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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| Hauptverfasser: | , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
02 July 2024
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| In: |
European radiology
Year: 2024, Jahrgang: 34, Heft: 12, Pages: 7909-7920 |
| ISSN: | 1432-1084 |
| DOI: | 10.1007/s00330-024-10818-0 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1007/s00330-024-10818-0
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| Verfasserangaben: | 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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| 520 | |a 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. | ||
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