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Abstract #1303

Characterisation of white matter lesion patterns in Systemic Lupus Erythematosus by an unsupervised machine learning approach.

Theodor Rumetshofer1, Tor Olof Strandberg2, Peter Mannfolk3, Andreas Jönsen4, Markus Nilsson1, Johan Mårtensson1, and Pia Maly Sundgren1,5
1Department of Clinical Sciences Lund/Diagnostic Radiology, Lund University, Lund, Sweden, 2Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Lund, Sweden, 3Clinical Imaging and Physiology, Skåne University Hospital, Lund, Sweden, 4Department of Reumatology, Skåne University Hospital, Lund, Sweden, 5Department of Clinical Sciences/Centre for Imaging and Function, Skåne University Hospital, Lund, Sweden

Evaluating white matter hyperintensities (WMHs) in neuropsychiatric systemic lupus erythematosus (NPSLE) is a challenging task. Multimodal MRI images in combination with unsupervised machine characterization can provide a powerful tool to investigate the spatial WHM distribution of relevant phenotypes. Automatically segmented WMH maps were spatially allocated to a white matter tract atlas. Cluster analysis was applied on this tract-wise lesion-load map to obtain subtypes with a distinct WMH damage profile. This approach on microstructural changes could help to identify specific progression pattern which may improve the accuracy of NPSLE classification.

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