Abstract #0608

A unsupervised machine learning approach for classification of white matter hyperintensity patterns applied to Systemic Lupus Erythematosus.

Theodor Rumetshofer¹, Francesca Inglese², Jeroen de Bresser², Peter Mannfolk³, Olof Strandberg⁴, Markus Nilsson¹, Itamar Ronen², Andreas Jönsen⁵, Linda Knutsson^6,7, Tom Huizinga⁸, Gerda Steup-Beekman⁸, and Pia Sundgren^1,9,10

¹Clinical Science Lund / Diagnostic Radiology, Lund University, Lund, Sweden, ²Department of Radiology, Leiden University Medical Center, Leiden, Netherlands, ³Department of Medical Imaging and Physiology, Skåne University Hospital, Lund, Sweden, ⁴Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Lund, Sweden, ⁵Department of Rheumatology, Lund University, Skåne University Hospital, Lund, Sweden, ⁶Department of Medical Radiation Physics, Lund University, Lund, Sweden, ⁷Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States, ⁸Department of Rheumatology, Leiden University Medical Center, Leiden, Netherlands, ⁹Department of Clinical Sciences/Centre for Imaging and Function, Skåne University Hospital, Lund, Sweden, ¹⁰Lund University BioImaging Center, Lund University, Lund, Sweden

White Matter Hyperintensities (WMH) are common clinical neuroimaging brain markers. However, WMH in Systemic Lupus Erythematosus (SLE) are non-specific. For this purpose, we developed and unsupervised machine learning approach based on individual WMH distribution to unveil hidden MRI phenotypes. Cluster analysis was performed on a two-site SLE dataset with significant different WMH burden and MRI acquisition protocols. The resulting MRI phenotypes show a clear lesion pattern on distinct WM tracts. This approach reduces the influence of the total WMH burden and MRI acquisition parameters and improves WMH characterization in SLE.

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