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Abstract #0854

Explaining variation in DTI parameters with meningioma microscopy: A comparison between a neural network and an image-feature-based approach

Jan Brabec1, Magda Friedjungová2, Daniel Vašata2, Elisabet Englund3, Linda Knutsson1,4,5, Filip Szczepankiewicz6, Pia C Sundgren6, and Markus Nilsson6
1Medical Radiation Physics, Lund University, Lund, Sweden, 2Department of Applied Mathematics, Faculty of Information Technology, Czech Technical University, Prague, Czech Republic, 3Division of Oncology and Pathology, Department of Clinical Sciences, Lund University, Skåne University Hospital, Lund, Sweden, 4Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States, 5F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States, 6Diagnostic Radiology, Clinical Sciences Lund, Lund University, Lund, Sweden


Diffusion MRI reflects tissue microstructure and mean diffusivity (MD) and fractional anisotropy (FA) are often readily interpreted in terms of cellularity and cell anisotropy, respectively. Here, we investigated to which degree histological features accounts for their variations in fixed sections of meningioma tumors. Histological slices were quantified in terms of cellularity and cell anisotropy, or by a neural network. Results show that in some cases the majority of the variation can be attributed to cellularity whereas in others none. Similarly, in some samples only a minority of the variability is attributable to the variability in FA explained by neural network.

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