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Clinically isolated syndromes suggestive of multiple sclerosis: an optical coherence tomography study.

ABSTRACT: BACKGROUND: Optical coherence tomography (OCT) is a simple, high-resolution technique to quantify the thickness of retinal nerve fiber layer (RNFL), which provides an indirect measurement of axonal damage in multiple sclerosis (MS). This study aimed to evaluate RNFL thickness in patients at presentation with clinically isolated syndromes (CIS) suggestive of MS. METHODOLOGY: This was a cross-sectional study. Twenty-four patients with CIS suggestive of MS (8 optic neuritis [ON], 6 spinal cord syndromes, 5 brainstem symptoms and 5 with sensory and other syndromes) were prospectively studied. The main outcome evaluated was RNFL thickness at CIS onset. Secondary objectives were to study the relationship between RNFL thickness and MRI criteria for disease dissemination in space (DIS) as well as the presence of oligoclonal bands in the cerebrospinal fluid. PRINCIPAL FINDINGS: Thirteen patients had decreased RNFL thickness in at least one quadrant. Mean RNFL thickness was 101.67±10.72 µm in retrobulbar ON eyes and 96.93±10.54 in unaffected eyes. Three of the 6 patients with myelitis had at least one abnormal quadrant in one of the two eyes. Eight CIS patients fulfilled DIS MRI criteria. The presence of at least one quadrant of an optic nerve with a RNFL thickness at a P<5% cut-off value had a sensitivity of 75% and a specificity of 56% for predicting DIS MRI. CONCLUSIONS: The findings from this study show that axonal damage measured by OCT is present in any type of CIS; even in myelitis forms, not only in ON as seen up to now. OCT can detect axonal damage in very early stages of disease and seems to have high sensitivity and moderate specificity for predicting DIS MRI. Studies with prospective long-term follow-up would be needed to establish the prognostic value of baseline OCT findings.

SUBMITTER: Oreja-Guevara C

PROVIDER: S-EPMC3309007 | biostudies-other | 2012

REPOSITORIES: biostudies-other

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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.

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Clinically isolated syndromes suggestive of multiple sclerosis: an optical coherence tomography study.

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