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bioRxiv · 10.1101/2020.08.31.276238

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

Lou, C.·Sati, P.·Absinta, M.·Clark, K.·Dworkin, J. D.·Valcarcel, A. M.·Schindler, M. K.·Reich, D. S.·Sweeney, E. M.·Shinohara, R. T.
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Abstract

Background and PurposeThe 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. MethodsT1-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. ResultsThe 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]). ConclusionThis 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. HighlightsO_LIA fully automated method for both the identification and classification of paramagnetic rim lesions is proposed. C_LIO_LIRadiomic features in conjunction with machine learning algorithms can accurately classify paramagnetic rim lesions. C_LIO_LIChallenges for classification are largely driven by heterogeneity between lesions, including equivocal rim signatures and lesion location. C_LI

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Lou, C., Sati, P., Absinta, M., Clark, K., Dworkin, J. D., Valcarcel, A. M., Schindler, M. K., Reich, D. S., Sweeney, E. M., Shinohara, R. T.. 2020-09-02. Fully Automated Detection of Paramagnetic Rims in Multiple Sclerosis Lesions on 3T Susceptibility-Based MR Imaging. https://doi.org/10.1101/2020.08.31.276238

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