A Systematic Review of the Diagnostic Accuracy of Deep Learning Models for the Automatic Detection, Localization, and Characterization of Clinically Significant Prostate Cancer on Magnetic Resonance Imaging.

Fiche publication

Date publication

novembre 2024

Journal

European urology oncology

Auteurs

Membres identifiés du Cancéropôle Est :
Dr MOLIERE Sébastien

Tous les auteurs :
Molière S, Hamzaoui D, Ploussard G, Mathieu R, Fiard G, Baboudjian M, Granger B, Roupret M, Delingette H, Renard-Penna R

Lien Pubmed

https://www.ncbi.nlm.nih.gov/pubmed/39547898

Résumé

Magnetic resonance imaging (MRI) plays a critical role in prostate cancer diagnosis, but is limited by variability in interpretation and diagnostic accuracy. This systematic review evaluates the current state of deep learning (DL) models in enhancing the automatic detection, localization, and characterization of clinically significant prostate cancer (csPCa) on MRI.