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 Sundgren^1,5

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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