Abstract #1885

Prediction of active Multiple Sclerosis lesions through use of logistic regression classifier and first-order features

Vivian S. Nguyen^1,2, Adam J. Hasse³, Emily Tao¹, Jihye Jang⁴, Adil Javed⁵, Timothy J. Carroll³, and Keigo Kawaji^1,2

¹Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, United States, ²Medicine - Cardiology, University of Chicago Medical Center, Chicago, IL, United States, ³Radiology, University of Chicago Medical Center, Chicago, IL, United States, ⁴Philips Healthcare, Gainesville, FL, United States, ⁵Neurology, University of Chicago Medical Center, Chicago, IL, United States

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Multiple SclerosisMultiple Sclerosis is a neuroinflammatory disease in which the immune system attacks nerve fibers and myelin sheaths, leading to the formation of lesions through white matter. Gadolinium-enhanced MRI is used to diagnose and track the progression of MS. Active MS lesions enhance with gadolinium, but there is an interest in prediction of lesion enhancement based on lesion features. In this study, we examined first-order features derived from T1w pre-contrast MS lesions acquired on multiple 3T imagers at a single center to train a logistic regression classifier to classify lesions as active or inactive.

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