament protein), which were quantitatively compared with the MR maps.

RESULTS

Excellent correspondence was found between high-resolution MR parameter maps and histology, particularly for apparent proton density MRI and myelin basic protein staining.

CONCLUSION

High-resolution quantitative MRI of the spinal cord provides biologically meaningful measures, and could be beneficial to diagnose and track multiple sclerosis lesions in the spinal cord.

Magnetic Resonance Imaging and Clinical Features of the Demyelinating Degeneration of White Matter in Young Patients

Objective

Magnetic resonance imaging (MRI) of brain white matter demyelination often focuses on demyelinating disease, cerebral small vascular disease diagnosis, and follow-up of cognitive dysfunction for observation. This study explored MRI findings and clinical manifestations of demyelinating degeneration of white matter in young patients.

Methods

A total of ninety-four patients with white matter degeneration diagnosed with MRI were enrolled in this study from January 2014 to July 2018. These patients were divided into two groups: the demyelinating disease group (n = 43) and the non-demyelinating disease group (n = 51). The imaging findings and clinical manifestations of the two groups were analyzed.

Results

Compared with the non-demyelinating group, there were more female than male patients in the demyelinating group (P < 0.05). In addition, of the 45 patients with an imaging result of “demyelinating degeneration of white matter and multiple sclerosis,” 39 patients met the diagnosis of multiple sclerosis (86.7%). In comparison, of the 49 patients with an imaging result of “demyelinating degeneration of white matter,” only four patients met the diagnosis for demyelinating disease (8.2%).

Conclusion

In patients complaining of headaches, dizziness, vertigo, and other symptoms and in the case of an imaging result showing the demyelinating degeneration of white matter alone, the possibility of a clinical diagnosis of a demyelinating disease is minimal.

Detection of Optic Neuritis on Routine Brain MRI without and with the Assistance of an Image Postprocessing Algorithm

BACKGROUND AND PURPOSE

At times, there is a clinical need for using routine brain MR imaging performed close to the time of onset of patients' visual symptoms to firmly establish the diagnosis of optic neuritis. Our aim was to assess the diagnostic performance of radiologists in detecting optic neuritis on routine brain MR images and whether this performance could be enhanced using a postprocessing algorithm.

MATERIALS AND METHODS

In this retrospective case-control study of 60 patients (37 women, 23 men; mean age, 47.2 [SD, 17.9] years), 2 blinded neuroradiologists evaluated T2-weighted FLAIR and contrast-enhanced T1WI from brain MR imaging for the presence of imaging evidence of optic neuritis. Images were processed using an image-processing algorithm that aimed to selectively accentuate the signal intensity of diseased optic nerves. We assessed the effect of image processing on the contrast-to-noise ratio between the optic nerves and normal-appearing white matter and on the diagnostic performance of the neuroradiologists, including the interobserver reliability.

RESULTS

The average sensitivity of readers was 55%, 56.5%, and 30.0% on FLAIR, coronal contrast-enhanced T1WI, and axial contrast-enhanced T1WI, respectively. Sensitivities were lower in the absence of fat saturation on FLAIR (P = .001) and coronal contrast-enhanced T1WI (P = .04). Processing increased the contrast-to-noise ratio of diseased (P value range = .03 to <.001) but not of control optic nerves. Processing did not improve the sensitivity but improved the specificity and positive predictive value. Interobserver agreement improved from slight to good.

CONCLUSIONS

Detection of optic neuritis on routine brain MR imaging is challenging. Specificity, positive predictive value, and interobserver agreement can be improved by postprocessing of MR images.

Recent advances in development of nanomedicines for multiple sclerosis diagnosis

Multiple sclerosis (MS) is a neurodegenerative disease with a high morbidity and disease burden. It is characterized by the loss of the myelin sheath, resulting in the disruption of neuron electrical signal transmissions and sensory and motor ability deficits. The diagnosis of MS is crucial to its management, but the diagnostic sensitivity and specificity are always a challenge. To overcome this challenge, nanomedicines have recently been employed to aid the diagnosis of MS with an improved diagnostic efficacy. Advances in nanomedicine-based contrast agents in magnetic resonance imaging scanning of MS lesions, and nanomedicine-derived sensors for detecting biomarkers in the cerebrospinal fluid biopsy, or analyzing the composition of exhaled breath gas, have demonstrated the potential of using nanomedicines in the accurate diagnosis of MS. This review aims to provide an overview of recent advances in the application of nanomedicines for the diagnosis of MS and concludes with perspectives of using nanomedicines for the development of safe and effective MS diagnostic nanotools.

Retinal and brain damage during multiple sclerosis course: inflammatory activity is a key factor in the first 5 years

Understanding of the role of focal inflammation, a treatable feature, on neuro-axonal injury, is paramount to optimize neuroprotective strategy in MS. To quantify the impact of focal inflammatory activity on the rate of neuro-axonal injury over the MS course. We quantified the annualized rates of change in peripapillary retinal nerve fiber layer, ganglion cell plus inner plexiform layer (GCIPL), whole-brain, gray matter and thalamic volumes in patients with and without focal inflammatory activity in 161 patients followed over 5 years. We used mixed models including focal inflammatory activity (the presence of at least one relapse or a new/enlarging T2-FLAIR or gadolinium- enhancing lesion), and its interaction with time adjusted by age, sex, use of disease-modifying therapies and steroids, and prior optic neuritis. The increased rate of neuro-axonal injury during the first five years after onset was more prominent among active patients, as reflected by the changes in GCIPL thickness (p = 0.02), whole brain (p = 0.002) and thalamic volumes (p < 0.001). Thereafter, rates of retinal and brain changes stabilized and were similar in active and stable patients. Focal inflammatory activity is associated with neurodegeneration early in MS which reinforces the use of an early intensive anti-inflammatory therapy to prevent neurodegeneration in MS.

Serum Exosome MicroRNAs Predict Multiple Sclerosis Disease Activity after Fingolimod Treatment

We and others have previously demonstrated the potential for circulating exosome microRNAs to aid in disease diagnosis. In this study, we sought the possible utility of serum exosome microRNAs as biomarkers for disease activity in multiple sclerosis patients in response to fingolimod therapy. We studied patients with relapsing-remitting multiple sclerosis prior to and 6 months after treatment with fingolimod. Disease activity was determined using gadolinium-enhanced magnetic resonance imaging. Serum exosome microRNAs were profiled using next-generation sequencing. Data were analysed using univariate/multivariate modelling and machine learning to determine microRNA signatures with predictive utility. Accordingly, we identified 15 individual miRNAs that were differentially expressed in serum exosomes from post-treatment patients with active versus quiescent disease. The targets of these microRNAs clustered in ontologies related to the immune and nervous systems and signal transduction. While the power of individual microRNAs to predict disease status post-fingolimod was modest (average 77%, range 65 to 91%), several combinations of 2 or 3 miRNAs were able to distinguish active from quiescent disease with greater than 90% accuracy. Further stratification of patients identified additional microRNAs associated with stable remission, and a positive response to fingolimod in patients with active disease prior to treatment. Overall, these data underscore the value of serum exosome microRNA signatures as non-invasive biomarkers of disease in multiple sclerosis and suggest they may be used to predict response to fingolimod in future clinical practice. Additionally, these data suggest that fingolimod may have mechanisms of action beyond its known functions.

Observational designs in clinical multiple sclerosis research: Particulars, practices and potentialities

Observational studies investigate a wide range of topics in multiple sclerosis research. This paper presents an overview of the various observational designs and their applications in clinical studies. Observational studies are well suited for making discoveries and assessing new explanations of phenomena, but less so for establishing causal relationships, due to confounding by indication (selection bias), co-morbidity, socio-economic or other factors. Whether observational findings are demonstrative, indicative or only suggestive, depends on the research question, whether and how the design fits this question, analytical techniques, and the quality of data. Observational studies may be cross-sectional vs. longitudinal, and prospective vs. retrospective. The term 'retrograde' is proposed to explicate that cross-sectional studies may obtain data that cover (long) preceding periods. Case reports and case series are usually based on accidental observations or routinely collected data. Cross-sectional studies, by simultaneously assessing clinical phenomena and external factors, enable the discovery and quantification of associations. In ecological studies the unit of analysis is population or group, and relationships on patient level cannot be established. A cohort study is a longitudinal study that investigates patients with a defining characteristic, e.g. diagnosis or specific treatment, by analyzing data acquired at various intervals. Prospective cohort studies use (some) data that are not yet available at the time the research is conceived, whereas in retrospective studies the data already exist. In a case-control study a representative group of patients with a specific clinical feature is compared with controls, and the frequencies at which an external factor, e.g. infection, has occurred in each group is compared; in a nested case-control study controls are drawn from a fully known cohort. Randomized controlled trial (RCT)-extension studies are informative because, due to RCT randomization, they are free from confounding by indication. Patient or disease registries are organised systems for the long-term collection of uniform data on a population that is defined by a particular disease, condition or exposure, with the purpose to study changes over time. In pharmacotherapeutic research, accidental observations of unexpected beneficial effects may lead to further research into a drug's efficacy in other conditions. Uncontrolled phase 1 studies investigate safety and dosing aspects. Observational studies are alternatives to RCTs when these are not feasible for ethical or practical reasons. Phase 4 observational studies play a crucial role in the evaluation of the effectiveness of treatments in daily practice, the validation of RCT-based side effect profiles, and the discovery of late occurring or rare, potentially life-threatening side effects. Combinations of multidisciplinary longitudinal data bases into large data sets enable the development of algorithms for personalized treatments. To improve the reporting of observational findings on treatment effectiveness, it is proposed that abstracts define the research question(s) the study was meant to answer, study design and analytical methods, and identify and quantify the patient population, treatment of interest, relevant outcomes and the study's strengths and limitations. The development of guidelines for Strengthening the Reporting of Observational Studies in Effectiveness Research (STROBER), as an extension of the guidelines used in epidemiology, is wanted.

Comparing longitudinal brain atrophy measurement techniques in a real-world multiple sclerosis clinical practice cohort: towards clinical integration?

Background:

Whole brain atrophy (WBA) estimates in multiple sclerosis (MS) correlate more robustly with clinical disability than traditional, lesion-based metrics. We compare Structural Image Evaluation using Normalisation of Atrophy (SIENA) with the icobrain longitudinal pipeline (icobrain long), for assessment of longitudinal WBA in MS patients.

Methods:

Magnetic resonance imaging (MRI) scan pairs [1.05 (±0.15) year separation] from 102 MS patients were acquired on the same 3T scanner. Three-dimensional (3D) T1-weighted and two-dimensional (2D)/3D fluid-attenuated inversion-recovery sequences were analysed. Percentage brain volume change (PBVC) measurements were calculated using SIENA and icobrain long. Statistical correlation, agreement and consistency between methods was evaluated; MRI brain volumetric and clinical data were compared. The proportion of the cohort with annualized brain volume loss (aBVL) rates ⩾ 0.4%, ⩾0.8% and ⩾0.94% were calculated. No evidence of disease activity (NEDA) 3 and NEDA 4 were also determined.

Results:

Mean annualized PBVC was −0.59 (±0.65)% and −0.64 (±0.73)% as measured by icobrain long and SIENA. icobrain long and SIENA-measured annualized PBVC correlated strongly, r = 0.805 (p < 0.001), and the agreement [intraclass correlation coefficient (ICC) 0.800] and consistency (ICC 0.801) were excellent. Weak correlations were found between MRI metrics and Expanded Disability Status Scale scores. Over half the cohort had aBVL ⩾ 0.4%, approximately a third ⩾0.8%, and aBVL was ⩾0.94% in 28.43% and 23.53% using SIENA and icobrain long, respectively. NEDA 3 was achieved in 35.29%, and NEDA 4 in 15.69% and 16.67% of the cohort, using SIENA and icobrain long to derive PBVC, respectively.

Discussion:

icobrain long quantified longitudinal WBA with a strong level of statistical agreement and consistency compared to SIENA in this real-world MS population. Utility of WBA measures in individuals remains challenging, but show promise as biomarkers of neurodegeneration in MS clinical practice. Optimization of MRI analysis algorithms/techniques are needed to allow reliable use in individuals. Increased levels of automation will enable more rapid clinical translation.

Future Brain and Spinal Cord Volumetric Imaging in the Clinic for Monitoring Treatment Response in MS

PURPOSE OF REVIEW

Volumetric analysis of brain imaging has emerged as a standard approach used in clinical research, e.g., in the field of multiple sclerosis (MS), but its application in individual disease course monitoring is still hampered by biological and technical limitations. This review summarizes novel developments in volumetric imaging on the road towards clinical application to eventually monitor treatment response in patients with MS.

RECENT FINDINGS

In addition to the assessment of whole-brain volume changes, recent work was focused on the volumetry of specific compartments and substructures of the central nervous system (CNS) in MS. This included volumetric imaging of the deep brain structures and of the spinal cord white and gray matter. Volume changes of the latter indeed independently correlate with clinical outcome measures especially in progressive MS. Ultrahigh field MRI and quantitative MRI added to this trend by providing a better visualization of small compartments on highly resolving MR images as well as microstructural information. New developments in volumetric imaging have the potential to improve sensitivity as well as specificity in detecting and hence monitoring disease-related CNS volume changes in MS.