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

• Activity Number: 26 - Imaging Speed Session
• Type: Contributed
• Date/Time: Sunday, August 8, 2021 : 1:30 PM to 3:20 PM
• Sponsor: Section on Statistics in Imaging
• Author(s): Carolyn Lou* and Pascal Sati and Martina Absinta and Kelly Clark and Jordan D Dworkin and Alessandra M Valcarcel and Matthew K Schindler and Daniel S Reich and Elizabeth M Sweeney and Russell Shinohara
• Companies: University of Pennsylvania and Cedars-Sinai Medical Center and National Institutes of Health and University of Pennsylvania and Columbia University Medical Center and Genentech, Inc. and University of Pennsylvania and National Institutes of Health and Weill Cornell Medicine and University of Pennsylvania
• Keywords: Neuroimaging; Machine Learning; Multiple Sclerosis; MRI

Abstract

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, but manual identification of PRLs is time-consuming. We present a method to automatically detect PRLs on 3T T2*-phase images. T1-weighted, T2-FLAIR, and T2*-phase MRI of the brain were collected at 3T for 19 subjects with MS. Images were then processed with automated lesion segmentation, lesion labelling, and lesion-level radiomic feature extraction. A total of 877 lesions were identified, 118 (13%) of which contained a paramagnetic rim. We fit a random forest classification model on a training set and assessed our ability to classify PRL lesions on a test set. 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 achieved an area under the curve of 0.80 (95% CI [0.67, 0.86]).