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Abstract #2693

Are Two Samples of Parametric Maps Statistically Different? Indexed Distribution Analysis (IDA) Can Provide Better Inferences Than Conventional and Histogram Analysis Methods

Chris J. Rose1, James P. O'Connor1, 2, Tim F. Cootes1, Chris J. Taylor1, Gordon C. Jayson3, Geoff J. M. Parker1, John C. Waterton1, 4

1Center for Imaging Sciences, Manchester Academic Health Science Center, The University of Manchester, Manchester, Greater Manchester, United Kingdom; 2Department of Radiology, The Christie, Manchester, Greater Manchester, United Kingdom; 3Department of Medical Oncology, The Christie, Manchester, Greater Manchester, United Kingdom; 4AstraZeneca, Alderley Park, Cheshire, United Kingdom

MRI can spatially map biophysical and physiological parameters across organs and tumors, and is often used in natural history studies and preclinical and clinical trials of novel drugs. In such research, there is a need to draw statistical inferences about the population, based on a sample. It is common to perform hypothesis tests by taking averages over each structure, but spatially heterogeneous differences can attenuate statistical power. We compare the recently-proposed indexed distribution analysis (IDA) to the conventional and histogram analysis approaches using well-controlled simulated and clinical data. IDA has several advantages over conventional and histogram analysis methods.