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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