scholarly journals A R-Script for Generating Multiple Sclerosis Lesion Pattern Discrimination Plots

2021 ◽  
Vol 11 (1) ◽  
pp. 90
Author(s):  
Robert Marschallinger ◽  
Carmen Tur ◽  
Hannes Marschallinger ◽  
Johann Sellner
Keyword(s):  
Multiple Sclerosis ◽  
White Matter ◽  
Demyelinating Disease ◽  
White Matter Lesions ◽  
Pattern Discrimination ◽  
Small Sample ◽  
Multiple Sclerosis Lesion ◽  
Normal Brain ◽  
Data Set ◽  
Lesion Pattern

One significant characteristic of Multiple Sclerosis (MS), a chronic inflammatory demyelinating disease of the central nervous system, is the evolution of highly variable patterns of white matter lesions. Based on geostatistical metrics, the MS-Lesion Pattern Discrimination Plot reduces complex three- and four-dimensional configurations of MS-White Matter Lesions to a well-arranged and standardized two-dimensional plot that facilitates follow-up, cross-sectional and medication impact analysis. Here, we present a script that generates the MS-Lesion Pattern Discrimination Plot, using the widespread statistical computing environment R. Input data to the script are Nifti-1 or Analyze-7.5 files with individual MS-White Matter Lesion masks in Montreal Normal Brain geometry. The MS-Lesion Pattern Discrimination Plot, variogram plots and associated fitting statistics are output to the R console and exported to standard graphics and text files. Besides reviewing relevant geostatistical basics and commenting on implementation details for smooth customization and extension, the paper guides through generating MS-Lesion Pattern Discrimination Plots using publicly available synthetic MS-Lesion patterns. The paper is accompanied by the R script LDPgenerator.r, a small sample data set and associated graphics for comparison.

scholarly journals Assessment of Artificial Intelligence Automatic Multiple Sclerosis Lesion Delineation Tool for Clinical Use

2021 ◽  
Author(s):  
Amalie Monberg Hindsholm ◽  
Stig Præstekjær Cramer ◽  
Helle Juhl Simonsen ◽  
Jette Lautrup Frederiksen ◽  
Flemming Andersen ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
White Matter ◽  
Quantitative Evaluation ◽  
White Matter Lesions ◽  
Qualitative Evaluation ◽  
White Matter Hyperintensity ◽  
Multiple Sclerosis Lesion ◽  
Dice Similarity Coefficient ◽  
Accuracy Score ◽  
Clinical Value

Abstract Purpose To implement and validate an existing algorithm for automatic delineation of white matter lesions on magnetic resonance imaging (MRI) in patients with multiple sclerosis (MS) on a local single-center dataset. Methods We implemented a white matter hyperintensity segmentation model, based on a 2D convolutional neural network, using the conventional T2-weighted fluid attenuated inversion recovery (FLAIR) MRI sequence as input. The model was adapted for delineation of MS lesions by further training on a local dataset of 93 MS patients with a total of 3040 lesions. A quantitative evaluation was performed on ten test patients, in which model-generated masks were compared to manually delineated masks from two expert delineators. A subsequent qualitative evaluation of the implemented model was performed by two expert delineators, in which generated delineation masks on a clinical dataset of 53 patients were rated acceptable (< 10% errors) or unacceptable (> 10% errors) based on the total number of true lesions. Results The quantitative evaluation resulted in an average accuracy score (F1) of 0.71, recall of 0.77 and dice similarity coefficient of 0.62. Our implemented model obtained the highest scores in all three metrics, when compared to three out of the box lesion segmentation models. In the clinical evaluation an average of 94% of our 53 model-generated masks were rated acceptable. Conclusion After adaptation to our local dataset, the implemented segmentation model was able to delineate MS lesions with a high clinical value as rated by delineation experts while outperforming popular out of the box applications. This serves as a promising step towards implementation of automatic lesion delineation in our MS clinic.

Author response for "White matter lesions in multiple sclerosis are enriched for CD20 dim CD8 + tissue‐resident memory T cells"

2020 ◽  
Author(s):  
Cheng‐Chih Hsiao ◽  
Nina L. Fransen ◽  
Aletta M.R. den Bosch ◽  
Kim I.M. Brandwijk ◽  
Inge Huitinga ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
T Cells ◽  
White Matter ◽  
Memory T Cells ◽  
White Matter Lesions ◽  
Author Response ◽  
Resident Memory T Cells

scholarly journals Phenotypic and functional characterization of T cells in white matter lesions of multiple sclerosis patients

2017 ◽  
Vol 134 (3) ◽  
pp. 383-401 ◽  
Author(s):  
Gijsbert P. van Nierop ◽  
Marvin M. van Luijn ◽  
Samira S. Michels ◽  
Marie-Jose Melief ◽  
Malou Janssen ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
T Cells ◽  
White Matter ◽  
Functional Characterization ◽  
White Matter Lesions ◽  
Multiple Sclerosis Patients

scholarly journals Blood-brain barrier breakdown in non-enhancing multiple sclerosis lesions detected by 7-Tesla MP2RAGE ΔT1 mapping

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249973
Author(s):  
Seongjin Choi ◽  
Margaret Spini ◽  
Jun Hua ◽  
Daniel M. Harrison
Keyword(s):  
Multiple Sclerosis ◽  
Relaxation Time ◽  
White Matter ◽  
Blood Brain Barrier ◽  
White Matter Lesions ◽  
Brain Barrier ◽  
7 Tesla ◽  
T1 Relaxation Time ◽  
T1 Relaxation ◽  
Blood Brain

Although the blood-brain barrier (BBB) is altered in most multiple sclerosis (MS) lesions, gadolinium enhancement is seen only in acute lesions. In this study, we aimed to investigate gadolinium-induced changes in T1 relaxation time in MS lesions on 7-tesla (7T) MRI as a means to quantify BBB breakdown in non-enhancing MS lesions. Forty-seven participants with MS underwent 7T MRI of the brain with a magnitude-prepared rapid acquisition of 2 gradient echoes (MP2RAGE) sequence before and after contrast. Subtraction of pre- and post-contrast T1 maps was used to measure T1 relaxation time change (ΔT1) from gadolinium. ΔT1 values were interrogated in enhancing white matter lesions (ELs), non-enhancing white matter lesions (NELs), and normal appearing white matter (NAWM) and metrics were compared to clinical data. ΔT1 was measurable in NELs (median: -0.139 (-0.304, 0.174) seconds; p < 0.001) and was negligible in NAWM (median: -0.001 (-0.036, 0.155) seconds; p = 0.516). Median ΔT1 in NELs correlated with disability as measured by Expanded Disability Status Scale (EDSS) (rho = -0.331, p = 0.026). Multiple measures of NEL ΔT1 variability also correlated with EDSS. NEL ΔT1 values were greater and more variable in patients with progressive forms of MS and greater in those not on MS treatment. Measurement of the changes in T1 relaxation time caused by contrast on 7T MP2RAGE reveals clinically relevant evidence of BBB breakdown in NELs in MS. This data suggests that NEL ΔT1 should be evaluated further as a biomarker for disease severity and treatment effect in MS.

scholarly journals Expression of disease‐related mi RNA s in white‐matter lesions of progressive multiple sclerosis brains

2019 ◽  
Vol 6 (5) ◽  
pp. 854-862 ◽  
Author(s):  
Ajai Tripathi ◽  
Christina Volsko ◽  
Ushasi Datta ◽  
Keren Regev ◽  
Ranjan Dutta
Keyword(s):  
Multiple Sclerosis ◽  
White Matter ◽  
White Matter Lesions ◽  
Progressive Multiple Sclerosis
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