Geometric evaluation of a deep learning method for segmentation of urinary OARs on magnetic resonance imaging for prostate cancer radiotherapy.

Fiche publication

Date publication

janvier 2026

Journal

Clinical and translational radiation oncology

Auteurs

Membres identifiés du Cancéropôle Est :
Dr BESSIERES Igor

Tous les auteurs :
Le Guévelou J, Castro M, Texier B, Barateau A, Martin RA, Lafond C, Bessières I, Nunes JC, Crevoisier R, Acosta O

Lien Pubmed

https://www.ncbi.nlm.nih.gov/pubmed/41458146

Résumé

While urinary organs at risk (OARs) such as the intraprostatic urethra and the bladder trigone are increasingly recognized as associated with severe genitourinary toxicity, their delineation in clinical practice is time consuming and probably associated with a large interobserver variability. The aim of this study was to propose a magnetic resonance (MR) deep learning segmentation of urinary OARs for prostate cancer (PCa) radiotherapy (RT), based on a validated atlas.