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Abstract #1066

Pixel-Wise Multi-Parametric Assessment of Prostate Cancer from Co-registered regions of Pathologically defined Disease.

Chaitanya Kalavagunta 1 , Xiangmin Zhou 2 , Stephen Schmechel 3 , Joseph S Koopmeiners 4 , Christopher A Warlick 5 , Badrinath Konety 5 , and Gregory J Metzger 1

1 Center of Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, United States, 2 Center for Research in Education and Simulation Technologies (CREST), University of Minnesota, Minnesota, United States, 3 Department of Laboratory Medicine and Pathology, University of Minnesota, Minnesota, United States, 4 Division of Biostatistics, School of Public Health, University of Minnesota, Minnesota, United States, 5 Institute for Prostate and Urologic Cancers, University of Minnesota, Minnesota, United States

Our study shows the combined power of the multiparametric MRI parameters in the detection of prostate cancer and their association with grade. These results are highly unique and relevant as 1) Region of interest (ROI) definitions were dictated by registered pathology regions and not by manual interpretations of pathologically identified disease and 2) pixel-wise analysis was performed as opposed to the use of summary statistics from within the ROIs. Performing a pixel-wise analysis allows the apparent non-coincidence of some of the quantitative MR parameters to be investigated. We propose this approach is a more appropriate way to apply predictive models moving forward.

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