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Abstract #4207

Hierarchical Versus Voxel-Wise Models for DCE-MRI in a Head and Neck Study with Lapatinib

Brandon Whitcher1, Volker J. Schmid2, David Collins3, Matthew Orton3, Dow-Mu Koh3,4, Josep M. del Campo5, Kevin Harrington6, Iman A. El-Hariry7

1Clinical Imaging Centre, GlaxoSmithKline, London, UK; 2Institute of Biomedical Engineering, Imperial College, London, UK; 3CRUK Clinical Magnetic Resonance Research Group, Institute of Cancer Research, Sutton, Surrey, UK; 4Department of Radiology, Royal Marsden Hospital, Sutton, Surrey, UK; 5Department of Medical Oncology, Vall d'Hebron Hospital, Barcelona, Spain; 6Targeted Therapy Team, Institute of Cancer Research, London, UK; 7Oncology Medicine Development Centre, GlaxoSmithKline, London, UK

We compare the results from a quantitative analysis of DCE-MRI data using summary statistics from a non-linear regression analysis, using both optimization and Bayesian methods, and the output from a Bayesian hierarchical model in a phase II study of lapatinib in patients with locally advanced squamous cell carcinoma of the head and neck.