TY  - JOUR
AU  - Schrader, Adrian
AU  - Netzer, Nils
AU  - Hielscher, Thomas
AU  - Görtz, Magdalena
AU  - Zhang, Kevin Sun
AU  - Schütz, Viktoria
AU  - Stenzinger, Albrecht
AU  - Hohenfellner, Markus
AU  - Schlemmer, Heinz-Peter
AU  - Bonekamp, David
TI  - Prostate cancer risk assessment and avoidance of prostate biopsies using fully automatic deep learning in prostate MRI: comparison to PI-RADS and integration with clinical data in nomograms.
JO  - European radiology
VL  - 34
SN  - 0938-7994
CY  - Heidelberg
PB  - Springer
M1  - DKFZ-2024-01408
SP  - 7909–7920
PY  - 2024
N1  - #EA:E010#LA:E010# /  Volume 34, pages 7909–7920, (2024)
AB  - Risk calculators (RCs) improve patient selection for prostate biopsy with clinical/demographic information, recently with prostate MRI using the prostate imaging reporting and data system (PI-RADS). Fully-automated deep learning (DL) analyzes MRI data independently, and has been shown to be on par with clinical radiologists, but has yet to be incorporated into RCs. The goal of this study is to re-assess the diagnostic quality of RCs, the impact of replacing PI-RADS with DL predictions, and potential performance gains by adding DL besides PI-RADS.One thousand six hundred twenty-seven consecutive examinations from 2014 to 2021 were included in this retrospective single-center study, including 517 exams withheld for RC testing. Board-certified radiologists assessed PI-RADS during clinical routine, then systematic and MRI/Ultrasound-fusion biopsies provided histopathological ground truth for significant prostate cancer (sPC). nnUNet-based DL ensembles were trained on biparametric MRI predicting the presence of sPC lesions (UNet-probability) and a PI-RADS-analogous five-point scale (UNet-Likert). Previously published RCs were validated as is; with PI-RADS substituted by UNet-Likert (UNet-Likert-substituted RC); and with both UNet-probability and PI-RADS (UNet-probability-extended RC). Together with a newly fitted RC using clinical data, PI-RADS and UNet-probability, existing RCs were compared by receiver-operating characteristics, calibration, and decision-curve analysis.Diagnostic performance remained stable for UNet-Likert-substituted RCs. DL contained complementary diagnostic information to PI-RADS. The newly-fitted RC spared 49
KW  - Deep learning (Other)
KW  - Nomograms (Other)
KW  - Prostatic neoplasms, Multiparametric magnetic resonance imaging (Other)
KW  - Risk assessment (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:38955845
DO  - DOI:10.1007/s00330-024-10818-0
UR  - https://inrepo02.dkfz.de/record/291445
ER  -