ESTRO 2025 - Abstract Book

S2482

Physics - Autosegmentation

ESTRO 2025

Keywords: AI, autocontouring, inter-observer variability

References: 1 Chatterjee D, Kanhere A, Doo FX, Zhao J, Chan A, Welsh A, Kulkarni P, Trang A, Parekh VS, Yi PH. Children Are Not Small Adults: Addressing Limited Generalizability of an Adult Deep Learning CT Organ Segmentation Model to the Pediatric Population. J Imaging Inform Med. 2024 Sep 19. https://doi.org/10.1007/s10278-024-01273-w. 2 Ding M, Maspero M, Littooij AS, van Grotel M, Fajardo RD, van Noesel MM, van den Heuvel-Eibrink MM, Janssens GO. Deep learning-based auto-contouring of organs/structures-at-risk for pediatric upper abdominal radiotherapy. 2024. https://arxiv.org/abs/2411.00594. 

2630

Mini-Oral Pioneering AI delineation in online adaptive MRI-guided radiotherapy: Prospective study shows time gains for prostate cancer Maximilian Lukas Konrad 1,2 , Carsten Brink 1,2 , Anders Smedegaard Bertelsen 2 , Ebbe Laugaard Lorenzen 2,1 , Christina Junker Nyborg 3 , Lars Dysager 3 , Tine Schytte 3,1 , Uffe Bernchou 2,1 1 Department of Clinical Research, University of Southern Denmark, Odense, Denmark. 2 Laboratory of Radiation Physics, Odense University Hospital, Odense, Denmark. 3 Department of Oncology, Odense University Hospital, Odense, Denmark Purpose/Objective: Daily magnetic resonance image (MRI)-guided radiotherapy plan adaptation requires time-consuming manual contour edits of targets and organs at risk in the online workflow. Recent advances in auto-segmentation promise to deliver high-quality delineations within a short time frame. However, the actual time benefit in a clinical setting is