TY  - JOUR
AU  - Netzer, Nils
AU  - Eith, Carolin
AU  - Bethge, Oliver
AU  - Hielscher, Thomas
AU  - Schwab, Constantin
AU  - Stenzinger, Albrecht
AU  - Gnirs, Regula
AU  - Schlemmer, Heinz-Peter
AU  - Maier-Hein, Klaus H
AU  - Schimmöller, Lars
AU  - Bonekamp, David
TI  - Application of a validated prostate MRI deep learning system to independent same-vendor multi-institutional data: demonstration of transferability.
JO  - European radiology
VL  - 33
IS  - 11
SN  - 0938-7994
CY  - Heidelberg
PB  - Springer
M1  - DKFZ-2023-01525
SP  - 7463-7476
PY  - 2023
N1  - #EA:E010#LA:E010# / 2023 Nov;33(11):7463-7476
AB  - To evaluate a fully automatic deep learning system to detect and segment clinically significant prostate cancer (csPCa) on same-vendor prostate MRI from two different institutions not contributing to training of the system.In this retrospective study, a previously bi-institutionally validated deep learning system (UNETM) was applied to bi-parametric prostate MRI data from one external institution (A), a PI-RADS distribution-matched internal cohort (B), and a csPCa stratified subset of single-institution external public challenge data (C). csPCa was defined as ISUP Grade Group ≥ 2 determined from combined targeted and extended systematic MRI/transrectal US-fusion biopsy. Performance of UNETM was evaluated by comparing ROC AUC and specificity at typical PI-RADS sensitivity levels. Lesion-level analysis between UNETM segmentations and radiologist-delineated segmentations was performed using Dice coefficient, free-response operating characteristic (FROC), and weighted alternative (waFROC). The influence of using different diffusion sequences was analyzed in cohort A.In 250/250/140 exams in cohorts A/B/C, differences in ROC AUC were insignificant with 0.80 (95
KW  - Deep learning (Other)
KW  - Magnetic resonance imaging (Other)
KW  - Prostatic neoplasms (Other)
KW  - Validation study (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:37507610
DO  - DOI:10.1007/s00330-023-09882-9
UR  - https://inrepo02.dkfz.de/record/277857
ER  -