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Abstract #4963

Spatially-sensitive model for detection of prostate cancer on multiparametric MRI

Ethan Leng1, Jin Jin2, Lin Zhang2, Christopher A. Warlick3, Benjamin Spilseth4, Joseph S. Koopmeiners2, and Gregory J. Metzger1

1Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States, 2Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, 3Department of Urologic Surgery, Institute of Prostate and Urologic Cancers, University of Minnesota, Minneapolis, MN, United States, 4Department of Radiology, University of Minnesota, Minneapolis, MN, United States

A novel predictive model of prostate cancer (PCa) on multiparametric MRI was developed that takes into account the spatial distribution of PCa within the prostate and the spatially-autocorrelated nature of mpMRI data. The performance of the proposed model was compared to the LASSO-based model we previously described on 34 PCa cases using both voxel-wise metrics (AUC) and slice-wise metrics ($$$s_s$$$) we recently developed. The proposed model achieved superior predictive performance both in terms of AUC (0.81 vs 0.77) and $$$s_s$$$ (0.45 vs. 0.35) over the 34 cases, with significant improvements for the majority of cases.

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