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Abstract #3831

Detecting Glioblastoma invasion using Multi-parametric MRI and Quantitative Assessment with in-plane Histology

Haitham Farooq Al-Mubarak1, Antoine Vallatos1, Jim Birch1, Lindsay Gallagher1, Joanna Birch2, Lesley Glmour2, John Foster3, William Holmes1, and Anthony Chalmers2

1Glasgow Experimental MRI center, University of Glasgow, Glasgow, United Kingdom, 2Institute of Cancer Sciences, University of Glasgow, Glasgow, United Kingdom, 3MRI and clinical physics, Glasgow, United Kingdom

We evaluate the ability of a range of MRI techniques to probe glioblastoma invasion in a mouse model by comparison with in-plane stacked histology, enabling a direct voxel-to-voxel comparison between MRI and histology (HLA stain). We used the G7 mouse model which exhibits highly invasive tumour margins and scanned using T1 weighted, T2 weighted, T2map, Diffusion Tensor Imagining (DTI) and Perfusion Weighting Imaging (PWI). Registration of MRI datasets with stacked in-plane histology allows direct quantitative validation using DICE and ROC analysis, showing that PWI gave the best indication of tumour cell invasion.

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