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Abstract #4376

Does Machine Learning, As An Independent Arbitrator Of MR Contrast-Ranking In Prostate Cancer Exams, Agree With PI-RADS version 2?

Steve Patterson1, Peter Lee2, Chris V. Bowen3,4,5, Jennifer Merrimen6, Cheng Wang6, Steven D. Beyea3,4,5, and Sharon E. Clarke3,4,5

1Steve Patterson, Nova Scotia Health Research Foundation, Halifax, NS, Canada, 2Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada, 3Diagnostic Radiology, Dalhousie University, Halifax, NS, Canada, 4Biomedical Translational Imaging Centre, Nova Scotia Health Authority, Halifax, NS, Canada, 5Physics and Atmospheric Science, Dalhousie University, Halifax, NS, Canada, 6Anatomical Pathology, Dalhousie University, Halifax, NS, Canada

We show that a simple machine learning algorithm validated most, but not all, aspects of the Prostate Imaging Reporting and Data System (PI-RADS) version 2 formalism derived exclusively from clinical perspectives. Specifically, the value of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) sequences in the peripheral zone was confirmed. In contradistinction to PI-RADS, DWI was found to be more valuable in the transition zone than T2 weighted imaging; however, a T2 texture feature afforded a small but significant increase in classifier accuracy in this zone.

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