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Original Contribution |

Reliability of Classifying Multiple Sclerosis Disease Activity Using Magnetic Resonance Imaging in a Multiple Sclerosis Clinic

Ebru Erbayat Altay, MD; Elizabeth Fisher, PhD; Stephen E. Jones, MD, PhD; Claire Hara-Cleaver, MSN; Jar-Chi Lee, MS; Richard A. Rudick, MD
JAMA Neurol. 2013;70(3):338-344. doi:10.1001/2013.jamaneurol.211.
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Published online

Objective  To assess the reliability of new magnetic resonance imaging (MRI) lesion counts by clinicians in a multiple sclerosis specialty clinic.

Design  An observational study.

Setting  A multiple sclerosis specialty clinic.

Patients  Eighty-five patients with multiple sclerosis participating in a National Institutes of Health–supported longitudinal study were included.

Intervention  Each patient had a brain MRI scan at entry and 6 months later using a standardized protocol.

Main Outcome Measures  The number of new T2 lesions, newly enlarging T2 lesions, and gadolinium-enhancing lesions were measured on the 6-month MRI using a computer-based image analysis program for the original study. For this study, images were reanalyzed by an expert neuroradiologist and 3 clinician raters. The neuroradiologist evaluated the original image pairs; the clinicians evaluated image pairs that were modified to simulate clinical practice. New lesion counts were compared across raters, as was classification of patients as MRI active or inactive.

Results  Agreement on lesion counts was highest for gadolinium-enhancing lesions, intermediate for new T2 lesions, and poor for enlarging T2 lesions. In 18% to 25% of the cases, MRI activity was classified differently by the clinician raters compared with the neuroradiologist or computer program. Variability among the clinical raters for estimates of new T2 lesions was affected most strongly by the image modifications that simulated low image quality and different head position.

Conclusions  Between-rater variability in new T2 lesion counts may be reduced by improved standardization of image acquisitions, but this approach may not be practical in most clinical environments. Ultimately, more reliable, robust, and accessible image analysis methods are needed for accurate multiple sclerosis disease-modifying drug monitoring and decision making in the routine clinic setting.

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Grahic Jump Location

Figure 1. Examples of image pairs including original and modified images for 3 different subjects. Pairs that were compared are outlined in red. For patient 1 (A-C), the baseline image (m00) was modified by adding noise (B). For Patient 2 (D-F), the baseline image was modified by resampling the data to have a slice thickness of 3 mm instead of 5 mm (E). For Patient 3 (G-I), the month 6 image (m06) was modified by applying a rigid transformation to rotate the volume (H).

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Grahic Jump Location

Figure 2. Percentage of discrepant counts for new T2 lesions and gadolinium-enhancing (Gad) lesions and percentage of discrepant classification for magnetic resonance imaging activity status.

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