ESTRO 2025 - Abstract Book

S4315

RTT - Treatment planning, OAR and target definitions

ESTRO 2025

Conclusion: Current guidelines of non-uniform CTV to PTV margins of 1-1.5cm for the primary tumour, and 0.7cm for the PAN, are sufficient to ensure adequate coverage for setup errors. However, the study suggested that a smaller, uniform margin of 0.6cm, for both the primary and PAN region, is equally effective with the use of AIO and daily imaging. Reducing these margins enhances treatment precision, while potentially sparing surrounding organs at risk from excessive radiation exposure, thereby diminishing the incidence of treatment-related toxicities (4,5). However, further research with larger sample sizes is needed to confirm its effectiveness and enhance the reliability of these findings. References: 1 Thamronganantasakul et al (2018). Extended-field radiotherapy for locally advanced cervical cancer. 2 Sun et al (2016). CTV to PTV in cervical cancer:From static margins to adaptive radiotherapy. 3 Jadon et al (2014). A Systematic review of organ motion and image-guided strategies in external beam radiotherapy for cervical Cancer. 4 Gupta, M (2019). Early toxicity and treatment outcomes of extended field-intensity modulated radiotherapy for cervical cancer patients with para-aortic nodal metastasis. 5 Yegya-Raman et al (2018). Association of Target Volume Margins With Locoregional Control and Acute Toxicities for Non-small cell lung cancer Treated With Concurrent Chemoradiation Therapy. Keywords: CTV, PTV, margin expansions Proffered Paper AI-driven segmentation: Efficiency gains and workflow challenges in radiotherapy Ciaran Malone 1 , Jill Nicholson 1,2 , Samantha Ryan 1 , Frances Duane 1,2 , Pierre Thirion 1,3 , Peter McBride 1 , Ruth Woods 1 , Orla McArdle 1 , Gerard G Hanna 2,1 , Brendan McClean 1,4 , Sinead Brennan 1,2 1 Department of Radiation Oncology, St.Luke's Radiation Oncology Network, Dublin, Ireland. 2 Applied Radiation Therapy Trinity, Discipline of Radiation Therapy & Trinity St James’s Cancer Institute, Trinity College Dublin, Dublin, Ireland. 3 School of Medicine, Discipline of Radiation Therapy & Trinity St James’s Cancer Institute, Trinity College Dublin, Dublin, Ireland. 4 Department of Physics, UCD, Dublin, Ireland Purpose/Objective: Integration of artificial intelligence (AI) into clinical workflows may transform timelines for patients waiting to commence radiotherapy. It remains unclear whether improving contouring efficiency using AI-driven contouring (AIseg) consistently shortens the contouring task time in a real world setting (i.e. not necessarily undertaken in one sitting), or the overall radiotherapy planning-CT to treatment time. This multidisciplinary initiative aims to evaluate how AIseg changes the duration taken for contouring tasks as well as the time to complete the overall treatment planning carepath. Material/Methods: In November 2023, MVision's AIseg solution was implemented for all radiotherapy patient groups at our institution. Data on task availability, initiation, and completion were recorded from ARIA patient records across a two-year 339