Abstract #2814

Longitudinal automated assessment of paramagnetic rim lesions in multiple sclerosis using RimNet

Maxence Wynen¹, Francesco La Rosa^2,3,4, Amina Sellimi⁵, Germán Barquero^2,3,4, Gaetano Perrotta⁶, Valentina Lolli⁷, Vincent Van Pesch⁵, Cristina Granziera^8,9, Tobias Kober¹⁰, Pascal Sati^11,12, Benoît Macq¹³, Daniel S. Reich¹¹, Martina Absinta^11,14, Meritxell Bach Cuadra^2,3,4, and Pietro Maggi^5,15

¹Ecole Polytechnique de Louvain, Université Catholique de Louvain, Louvain-la-Neuve, Belgium, ²Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, ³Medical Image Analysis Laboratory, Center for Biomedical Imaging (CIBM), University of Lausanne, Lausanne, Switzerland, ⁴Radiology Department, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, ⁵Department of Neurology, Cliniques Universitaires Saint-Luc, Université Catholique de Louvain, Brussels, Belgium, ⁶Department of Neurology, Erasme University Hospital, Université Libre de Bruxelles, Brussels, Belgium, ⁷Department of Radiology, Erasme University Hospital, Université Libre de Bruxelles, Brussels, Belgium, ⁸Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland, ⁹Translational Imaging in Neurology (ThINk) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland, ¹⁰Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland, ¹¹Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States, ¹²Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, United States, ¹³ICTEAM Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium, ¹⁴Department of Neurology, Johns Hopkins University, Baltimore, MD, United States, ¹⁵Department of Neurology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland

The automatic assessment of paramagnetic rim lesions in multiple sclerosis is important, and a deep learning-based algorithm called RimNet has recently been proposed. This work evaluates the generalizability of RimNet and its longitudinal performance on MRI data acquired at different clinical centers. We found that RimNet’s performance was nearly as good on totally unseen data as in the original paper (receiver-operating-characteristic area-under-the-curve (AUC) 0.88 vs. 0.94, precision-recall AUC 0.69 vs. 0.70), and it made consistent predictions on longitudinal data (binary consistency 82%, probability consistency 93%).

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