Feasibility of Brain Atrophy Measurement in Clinical Routine without Prior Standardization of the MRI Protocol: Results from MS-MRIUS, a Longitudinal Observational, Multicenter Real-World Outcome Study in Patients with Relapsing-Remitting MS

Utility of icobrain for brain volumetry in multiple sclerosis clinical practice

BACKGROUND

Few studies on multiple sclerosis (MS) have explored the variability of percentage brain volume change (PBVC) measurements obtained from different clinical MRIs. In a retrospective multicentre cohort study, we quantified the variability of annualised PBVC in clinical MRIs.

METHODS

Clinical MRIs of relapse-onset MS patients were assessed by icobrain. Volumetric data were analysed on same-scanner and different-scanner MRI pairs if they passed quality control criteria. Alignment similarity between two images had to be comparable to same-scanner scan-rescan images.

RESULTS

Of 6826 MRIs, 85 % had appropriate volumetric sequences and 4446 serial MRI pairs were analysed. 3334 (75 %) MRI pairs from 1207 patients met the inclusions. The PBVC of included MRI pairs showed variance of 0.78 % for same-scanner pairs and 0.80 % for different-scanner pairs. Further selection of included MRI pairs with the best variance resulted in 1885 (42 %) MRI pairs with PBVC variance of 0.34 %. Excluded MRI pairs with poor alignment similarity had variances of 2.97 % for same-scanner pairs and 20.79 % for different-scanner pairs.

CONCLUSION

Icobrain should be utilised for PBVC determination only on selected MRIs with the best alignment similarity. Applying strict selection criteria for the included MRI pairs and longitudinal imaging on the same scanner remain mandatory to reduce PBVC variability.

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

INTRODUCTION

Brain atrophy is a well-established MRI outcome for predicting clinical progression and monitoring treatment response in persons with multiple sclerosis (pwMS) at the group level. Despite the important progress made, the translation of brain atrophy assessment into clinical practice faces several challenges.

AREAS COVERED

In this review, the authors discuss technical- and subject-related barriers for implementing brain atrophy assessment as part of the clinical routine at the individual level. Substantial progress has been made to understand and mitigate technical barriers behind MRI acquisition. Numerous research and commercial segmentation techniques for volume estimation are available and technically validated, but their clinical value has not been fully established. A systematic assessment of subject-related barriers, which include genetic, environmental, biological, lifestyle, comorbidity, and aging confounders, is critical for the interpretation of brain atrophy measures at the individual subject level. Educating both medical providers and pwMS will help better clarify the benefits and limitations of assessing brain atrophy for disease monitoring and prognosis.

EXPERT OPINION

Integrating brain atrophy assessment into clinical practice for pwMS requires overcoming technical and subject-related challenges. Advances in MRI standardization, artificial intelligence, and clinician education will facilitate this process, improving disease management and potentially reducing long-term healthcare costs.

Neuroimaging assessment of facility-bound severely-affected MS reveals the critical role of cortical gray matter pathology: results from the CASA-MS case-controlled study

BACKGROUND

A subgroup of people with multiple sclerosis (pwMS) will develop severe disability. The pathophysiology underlying severe MS is unknown. The comprehensive assessment of severely affected MS (CASA-MS) was a case-controlled study that compared severely disabled in skilled nursing (SD/SN) (EDSS ≥ 7.0) to less-disabled (EDSS 3.0-6.5) community dwelling (CD) progressive pwMS, matched on age-, sex- and disease-duration (DDM).

OBJECTIVES

To identify neuroimaging and molecular biomarker characteristics that distinguish SD/SN from DDM-CD progressive pwMS.

METHODS

This study was carried at SN facility and at a tertiary MS center. The study collected clinical, molecular (serum neurofilament light chain, sNfL and glial acidic fibrillary protein, sGFAP) and MRI quantitative lesion-, brain volume-, and tissue integrity-derived measures. Statistical analyses were controlled for multiple comparisons.

RESULTS

42 SD/SN and 42 DDM-CD were enrolled. SD/SN pwMS showed significantly lower cortical volume (CV) (p < 0.001, d = 1.375) and thalamic volume (p < 0.001, d = 0.972) compared to DDM-CD pwMS. In a logistic stepwise regression model, the SD/SN pwMS were best differentiated from the DDM-CD pwMS by lower CV (p < 0.001) as the only significant predictor, with the accuracy of 82.3%. No significant differences between the two groups were observed for medulla oblongata volume, a proxy for spinal cord atrophy and white matter lesion burden, while there was a statistical trend for numerically higher sGFAP in SD/SN pwMS.

CONCLUSIONS

The CASA-MS study showed significantly more gray matter atrophy in severe compared to less-severe progressive MS.

Thalamic atrophy and dysconnectivity are associated with cognitive impairment in a multi-center, clinical routine, real-word study of people with relapsing-remitting multiple sclerosis

BACKGROUND

Prior research has established a link between thalamic pathology and cognitive impairment (CI) in people with multiple sclerosis (pwMS). However, the translation of these findings to pwMS in everyday clinical settings has been insufficient.

OBJECTIVE

To assess which global and/or thalamic imaging biomarkers can be used to identify pwMS at risk for CI and cognitive worsening (CW) in a real-world setting.

METHODS

This was an international, multi-center (11 centers), longitudinal, retrospective, real-word study of people with relapsing-remitting MS (pwRRMS). Brain MRI exams acquired at baseline and follow-up were collected. Cognitive status was evaluated using the Symbol Digit Modalities Test (SDMT). Thalamic volume (TV) measurement was performed on T2-FLAIR, as well as on T1-WI, when available. Thalamic dysconnectivity, T2-lesion volume (T2-LV), and volumes of gray matter (GM), whole brain (WB) and lateral ventricles (LVV) were also assessed.

RESULTS

332 pwMS were followed for an average of 2.8 years. At baseline, T2-LV, LVV, TV and thalamic dysconnectivity on T2-FLAIR (p < 0.016), and WB, GM and TV volumes on T1-WI (p < 0.039) were significantly worse in 90 (27.1 %) CI vs. 242 (62.9 %) non-CI pwRRMS. Greater SDMT decline over the follow-up was associated with lower baseline TV on T2-FLAIR (standardized β = 0.203, p = 0.002) and greater thalamic dysconnectivity (standardized β = -0.14, p = 0.028) in a linear regression model.

CONCLUSIONS

PwRRMS with thalamic atrophy and worse thalamic dysconnectivity present more frequently with CI and experience greater CW over mid-term follow-up in a real-world setting.

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

INTRODUCTION

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

AREAS COVERED

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

EXPERT OPINION

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

Resting-state functional MRI in multicenter studies on multiple sclerosis: a report on raw data quality and functional connectivity features from the Italian Neuroimaging Network Initiative

The Italian Neuroimaging Network Initiative (INNI) is an expanding repository of brain MRI data from multiple sclerosis (MS) patients recruited at four Italian MRI research sites. We describe the raw data quality of resting-state functional MRI (RS-fMRI) time-series in INNI and the inter-site variability in functional connectivity (FC) features after unified automated data preprocessing. MRI datasets from 489 MS patients and 246 healthy control (HC) subjects were retrieved from the INNI database. Raw data quality metrics included temporal signal-to-noise ratio (tSNR), spatial smoothness (FWHM), framewise displacement (FD), and differential variation in signals (DVARS). Automated preprocessing integrated white-matter lesion segmentation (SAMSEG) into a standard fMRI pipeline (fMRIPrep). FC features were calculated on pre-processed data and harmonized between sites (Combat) prior to assessing general MS-related alterations. Across centers (both groups), median tSNR and FWHM ranged from 47 to 84 and from 2.0 to 2.5, and median FD and DVARS ranged from 0.08 to 0.24 and from 1.06 to 1.22. After preprocessing, only global FC-related features were significantly correlated with FD or DVARS. Across large-scale networks, age/sex/FD-adjusted and harmonized FC features exhibited both inter-site and site-specific inter-group effects. Significant general reductions were obtained for somatomotor and limbic networks in MS patients (vs. HC). The implemented procedures provide technical information on raw data quality and outcome of fully automated preprocessing that might serve as reference in future RS-fMRI studies within INNI. The unified pipeline introduced little bias across sites and appears suitable for multisite FC analyses on harmonized network estimates.

Multisite MRI reproducibility of lateral ventricular volume using the NAIMS cooperative pilot dataset

BACKGROUND AND PURPOSE

The North American Imaging in Multiple Sclerosis (NAIMS) multisite project identified interscanner reproducibility issues with T1-based whole brain volume (WBV). Lateral ventricular volume (LVV) acquired on T2-fluid-attenuated inverse recovery (FLAIR) scans has been proposed as a robust proxy measure. Therefore, we sought to determine the relative magnitude of scanner-induced T2-FLAIR-based LVV and T1-based WBV measurement errors in relation to clinically meaningful changes.

METHODS

This was a post hoc analysis of the NAIMS pilot dataset in which a relapsing-remitting MS patient with no intrastudy clinical or radiological activity was imaged twice on seven different Siemens scanners across the United States. LVV was determined using the automated NeuroSTREAM technique on T2-FLAIR and WBV was determined with SIENAX on high-resolution T1-MPRAGE. Average LVV and WBV were measured, and absolute intrascanner and interscanner coefficients of variation (CoVs) were calculated. The variabilities were compared to previously established annual pathological and clinically meaningful cutoffs of 0.40% for WBV and of 3.51% for LVV.

RESULTS

Mean LVV across all seven scan/rescan pairs was 45.87 ± 1.15 ml. Average LVV intrascanner CoV was 1.42% and interscanner CoV was 1.78%, both smaller than the reported annualized clinically meaningful cutoff of 3.51%. In contrast, intra- and interscanner CoVs for WBV (0.99% and 1.15%) were both higher than the established cutoff of 0.40%. Individually, 1/7 intrasite and 2/7 intersite pair-wise LVV comparisons were above the 3.51% cutoff, whereas 4/7 intrasite and 7/7 intersite WBV comparisons were above the 0.40% cutoff.

CONCLUSION

Fully automated LVV segmentation has higher absolute variability than WBV, but much lower relative variability compared to clinically relevant changes, and may therefore be a meaningful proxy outcome measure of neurodegeneration.

Thalamic atrophy measured by artificial intelligence in a multicentre clinical routine real-word study is associated with disability progression

BACKGROUND

The thalamus is a key grey matter structure, and sensitive marker of neurodegeneration in multiple sclerosis (MS). Previous reports indicated that thalamic volumetry using artificial intelligence (AI) on clinical-quality T2-fluid-attenuated inversion recovery (FLAIR) images alone is fast and reliable.

OBJECTIVE

To investigate whether thalamic volume (TV) loss, measured longitudinally by AI, is associated with disability progression (DP) in patients with MS, participating in a large multicentre study.

METHODS

The DeepGRAI (Deep Grey Rating via Artificial Intelligence) Registry is a multicentre (30 USA sites), longitudinal, observational, retrospective, real-word study of relapsing-remitting (RR) MS patients. Each centre enrolled between 30 and 35 patients. Brain MRI exams acquired at baseline and follow-up on 1.5T or 3T scanners with no prior standardisation were collected. TV measurement was performed on T2-FLAIR using DeepGRAI, and on two dimensional (D)-weighted and 3D T1-weighted images (WI) by using FMRIB's Integrated Registration and Segmentation Tool software where possible.

RESULTS

1002 RRMS patients were followed for an average of 2.6 years. Longitudinal TV analysis was more readily available on T2-FLAIR (96.1%), compared with 2D-T1-WI (61.8%) or 3D-T1-WI (33.2%). Over the follow-up, DeepGRAI TV loss was significantly higher in patients with DP, compared with those with disability improvement (DI) or disease stability (-1.35% in DP, -0.87% in DI and -0.57% in Stable, p=0.045, Bonferroni-adjusted, age-adjusted and follow-up time-adjusted analysis of covariance). In a regression model including MRI scanner change, age, sex, disease duration and follow-up time, DP was associated with DeepGRAI TV loss (p=0.022).

CONCLUSIONS

Thalamic atrophy measured by AI in a multicentre clinical routine real-word setting is associated with DP over mid-term follow-up.

Brain atrophy and lesion burden are associated with disability progression in a multiple sclerosis real-world dataset using only T2-FLAIR: The NeuroSTREAM MSBase study

BACKGROUND

Methodological challenges limit the use of brain atrophy and lesion burden measures in the follow-up of multiple sclerosis (MS) patients on clinical routine datasets.

OBJECTIVE

To determine the feasibility of T2-FLAIR-only measures of lateral ventricular volume (LVV) and salient central lesion volume (SCLV), as markers of disability progression (DP) in MS.

METHODS

A total of 3,228 MS patients from 9 MSBase centers in 5 countries were enrolled. Of those, 2,875 (218 with clinically isolated syndrome, 2,231 with relapsing-remitting and 426 with progressive disease subtype) fulfilled inclusion and exclusion criteria. Patients were scanned on either 1.5 T or 3 T MRI scanners, and 5,750 brain scans were collected at index and on average after 42.3 months at post-index. Demographic and clinical data were collected from the MSBase registry. LVV and SCLV were measured on clinical routine T2-FLAIR images.

RESULTS

Longitudinal LVV and SCLV analyses were successful in 96% of the scans. 57% of patients had scanner-related changes over the follow-up. After correcting for age, sex, disease duration, disability, disease-modifying therapy and LVV at index, and follow-up time, MS patients with DP (n = 671) had significantly greater absolute LVV change compared to stable (n = 1,501) or disability improved (DI, n = 248) MS patients (2.0 mL vs. 1.4 mL vs. 1.1 mL, respectively, ANCOVA p < 0.001, post-hoc pair-wise DP vs. Stable p = 0.003; and DP vs. DI, p = 0.002). Similar ANCOVA model was also significant for SCLV (p = 0.03).

CONCLUSIONS

LVV-based atrophy and SCLV-based lesion outcomes are feasible on clinically acquired T2-FLAIR scans in a multicenter fashion and are associated with DP over mid-term.

Quantifying disease pathology and predicting disease progression in multiple sclerosis with only clinical routine T2-FLAIR MRI

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We explored five brain pathology measures from clinical-quality T2-FLAIR MRI in MS.

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These included LVV, thalamus volume, MOV, SCLV and network efficiency.

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T2-FLAIR measures predicted a majority of the variance in research-quality MRI.

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T2-FLAIR measures correlated with neurologic disability and cognitive function.

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T2-FLAIR measures predicted disability progression over five-years.

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T2-FLAIR measures can be used in legacy clinical datasets.

Background

Although quantitative measures from research-quality MRI provide a means to study multiple sclerosis (MS) pathology in vivo, these metrics are often unavailable in legacy clinical datasets.

Objective

To determine how well an automatically-generated quantitative snapshot of brain pathology, measured only on clinical routine T2-FLAIR MRI, can substitute for more conventional measures on research MRI in terms of capturing multi-factorial disease pathology and providing similar clinical relevance.

Methods

MRI with both research-quality sequences and conventional clinical T2-FLAIR was acquired for 172 MS patients at baseline, and neurologic disability was assessed at baseline and five-years later. Five measures (thalamus volume, lateral ventricle volume, medulla oblongata volume, lesion volume, and network efficiency) for quantifying disparate aspects of neuropathology from low-resolution T2-FLAIR were applied to predict standard research-quality MRI measures. They were compared in regard to association with future neurologic disability and disease progression over five years.

Results

The combination of the five T2-FLAIR measures explained most of the variance in standard research-quality MRI. T2-FLAIR measures were associated with neurologic disability and cognitive function five-years later (R² = 0.279, p < 0.001; R² = 0.382, p < 0.001), similar to standard research-quality MRI (R² = 0.279, p < 0.001; R² = 0.366, p < 0.001). They also similarly predicted disability progression over five years (%-correctly-classified = 69.8, p = 0.034), compared to standard research-quality MRI (%-correctly-classified = 72.4%, p = 0.022) in relapsing-remitting MS.

Conclusion

A set of five T2-FLAIR-only measures can substitute for standard research-quality MRI, especially in relapsing-remitting MS. When only clinical T2-FLAIR is available, it can be used to obtain substantially more quantitative information about brain pathology and disability than is currently standard practice.

DeepGRAI (Deep Gray Rating via Artificial Intelligence): Fast, feasible, and clinically relevant thalamic atrophy measurement on clinical quality T2-FLAIR MRI in multiple sclerosis

BACKGROUND

Thalamic volume loss is a key marker of neurodegeneration in multiple sclerosis (MS). T2-FLAIR MRI is a common denominator in clinical routine MS imaging, but current methods for thalamic volumetry are not applicable to it.

OBJECTIVE

To develop and validate a robust algorithm to measure thalamic volume using clinical routine T2-FLAIR MRI.

METHODS

A dual-stage deep learning approach based on 3D U-net (DeepGRAI - Deep Gray Rating via Artificial Intelligence) was created and trained/validated/tested on 4,590 MRI exams (4288 2D-FLAIR, 302 3D-FLAIR) from 59 centers (80/10/10 train/validation/test split). As training/test targets, FIRST was used to generate thalamic masks from 3D T1 images. Masks were reviewed, corrected, and aligned into T2-FLAIR space. Additional validation was performed to assess inter-scanner reliability (177 subjects at 1.5 T and 3 T within one week) and scan-rescan-reliability (5 subjects scanned, repositioned, and then re-scanned). A longitudinal dataset including assessment of disability and cognition was used to evaluate the predictive value of the approach.

RESULTS

DeepGRAI automatically quantified thalamic volume in approximately 7 s per case, and has been made publicly available. Accuracy on T2-FLAIR relative to 3D T1 FIRST was 99.4% (r = 0.94, p < 0.001,TPR = 93.0%, FPR = 0.3%). Inter-scanner error was 3.21%. Scan-rescan error with repositioning was 0.43%. DeepGRAI-derived thalamic volume was associated with disability (r = -0.427,p < 0.001) and cognition (r = -0.537,p < 0.001), and was a significant predictor of longitudinal cognitive decline (R2 = 0.081, p = 0.024; comparatively, FIRST-derived volume was R2 = 0.080, p = 0.025).

CONCLUSIONS

DeepGRAI provides fast, reliable, and clinically relevant thalamic volume measurement on multicenter clinical-quality T2-FLAIR images. This indicates potential for real-world thalamic volumetry, as well as quantification on legacy datasets without 3D T1 imaging.

Spectrum of neuroimaging findings post-proton beam therapy in a large pediatric cohort

PURPOSE

Proton beam therapy (PBT) is now well established for the treatment of certain pediatric brain tumors. The intrinsic properties of PBT are known to reduce long-term negative effects of photon radiotherapy (PRT). To better understand the intracranial effects of PBT, we analyzed the longitudinal imaging changes in a cohort of children with brain tumors treated by PBT with clinical and radiotherapy dose correlations.

MATERIALS AND METHODS

Retrospective imaging review of 46 patients from our hospital with brain tumors treated by PBT. The imaging findings were correlated with clinical and dose parameters.

RESULTS

Imaging changes were assessed by reviewing serial magnetic resonance imaging (MRI) scans following PBT over a follow-up period ranging from 1 month to 7 years. Imaging changes were observed in 23 patients undergoing PBT and categorized as pseudoprogression (10 patients, 43%), white matter changes (6 patients, 23%), parenchymal atrophy (6 patients, 23%), and cerebral large vessel arteriopathy (5 patients, 25%). Three patients had more than one type of imaging change. Clinical symptoms attributable to PBT were observed in 13 (28%) patients.

CONCLUSION

In accordance with published literature, we found evidence of varied intracranial imaging changes in pediatric brain tumor patients treated with PBT. There was a higher incidence (10%) of large vessel cerebral arteriopathy in our cohort than previously described in the literature. Twenty-eight percent of patients had clinical sequelae as a result of these changes, particularly in the large vessel arteriopathy subgroup, arguing the need for angiographic and perfusion surveillance to pre-empt any morbidities and offer potential neuro-protection.

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.

FLAIR-only joint volumetric analysis of brain lesions and atrophy in clinically isolated syndrome (CIS) suggestive of multiple sclerosis

Background

MRI assessment in multiple sclerosis (MS) focuses on the presence of typical white matter (WM) lesions. Neurodegeneration characterised by brain atrophy is recognised in the research field as an important prognostic factor. It is not routinely reported clinically, in part due to difficulty in achieving reproducible measurements. Automated MRI quantification of WM lesions and brain volume could provide important clinical monitoring data. In general, lesion quantification relies on both T1 and FLAIR input images, while tissue volumetry relies on T1. However, T1-weighted scans are not routinely included in the clinical MS protocol, limiting the utility of automated quantification.

Objectives

We address an aspect of this important translational challenge by assessing the performance of FLAIR-only lesion and brain segmentation, against a conventional approach requiring multi-contrast acquisition. We explore whether FLAIR-only grey matter (GM) segmentation yields more variability in performance compared with two-channel segmentation; whether this is related to field strength; and whether the results meet a level of clinical acceptability demonstrated by the ability to reproduce established biological associations.

Methods

We used a multicentre dataset of subjects with a CIS suggestive of MS scanned at 1.5T and 3T in the same week. WM lesions were manually segmented by two raters, ‘manual 1′ guided by consensus reading of CIS-specific lesions and ‘manual 2′ by any WM hyperintensity. An existing brain segmentation method was adapted for FLAIR-only input. Automated segmentation of WM hyperintensity and brain volumes were performed with conventional (T1/T1 + FLAIR) and FLAIR-only methods.

Results

WM lesion volumes were comparable at 1.5T between ‘manual 2′ and FLAIR-only methods and at 3T between ‘manual 2′, T1 + FLAIR and FLAIR-only methods. For cortical GM volume, linear regression measures between conventional and FLAIR-only segmentation were high (1.5T: α = 1.029, R² = 0.997, standard error (SE) = 0.007; 3T: α = 1.019, R² = 0.998, SE = 0.006). Age-associated change in cortical GM volume was a significant covariate in both T1 (p = 0.001) and FLAIR-only (p = 0.005) methods, confirming the expected relationship between age and GM volume for FLAIR-only segmentations.

Conclusions

FLAIR-only automated segmentation of WM lesions and brain volumes were consistent with results obtained through conventional methods and had the ability to demonstrate biological effects in our study population. Imaging protocol harmonisation and validation with other MS phenotypes could facilitate the integration of automated WM lesion volume and brain atrophy analysis as clinical tools in radiological MS reporting.

Disability Improvement Is Associated with Less Brain Atrophy Development in Multiple Sclerosis

BACKGROUND AND PURPOSE

It is unknown whether deceleration of brain atrophy is associated with disability improvement in patients with MS. Our aim was to investigate whether patients with MS with disability improvement develop less brain atrophy compared with those who progress in disability or remain stable.

MATERIALS AND METHODS

We followed 980 patients with MS for a mean of 4.8 ± 2.4 years. Subjects were divided into 3 groups: progress in disability (n = 241, 24.6%), disability improvement (n = 101, 10.3%), and stable (n = 638, 65.1%) at follow-up. Disability improvement and progress in disability were defined on the basis of the Expanded Disability Status Scale score change using standardized guidelines. Stable was defined as nonoccurrence of progress in disability or disability improvement. Normalized whole-brain volume was calculated using SIENAX on 3D T1WI, whereas the lateral ventricle was measured using NeuroSTREAM on 2D-T2-FLAIR images. The percentage brain volume change and percentage lateral ventricle volume change were calculated using SIENA and NeuroSTREAM, respectively. Differences among groups were investigated using ANCOVA, adjusted for age at first MR imaging, race, T2 lesion volume, and corresponding baseline structural volume and the Expanded Disability Status Scale.

RESULTS

At first MR imaging, there were no differences among progress in disability, disability improvement, and the stable groups in whole-brain volume (P = .71) or lateral ventricle volume (P = .74). During follow-up, patients with disability improvement had the lowest annualized percentage lateral ventricle volume change (1.6% ± 2.7%) followed by patients who were stable (2.1% ± 3.7%) and had progress in disability (4.1% ± 5.5%), respectively (P < .001). The annualized percentage brain volume change values were -0.7% ± 0.7% for disability improvement, -0.8% ± 0.7% for stable, and -1.1% ± 1.1% for progress in disability (P = .001).

CONCLUSIONS

Patients with MS who improve in their clinical disability develop less brain atrophy across time compared with those who progress.

MRI quality control for the Italian Neuroimaging Network Initiative: moving towards big data in multiple sclerosis

The Italian Neuroimaging Network Initiative (INNI) supports the creation of a repository, where MRI, clinical, and neuropsychological data from multiple sclerosis (MS) patients and healthy controls are collected from Italian Research Centers with internationally recognized expertise in MRI applied to MS. However, multicenter MRI data integration needs standardization and quality control (QC). This study aimed to implement quantitative measures for characterizing the standardization and quality of MRI collected within INNI. MRI scans of 423 MS patients, including 3D T₁- and T₂-weighted, were obtained from INNI repository (from Centers A, B, C, and D). QC measures were implemented to characterize: (1) head positioning relative to the magnet isocenter; (2) intensity inhomogeneity; (3) relative image contrast between brain tissues; and (4) image artefacts. Centers A and D showed the most accurate subject positioning within the MR scanner (median z-offsets = - 2.6 ± 1.7 cm and - 1.1 ± 2 cm). A low, but significantly different, intensity inhomogeneity on 3D T₁-weighted MRI was found between all centers (p < 0.05), except for Centers A and C that showed comparable image bias fields. Center D showed the highest relative contrast between gray and normal appearing white matter (NAWM) on 3D T₁-weighed MRI (0.63 ± 0.04), while Center B showed the highest relative contrast between NAWM and MS lesions on FLAIR (0.21 ± 0.06). Image artefacts were mainly due to brain movement (60%) and ghosting (35%). The implemented QC procedure ensured systematic data quality assessment within INNI, thus making available a huge amount of high-quality MRI to better investigate pathophysiological substrates and validate novel MRI biomarkers in MS.

Aging and Brain Atrophy in Multiple Sclerosis

BACKGROUND AND PURPOSE

Brain atrophy accelerates at the age of 60 in healthy individuals (HI) and at disease onset in multiple sclerosis (MS) patients. Whether there is an exacerbating effect of aging superimposed on MS-related brain atrophy is unknown. We estimated the aging effect on lateral ventricular volume (LVV) and whole brain volume (WBV) changes in MS patients.

METHODS

1,982 MS patients (mean follow-up: 4.8 years) and 351 HI (mean follow-up: of 3.1 years), aged from 20 to 79 years old (yo), were collected retrospectively. Percent LVV change (PLVVC) and percent brain volume change (PBVC) on 1.5T and 3T MRI scanners (median of 3.9 scans per subject) were calculated. These were determined between all-time points and subjects were divided in six-decade age groups. MRI differences between age groups were calculated using analysis of covariance (ANCOVA).

RESULTS

Compared to HI, at first MRI, MS patients had significantly increased LVV in the age groups: 30-39 yo, 40-49 yo, 50-59 yo, 60-69 yo (all P < .0001), and 70-79 yo (P = .029), and decreased WBV in the age groups: 20-29 yo (P = .024), 30-39 yo (P = .031), 40-49 yo, and 50-59 yo (all P < .0001). Annualized PLVVC was significantly different between the age groups 20-59 and 60-79 yo in MS patients (P = .005) and HI (P < .0001), as was for PBVC in MS patients (P = .001), but not for HI (P = .521). There was a significant aging interaction effect in the annualized PLVVC (P = .001) between HI and MS patients, which was not observed for the annualized PBVC (P = .380).

CONCLUSIONS

Development of brain atrophy manifests progressively in MS patients, and occurs with a different pattern, as compared to aging HI. PLVVC increased across age in HI as compared to MS, while PBVC decreased across ages in both HI and MS.

Dietary and lifestyle factors in multiple sclerosis progression: results from a 5-year longitudinal MRI study

BACKGROUND

Evidence regarding the role, if any, of dietary and lifestyle factors in the pathogenesis of multiple sclerosis (MS) is poorly understood.

OBJECTIVE

To assess the effect of lifestyle-based risk factors linked to cardiovascular disease (CVD) on clinical and MRI-derived MS outcomes.

METHODS

The study enrolled 175 MS or clinically isolated syndrome (CIS) patients and 42 age- and sex-matched healthy controls (HCs) who were longitudinally followed for 5.5 years. The 20-year CVD risk was calculated by Healthy Heart Score (HHS) prediction model which includes age, smoking, body mass index, dietary intake, exercise, and alcohol consumption. Baseline and follow-up MRI scans were obtained and cross-sectional and longitudinal changes of T2-lesion volume (LV), whole brain volume (WBV), white matter volume (WMV), gray matter volume (GMV), and lateral ventricular volume (LVV) were calculated.

RESULTS

After correcting for disease duration, the baseline HHS values of the MS group were associated with baseline GMV (r_s = - 0.20, p = 0.01), and longitudinal LVV change (r_s = 0.19, p = 0.01). The association with LVV remained significant after adjusting for baseline LVV volumes (r_s = 0.2, p = 0.008) in MS patients. The diet component of the HHS was associated with the 5-year T2-LV accrual (r_s = - 0.191, p = 0.04) in MS. In the HC group, the HHS was associated with LVV (r_s = 0.58, p < 0.001), GMV (r_s = - 0.57, p < 0.001), WBV (r_s = - 0.55, p = 0.001), T2-LV (r_s = 0.41, p = 0.027), and WMV (r_s = - 0.38, p = 0.042). Additionally, the HC HHS was associated with the 5-year change in LVV (r_s = 0.54, p = 0.001) and in WBV (r_s = - 0.45, p = 0.011).

CONCLUSION

Lifestyle risk factors contribute to accelerated central brain atrophy in MS patients, whereas unhealthier diet is associated with MS lesion accrual. Despite the lower overall effect when compared to HCs, lifestyle-based modifications may still provide a beneficial effect on reducing brain atrophy in MS patients.

Comparative effectiveness of teriflunomide and dimethyl fumarate in patients with relapsing forms of MS in the retrospective real-world Teri-RADAR study

AIM

Head-to-head clinical trials of teriflunomide (TFM) versus dimethyl fumarate (DMF) have not been conducted.

OBJECTIVES

To compare the real-world effectiveness of TFM versus DMF.

METHODS

Anonymized data were collected from patients with relapsing multiple sclerosis (MS) initiating treatment with teriflunomide (N = 50) or DMF (N = 50).

RESULTS

On follow-up magnetic resonance imaging (MRI) compared with baseline, with TFM versus DMF treatment, the proportion of patients with new/enlarging T2 or gadolinium-enhancing lesions was 30.0 versus 40.0% (p = 0.2752). However, median annualized percent whole brain volume change was -0.1 versus -0.5 (p = 0.0212). There were no significant treatment differences on additional MRI and clinical end points and no unexpected safety signals.

CONCLUSION

The effectiveness of teriflunomide was superior to DMF on whole brain atrophy and similar to DMF on other MRI/clinical end points.

Brain Atrophy Is Associated with Disability Progression in Patients with MS followed in a Clinical Routine

BACKGROUND AND PURPOSE

The assessment of brain atrophy in a clinical routine is not performed routinely in multiple sclerosis. Our aim was to determine the feasibility of brain atrophy measurement and its association with disability progression in patients with MS followed in a clinical routine for 5 years.

MATERIALS AND METHODS

A total of 1815 subjects, 1514 with MS and 137 with clinically isolated syndrome and 164 healthy individuals, were collected retrospectively. Of 11,794 MR imaging brain scans included in the analysis, 8423 MRIs were performed on a 3T, and 3371 MRIs, on a 1.5T scanner. All patients underwent 3D T1WI and T2-FLAIR examinations at all time points of the study. Whole-brain volume changes were measured by percentage brain volume change/normalized brain volume change using SIENA/SIENAX on 3D T1WI and percentage lateral ventricle volume change using NeuroSTREAM on T2-FLAIR.

RESULTS

Percentage brain volume change failed in 36.7% of the subjects; percentage normalized brain volume change, in 19.2%; and percentage lateral ventricle volume change, in 3.3% because of protocol changes, poor scan quality, artifacts, and anatomic variations. Annualized brain volume changes were significantly different between those with MS and healthy individuals for percentage brain volume change (P < .001), percentage normalized brain volume change (P = .002), and percentage lateral ventricle volume change (P = .01). In patients with MS, mixed-effects model analysis showed that disability progression was associated with a 21.9% annualized decrease in percentage brain volume change (P < .001) and normalized brain volume (P = .002) and a 33% increase in lateral ventricle volume (P = .004).

CONCLUSIONS

All brain volume measures differentiated MS and healthy individuals and were associated with disability progression, but the lateral ventricle volume assessment was the most feasible.

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.

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.

Establishing pathological cut-offs for lateral ventricular volume expansion rates

Background

A percent brain volume change (PBVC) cut-off of −0.4% per year has been proposed to distinguish between pathological and physiological changes in multiple sclerosis (MS). Unfortunately, standardized PBVC measurement is not always feasible on scans acquired outside research studies or academic centers. Percent lateral ventricular volume change (PLVVC) is a strong surrogate measure of PBVC, and may be more feasible for atrophy assessment on real-world scans. However, the PLVVC rate corresponding to the established PBVC cut-off of −0.4% is unknown.

Objective

To establish a pathological PLVVC expansion rate cut-off analogous to −0.4% PBVC.

Methods

We used three complementary approaches. First, the original follow-up-length-weighted receiver operating characteristic (ROC) analysis method establishing whole brain atrophy rates was adapted to a longitudinal ventricular atrophy dataset of 177 relapsing-remitting MS (RRMS) patients and 48 healthy controls. Second, in the same dataset, SIENA PBVCs were used with non-linear regression to directly predict the PLVVC value corresponding to −0.4% PBVC. Third, in an unstandardized, real world dataset of 590 RRMS patients from 33 centers, the cut-off maximizing correspondence to PBVC was found. Finally, correspondences to clinical outcomes were evaluated in both datasets.

Results

ROC analysis suggested a cut-off of 3.09% (AUC = 0.83, p < 0.001). Non-linear regression R² was 0.71 (p < 0.001) and a − 0.4% PBVC corresponded to a PLVVC of 3.51%. A peak in accuracy in the real-world dataset was found at a 3.51% PLVVC cut-off. Accuracy of a 3.5% cut-off in predicting clinical progression was 0.62 (compared to 0.68 for PBVC).

Conclusions

Ventricular expansion of between 3.09% and 3.51% on T2-FLAIR corresponds to the pathological whole brain atrophy rate of 0.4% for RRMS. A conservative cut-off of 3.5% performs comparably to PBVC for clinical outcomes.

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Pathological atrophy in MS can be measured on clinical T2-FLAIR images alone.

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Ventricular enlargement of 3.5% per year separates MS/HC as well as PBVC on T1 images.

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Ventricular cut-offs also correspond to clinical outcome.

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This cut-off can substitute in NEDA-4 when only clinical T2-FLAIR images are available.