Imaging as an Outcome Measure in Multiple Sclerosis

Ontaneda, Daniel; Fox, Robert J.

doi:10.1007/s13311-016-0479-6

Cited by 55 publications

(43 citation statements)

References 147 publications

(145 reference statements)

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“…What explains the variation in chronic immune responses between individuals? In conjunction with additional imaging tools for assessment of demyelination and remyelination, neurodegeneration and synaptic loss [151][152][153][154] , the impact of chronic neuroinflammation and its modulation in the CNS can now be studied directly.…”

Section: {H1} Conclusionmentioning

confidence: 99%

Chronic inflammation in multiple sclerosis — seeing what was always there

Matthews

2019

Nat Rev Neurol

106

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• Advanced MRI and PET methods enable visualization of features related to chronic inflammation in progressive and relapsing-remitting forms of multiple sclerosis (MS). • Quantitative analysis of uptake of gadolinium contrast agent and ultra-small paramagnetic particles provide in vivo evidence of chronic, low-grade inflammation in people with progressive or relapsing-remitting MS. • Lesions associated with activated macrophages/microglia (slowly expanding T2 hyperintense lesions and lesions with high susceptibility-weighted MRI signals at their rims) are more common in progressive MS than in RRMS. • Persistent focal leptomeningeal inflammation, detectable with gadolinium contrastenhanced T2 fluid attenuation inversion recovery MRI in many people with MS (particularly progressive MS), is associated with cortical lesions and accelerated cortical atrophy. • Translocator protein (TSPO) PET can detect increased innate immune activation in brains of people with MS with typically greater activation in secondary progressive MS than in relapsing-remitting MS. Indirect evidence suggests that magnetic resonance spectroscopy measures of myo-inositol and some more recently introduced PET measures both can reflect contributions of astrocyte activation to brain innate immune responses.

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Section: {H1} Conclusionmentioning

confidence: 99%

Chronic inflammation in multiple sclerosis — seeing what was always there

Matthews

2019

Nat Rev Neurol

106

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“…Axonal injury is a key element in the pathology of multiple sclerosis (MS) and a major determinant of patients’ disability . Because capturing and characterizing axonal damage in MS patients in vivo is challenging, there remains a pressing need to identify a magnetic resonance imaging (MRI) biometric indicative of axonal quantity. This biomarker would allow for the detection of microscopic damage before symptoms and irreversible tissue damage appear, potentially optimizing treatment personalization based on an accurate characterization of tissue injury.…”

Section: Introductionmentioning

confidence: 99%

“…MR spectroscopy (MRS) and the quantification of the neuronal metabolite N‐acetyl aspartate (NAA) has made it possible to infer on axonal damage and secondary loss in MS . Technical disadvantages such as the long acquisition time and poor image resolution challenge the advancement of MRS to clinical and research utilization …”

Section: Introductionmentioning

confidence: 99%

Probing axons using multi‐compartmental diffusion in multiple sclerosis

Bagnato

Franco

et al. 2019

Ann Clin Transl Neurol

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Objects The diffusion‐based spherical mean technique (SMT) provides a novel model to relate multi‐b‐value diffusion magnetic resonance imaging (MRI) data to features of tissue microstructure. We propose the first clinical application of SMT to image the brain of patients with multiple sclerosis (MS) and investigate clinical feasibility and translation. Methods Eighteen MS patients and nine age‐ and sex‐matched healthy controls (HCs) underwent a 3.0 Tesla scan inclusive of clinical sequences and SMT images (isotropic resolution of 2 mm). Axial diffusivity (AD), apparent axonal volume fraction (Vax), and effective neural diffusivity (Dax) parametric maps were fitted. Differences in AD, Vax, and Dax between anatomically matched regions reflecting different tissues types were estimated using generalized linear mixed models for binary outcomes. Results Differences were seen in all SMT‐derived parameters between chronic black holes (cBHs) and T2‐lesions (P ≤ 0.0016), in Vax and AD between T2‐lesions and normal appearing white matter (NAWM) (P < 0.0001), but not between the NAWM and normal WM in HCs. Inverse correlations were seen between Vax and AD in cBHs (r = −0.750, P = 0.02); in T2‐lesions Dax values were associated with Vax (r = 0.824, P < 0.0001) and AD (r = 0.570, P = 0.014). Interpretations SMT‐derived metrics are sensitive to pathological changes and hold potential for clinical application in MS patients.

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“…WHOLE, TISSUE‐SPECIFIC, and regional brain atrophy in addition to white matter (WM) lesions are major findings in multiple sclerosis (MS), the most common demyelinating disease in young adults . Atrophy and lesion load in MS are most commonly estimated on structural magnetic resonance imaging (MRI) and are increasingly used for managing patients and evaluating newer treatments in multicenter trials . Robust and automatic segmentation techniques are critical for estimating lesion load and brain atrophy without operator bias.…”

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confidence: 99%

Deep‐Learning‐Based Neural Tissue Segmentation of MRI in Multiple Sclerosis: Effect of Training Set Size

Narayana

Coronado

Sujit

et al. 2019

Magnetic Resonance Imaging

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Background: The dependence of deep-learning (DL)-based segmentation accuracy of brain MRI on the training size is not known. Purpose: To determine the required training size for a desired accuracy in brain MRI segmentation in multiple sclerosis (MS) using DL. Study Type: Retrospective analysis of MRI data acquired as part of a multicenter clinical trial. Study Population: In all, 1008 patients with clinically definite MS. Field Strength/Sequence: MRIs were acquired at 1.5T and 3T scanners manufactured by GE, Philips, and Siemens with dual turbo spin echo, FLAIR, and T 1 -weighted turbo spin echo sequences. Assessment: Segmentation results using an automated analysis pipeline and validated by two neuroimaging experts served as the ground truth. A DL model, based on a fully convolutional neural network, was trained separately using 16 different training sizes. The segmentation accuracy as a function of the training size was determined. These data were fitted to the learning curve for estimating the required training size for desired accuracy. Statistical Tests: The performance of the network was evaluated by calculating the Dice similarity coefficient (DSC), and lesion true-positive and false-positive rates. Results: The DSC for lesions showed much stronger dependency on the sample size than gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). When the training size was increased from 10 to 800 the DSC values varied from 0.00 to 0.86 AE 0.016 for T 2 lesions, 0.87 AE 009 to 0.94 AE 0.004 for GM, 0.86 AE 0.08 to 0.94 AE 0.005 for WM, and 0.91 AE 0.009 to 0.96 AE 0.003 for CSF. Data Conclusion: Excellent segmentation was achieved with a training size as small as 10 image volumes for GM, WM, and CSF. In contrast, a training size of at least 50 image volumes was necessary for adequate lesion segmentation.

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Imaging as an Outcome Measure in Multiple Sclerosis

Cited by 55 publications

References 147 publications

Chronic inflammation in multiple sclerosis — seeing what was always there

Chronic inflammation in multiple sclerosis — seeing what was always there

Probing axons using multi‐compartmental diffusion in multiple sclerosis

Deep‐Learning‐Based Neural Tissue Segmentation of MRI in Multiple Sclerosis: Effect of Training Set Size

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