Recommendations to improve imaging and analysis of brain lesion load and atrophy in longitudinal studies of multiple sclerosis

Systemic inflammation associates with and precedes cord atrophy in progressive multiple sclerosis

In preclinical models of multiple sclerosis, systemic inflammation has an impact on the compartmentalized inflammatory process within the central nervous system and results in axonal loss. It remains to be shown whether this is the case in humans, specifically whether systemic inflammation contributes to spinal cord or brain atrophy in multiple sclerosis. Hence, an observational longitudinal study was conducted to delineate the relationship between systemic inflammation and atrophy using magnetic resonance imaging: the SIMS (Systemic Inflammation in Multiple Sclerosis) study. Systemic inflammation and progression were assessed in people with progressive multiple sclerosis (n = 50) over two and a half years. Eligibility criteria included: (i) primary or secondary progressive multiple sclerosis; (ii) age ≤ 70; and (iii) Expanded Disability Status Scale ≤ 6.5. First morning urine was collected weekly to quantify systemic inflammation by measuring the urinary neopterin-to-creatinine ratio using a validated ultra-performance liquid chromatography mass spectrometry technique. The urinary neopterin-to-creatinine ratio temporal profile was characterized by short-term responses overlaid on a background level of inflammation, so these two distinct processes were considered as separate variables: background inflammation and inflammatory response. Participants underwent MRI at the start and end of the study, to measure cervical spinal cord and brain atrophy. Brain and cervical cord atrophy occurred on the study, but the most striking change was seen in the cervical spinal cord, in keeping with the corticospinal tract involvement that is typical of progressive disease. Systemic inflammation predicted cervical cord atrophy. An association with brain atrophy was not observed in this cohort. A time lag between systemic inflammation and cord atrophy was evident, suggesting but not proving causation. The association of the inflammatory response with cord atrophy depended on the level of background inflammation, in keeping with experimental data in preclinical models where the effects of a systemic inflammatory challenge on tissue injury depended on prior exposure to inflammation. A higher inflammatory response was associated with accelerated cord atrophy in the presence of background systemic inflammation below the median for the study population. Higher background inflammation, while associated with cervical cord atrophy itself, subdued the association of the inflammatory response with cord atrophy. Findings were robust to sensitivity analyses adjusting for potential confounders and excluding cases with new lesion formation. In conclusion, systemic inflammation associates with, and precedes, multiple sclerosis progression. Further work is needed to prove causation since targeting systemic inflammation may offer novel treatment strategies for slowing neurodegeneration in multiple sclerosis.

Image harmonization improves consistency of intra-rater delineations of MS lesions in heterogeneous MRI

Clinical magnetic resonance images (MRIs) lack a standard intensity scale due to differences in scanner hardware and the pulse sequences used to acquire the images. When MRIs are used for quantification, as in the evaluation of white matter lesions (WMLs) in multiple sclerosis, this lack of intensity standardization becomes a critical problem affecting both the staging and tracking of the disease and its treatment. This paper presents a study of harmonization on WML segmentation consistency, which is evaluated using an object detection classification scheme that incorporates manual delineations from both the original and harmonized MRIs. A cohort of ten people scanned on two different imaging platforms was studied. An expert rater, blinded to the image source, manually delineated WMLs on images from both scanners before and after harmonization. It was found that there is closer agreement in both global and per-lesion WML volume and spatial distribution after harmonization, demonstrating the importance of image harmonization prior to the creation of manual delineations. These results could lead to better truth models in both the development and evaluation of automated lesion segmentation algorithms.

Understanding the Pathophysiology and Magnetic Resonance Imaging of Multiple Sclerosis and Neuromyelitis Optica Spectrum Disorders

Magnetic resonance imaging (MRI) has been extensively applied in the study of multiple sclerosis (MS), substantially contributing to diagnosis, differential diagnosis, and disease monitoring. MRI studies have significantly contributed to the understanding of MS through the characterization of typical radiological features and their clinical or prognostic implications using conventional MRI pulse sequences and further with the application of advanced imaging techniques sensitive to microstructural damage. Interpretation of results has often been validated by MRI-pathology studies. However, the application of MRI techniques in the study of neuromyelitis optica spectrum disorders (NMOSD) remains an emerging field, and MRI studies have focused on radiological correlates of NMOSD and its pathophysiology to aid in diagnosis, improve monitoring, and identify relevant prognostic factors. In this review, we discuss the main contributions of MRI to the understanding of MS and NMOSD, focusing on the most novel discoveries to clarify differences in the pathophysiology of focal inflammation initiation and perpetuation, involvement of normal-appearing tissue, potential entry routes of pathogenic elements into the CNS, and existence of primary or secondary mechanisms of neurodegeneration.

Methods for Brain Atrophy MR Quantification in Multiple Sclerosis: Application to the Multicenter INNI Dataset

BACKGROUND

Current therapeutic strategies in multiple sclerosis (MS) target neurodegeneration. However, the integration of atrophy measures into the clinical scenario is still an unmet need.

PURPOSE

To compare methods for whole-brain and gray matter (GM) atrophy measurements using the Italian Neuroimaging Network Initiative (INNI) dataset.

STUDY TYPE

Retrospective (data available from INNI).

POPULATION

A total of 466 patients with relapsing-remitting MS (mean age = 37.3 ± 10 years, 323 women) and 279 healthy controls (HC; mean age = 38.2 ± 13 years, 164 women).

FIELD STRENGTH/SEQUENCE

A 3.0-T, T1-weighted (spin echo and gradient echo without gadolinium injection) and T2-weighted spin echo scans at baseline and after 1 year (170 MS, 48 HC).

ASSESSMENT

Structural Image Evaluation using Normalization of Atrophy (SIENA-X/XL; version 5.0.9), Statistical Parametric Mapping (SPM-v12); and Jim-v8 (Xinapse Systems, Colchester, UK) software were applied to all subjects.

STATISTICAL TESTS

In MS and HC, we evaluated the intraclass correlation coefficient (ICC) among FSL-SIENA(XL), SPM-v12, and Jim-v8 for cross-sectional whole-brain and GM tissue volumes and their longitudinal changes, the effect size according to the Cohen's d at baseline and the sample size requirement for whole-brain and GM atrophy progression at different power levels (lowest = 0.7, 0.05 alpha level). False discovery rate (Benjamini-Hochberg procedure) correction was applied. A P value <0.05 was considered statistically significant.

RESULTS

SPM-v12 and Jim-v8 showed significant agreement for cross-sectional whole-brain (ICC = 0.93 for HC and ICC = 0.84 for MS) and GM volumes (ICC = 0.66 for HC and ICC = 0.90) and longitudinal assessment of GM atrophy (ICC = 0.35 for HC and ICC = 0.59 for MS), while no significant agreement was found in the comparisons between whole-brain and GM volumes for SIENA-X/XL and both SPM-v12 (P = 0.19 and P = 0.29, respectively) and Jim-v8 (P = 0.21 and P = 0.32, respectively). SPM-v12 and Jim-v8 showed the highest effect size for cross-sectional GM atrophy (Cohen's d = -0.63 and -0.61). Jim-v8 and SIENA(XL) showed the smallest sample size requirements for whole-brain (58) and GM atrophy (152), at 0.7 power level.

DATA CONCLUSION

The findings obtained in this study should be considered when selecting the appropriate brain atrophy pipeline for MS studies.

EVIDENCE LEVEL

4.

TECHNICAL EFFICACY

Stage 1.

Learning from pseudo-labels: deep networks improve consistency in longitudinal brain volume estimation

Introduction

Brain atrophy is a critical biomarker of disease progression and treatment response in neurodegenerative diseases such as multiple sclerosis (MS). Confounding factors such as inconsistent imaging acquisitions hamper the accurate measurement of brain atrophy in the clinic. This study aims to develop and validate a robust deep learning model to overcome these challenges; and to evaluate its impact on the measurement of disease progression.

Methods

Voxel-wise pseudo-atrophy labels were generated using SIENA, a widely adopted tool for the measurement of brain atrophy in MS. Deformation maps were produced for 195 pairs of longitudinal 3D T1 scans from patients with MS. A 3D U-Net, namely DeepBVC, was specifically developed overcome common variances in resolution, signal-to-noise ratio and contrast ratio between baseline and follow up scans. The performance of DeepBVC was compared against SIENA using McLaren test-retest dataset and 233 in-house MS subjects with MRI from multiple time points. Clinical evaluation included disability assessment with the Expanded Disability Status Scale (EDSS) and traditional imaging metrics such as lesion burden.

Results

For 3 subjects in test-retest experiments, the median percent brain volume change (PBVC) for DeepBVC and SIENA was 0.105 vs. 0.198% (subject 1), 0.061 vs. 0.084% (subject 2), 0.104 vs. 0.408% (subject 3). For testing consistency across multiple time points in individual MS subjects, the mean (± standard deviation) PBVC difference of DeepBVC and SIENA were 0.028% (± 0.145%) and 0.031% (±0.154%), respectively. The linear correlation with baseline T2 lesion volume were r = -0.288 (p < 0.05) and r = -0.249 (p < 0.05) for DeepBVC and SIENA, respectively. There was no significant correlation of disability progression with PBVC as estimated by either method (p = 0.86, p = 0.84).

Discussion

DeepBVC is a deep learning powered brain volume change estimation method for assessing brain atrophy used T1-weighted images. Compared to SIENA, DeepBVC demonstrates superior performance in reproducibility and in the context of common clinical scan variances such as imaging contrast, voxel resolution, random bias field, and signal-to-noise ratio. Enhanced measurement robustness, automation, and processing speed of DeepBVC indicate its potential for utilisation in both research and clinical environments for monitoring disease progression and, potentially, evaluating treatment effectiveness.

Brain magnetic resonance imaging surface-based analysis and cortical thickness measurement in relapsing remission multiple sclerosis

Background

Damage occurs in the brain tissue in MS which appears normal on standard conventional imaging (normal appearing brain tissue). This slow, evolving damage can be monitored by nonconventional advanced MR imaging techniques. New techniques for the measurement of cortical thickness have been validated against histological analysis and manual measurements. The aim of our study was to study the role of MRI surface-based analysis and cortical thickness measurement in the evaluation of patients with Relapsing Remitting Multiple Sclerosis and to detect if there is localized rather than generalized cortical atrophy in Multiple Sclerosis patients and correlating these findings with clinical data.

Results

30 patients and 30 healthy control were included in this study and they were subjected to cortical thickness analysis using MRI. The patients in our study showed decreased thickness of the precentral, paracentral, postcentral, posterior cingulate cortices and mean cortical thickness in both hemispheres when compared with the normal control group. Statistical analysis was significant (P value < 0.05) for the precentral, paracentral, postcentral, posterior cingulate cortices and mean cortical thickness in both hemispheres. On the other hand, statistical analysis was not significant (P value > 0.05) for other cortices. There was a significant negative correlation between the precentral, paracentral, postcentral, posterior cingulate cortices and mean cortical thickness in both hemispheres and EDSS scores with correlation coefficients ranging from − 0.9878 to − 0.7977.

Conclusions

MRI and post-processing segmentation analysis for cortical thickness is non-invasive imaging techniques that can increase the level of diagnostic confidence in diagnosis of MS patients and should be included as routine modality when evaluating patients with MS.

Logistic Regression-Based Model Is More Efficient Than U-Net Model for Reliable Whole Brain Magnetic Resonance Imaging Segmentation

OBJECTIVES

Automated whole brain segmentation from magnetic resonance images is of great interest for the development of clinically relevant volumetric markers for various neurological diseases. Although deep learning methods have demonstrated remarkable potential in this area, they may perform poorly in nonoptimal conditions, such as limited training data availability. Manual whole brain segmentation is an incredibly tedious process, so minimizing the data set size required for training segmentation algorithms may be of wide interest. The purpose of this study was to compare the performance of the prototypical deep learning segmentation architecture (U-Net) with a previously published atlas-free traditional machine learning method, Classification using Derivative-based Features (C-DEF) for whole brain segmentation, in the setting of limited training data.

MATERIALS AND METHODS

C-DEF and U-Net models were evaluated after training on manually curated data from 5, 10, and 15 participants in 2 research cohorts: (1) people living with clinically diagnosed HIV infection and (2) relapsing-remitting multiple sclerosis, each acquired at separate institutions, and between 5 and 295 participants' data using a large, publicly available, and annotated data set of glioblastoma and lower grade glioma (brain tumor segmentation). Statistics was performed on the Dice similarity coefficient using repeated-measures analysis of variance and Dunnett-Hsu pairwise comparison.

RESULTS

C-DEF produced better segmentation than U-Net in lesion (29.2%-38.9%) and cerebrospinal fluid (5.3%-11.9%) classes when trained with data from 15 or fewer participants. Unlike C-DEF, U-Net showed significant improvement when increasing the size of the training data (24%-30% higher than baseline). In the brain tumor segmentation data set, C-DEF produced equivalent or better segmentations than U-Net for enhancing tumor and peritumoral edema regions across all training data sizes explored. However, U-Net was more effective than C-DEF for segmentation of necrotic/non-enhancing tumor when trained on 10 or more participants, probably because of the inconsistent signal intensity of the tissue class.

CONCLUSIONS

These results demonstrate that classical machine learning methods can produce more accurate brain segmentation than the far more complex deep learning methods when only small or moderate amounts of training data are available (n ≤ 15). The magnitude of this advantage varies by tissue and cohort, while U-Net may be preferable for deep gray matter and necrotic/non-enhancing tumor segmentation, particularly with larger training data sets (n ≥ 20). Given that segmentation models often need to be retrained for application to novel imaging protocols or pathology, the bottleneck associated with large-scale manual annotation could be avoided with classical machine learning algorithms, such as C-DEF.

MAGNIMS recommendations for harmonization of MRI data in MS multicenter studies

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Sharing data from cooperative studies is essential to develop new biomarkers in MS.

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Differences in MRI acquisition, analysis, storage represent a substantial constraint.

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We review the state of the art and developments in the harmonization of MRI.

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We provide recommendations to harmonize large MRI datasets in the MS field.

There is an increasing need of sharing harmonized data from large, cooperative studies as this is essential to develop new diagnostic and prognostic biomarkers. In the field of multiple sclerosis (MS), the issue has become of paramount importance due to the need to translate into the clinical setting some of the most recent MRI achievements. However, differences in MRI acquisition parameters, image analysis and data storage across sites, with their potential bias, represent a substantial constraint. This review focuses on the state of the art, recent technical advances, and desirable future developments of the harmonization of acquisition, analysis and storage of large-scale multicentre MRI data of MS cohorts. Huge efforts are currently being made to achieve all the requirements needed to provide harmonized MRI datasets in the MS field, as proper management of large imaging datasets is one of our greatest opportunities and challenges in the coming years. Recommendations based on these achievements will be provided here. Despite the advances that have been made, the complexity of these tasks requires further research by specialized academical centres, with dedicated technical and human resources. Such collective efforts involving different professional figures are of crucial importance to offer to MS patients a personalised management while minimizing consumption of resources.

Slowly expanding lesions relate to persisting black-holes and clinical outcomes in relapse-onset multiple sclerosis

BACKGROUND

Slowly expanding lesions (SELs) are MRI markers of chronic active lesions in multiple sclerosis (MS). T1-hypointense black holes, and reductions in magnetization transfer ratio (MTR) are pathologically correlated with myelin and axonal loss. While all associated with progressive MS, the relationship between these lesion's metrics and clinical outcomes in relapse-onset MS has not been widely investigated.

OBJECTIVES

To explore the relationship of SELs with T1-hypointense black holes, and longitudinal T1 intensity contrast ratio and MTR, their correlation to brain volume, and their contribution to MS disability in relapse-onset patients.

METHODS

135 patients with relapsing-remitting MS (RRMS) were studied with clinical assessments and brain MRI (T2/FLAIR and T1-weighted scans at 1.5/3 T) at baseline and two subsequent follow-ups; a subset of 83 patients also had MTR acquisitions. Early-onset patients were defined when the baseline disease duration was ≤ 5 years (n = 85). SELs were identified using deformation field maps from the manually segmented baseline T2 lesions and differentiated from the non-SELs. Persisting black holes (PBHs) were defined as a subset of T2 lesions with a signal below a patient-specific grey matter T1 intensity in a semi-quantitative manner. SELs, PBH counts, and brain volume were computed, and their associations were assessed through Spearman and Pearson correlation. Clusters of patients according to low (up to 2), intermediate (3 to 10), or high (more than 10) SEL counts were determined with a Gaussian generalised mixture model. Mixed-effects and logistic regression models assessed volumes, T1 and MTR within SELs, and their correlation with Expanded Disability Status Scale (EDSS) and confirmed disability progression (CDP).

RESULTS

Mean age at study onset was 35.5 years (73% female), disease duration 5.5 years and mean time to last follow-up 6.5 years (range 1 to 12.5); median baseline EDSS 1.5 (range 0 to 5.5) and a mean EDSS change of 0.31 units at final follow-up. Among 4007 T2 lesions, 27% were classified as SELs and 10% as PBHs. Most patients (n = 65) belonged to the cluster with an intermediate SEL count (3 to 10 SELs). The percentage of PBHs was higher in SELs than non-SELs (up to 61% vs 44%, p < 0.001) and within-patient SEL volumes positively correlated with PBH volumes (r = 0.53, p < 0.001). SELs showed a decrease in T1 intensity over time (beta = -0.004, 95%CI -0.005 to -0.003, p < 0.001), accompanied by lower cross-sectional baseline and follow-up MTR. In mixed-effects models, EDSS worsening was predicted by the SEL log-volumes increase over time (beta = 0.11, 95%CI 0.03 to 0.20, p = 0.01), which was confirmed in the sub-cohort of patients with early onset MS (beta = 0.14, 95%CI 0.04 to 0.25, p = 0.008). In logistic regressions, a higher risk for CDP was associated with SEL volumes (OR = 5.15, 95%CI 1.60 to 16.60, p = 0.006).

CONCLUSIONS

SELs are associated with accumulation of more destructive pathology as indicated by an association with PBH volume, longitudinal reduction in T1 intensity and MTR. Higher SEL volumes are associated with clinical progression, while lower ones are associated with stability in relapse-onset MS.

Validation of an automatic tool for the rapid measurement of brain atrophy and white matter hyperintensity: QyScore®

OBJECTIVES

QyScore® is an imaging analysis tool certified in Europe (CE marked) and the US (FDA cleared) for the automatic volumetry of grey and white matter (GM and WM respectively), hippocampus (HP), amygdala (AM), and white matter hyperintensity (WMH). Here we compare QyScore® performances with the consensus of expert neuroradiologists.

METHODS

Dice similarity coefficient (DSC) and the relative volume difference (RVD) for GM, WM volumes were calculated on 50 3DT1 images. DSC and the F1 metrics were calculated for WMH on 130 3DT1 and FLAIR images. For each index, we identified thresholds of reliability based on current literature review results. We hypothesized that DSC/F1 scores obtained using QyScore® markers would be higher than the threshold. In contrast, RVD scores would be lower. Regression analysis and Bland-Altman plots were obtained to evaluate QyScore® performance in comparison to the consensus of three expert neuroradiologists.

RESULTS

The lower bound of the DSC/F1 confidence intervals was higher than the threshold for the GM, WM, HP, AM, and WMH, and the higher bounds of the RVD confidence interval were below the threshold for the WM, GM, HP, and AM. QyScore®, compared with the consensus of three expert neuroradiologists, provides reliable performance for the automatic segmentation of the GM and WM volumes, and HP and AM volumes, as well as WMH volumes.

CONCLUSIONS

QyScore® represents a reliable medical device in comparison with the consensus of expert neuroradiologists. Therefore, QyScore® could be implemented in clinical trials and clinical routine to support the diagnosis and longitudinal monitoring of neurological diseases.

KEY POINTS

• QyScore® provides reliable automatic segmentation of brain structures in comparison with the consensus of three expert neuroradiologists. • QyScore® automatic segmentation could be performed on MRI images using different vendors and protocols of acquisition. In addition, the fast segmentation process saves time over manual and semi-automatic methods. • QyScore® could be implemented in clinical trials and clinical routine to support the diagnosis and longitudinal monitoring of neurological diseases.

Automated quantification of deep grey matter structures and white matter lesions using magnetic resonance imaging in relapsing remission multiple sclerosis

Background

Brain volume loss (BVL) is widespread in MS and occurs throughout the disease course at a rate considerably greater than in the general population. In MS, brain volume correlates with and predicts future disability, making BVL a relevant measure of diffuse CNS damage leading to clinical disease progression, as well as serving as a useful outcome in evaluating MS therapies. The aim of our study was to evaluate the role of automated segmentation and quantification of deep grey matter structures and white matter lesions in Relapsing Remitting Multiple Sclerosis patients using MR images and to correlate the volumetric results with different degrees of disability based on expanded disability status scale (EDSS) scores.

Results

All the patients in our study showed relative atrophy of the thalamus and the putamen bilaterally when compared with the normal control group. Statistical analysis was significant for the thalamus and the putamen atrophy (P value < 0.05). On the other hand, statistical analysis was not significant for the caudate and the hippocampus (P value > 0.05); there was a significant positive correlation between the white matter lesions volume and EDSS scores (correlation coefficient of 0.7505). On the other hand, there was a significant negative correlation between the thalamus and putamen volumes, and EDSS scores (correlation coefficients < − 0.9), while the volumes of the caudate and the hippocampus had a very weak and non-significant correlation with the EDSS scores (correlation coefficients > − 0.35).

Conclusions

The automated segmentation and quantification tools have a great role in the assessment of brain structural changes in RRMS patients, and that it became essential to integrate these tools in the daily medical practice for the great value they add to the current evaluation measures.

Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression

BACKGROUND AND OBJECTIVES

Research in Multiple Sclerosis (MS) has recently focused on extracting knowledge from real-world clinical data sources. This type of data is more abundant than data produced during clinical trials and potentially more informative about real-world clinical practice. However, this comes at the cost of less curated and controlled data sets. In this work we aim to predict disability progression by optimally extracting information from longitudinal patient data in the real-world setting, with a special focus on the sporadic sampling problem.

METHODS

We use machine learning methods suited for patient trajectories modeling, such as recurrent neural networks and tensor factorization. A subset of 6682 patients from the MSBase registry is used.

RESULTS

We can predict disability progression of patients in a two-year horizon with an ROC-AUC of 0.85, which represents a 32% decrease in the ranking pair error (1-AUC) compared to reference methods using static clinical features.

CONCLUSIONS

Compared to the models available in the literature, this work uses the most complete patient history for MS disease progression prediction and represents a step forward towards AI-assisted precision medicine in MS.

Brain atrophy rates in patients with multiple sclerosis on long term natalizumab resembles healthy controls

BACKGROUND

Clinically stable multiple sclerosis (MS) patients often have negligible inflammatory MRI changes. Brain atrophy may provide insight into subclinical disease progression. The objective was to compare brain atrophy rates in stable patients on long term natalizumab treatment vs. age and gender matched healthy non-MS controls (HC) prospectively over two-years examining brain volume, cognition, and patient reported outcomes (PROs).

METHODS

MS patients treated with natalizumab for a minimum of 2 years, age 18-60 were recruited and compared with age- and gender-matched healthy controls (HC). Both groups were followed prospectively to obtain two years of consecutive magnetic resonance imaging, clinical and PRO data. Baseline normalized brain volume (NBV), yearly T2 lesion volume (T2LV), and percent brain volume change (PBVC) were measured using SIENAX, JIM 6.0, and SIENA respectively. Neuropsychological tests from the MACFIMS battery were selected to optimize assessments for impairments in the domains of information processing speed and memory. Patient reported outcomes (PROs) for domains of physical, mental and social quality of life were evaluated using the NeuroQol short forms.

RESULTS

Forty-eight natalizumab and 62 HC completed all study visits. At baseline, unadjusted mean NBV (natalizumab=1508.80cm (Popescu et al., 2013) vs. HC=1539.23cm (Popescu et al., 2013); p=0.033) and median baseline T2LV (natalizumab=1724.62mm (Popescu et al., 2013) vs. HC=44.20mm (Popescu et al., 2013); p=<0.0001) were different. The mean PBVC at year 2, adjusted for gender and baseline age was -0.57% (CI: 0.7620, -0.3716) for natalizumab and -0.50% (-0.7208, -0.2831) for HC, but the difference between groups was not statistically significant (0.073%; p=0.62). Over the 2-year period, HC demonstrated mild improvements in some cognitive tests vs. natalizumab subjects. However, PROs were similar between the two groups.

CONCLUSION

Stable MS patients on natalizumab have similar brain volume loss as people who do not have MS, suggesting normalization of brain atrophy.

Overestimation of grey matter atrophy in glioblastoma patients following radio(chemo)therapy

Objective

Brain atrophy has the potential to become a biomarker for severity of radiation-induced side-effects. Particularly brain tumour patients can show great MRI signal changes over time caused by e.g. oedema, tumour progress or necrosis. The goal of this study was to investigate if such changes affect the segmentation accuracy of normal appearing brain and thus influence longitudinal volumetric measurements.

Materials and methods

T1-weighted MR images of 52 glioblastoma patients with unilateral tumours acquired before and three months after the end of radio(chemo)therapy were analysed. GM and WM volumes in the contralateral hemisphere were compared between segmenting the whole brain (full) and the contralateral hemisphere only (cl) with SPM and FSL. Relative GM and WM volumes were compared using paired t tests and correlated with the corresponding mean dose in GM and WM, respectively.

Results

Mean GM atrophy was significantly higher for full segmentation compared to cl segmentation when using SPM (mean ± std: ΔV_GM,full = − 3.1% ± 3.7%, ΔV_GM,cl = − 1.6% ± 2.7%; p < 0.001, d = 0.62). GM atrophy was significantly correlated with the mean GM dose with the SPM cl segmentation (r = − 0.4, p = 0.004), FSL full segmentation (r = − 0.4, p = 0.004) and FSL cl segmentation (r = -0.35, p = 0.012) but not with the SPM full segmentation (r = − 0.23, p = 0.1).

Conclusions

For accurate normal tissue volume measurements in brain tumour patients using SPM, abnormal tissue needs to be masked prior to segmentation, however, this is not necessary when using FSL.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10334-021-00922-3.

Effects of Ibudilast on MRI Measures in the Phase 2 SPRINT-MS Study

OBJECTIVE

To determine whether ibudilast has an effect on brain volume and new lesions in progressive forms of multiple sclerosis (MS).

METHODS

A randomized, placebo-controlled, blinded study evaluated ibudilast at a dose of up to 100 mg over 96 weeks in primary and secondary progressive MS. In this secondary analysis of a previously reported trial, secondary and tertiary endpoints included gray matter atrophy, new or enlarging T2 lesions as measured every 24 weeks, and new T1 hypointensities at 96 weeks. Whole brain atrophy measured by structural image evaluation, using normalization, of atrophy (SIENA) was a sensitivity analysis.

RESULTS

A total of 129 participants were assigned to ibudilast and 126 to placebo. New or enlarging T2 lesions were observed in 37.2% on ibudilast and 29.0% on placebo (p = 0.82). New T1 hypointense lesions at 96 weeks were observed in 33.3% on ibudilast and 23.5% on placebo (p = 0.11). Gray matter atrophy was reduced by 35% for those on ibudilast vs placebo (p = 0.038). Progression of whole brain atrophy by SIENA was slowed by 20% in the ibudilast group compared with placebo (p = 0.08).

CONCLUSION

Ibudilast treatment was associated with a reduction in gray matter atrophy. Ibudilast treatment was not associated with a reduction in new or enlarging T2 lesions or new T1 lesions. An effect on brain volume contributes to prior data that ibudilast appears to affect markers associated with neurodegenerative processes, but not inflammatory processes.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that for people with MS, ibudilast does not significantly reduce new or enlarging T2 lesions or new T1 lesions.

Clinical feasibility of longitudinal lateral ventricular volume measurements on T2-FLAIR across MRI scanner changes

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Central and whole brain atrophy are faster in MS patients with disability progression.

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These measures can be reliably assessed on clinically-available FLAIR images.

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They are meaningful even with longitudinal scanner and field strength changes.

Background

Greater brain atrophy is associated with disability progression (DP) in patients with multiple sclerosis (PwMS). However, methodological challenges limit its routine clinical use.

Objective

To determine the feasibility of atrophy measures as markers of DP in PwMS scanned across different MRI field strengths.

Methods

A total of 980 PwMS were scanned on either 1.5 T or 3.0 T MRI scanners. Demographic and clinical data were retrospectively collected, and the presence of DP was determined according to standard clinical trial criteria. Lateral ventricular volume (LVV) change was measured with the NeuroSTREAM technique on clinical routine T2-FLAIR images. Percent brain volume change (PBVC) was measured using SIENA and ventricular cerebrospinal fluid (vCSF) % change was measured using VIENA and SIENAX algorithms on 3D T1-weighted images (WI). Stable vs. DP PwMS were compared using analysis of covariance (ANCOVA). Mixed modeling determined the effect of MRI scanner change on MRI-derived atrophy measures.

Results

Longitudinal LVV analysis was successful in all PwMS. SIENA-based PBVC and VIENA-based changes failed in 37.6% of cases, while SIENAX-based vCSF failed in 12.9% of cases. PwMS with DP (n = 241) had significantly greater absolute (20.9% vs. 8.7%, d = 0.66, p < 0.001) and annualized LVV % change (4.1% vs. 2.3%, d = 0.27, p < 0.001) when compared to stable PwMS (n = 739). In subjects with both analyses available, both 3D-T1 and T2-FLAIR-based analyses differentiated PwMS with DP (n = 149). However, only NeuroSTREAM and VIENA-based LVV/vCSF were able to show greater atrophy in PwMS that were scanned on different scanners. PBVC and SIENAX-based vCSF % changes were significantly affected by scanner change (Beta = −0.16, t-statistics = −2.133, p = 0.033 and Beta = −2.08, t-statistics = −4.084, p < 0.001), whereas no MRI scanner change effects on NeuroSTREAM-based PLVVC and VIENA-based vCSF % change were noted.

Conclusions

LVV-based atrophy on T2-FLAIR is a clinically relevant measure in spite of MRI scanner changes and mild disability levels.

Physical activity monitoring to assess disability progression in multiple sclerosis

Background

Clinical outcome measurement in multiple sclerosis (MS) usually requires a physical visit. Remote activity monitoring (RAM) using wearable technology provides a rational alternative, especially desirable when distance is involved or in a pandemic setting.

Objective

To validate RAM in progressive MS using (1) traditional psychometric methods (2) brain atrophy.

Methods

56 people with progressive MS participated in a longitudinal study over 2.5 years. An arm-worn RAM device measured activity over six days, every six months, and incorporated triaxial accelerometry and transcutaneous physiological variable measurement. Five RAM variables were assessed: physical activity duration, step count, active energy expenditure, metabolic equivalents and a composite RAM score incorporating all four variables. Other assessments every six months included EDSS, MSFC, MSIS-29, Chalder Fatigue Scale and Beck’s Depression Inventory. Annualized brain atrophy was measured using SIENA.

Results

RAM was tolerated well by people with MS; the device was worn 99.4% of the time. RAM had good convergent and divergent validity and was responsive, especially with respect to step count. Measurement of physical activity over one day was as responsive as six days. The composite RAM score positively correlated with brain volume loss.

Conclusion

Remote activity monitoring is a valid and acceptable outcome measure in MS.

Adding brain volume measures into response criteria in multiple sclerosis: the Río-4 score

PURPOSE

Brain volume changes (BVC) on therapy in MS are being considered as predictor for treatment response at an individual level. We ought to assess whether adding BVC as a factor to monitor interferon-beta response improves the predictive ability of the (no) evidence of disease activity (EDA-3) and Río score (RS-3) criteria for confirmed disability progression in a historical cohort.

METHODS

One hundred one patients from an observational cohort treated with interferon-beta were assessed for different cutoff points of BVC (ranged 0.2-1.2%), presence of active lesions (≥ 1 for EDA/≥ 3 for RS), relapses, and 6-month confirmed disability progression (CDP), measured by the Expanded Disability Status Scale, after 1 year. Sensitivity, specificity, and positive and negative predictive values for predicting confirmed disability progression at 4 years in original EDA (EDA-3) and RS (RS-3) as well as EDA and RS including BVC (EDA-4 and RS-4) were compared.

RESULTS

Adding BVC to EDA slightly increased sensitivity, but not specificity or predictive values, nor the OR for predicting CDP; only EDA-3 showed a trend for predicting CDP (OR 3.701, p = 0.050). Adding BVC to RS-3 (defined as ≥ 2 criteria) helped to improve sensitivity and negative predictive value, and increased OR for predicting CDP using a cutoff of ≤ - 0.86% (RS-3 OR 23.528, p < 0.001; RS-4 for all cutoffs ranged from 15.06 to 32, p < 0.001). RS-4 showed areas under the curve larger than RS-3 for prediction of disability at 4 years.

CONCLUSION

Addition of BVC to RS improves its prediction of response to interferon-beta.

A contrast-adaptive method for simultaneous whole-brain and lesion segmentation in multiple sclerosis

Here we present a method for the simultaneous segmentation of white matter lesions and normal-appearing neuroanatomical structures from multi-contrast brain MRI scans of multiple sclerosis patients. The method integrates a novel model for white matter lesions into a previously validated generative model for whole-brain segmentation. By using separate models for the shape of anatomical structures and their appearance in MRI, the algorithm can adapt to data acquired with different scanners and imaging protocols without retraining. We validate the method using four disparate datasets, showing robust performance in white matter lesion segmentation while simultaneously segmenting dozens of other brain structures. We further demonstrate that the contrast-adaptive method can also be safely applied to MRI scans of healthy controls, and replicate previously documented atrophy patterns in deep gray matter structures in MS. The algorithm is publicly available as part of the open-source neuroimaging package FreeSurfer.

Brain Atrophy Rates for Stable Multiple Sclerosis Patients on Long-Term Fingolimod versus Glatiramer Acetate

Background: Clinically stable multiple sclerosis (MS) patients on long-term therapy often have negligible acute inflammation on MRI. Brain atrophy may provide insight into subclinical disease progression in such populations. Objective: This study aims to compare brain atrophy for age- and gender-matched MS patients treated for >2 years with fingolimod (FTY) or glatiramer acetate (GA), examining brain volume, cognition, and patient-reported outcomes (PROs). Methods: Stable relapsing-MS patients, age 18-60, on FTY or GA for >2 years were followed up for 2 years. MRI brain and lesion volumes, cognitive measures, and PROs were collected at baseline and annually. Results: Forty-four FTY and forty-three GA patients completed baseline and year 2 visits. No differences in age, gender, or education were observed. Median EDSS was 2.0_GA and 2.5_FTY (p = 0.22). Treatment duration was longer for GA, 6.50_GA vs. 3.73_FTY years (p < 0.001). Baseline geometric mean T2LV were different, GA = 1,009.29 cm³ vs. FTY = 2,404.67 cm³ (p = 0.0071). Baseline brain volumes were similar, GA = 1,508 cm³ vs. FTY = 1,489 cm³ (p = 0.2381). Annualized atrophy rates, adjusted for baseline and at mean baseline value, were GA = -0.2775% vs. FTY = -0.2967% (p = 0.7979). No differences in cognitive measures or PROs were observed. Conclusions: Stable MS patients on long-term treatment with FTY and GA have similar brain volume loss rates. Differences in baseline disease severity may suggest patients with more aggressive disease treated with FTY may achieve similar brain volume loss rates as patients with milder baseline disease on GA.

Brain magnetic resonance imaging features in multiple sclerosis and neuromyelitis optica spectrum disorders patients with or without aquaporin-4 antibody in a Latin American population

INTRODUCTION

There is scarce evidence comparing the behavior in magnetic resonance (MRI) between positive and negative aquaporin-4 antibody neuromyelitis optica spectrum disorders (P-NMOSD and NNMOSD, respectively). The aim of this study was to describe and compare MRI features through a quantitative and qualitative analysis between P-NMOSD and NNMOSD patients in a cohort from Latin American (LATAM) patients.

METHODS

We retrospectively reviewed the MRI and medical records of NMOSD patients as defined by the 2015 validated diagnostic criteria, and with at least 3 years of follow-up from disease onset (first symptom). We included patients from Argentina, Brazil and Venezuela. To be included, NMOSD patients must have had AQP4-ab status measured by a cell-based assay. Brain MRIs were obtained for each participant at disease onset and every 12 months for 3 years. Demographics, clinical and MRI variables (T2 lesion volume [T2LV], lesion distribution, cortical thickness [CT] and percentage of brain volume loss [PBVL]) were analyzed and compared between groups (P-NMOSD; NNMOSD) at disease onset and follow-up. A multiple sclerosis (MS) control group of patients was also included.

RESULTS

We included 24 P-NMOSD, 15 NNMOSD and 35 MS patients. No differences in age, gender and follow-up time were observed between groups. Nor were differences found in lesion distribution at disease onset or in brain volumes during follow-up between P-NMOSD and NNMOSD patients (T2LV = 0.43, CT = 0.12, PBVL p = 0.45). Significant differences were observed in lesion distribution at disease onset, as well as in brain volumes during follow-up between NMOSD and MS (T2LV = p<0.001, CT = p<0.001, PBVL p = 0.01).

CONCLUSION

Different MRI features were observed between MS and NMOSD. However, no quantitative nor qualitative differences were observed between P-NMOSD and NNMOSD, not allowing us to differentiate NMOSD conditions by MRI.

"No evidence of disease activity": Is it an aspirational therapeutic goal in multiple sclerosis?

'No evidence of disease activity' (NEDA) that has been identified as a potential outcome measure for the evaluation of DMTs effects. The concept has been adopted from other diseases such as cancer where treatment is intended to free the patient from the disease. Disease-free status has been substituted by NEDA in MS, since we are limited when it comes to fully evaluating the underlying disease. In general, NEDA, otherwise termed as NEDA-3, is defined by the lack of disease activity based on the absence of clinical relapses, disability progression with the expanded disability status score (EDSS), and radiological activity. Recently, brain atrophy, a highly predictive marker of disability progression, has been added as a fourth component (NEDA-4). The use of this composite allowed a more comprehensive assessment of the disease activity. Indeed, it has an important role in clinical trials as a secondary outcome in addition to primary endpoints. However, the evidence is insufficient regarding the ability of NEDA to predict future disability and treatment response. Moreover, combining different composites does not eliminate the limitation of each, therefore the use of NEDA in clinical routine is still not implemented. The aim of this review is first to report from the literature the available definitions of NEDA and its different variants, and second, evaluate the importance of its use as a surrogate marker to assess the efficacy of different DMTs.

A challenging case of concurrent multiple sclerosis and anaplastic astrocytoma

Background:

Cases of gliomas coexisting with multiple sclerosis (MS) have been described over the past few decades. However, due to the complex clinical and radiological traits inherent to both entities, this concurrent phenomenon remains difficult to diagnose. Much has been debated about whether this coexistence is incidental or mirrors a poorly understood neoplastic phenomenon engaging glial cells in the regions of demyelination.

Case Description:

We present the case of a 41-year-old patient diagnosed with a left-sided frontal contrast enhancing lesion initially assessed as a tumefactive MS. Despite systemic treatment, the patient gradually developed signs of mass effect, which led to decompressive surgery. The initial microscopic evaluation demonstrated the presence of MS and oligodendroglioma; the postoperative evolution proved complex due to a series of MS-relapses and tumor recurrence. An ulterior revaluation of the samples for the purpose of this report showed an MS-concurrent anaplastic astrocytoma. We describe all relevant clinical aspects of this case and review the medical literature for possible causal mechanisms.

Conclusion:

Although cases of concurrent glioma and MS remain rare, we present a case illustrating this phenomenon and explore a number of theories behind a potential causal relationship.

Three-dimensional MRI sequences in MS diagnosis and research

The most recent guidelines for magnetic resonance imaging (MRI) in multiple sclerosis (MS) recommend three-dimensional (3D) MRI sequences over their two-dimensional (2D) counterparts. This development has been made possible by advances in MRI scanner hardware and software. In this article, we review the 3D versions of conventional sequences, including T1-weighted, T2-weighted and fluid-attenuated inversion recovery (FLAIR), as well as more advanced scans, including double inversion recovery (DIR), FLAIR2, FLAIR*, phase-sensitive inversion recovery, and susceptibility weighted imaging (SWI).

Automated segmentation of changes in FLAIR-hyperintense white matter lesions in multiple sclerosis on serial magnetic resonance imaging

Longitudinal analysis of white matter lesion changes on serial MRI has become an important parameter to study diseases with white-matter lesions. Here, we build on earlier work on cross-sectional lesion segmentation; we present a fully automatic pipeline for serial analysis of FLAIR-hyperintense white matter lesions. Our algorithm requires three-dimensional gradient echo T1- and FLAIR- weighted images at 3 Tesla as well as available cross-sectional lesion segmentations of both time points. Preprocessing steps include lesion filling and intrasubject registration. For segmentation of lesion changes, initial lesion maps of different time points are fused; herein changes in intensity are analyzed at the voxel level. Significance of lesion change is estimated by comparison with the difference distribution of FLAIR intensities within normal appearing white matter. The method is validated on MRI data of two time points from 40 subjects with multiple sclerosis derived from two different scanners (20 subjects per scanner). Manual segmentation of lesion increases served as gold standard. Across all lesion increases, voxel-wise Dice coefficient (0.7) as well as lesion-wise detection rate (0.8) and false-discovery rate (0.2) indicate good overall performance. Analysis of scans from a repositioning experiment in a single patient with multiple sclerosis did not yield a single false positive lesion. We also introduce the lesion change plot as a descriptive tool for the lesion change of individual patients with regard to both number and volume. An open source implementation of the algorithm is available at http://www.statistical-modeling.de/lst.html.

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Quantification of white matter lesion changes is important in multiple sclerosis.

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We developed and validated an algorithm for automated detection of lesion changes.

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Our algorithm requires T1-weighted and FLAIR images derived at 3 T as well as available cross-sectional lesion segmentations.

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With data from 2 different scanners, the tool showed good agreement with manual tracing.

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An open-source application is available.

Robust, atlas-free, automatic segmentation of brain MRI in health and disease

Background

Brain- and lesion-volumes derived from magnetic resonance images (MRI) serve as important imaging markers of disease progression in neurodegenerative diseases and aging. While manual segmentation of these volumes is both tedious and impractical in large cohorts of subjects, automated segmentation methods often fail in accurate segmentation of brains with severe atrophy or high lesion loads. The purpose of this study was to develop an atlas-free brain Classification using DErivative-based Features (C-DEF), which utilizes all scans that may be acquired during the course of a routine MRI study at any center.

Methods

Proton-density, T₂-weighted, T₁-weighted, brain-free water, 3D FLAIR, 3D T₂-weighted, and 3D T₂*-weighted images, collected routinely on patients with neuroinflammatory diseases at the NIH, were used to optimize the C-DEF algorithm on healthy volunteers and HIV + subjects (cohort 1). First, manually marked lesions and eroded FreeSurfer brain segmentation masks (compiled into gray and white matter, globus pallidus, CSF labels) were used in training. Next, the optimized C-DEF was applied on a separate cohort of HIV + subjects (cohort two), and the results were compared with that of FreeSurfer and Lesion-TOADS. Finally, C-DEF segmentation was evaluated on subjects clinically diagnosed with various other neurological diseases (cohort three).

Results

C-DEF algorithm was optimized using leave-one-out cross validation on five healthy subjects (age 36 ± 11 years), and five subjects infected with HIV (age 57 ± 2.6 years) in cohort one. The optimized C-DEF algorithm outperformed FreeSurfer and Lesion-TOADS segmentation in 49 other subjects infected with HIV (cohort two, age 54 ± 6 years) in qualitative and quantitative comparisons. Although trained only on HIV brains, sensitivity to detect lesions using C-DEF increased by 45% in HTLV-I-associated myelopathy/tropical spastic paraparesis (n = 5; age 58 ± 7 years), 33% in multiple sclerosis (n = 5; 42 ± 9 years old), and 4% in subjects with polymorphism of the cytotoxic T-lymphocyte-associated protein 4 gene (n = 5; age 24 ± 12 years) compared to Lesion-TOADS.

Conclusion

C-DEF outperformed other segmentation algorithms in the various neurological diseases explored herein, especially in lesion segmentation. While the results reported are from routine images acquired at the NIH, the algorithm can be easily trained and optimized for any set of contrasts and protocols for wider application. We are currently exploring various technical aspects of optimal implementation of CDEF in a clinical setting and evaluating a larger cohort of patients with other neurological diseases. Improving the accuracy of brain segmentation methodology will help better understand the relationship of imaging abnormalities to clinical and neuropsychological markers in disease.

Validation of CSF free light chain in diagnosis and prognosis of multiple sclerosis and clinically isolated syndrome: prospective cohort study in Buenos Aires

BACKGROUND

The objective was to evaluate the precision of kappa and lambda free light chains (KFLC and LFLC) in CSF for the diagnosis of multiple sclerosis (MS) and prognosis of clinically isolated syndrome (CIS).

METHODS

CSF and serum samples from CIS, MS and other neurological non-MS disease were collected between 2015 and 2017. FLC concentrations were measured using immunoassay Freelite™. Results were correlated with the patients' diagnoses and ROC curve analysis was used to determine accuracy. In CIS patients, analysis of FLC were compared in CIS converters vs. non-converter during follow-up.

RESULTS

In the MS group (n = 41), the optimal cut-off for KFLC determined was 7 mg/L, with a diagnostic sensitivity and specificity of 95% and 97%, respectively. The optimal cut-off for LFLC was 0.7 mg/L, with a diagnostic sensitivity and specificity of 71% and 81%, respectively. 36 CIS patients were included; mean follow-up time was 28 ± 9 months, and 22 (61.1%) patients converted to MS. The median concentration of CSF K and LFLCs at CIS diagnosis was slightly higher in CIS-converters compared to non-converters, but this did not reach statistical significance (KFLC: median 7 ± 5.3 mg/L vs. 5 ± 2.3 mg/L, p = 0.11; LFLC 0.7 ± 0.33 mg/L vs. 0.5 ± 0.23 mg/L p = 0.16). A strong correlation was observed between the concentration of K and L FLCs at diagnosis and the change in PBVC during follow-up (r = 0.72 and r = 0.65, respectively).

CONCLUSION

KFLCs have a high sensitivity and specificity for the diagnosis of MS. FLC concentrations at CIS diagnosis were not significantly higher in CIS-converters.

Repeatability and reproducibility of FreeSurfer, FSL-SIENAX and SPM brain volumetric measurements and the effect of lesion filling in multiple sclerosis

OBJECTIVES

To compare the cross-sectional robustness of commonly used volumetric software and effects of lesion filling in multiple sclerosis (MS).

METHODS

Nine MS patients (six females; age 38±13 years, disease duration 7.3±5.2 years) were scanned twice with repositioning on three MRI scanners (Siemens Aera 1.5T, Avanto 1.5T, Trio 3.0T) the same day. Volumetric T₁-weighted images were processed with FreeSurfer, FSL-SIENAX, SPM and SPM-CAT before and after 3D FLAIR lesion filling with LST. The whole-brain, grey matter (GM) and white matter (WM) volumes were calculated with and without normalisation to the intracranial volume or FSL-SIENAX scaling factor. Robustness was assessed using the coefficient of variation (CoV).

RESULTS

Variability in volumetrics was lower within than between scanners (CoV 0.17-0.96% vs. 0.65-5.0%, p<0.001). All software provided similarly robust segmentations of the brain volume on the same scanner (CoV 0.17-0.28%, p=0.076). Normalisation improved inter-scanner reproducibility in FreeSurfer and SPM-based methods, but the FSL-SIENAX scaling factor did not improve robustness. Generally, SPM-based methods produced the most consistent volumetrics, while FreeSurfer was more robust for WM volumes on different scanners. FreeSurfer had more robust normalised brain and GM volumes on different scanners than FSL-SIENAX (p=0.004). MS lesion filling changed the output of FSL-SIENAX, SPM and SPM-CAT but not FreeSurfer.

CONCLUSIONS

Consistent use of the same scanner is essential and normalisation to the intracranial volume is recommended for multiple scanners. Based on robustness, SPM-based methods are particularly suitable for cross-sectional volumetry. FreeSurfer poses a suitable alternative with WM segmentations less sensitive to MS lesions.

KEY POINTS

• The same scanner should be used for brain volumetry. If different scanners are used, the intracranial volume normalisation improves the FreeSurfer and SPM robustness (but not the FSL scaling factor). • FreeSurfer, FSL and SPM all provide robust measures of the whole brain volume on the same MRI scanner. SPM-based methods overall provide the most robust segmentations (except white matter segmentations on different scanners where FreeSurfer is more robust). • MS lesion filling with Lesion Segmentation Toolbox changes the output of FSL-SIENAX and SPM. FreeSurfer output is not affected by MS lesion filling since it already takes white matter hypointensities into account and is therefore particularly suitable for MS brain volumetry.

No evidence of disease activity in patients receiving fingolimod at private or academic centers in clinical practice: a retrospective analysis of the multiple sclerosis, clinical, and magnetic resonance imaging outcomes in the USA (MS-MRIUS) study

OBJECTIVE

The impact of multiple sclerosis (MS) center type on outcomes has not been investigated. This study aimed to evaluate baseline characteristics and clinical and magnetic resonance imaging (MRI) outcomes in patients with MS receiving fingolimod over 16 months' follow-up at private or academic centers in the USA.

METHODS

Clinical and MRI data collected in clinical practice from patients initiating fingolimod were stratified by center type and retrospectively analyzed. No evidence of disease activity (NEDA-3) was defined as patients with no new/enlarged T2/gadolinium-enhancing lesions, no relapses, and no disability progression (Expanded Disability Status Scale scores).

RESULTS

Data were collected for 398 patients from 25 private centers and 192 patients from eight academic centers. Patients were older (median age = 43 vs 41 years; p = .0047) and had a numerically shorter median disease duration (7.0 vs 8.5 years; p = .0985) at private vs academic centers. Annualized relapse rate (ARR) was higher in patients at private than academic centers in the pre-index (0.40 vs 0.29; p = .0127) and post-index (0.16 vs 0.08; p = .0334) periods. The opposite was true for T2 lesion volume in the pre-index (2.86 vs 5.23 mL; p = .0002) and post-index (2.86 vs 5.11 mL; p = .0016) periods; other MRI outcomes were similar between center types. After initiating fingolimod, ARRs were reduced, disability and most MRI outcomes remained stable, and a similar proportion of patients achieved NEDA-3 at private and academic centers (64.1% vs 56.1%; p = .0659).

CONCLUSION

Patient characteristics differ between private and academic centers. Over 55% of patients achieved NEDA-3 during fingolimod treatment at both center types.

Clinical trial design for progressive MS trials

The design of clinical trials is a key aspect to maximizing the possibility to detect a treatment effect. This fact is particularly challenging in progressive multiple sclerosis (PMS) studies due to the uncertainty about the right target and/or outcome in phase-2 studies. The aim of this review is to evaluate the current challenges facing the design of clinical trials for PMS. The selection of patients, the instrumental and clinical outcomes that can be used in PMS trials, and issues in their design will be covered in this report.

Improved Detection of New MS Lesions during Follow-Up Using an Automated MR Coregistration-Fusion Method

BACKGROUND AND PURPOSE

MR imaging is the key examination in the follow-up of patients with MS, by identification of new high-signal T2 brain lesions. However, identifying new lesions when scrolling through 2 follow-up MR images can be difficult and time-consuming. Our aim was to compare an automated coregistration-fusion reading approach with the standard approach by identifying new high-signal T2 brain lesions in patients with multiple sclerosis during follow-up MR imaging.

MATERIALS AND METHODS

This prospective monocenter study included 94 patients (mean age, 38.9 years) treated for MS with dimethyl fumarate from January 2014 to August 2016. One senior neuroradiologist and 1 junior radiologist checked for new high-signal T2 brain lesions, independently analyzing blinded image datasets with automated coregistration-fusion or the standard scroll-through approach with a 3-week delay between the 2 readings. A consensus reading with a second senior neuroradiologist served as a criterion standard for analyses. A Poisson regression and logistic and γ regressions were used to compare the 2 methods. Intra- and interobserver agreement was assessed by the κ coefficient.

RESULTS

There were significantly more new high-signal T2 lesions per patient detected with the coregistration-fusion method (7 versus 4, P < .001). The coregistration-fusion method detected significantly more patients with at least 1 new high-signal T2 lesion (59% versus 46%, P = .02) and was associated with significantly faster overall reading time (86 seconds faster, P < .001) and higher reader confidence (91% versus 40%, P < 1 × 10^-4). Inter- and intraobserver agreement was excellent for counting new high-signal T2 lesions.

CONCLUSIONS

Our study showed that an automated coregistration-fusion method was more sensitive for detecting new high-signal T2 lesions in patients with MS and reducing reading time. This method could help to improve follow-up care.

Longitudinal decline in structural networks predicts dementia in cerebral small vessel disease

OBJECTIVE

To determine whether longitudinal change in white matter structural network integrity predicts dementia and future cognitive decline in cerebral small vessel disease (SVD). To investigate whether network disruption has a causal role in cognitive decline and mediates the association between conventional MRI markers of SVD with both cognitive decline and dementia.

METHODS

In the prospective longitudinal SCANS (St George's Cognition and Neuroimaging in Stroke) Study, 97 dementia-free individuals with symptomatic lacunar stroke were followed with annual MRI for 3 years and annual cognitive assessment for 5 years. Conversion to dementia was recorded. Structural networks were constructed from diffusion tractography using a longitudinal registration pipeline, and network global efficiency was calculated. Linear mixed-effects regression was used to assess change over time.

RESULTS

Seventeen individuals (17.5%) converted to dementia, and significant decline in global cognition occurred (p = 0.0016). Structural network measures declined over the 3-year MRI follow-up, but the degree of change varied markedly between individuals. The degree of reductions in network global efficiency was associated with conversion to dementia (B = -2.35, odds ratio = 0.095, p = 0.00056). Change in network global efficiency mediated much of the association of conventional MRI markers of SVD with cognitive decline and progression to dementia.

CONCLUSIONS

Network disruption has a central role in the pathogenesis of cognitive decline and dementia in SVD. It may be a useful disease marker to identify that subgroup of patients with SVD who progress to dementia.

Lesion accumulation is predictive of long-term cognitive decline in multiple sclerosis

OBJECTIVE

To investigate the long-term progression of cognitive dysfunction and its neuroanatomical correlates and predictors in multiple sclerosis (MS).

METHODS

A cohort of 37 MS patients reflecting five decades of disease duration and all subtypes was followed over 17.5 years. Matched controls were recruited at the last follow-up. Global cognitive functioning was assessed using a principal component cognitive index based on comprehensive neuropsychological testing. During the last 8.5 years of the study, brain MRI was performed to analyze normalized volumetrics of three global tissue compartments (white and gray matter, lesions) and strategic regions (corpus callosum, thalamus, hippocampus).

RESULTS

Cognitive decline progressed continuously throughout the study paralleled by atrophy and lesion accumulation. The cognitive index partly correlated with Expanded Disability Status Scale (ρ = -0.47, p < 0.001) and was mainly associated with the lesion fraction (β = -0.48, p < 0.001) and callosal fraction (β = 0.39, p = 0.002) in multiple linear regression analysis. The lesion fraction was an independent predictor of the cognitive performance 8.5 years later (β = -0.35, p = 0.008). Symbol Digit Modalities Test was most frequently abnormal (40%), while Rey-Osterrieth Complex Figure Test was more sensitive to detect cognitive decline.

CONCLUSIONS

Cognitive impairment progresses continuously in MS, associated with atrophy and lesion accumulation, suggesting that interventions targeting these processes could be beneficial at all disease stages. Widespread cognitive functions are more profoundly affected, associated with lesions and corpus callosal atrophy, supporting the idea of an underlying disconnection mechanism for cognitive decline in MS.

The clinical perspective: How to personalise treatment in MS and how may biomarkers including imaging contribute to this?

BACKGROUND

Multiple sclerosis (MS) is a highly heterogeneous disease, both in its course and in its response to treatments. Effective biomarkers may help predict disability progression and monitor patients' treatment responses.

OBJECTIVE

The aim of this review was to focus on how biomarkers may contribute to treatment individualisation in MS patients.

METHODS

This review reflects the content of presentations, polling results and discussions on the clinical perspective of MS during the first and second Pan-European MS Multi-stakeholder Colloquia in Brussels in May 2014 and 2015.

RESULTS

In clinical practice, magnetic resonance imaging (MRI) measures play a significant role in the diagnosis and follow-up of MS patients. Together with clinical markers, the rate of MRI-visible lesion accrual once a patient has started treatment may also help to predict subsequent treatment responsiveness. In addition, several molecular (immunological, genetic) biomarkers have been established that may play a role in predictive models of MS relapses and progression. To reach personalised treatment decisions, estimates of disability progression and likely treatment response should be carefully considered alongside the risk of serious adverse events, together with the patient's treatment expectations.

CONCLUSION

Although biomarkers may be very useful for individualised decision making in MS, many are still research tools and need to be validated before implementation in clinical practice.

Assessing treatment outcomes in multiple sclerosis trials and in the clinical setting

Increasing numbers of drugs are being developed for the treatment of multiple sclerosis (MS). Measurement of relevant outcomes is key for assessing the efficacy of new drugs in clinical trials and for monitoring responses to disease-modifying drugs in individual patients. Most outcomes used in trial and clinical settings reflect either clinical or neuroimaging aspects of MS (such as relapse and accrual of disability or the presence of visible inflammation and brain tissue loss, respectively). However, most measures employed in clinical trials to assess treatment effects are not used in routine practice. In clinical trials, the appropriate choice of outcome measures is crucial because the results determine whether a drug is considered effective and therefore worthy of further development; in the clinic, outcome measures can guide treatment decisions, such as choosing a first-line disease-modifying drug or escalating to second-line treatment. This Review discusses clinical, neuroimaging and composite outcome measures for MS, including patient-reported outcome measures, used in both trials and the clinical setting. Its aim is to help clinicians and researchers navigate through the multiple options encountered when choosing an outcome measure. Barriers and limitations that need to be overcome to translate trial outcome measures into the clinical setting are also discussed.

Locally adaptive magnetic resonance intensity models for unsupervised segmentation of multiple sclerosis lesions

Multiple sclerosis (MS) is a neurological disease characterized by focal lesions and morphological changes in the brain captured on magnetic resonance (MR) images. However, extraction of the corresponding imaging markers requires accurate segmentation of normal-appearing brain structures (NABS) and the lesions in MR images. On MR images of healthy brains, the NABS can be accurately captured by MR intensity mixture models, which, in combination with regularization techniques, such as in Markov random field (MRF) models, are known to give reliable NABS segmentation. However, on MR images that also contain abnormalities such as MS lesions, obtaining an accurate and reliable estimate of NABS intensity models is a challenge. We propose a method for automated segmentation of normal-appearing and abnormal structures in brain MR images that is based on a locally adaptive NABS model, a robust model parameters estimation method, and an MRF-based segmentation framework. Experiments on multisequence brain MR images of 30 MS patients show that, compared to whole-brain MR intensity model and compared to four popular unsupervised lesion segmentation methods, the proposed method increases the accuracy of MS lesion segmentation.

Change in multimodal MRI markers predicts dementia risk in cerebral small vessel disease

Objective:

To determine whether MRI markers, including diffusion tensor imaging (DTI), can predict cognitive decline and dementia in patients with cerebral small vessel disease (SVD).

Methods:

In the prospective St George's Cognition and Neuroimaging in Stroke study, multimodal MRI was performed annually for 3 years and cognitive assessments annually for 5 years in a cohort of 99 patients with SVD, defined as symptomatic lacunar stroke and confluent white matter hyperintensities (WMH). Progression to dementia was determined in all patients. Progression of WMH, brain volume, lacunes, cerebral microbleeds, and a DTI measure (the normalized peak height of the mean diffusivity histogram distribution) as a marker of white matter microstructural damage were determined.

Results:

Over 5 years of follow-up, 18 patients (18.2%) progressed to dementia. A significant change in all MRI markers, representing deterioration, was observed. The presence of new lacunes, and rate of increase in white matter microstructural damage on DTI, correlated with both decline in executive function and global functioning. Growth of WMH and deterioration of white matter microstructure on DTI predicted progression to dementia. A model including change in MRI variables together with their baseline values correctly classified progression to dementia with a C statistic of 0.85.

Conclusions:

This longitudinal prospective study provides evidence that change in MRI measures including DTI, over time durations during which cognitive change is not detectable, predicts cognitive decline and progression to dementia. It supports the use of MRI measures, including DTI, as useful surrogate biomarkers to monitor disease and assess therapeutic interventions.

Agreement of MSmetrix with established methods for measuring cross-sectional and longitudinal brain atrophy

INTRODUCTION

Despite the recognized importance of atrophy in multiple sclerosis (MS), methods for its quantification have been mostly restricted to the research domain. Recently, a CE labelled and FDA approved MS-specific atrophy quantification method, MSmetrix, has become commercially available. Here we perform a validation of MSmetrix against established methods in simulated and in vivo MRI data.

METHODS

Whole-brain and gray matter (GM) volume were measured with the cross-sectional pipeline of MSmetrix and compared to the outcomes of FreeSurfer (cross-sectional pipeline), SIENAX and SPM. For this comparison we investigated 20 simulated brain images, as well as in vivo data from 100 MS patients and 20 matched healthy controls. In fifty of the MS patients a second time point was available. In this subgroup, we additionally analyzed the whole-brain and GM volume change using the longitudinal pipeline of MSmetrix and compared the results with those of FreeSurfer (longitudinal pipeline) and SIENA.

RESULTS

In the simulated data, SIENAX displayed the smallest average deviation compared with the reference whole-brain volume (+ 19.56 ± 10.34 mL), followed by MSmetrix (- 38.15 ± 17.77 mL), SPM (- 42.99 ± 17.12 mL) and FreeSurfer (- 78.51 ± 12.68 mL). A similar pattern was seen in vivo. Among the cross-sectional methods, Deming regression analyses revealed proportional errors particularly in MSmetrix and SPM. The mean difference percentage brain volume change (PBVC) was lowest between longitudinal MSmetrix and SIENA (+ 0.16 ± 0.91%). A strong proportional error was present between longitudinal percentage gray matter volume change (PGVC) measures of MSmetrix and FreeSurfer (slope = 2.48). All longitudinal methods were sensitive to the MRI hardware upgrade that occurred during the time of the study.

CONCLUSION

MSmetrix, FreeSurfer, FSL and SPM show differences in atrophy measurements, even at the whole-brain level, that are large compared to typical atrophy rates observed in MS. Especially striking are the proportional errors between methods. Cross-sectional MSmetrix behaved similarly to SPM, both in terms of mean volume difference as well as proportional error. Longitudinal MSmetrix behaved most similar to SIENA. Our results indicate that brain volume measurement and normalization from T1-weighted images remains an unsolved problem that requires much more attention.

Neuroradiologists Compared with Non-Neuroradiologists in the Detection of New Multiple Sclerosis Plaques

BACKGROUND AND PURPOSE

Multiple sclerosis monitoring is based on the detection of new lesions on brain MR imaging. Outside of study populations, MS imaging studies are reported by radiologists with varying expertise. The aim of this study was to investigate the accuracy of MS reporting performed by neuroradiologists (someone who had spent at least 1 year in neuroradiology subspecialty training) versus non-neuroradiologists.

MATERIALS AND METHODS

Patients with ≥2 MS studies with 3T MR imaging that included a volumetric T2 FLAIR sequence performed between 2009 and 2011 inclusive were recruited into this study. The reports for these studies were analyzed for lesions detected, which were categorized as either progressed or stable. The results from a previous study using a semiautomated assistive software for lesion detection were used as the reference standard.

RESULTS

There were 5 neuroradiologists and 5 non-neuroradiologists who reported all studies. In total, 159 comparison pairs (ie, 318 studies) met the selection criteria. Of these, 96 (60.4%) were reported by a neuroradiologist. Neuroradiologists had higher sensitivity (82% versus 42%), higher negative predictive value (89% versus 64%), and lower false-negative rate (18% versus 58%) compared with non-neuroradiologists. Both groups had a 100% positive predictive value.

CONCLUSIONS

Neuroradiologists detect more new lesions than non-neuroradiologists in reading MR imaging for follow-up of MS. Assistive software that aids in the identification of new lesions has a beneficial effect for both neuroradiologists and non-neuroradiologists, though the effect is more profound in the non-neuroradiologist group.

Brain atrophy 15 years after CIS: Baseline and follow-up clinico-radiological correlations

BACKGROUND

Brain atrophy in multiple sclerosis (MS) patients is present since the very early stages of the disease and it has been related to long-term disability.

OBJECTIVE

To estimate brain volume (BV) at 15 years after a clinically isolated syndrome (CIS) and to evaluate its relationship with disease outcomes.

METHODS

From a prospective cohort including patients presenting with a CIS, 54 patients with a brain magnetic resonance imaging (MRI) performed 15 years after CIS were included. Brain parenchymal fraction (BPF), grey matter fraction (GMF) and white matter fraction (WMF) at 15-year follow-up were obtained. Regression analyses were conducted to predict BV loss and reaching an Expanded Disability Status Scale (EDSS) of 3.0 in that 15-year period.

RESULTS

In multivariable analyses, lower values of BPF and WMF were significantly associated with being male, presenting 3-4 Barkhof criteria at baseline, presenting a second relapse, and with a decision to start treatment. In the multivariable logistic regression analysis, only lower GMF was associated with a greater risk of reaching EDSS 3.0 (odds ratio (OR) = 0.24, p = 0.028).

CONCLUSION

Lower BPF and WMF 15 years after CIS are associated with previous markers of inflammatory disease. Lower GMF 15 years after a CIS is associated with an increased risk of reaching an EDSS of 3.0.

Current and Emerging Therapies in Multiple Sclerosis: Implications for the Radiologist, Part 2-Surveillance for Treatment Complications and Disease Progression

An understanding of the new generation of MS drugs in conjunction with the key role MR imaging plays in the detection of disease progression, opportunistic infections, and drug-related adverse effects is of vital importance to the neuroradiologist. Part 1 of this review outlined the current treatment options available for MS and examined the mechanisms of action of the various medications. It also covered specific complications associated with each form of therapy. Part 2, in turn deals with the subject of pharmacovigilance and the optimal frequency of MRI monitoring for each individual patient, depending on his or her unique risk profile. Special attention is given to the diagnosing of progressive multifocal leukoencephalopathy in patients treated with natalizumab as this is a key area in which neuroradiologists can contribute to improved patient outcomes. This article also outlines the aims of treatment and reviews the possibility of "no evidence of disease activity" becoming a treatment goal with the availability of more effective therapies. Potential future areas and technologies including image subtraction, brain volume measurement and advanced imaging techniques such as double inversion recovery are also reviewed. It is anticipated that such advancements in this rapidly developing field will improve the accuracy of monitoring an individual patient's response to treatment.

Longitudinal multiple sclerosis lesion segmentation: Resource and challenge

In conjunction with the ISBI 2015 conference, we organized a longitudinal lesion segmentation challenge providing training and test data to registered participants. The training data consisted of five subjects with a mean of 4.4 time-points, and test data of fourteen subjects with a mean of 4.4 time-points. All 82 data sets had the white matter lesions associated with multiple sclerosis delineated by two human expert raters. Eleven teams submitted results using state-of-the-art lesion segmentation algorithms to the challenge, with ten teams presenting their results at the conference. We present a quantitative evaluation comparing the consistency of the two raters as well as exploring the performance of the eleven submitted results in addition to three other lesion segmentation algorithms. The challenge presented three unique opportunities: (1) the sharing of a rich data set; (2) collaboration and comparison of the various avenues of research being pursued in the community; and (3) a review and refinement of the evaluation metrics currently in use. We report on the performance of the challenge participants, as well as the construction and evaluation of a consensus delineation. The image data and manual delineations will continue to be available for download, through an evaluation website2 as a resource for future researchers in the area. This data resource provides a platform to compare existing methods in a fair and consistent manner to each other and multiple manual raters.

Effect of Fingolimod on Brain Volume Loss in Patients with Multiple Sclerosis

Brain atrophy occurs at a faster rate in patients with multiple sclerosis (MS) than in healthy individuals. In three randomized, controlled, phase III trials, fingolimod reduced the annual rate of brain volume loss (BVL) in patients with relapsing MS (RMS) by approximately one-third relative to that in individuals receiving placebo or intramuscular interferon beta-1a. Analysis of brain volume changes during study extensions has shown that this reduced rate of BVL is sustained in patients with RMS receiving fingolimod continuously. Subgroup analyses of the core phase III and extension studies have shown that reductions in the rate of BVL are observed irrespective of levels of inflammatory lesion activity seen by magnetic resonance imaging at baseline and on study; levels of disability at baseline; and treatment history. The rate of BVL in these studies was predicted independently by T2 lesion and gadolinium-enhancing lesion burdens at baseline, and correlations observed between BVL and increasing levels of disability strengthened over time. In another phase III trial in patients with primary progressive MS (PPMS), fingolimod did not reduce BVL overall relative to placebo; however, consistent with findings in RMS, there was a treatment effect on BVL in patients with PPMS with gadolinium-enhancing lesion activity at baseline. The association between treatment effects on BVL and future accumulation of disability argues in favor of measuring BVL on a more routine basis and with a more structured approach than is generally the case in clinical practice. Despite several practical obstacles, progress is being made in achieving this goal.

Multiple sclerosis: assay of free immunoglobulin light chains

Over the past five years, a number of papers have appeared describing the assay of free immunoglobulin light chains in cerebrospinal fluid to assist in the diagnosis of multiple sclerosis. The assay of kappa free immunoglobulin chains is being advocated as a technically simpler and cheaper quantitative alternative to the qualitative detection of oligoclonal bands. This article reviews the analytical and clinical characteristics of these immunoglobulin free light chain assays and places them in their historical context and possible future developments.

Structural sex differences at disease onset in multiple sclerosis patients

BACKGROUND

Male sex is associated with worsening disability and a more rapid progression of multiple sclerosis (MS). This study analysed structural sex differences in magnetic resonance images of the brain, comparing women whose disease started before and after the menopause with a control group of men.

METHODS

This was a case control study in which female patients whose MS started before (Group 1) and after (Group 2) the menopause were included. The control group was matched by age, disease duration, Expanded Disability Status Scale and disease-modifying treatment. Patients were analysed according to demographic and clinical variables, as well as in terms of radiological measurements at disease onset and during the first 12 months of follow-up. These measurements included normalised total brain volume (NTBV), normalised cortical volume (NCV), normalised white matter volume, left and right hippocampus, the thalamus, brain stem volume, lesion load and percentage brain volume change. A linear regression model was used to analyse the data.

RESULTS

A total of 97 patients were included: 53 in Group 1 (27 females) and 44 in Group 2 (22 females). In Group 1, we observed a reduction in brain volume in males compared with females at disease onset in NTBV (p = 0.01), NCV (p = 0.001) and brain stem volume (p = 0.01). We did not observe differences in Group 2 at disease onset in the brain volumes analysed.

CONCLUSION

We observed structural sex differences in brain volume at disease onset in the pre-menopausal group. However, no structural differences were observed at disease onset between the sexes after the menopause had started.