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Elizabeth Sweeney, PhD, Assistant Professor of Biostatistics, University of Pennsylvania
December 14, 2022 @ 4:00 pm - 5:00 pm
Biomedical Data Science Seminar Series, BRB Gaulton Auditorium
Title: Treatment Effect Analysis with Quantitative Susceptibility Maps in Multiple Sclerosis Lesions
This week hosted by PennSIVE
Multiple sclerosis (MS) is an inflammatory disease of the central nervous system characterized by lesions in the brain and spinal cord. Magnetic resonance images (MRI) are sensitive to these lesions. A particular type of lesion, called a chronic active lesion, is characterized by a hyperintense rim of iron-enriched, activated microglia and macrophages, and has been linked to greater tissue damage. An MRI technique called quantitative susceptibility mapping (QSM) provides efficient in vivo quantification of susceptibility changes related to iron deposition and identifies these chronic active lesions, called QSM rim positive (rim+) lesions. QSM rim+ MS lesions and their longitudinal behavior have the potential to serve as a biomarker of chronic inflammation and to be utilized to monitor disease progression and evaluate disease-modifying therapies in MS. In this talk, I will discuss the challenges of estimating treatment effects using the longitudinal behavior of QSM rim+ lesions. I will compare two disease-modifying treatments, Tecfidera® and Copaxone®, using linear mixed effects regression models with inverse probability of censor weighting. One of the major limitations of this model is that the inflammatory stage or age of the lesion is unknown, causing misregistration of the lesion-level data. I will also introduce methodology to estimate the age of MS lesions using both cross sectional and longitudinal MRI information. The cross sectional age estimation method uses lesion-level MRI radiomic features with machine learning models. The longitudinal method employs curve registration techniques from functional data analysis.
Dr. Sweeney’s methodological research centers on the analysis of structural magnetic resonance imaging (MRI), with a particular focus on the disease of multiple sclerosis (MS). She has worked on image segmentation, image normalization, cross-sectional and longitudinal modeling, as well as software development in this area. Her recent work has focused on analysis of the novel quantitative MRI sequence of quantitative susceptibility mapping, applied to MS. Her collaborative work spans many areas in neurology and radiology.