Abstract #1883

Biparametric MRI classification model for prostate cancer detection using a combination of prediction maps

Mohammed R. S. Sunoqrot^1,2, Rebecca Segre^1,3, Gabriel A. Nketiah^1,2, Alexandros Patsanis¹, Tone F. Bathen^1,2, and Mattijs Elschot^1,2

¹Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway, ²Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway, ³Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Torino, Italy

Synopsis

Keywords: Machine Learning/Artificial Intelligence, ProstateBiparametric MRI (bpMRI) is a valuable tool for the diagnosis of prostate cancer (PCa). Computer-aided detection and diagnosis (CAD) systems have the potential to improve the robustness and efficiency of PCa detection) compared with conventional radiological reading. This work explores the combination of the results of two PCa CAD systems as input to a new classification model. We show that this is promising approach that can potentially improve the final detection of PCa.

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