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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

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 w...

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Autori principali: Schrader, Adrian (Autore) , Netzer, Nils (Autore) , Hielscher, Thomas (Autore) , Görtz, Magdalena (Autore) , Zhang, Kevin Sun (Autore) , Schütz, Viktoria (Autore) , Stenzinger, Albrecht (Autore) , Hohenfellner, Markus (Autore) , Schlemmer, Heinz-Peter (Autore) , Bonekamp, David (Autore)
Natura: Article (Journal)
Lingua:inglese
Pubblicazione: 02 July 2024
In: European radiology
Year: 2024, Volume: 34, Fascicolo: 12, Pages: 7909-7920
ISSN:1432-1084
DOI:10.1007/s00330-024-10818-0
Accesso online:Verlag, kostenfrei, Volltext: https://doi.org/10.1007/s00330-024-10818-0
Testo
Note sull'autore:Adrian Schrader, Nils Netzer, Thomas Hielscher, Magdalena Görtz, Kevin Sun Zhang, Viktoria Schütz, Albrecht Stenzinger, Markus Hohenfellner, Heinz-Peter Schlemmer and David Bonekamp
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Riassunto: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.
Descrizione del documento:Gesehen am 16.12.2024
Descrizione fisica:Online Resource
ISSN:1432-1084
DOI:10.1007/s00330-024-10818-0

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