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Abstract #3339

Accurate quantification of chronic lesion expansion in MS: Introducing the GRADE algorithm

Maartje Alting1,2, Samuel Klistorner3, Bella Zin1,4, Dongang Wang1,4, Heidi Beadnall1,5, Alexander Klistorner3, Michael Barnett1,4,5, and Chenyu Wang1,4
1Brain and Mind Centre, University of Sydney, Sydney, Australia, 2University of Amsterdam, Amsterdam, Netherlands, 3Save Sight Institute, University of Sydney, Sydney, Australia, 4Sydney Neuroimaging Analysis Centre, Sydney, Australia, 5Department of Neurology, Royal Prince Alfred Hospital, Camperdown, Australia

Synopsis

Keywords: Data Processing, Quantitative Imaging, Chronic Expansion, Neuroinflammation

Motivation: Persistent disability progression despite successful relapse suppression presents a critical challenge in MS management. Chronic expansion of existing lesions is likely a key driver and therefore a potential biomarker of disease progression and target for novel DMTs. Currently, no automated MR pipeline exists that can accurately quantify chronic expansion.

Goal(s): To develop and validate an algorithm that accurately differentiates and quantifies chronic expansion from new confluent lesion volume within expanding MS lesions.

Approach: Longitudinal FLAIR lesion masks will be analyzed using our GRadient-Adjusted Decay Estimation (GRADE) algorithm, assigning voxel-wise chronic expansion probabilities. Validation against manual ground truth with 100+ patients.

Impact: Despite its signification role in MS progression, accurate quantification of chronic lesion expansion has remained elusive. This newly designed GRADE algorithm will be validated for routine clinical quantification of chronic expansion, potentially revolutionizing patient monitoring and development of targeted therapies.

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Keywords