MRI-based patient selection for active surveillance in prostate cancer using U-Found: a generalized deep learning model

Abstract Background Current MRI prostate cancer risk assessment methods focus mainly on detecting tumor lesions, ignoring the prostate gland macro-environment which may also impact disease progression. A generalized deep-learning model for prostate may help capture these gland-level characteristics...

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Hlavní autoři:	Noah C. Lowry, Adrian L. Breto, Veronica Wallaengen, Ahmad Algohary, Nicolas Tapia-Stoll, Sandra M. Gaston, Nachiketh S. Prakash, Pedro F. S. Freitas, Oleksandr N. Kryvenko, Patricia Castillo, Joel Saltz, Tahsin Kurc, Chad R. Ritch, Bruno Nahar, Mark L. Gonzalgo, Dipen J. Parekh, Brandon Mahal, Benjamin O. Spieler, Alan Dal Pra, Matthew C. Abramowitz, Alan Pollack, Sanoj Punnen, Radka Stoyanova
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	BMC 2026-01-01
Edice:	Cancer Imaging
Témata:	Deep learning Foundation models Contrastive learning Unsupervised deep learning Prostate cancer Active Surveillance
On-line přístup:	https://doi.org/10.1186/s40644-026-00988-z
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