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Abstract #3898

Two-stage classifier for detection of high-grade prostate cancer using quantitative MRI and radiomic features

Ethan Leng1, Joseph Koopmeiners2, Lin Zhang2, and Gregory John Metzger1
1Center for Magnetic Resonance Research, Minneapolis, MN, United States, 2School of Public Health, Division of Biostatistics, University of Minnesota, Minneapolis, MN, United States

It is important to not only identify prostate cancer (PCa) when it is present, but also to determine the aggressiveness of PCa. In this work, we developed a novel two-stage classification model for simultaneous detection of PCa on prostate MRI and localization of aggressive, high-grade PCa, using both quantitative MRI and radiomic features. The first-stage classifier was trained to detect cancer on a voxel-wise basis, and achieved an AUC of 0.818. The second-stage classifier was trained to predict the aggressiveness of candidate regions automatically derived from the voxel-wise predictions of the first stage, and achieved an AUC of 0.779.

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