Errata Corrige: The Neuroradiology Journal 25: 17-21, 2012. Multiple Sclerosis Lesions in the Brain: Computer-Assisted Assessment of Lesion Load Dynamics on 3D FLAIR MR Images

The detection and monitoring of brain lesions caused by multiple sclerosis is commonly performed with the use of magnetic resonance imaging. Analysis of a large number of images is a time-consuming challenge to the neuroradiologist, that can be accelerated with the assistance of computer-detection s...

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Published in:The neuroradiology journal 2012-07, Vol.25 (3), p.379-384
Main Authors: Bilello, M, Arkuszewski, M, Nasrallah, I, Wu, X, Erus, G, Krejza, J
Format: Article
Language:English
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Summary:The detection and monitoring of brain lesions caused by multiple sclerosis is commonly performed with the use of magnetic resonance imaging. Analysis of a large number of images is a time-consuming challenge to the neuroradiologist, that can be accelerated with the assistance of computer-detection software. In 98 baseline and follow-up brain magnetic resonance studies from 88 patients with a diagnosis of multiple sclerosis, we employed locally developed lesion-detection software to assess temporal change in the load of brain lesions and compared its results to routine clinical reports. Analyzing the differences between the follow-up study and the baseline study, the software displays the results in the form of a scrollable axial volume, with the changed lesions highlighted in different colors and superimposed on the baseline reference scan. Although disagreements between the software and the clinical readers in the detection of changed lesions were observed only in 12 (12.2%) cases, the difference reached statistical significance (p=0.04). The mean interpretation time with assistance of the software was 2.7±2.2 minutes. We conclude that the performance of the software-assisted interpretation in the analysis of change over time in multiple sclerosis brain lesions is different from the performance of clinical readers, with a possibly shorter assessment time. The software detected more changes from baseline than clinical readers, suggesting a higher sensitivity, which will have to be confirmed on further analysis.
ISSN:1971-4009