TY  - JOUR
AU  - Zhang, Kevin Sun
AU  - Schelb, Patrick
AU  - Netzer, Nils
AU  - Tavakoli, Anoshirwan Andrej
AU  - Keymling, Myriam
AU  - Wehrse, Eckhard
AU  - Hog, Robert
AU  - Rotkopf, Lukas Thomas
AU  - Wennmann, Markus
AU  - Glemser, Philip Alexander
AU  - Thierjung, Heidi
AU  - von Knebel Doeberitz, Nikolaus
AU  - Kleesiek, Jens
AU  - Görtz, Magdalena
AU  - Schütz, Viktoria
AU  - Hielscher, Thomas
AU  - Stenzinger, Albrecht
AU  - Hohenfellner, Markus
AU  - Schlemmer, Heinz-Peter
AU  - Maier-Hein, Klaus
AU  - Bonekamp, David
TI  - Pseudoprospective Paraclinical Interaction of Radiology Residents With a Deep Learning System for Prostate Cancer Detection: Experience, Performance, and Identification of the Need for Intermittent Recalibration.
JO  - Investigative radiology
VL  - 57
IS  - 9
SN  - 0020-9996
CY  - [s.l.]
PB  - Ovid
M1  - DKFZ-2022-00831
SP  - 601-612
PY  - 2022
N1  - #EA:E010#LA:E010# / 2022 Sep 1;57(9):601-612
AB  - The aim of this study was to estimate the prospective utility of a previously retrospectively validated convolutional neural network (CNN) for prostate cancer (PC) detection on prostate magnetic resonance imaging (MRI).The biparametric (T2-weighted and diffusion-weighted) portion of clinical multiparametric prostate MRI from consecutive men included between November 2019 and September 2020 was fully automatically and individually analyzed by a CNN briefly after image acquisition (pseudoprospective design). Radiology residents performed 2 research Prostate Imaging Reporting and Data System (PI-RADS) assessments of the multiparametric dataset independent from clinical reporting (paraclinical design) before and after review of the CNN results and completed a survey. Presence of clinically significant PC was determined by the presence of an International Society of Urological Pathology grade 2 or higher PC on combined targeted and extended systematic transperineal MRI/transrectal ultrasound fusion biopsy. Sensitivities and specificities on a patient and prostate sextant basis were compared using the McNemar test and compared with the receiver operating characteristic (ROC) curve of CNN. Survey results were summarized as absolute counts and percentages.A total of 201 men were included. The CNN achieved an ROC area under the curve of 0.77 on a patient basis. Using PI-RADS ≥3-emulating probability threshold (c3), CNN had a patient-based sensitivity of 81.8
LB  - PUB:(DE-HGF)16
C6  - pmid:35467572
DO  - DOI:10.1097/RLI.0000000000000878
UR  - https://inrepo02.dkfz.de/record/179659
ER  -