Using Radiologic Markers to Predict Chronic Active Lesion Evolution in MS: Matthew Schindler, MD, PhD


The assistant professor of neurology at the University of Pennsylvania discussed his abstract at ACTRIMS Forum using 7T MRI features of newly developed MS lesions to predict chronic active lesions. [WATCH TIME: 4 minutes]

WATCH TIME: 4 minutes

"Much of the work is focused on sort of the after of when a CAL or PRL [paramagnetic rim lesion] has formed and the associations. But we don’t know about which lesions will actually form into them. That’s important because, if we’re going to start to try to target treatments against this type of lesion or patient, we want to be able to predict who’s going to develop them."

Chronic active lesions (CALs), a subset of focal multiple sclerosis (MS) lesions, have pathologically been associated with smoldering inflammatory demyelination at the edge, remyelination failure, and axonal degeneration. To better understand which lesions will ultimately become CALs, senior author Matthew Schindler, MD, PhD, and his colleagues developed a predictive model using noncontrast 7T MRI, T2 weighed multiecho gradient echo sequence, quantitative qT1 maps, and thalamic segmentations.

His study, presented at the Americas Committee for Treatment and Research in Multiple Sclerosis (ACTRIMS) Forum 2022, February 24-26, in West Palm Beach, Florida, included 14 patients with 60 incident lesions that were assessed at baseline, 3 months, and 6 months. At the 6-month time point, 12 incident lesions had a hypointense rim on phase contrast and were thus characterized as a CAL. For lesions that evolved into CALs, investigators observed a significantly different baseline qT1 compared to those that did not (P = .03).

NeurologyLive® sat down with Schindler at ACTRIMS Forum 2022 to get his thoughts on the clinical importance of this type of research and the findings he observed. Schindler, assistant professor of neurology, University of Pennsylvania, also provided insight on how he constructed his predictive model and whether this type of approach has potential to be paired with artificial intelligence.

For more coverage of ACTRIMS Forum 2022, click here.

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