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Fully Automated Detection of Paramagnetic Rims in Multiple Sclerosis Lesions on 3T Susceptibility-Based MR Imaging

View ORCID ProfileCarolyn Lou, View ORCID ProfilePascal Sati, View ORCID ProfileMartina Absinta, Kelly Clark, View ORCID ProfileJordan D. Dworkin, Alessandra M. Valcarcel, Matthew K. Schindler, Daniel S. Reich, Elizabeth M. Sweeney, Russell T. Shinohara
doi: https://doi.org/10.1101/2020.08.31.276238
Carolyn Lou
1Penn Statistics in Imaging and Visualization Endeavor (PennSIVE) Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
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  • ORCID record for Carolyn Lou
Pascal Sati
2Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH), Bethesda, Maryland, USA
3Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, California, USA
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Martina Absinta
2Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH), Bethesda, Maryland, USA
4Department of Neurology, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
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Kelly Clark
1Penn Statistics in Imaging and Visualization Endeavor (PennSIVE) Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
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Jordan D. Dworkin
5Department of Psychiatry, Columbia University Medical Center, New York, New York, USA
6New York State Psychiatric Institute, New York, New York, USA
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Alessandra M. Valcarcel
1Penn Statistics in Imaging and Visualization Endeavor (PennSIVE) Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
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Matthew K. Schindler
7Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
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Daniel S. Reich
2Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH), Bethesda, Maryland, USA
4Department of Neurology, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
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Elizabeth M. Sweeney
8Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, USA
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  • For correspondence: ems4003@med.cornell.edu rshi@pennmedicine.upenn.edu
Russell T. Shinohara
1Penn Statistics in Imaging and Visualization Endeavor (PennSIVE) Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
9Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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  • For correspondence: ems4003@med.cornell.edu rshi@pennmedicine.upenn.edu
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Abstract

Background and Purpose The presence of a paramagnetic rim around a white matter lesion has recently been shown to be a hallmark of a particular pathological type of multiple sclerosis (MS) lesion. Increased prevalence of these paramagnetic rim lesions (PRLs) is associated with a more severe disease course in MS. The identification of these lesions is time-consuming to perform manually. We present a method to automatically detect PRLs on 3T T2*-phase images.

Methods T1-weighted, T2-FLAIR, and T2*-phase MRI of the brain were collected at 3T for 19 subjects with MS. The images were then processed with lesion segmentation, lesion center detection, lesion labelling, and lesion-level radiomic feature extraction. A total of 877 lesions were identified, 118 (13%) of which contained a paramagnetic rim. We divided our data into a training set (15 patients, 673 lesions) and a testing set (4 patients, 204 lesions). We fit a random forest classification model on the training set and assessed our ability to classify lesions as PRL on the test set.

Results The number of PRLs per subject identified via our automated lesion labelling method was highly correlated with the gold standard count of PRLs per subject, r = 0.91 (95% CI [0.79, 0.97]). The classification algorithm using radiomic features can classify a lesion as PRL or not with an area under the curve of 0.80 (95% CI [0.67, 0.86]).

Conclusion This study develops a fully automated technique for the detection of paramagnetic rim lesions using standard T1 and FLAIR sequences and a T2*phase sequence obtained on 3T MR images.

Highlights

  • A fully automated method for both the identification and classification of paramagnetic rim lesions is proposed.

  • Radiomic features in conjunction with machine learning algorithms can accurately classify paramagnetic rim lesions.

  • Challenges for classification are largely driven by heterogeneity between lesions, including equivocal rim signatures and lesion location.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted September 02, 2020.
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Fully Automated Detection of Paramagnetic Rims in Multiple Sclerosis Lesions on 3T Susceptibility-Based MR Imaging
Carolyn Lou, Pascal Sati, Martina Absinta, Kelly Clark, Jordan D. Dworkin, Alessandra M. Valcarcel, Matthew K. Schindler, Daniel S. Reich, Elizabeth M. Sweeney, Russell T. Shinohara
bioRxiv 2020.08.31.276238; doi: https://doi.org/10.1101/2020.08.31.276238
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Fully Automated Detection of Paramagnetic Rims in Multiple Sclerosis Lesions on 3T Susceptibility-Based MR Imaging
Carolyn Lou, Pascal Sati, Martina Absinta, Kelly Clark, Jordan D. Dworkin, Alessandra M. Valcarcel, Matthew K. Schindler, Daniel S. Reich, Elizabeth M. Sweeney, Russell T. Shinohara
bioRxiv 2020.08.31.276238; doi: https://doi.org/10.1101/2020.08.31.276238

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