%0 Journal Article
%A Schelb, Patrick
%A Kohl, Simon
%A Radtke, Jan Philipp
%A Wiesenfarth, Manuel
%A Kickingereder, Philipp
%A Bickelhaupt, Sebastian
%A Kuder, Tristan Anselm
%A Stenzinger, Albrecht
%A Hohenfellner, Markus
%A Schlemmer, Heinz-Peter
%A Maier-Hein, Klaus
%A Bonekamp, David
%T Classification of Cancer at Prostate MRI: Deep Learning versus Clinical PI-RADS Assessment.
%J Radiology
%V 293
%N 3
%@ 1527-1315
%C Oak Brook, Ill.
%I Soc.
%M DKFZ-2019-02344
%P 607-617
%D 2019
%Z 2019 Dec;293(3):607-617
%X Background Men suspected of having clinically significant prostate cancer (sPC) increasingly undergo prostate MRI. The potential of deep learning to provide diagnostic support for human interpretation requires further evaluation. Purpose To compare the performance of clinical assessment to a deep learning system optimized for segmentation trained with T2-weighted and diffusion MRI in the task of detection and segmentation of lesions suspicious for sPC. Materials and Methods In this retrospective study, T2-weighted and diffusion prostate MRI sequences from consecutive men examined with a single 3.0-T MRI system between 2015 and 2016 were manually segmented. Ground truth was provided by combined targeted and extended systematic MRI-transrectal US fusion biopsy, with sPC defined as International Society of Urological Pathology Gleason grade group greater than or equal to 2. By using split-sample validation, U-Net was internally validated on the training set (80
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:31592731
%R 10.1148/radiol.2019190938
%U https://inrepo02.dkfz.de/record/147218