Dataset Information

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

ABSTRACT:

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: ? = 1.029, R² = 0.997, standard error (SE) = 0.007; 3T: ? = 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.

SUBMITTER: Goodkin O

PROVIDER: S-EPMC7804983 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Publications

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

Goodkin O O Prados F F Vos S B SB Pemberton H H Collorone S S Hagens M H J MHJ Cardoso M J MJ Yousry T A TA Thornton J S JS Sudre C H CH Barkhof F F

NeuroImage. Clinical 20201225

<h4>Background</h4>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 ...[more]

PMID: 33418171

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Project description:Machine learning classification is an attractive approach to automatically differentiate patients from healthy subjects, and to predict future disease outcomes. A clinically isolated syndrome (CIS) is often the first presentation of multiple sclerosis (MS), but it is difficult at onset to predict who will have a second relapse and hence convert to clinically definite MS. In this study, we thus aimed to distinguish CIS converters from non-converters at onset of a CIS, using recursive feature elimination and weight averaging with support vector machines. We also sought to assess the influence of cohort size and cross-validation methods on the accuracy estimate of the classification. We retrospectively collected 400 patients with CIS from six European MAGNIMS MS centres. Patients underwent brain MRI at onset of a CIS according to local standard-of-care protocols. The diagnosis of clinically definite MS at one-year follow-up was the standard against which the accuracy of the model was tested. For each patient, we derived MRI-based features, such as grey matter probability, white matter lesion load, cortical thickness, and volume of specific cortical and white matter regions. Features with little contribution to the classification model were removed iteratively through an interleaved sample bootstrapping and feature averaging approach. Classification of CIS outcome at one-year follow-up was performed with 2-fold, 5-fold, 10-fold and leave-one-out cross-validation for each centre cohort independently and in all patients together. The estimated classification accuracy across centres ranged from 64.9% to 88.1% using 2-fold cross-validation and from 73% to 92.9% using leave-one-out cross-validation. The classification accuracy estimate was higher in single-centre, smaller data sets than in combinations of data sets, being the lowest when all patients were merged together. Regional MRI features such as WM lesions, grey matter probability in the thalamus and the precuneus or cortical thickness in the cuneus and inferior temporal gyrus predicted the occurrence of a second relapse in patients at onset of a CIS using support vector machines. The increased accuracy estimate of the classification achieved with smaller and single-centre samples may indicate a model bias (overfitting) when data points were limited, but also more homogeneous. We provide an overview of classifier performance from a range of cross-validation schemes to give insight into the variability across schemes. The proposed recursive feature elimination approach with weight averaging can be used both in single- and multi-centre data sets in order to bridge the gap between group-level comparisons and making predictions for individual patients.

Project description:Grey matter atrophy has been shown in primary progressive multiple sclerosis (PPMS), but its association with physical incapacity is unclear. We submitted 19 patients with PPMS to a neurological evaluation and brain magnetic resonance imaging (MRI) with volumetric analysis using FreeSurfer. We found no relation between the Expanded Disability Status Scale or disease duration and the grey matter or white matter structures analysed. Lesion load was negatively correlated with cortical and subcortical grey matter volumes, but not with total white matter volume. We concluded that physical disability in PPMS is not directly related to brain atrophy and that focal inflammatory white matter lesions may contribute to progressive neuronal degeneration.Primary progressive multiple sclerosis (PPMS) is characterized by chronic progression since onset, with predominant involvement of the spinal cord and prominent neurodegeneration. Grey matter atrophy has been shown in patients with PPMS, but its association with clinical incapacity is uncertain. We investigated the relationship between regional brain atrophy and physical disability in patients with PPMS.Patients with an established diagnosis of PPMS underwent a neurological evaluation followed by brain MRI at 1.5 T. Volumetric analysis was performed with FreeSurfer software, and evaluated the neocortex, total white matter, total subcortical grey matter, putamen, caudate, globus pallidus, thalamus, hippocampus, brainstem, corpus callosum and pre-central gyrus volumes. Clinical data obtained included physical disability as measured by the Expanded Disability Status Scale (EDSS).Nineteen patients were included, 14 female (73.7%), mean age of 55.7 (SD 7.6) and mean disease duration of 13.0 years (SD 8.8). Median EDSS score was 6.0 (3.5-8.0). The average T1 lesion load (4.9 cm³, SD 3.4) and T2 load (10.5 cm³, SD 9.9) did not relate to disease duration. There was no significant correlation between EDSS score or disease duration and the cortical grey matter, deep grey matter or white matter structures analysed. Lesion load was negatively correlated with cortical and subcortical grey matter volumes (p < 0.05), but not with total white matter volume.Physical disability in PPMS is not directly related to brain volume loss. Grey matter atrophy correlates with lesion load in patients with PPMS, indicating that focal inflammatory white matter lesions may contribute to progressive neuronal degeneration.

Dataset Information

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

Background

Objectives

Methods

Results

Conclusions

Publications

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

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