Thibaut Couchoux, Tristan Jaouen, Christelle Melodelima-Gonindard, Pierre Baseilhac, Arthur Branchu, Nicolas Arfi, Richard Aziza, Nicolas Barry Delongchamps, Franck Bladou, Flavie Bratan, Serge Brunelle, Pierre Colin, Jean-Michel Correas, François Cornud, Jean-Luc Descotes, Pascal Eschwege, Gaelle Fiard, Bénédicte Guillaume, Rémi Grange, Nicolas Grenier, Hervé Lang, Frédéric Lefèvre, Bernard Malavaud, Clément Marcelin, Paul C Moldovan, Nicolas Mottet, Pierre Mozer, Eric Potiron, Daniel Portalez, Philippe Puech, Raphaele Renard-Penna, Matthieu Roumiguié, Catherine Roy, Marc-Olivier Timsit, Thibault Tricard, Arnauld Villers, Jochen Walz, Sabine Debeer, Adeline Mansuy, Florence Mège-Lechevallier, Myriam Decaussin-Petrucci, Lionel Badet, Marc Colombel, Alain Ruffion, Sébastien Crouzet, Muriel Rabilloud, Rémi Souchon, Olivier Rouvière
BACKGROUND AND OBJECTIVE: Prostate multiparametric magnetic resonance imaging (MRI) shows high sensitivity for International Society of Urological Pathology grade group (GG) ≥2 cancers. Many artificial intelligence algorithms have shown promising results in diagnosing clinically significant prostate cancer on MRI. To assess a region-of-interest-based machine-learning algorithm aimed at characterising GG ≥2 prostate cancer on multiparametric MRI. METHODS: The lesions targeted at biopsy in the MRI-FIRST dataset were retrospectively delineated and assessed using a previously developed algorithm...
March 15, 2024: European Urology Oncology