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Abstract #1846

mpMRI-based Tumor Probability Maps for Guidance of Targeted Prostate Biopsies

Gabriel Addio Nketiah1, Nienke Bakx1,2, Kirsten Margrete Selnæs 1,3, Adrian Lazaro Breto4, Radka Stoyanova 4,5, Mattijs Elschot 1, and Tone Frost Bathen1

1Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway, 2Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands, 3Clinic of Radiology and Nuclear Medicine, St. Olavs University Hospital, Trondheim, Norway, 4Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL, United States, 5Department of Radiation Oncology, University of Miami, Miami, FL, United States

Despite the improvement offered by the integration of multiparametric magnetic resonance imaging (mpMRI) in biopsy acquisition for prostate cancer diagnosis, the number of negative biopsies remains high with increasing risk of post-biopsy infection and complications. We evaluated the utility of machine learning-based tumor probability maps computed from pre-biopsy mpMR images for predicting and visualizing potential biopsy targets representing clinically significant cancer foci. The median [range] AUC, sensitivity and specificity of the classifier were 0.87 [0.82–0.92], 0.77 [0.71-0.83] and 0.82 [0.76-0.86], respectively. This approach has a potential to reduce the number of biopsy cores, and thus the risk of post-biopsy infection/complications.

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