ESTRO 2024 - Abstract Book

S2726

Interdisciplinary - Global health

ESTRO 2024

Conclusion:

Our study highlights the significant environmental impact of breast cancer treatment, with travel emissions playing a crucial role. Ultra-hypofractionated treatment, consisting of five fractions reduces carbon emissions by up to 75% compared to the 25-fractions schedule. While this research primarily only focuses on transportation related emissions, the potential environmental benefits of hypofractionation are evident. These findings emphasize the importance of adopting sustainable treatment protocols to reduce the carbon footprint of radiation therapy travels.

Keywords: carbon footprint, climate change, CO2

References:

European Automobile Manufacturers' Association. Average CO2 emissions from new passenger cars, by EU country [Internet]. 2022. Available at: https://www.acea.auto/figure/average-co2-emissions-from-new-passenger cars-by-eu-country/

385

Mini-Oral

Teaching and AI-Assistance Improve Organs-At-Risk Contouring for Head-Neck Cancer in a Global Study

Mathis Ersted Rasmussen 1 , Kamal Akbarov 2 , Egor Titowich 2 , Jasper Albertus Nijkamp 3 , Wouter Van Elmpt 4 , Pernille Lassen Hanne Primdahl 3 , Jon Cacicedo 5 , A.F.M. Kamal Uddin, Ahmed Mohamed 6 , Ben Prajogi, Brohet Kartika Erida 6 , Catherine Nyongesa, Darejan Lomidze 6 , Gisupnikha Prasiko, Gustavo Ferraris 6 , Humera Mahmood, Igor Stojkovski 6 , Isa Isayev, Issa Mohamad 6 , Leivon Shirley, Lotfi Kochbati 6 , Ludmila Eftodiev, Maksim Piatkevich 6 , aria Matilde Bonilla Jara, Orges Spahiu 6 , Rakhat Aralbayev, Raushan Zakirova 6 , Sandya Subramaniam, Solomon Kibudde 6 , Uranchimeg Tsegmed 6 , Stine Sofia Korreman 7 , Jesper Grau Eriksen 7 1 Investigator, ELAISA Consortium, Aarhus, Denmark. 2 International Atomic Energy Agency, ELAISA Consortium, Vienna, Austria. 3 Consultant, ELAISA Consortium, Aarhus, Denmark. 4 Consultant, ELAISA Consortium, Maastricht, Netherlands. 5 Consultant, ELAISA Consortium, Bilbao, Spain. 6 Chief Scientific Investigator, ELAISA Consortium, Vienna, Austria. 7 Principal Investigator and Shared Last Author, ELAISA Consortium, Aarhus, Denmark

Purpose/Objective:

The use of deep learning-based auto-contours as templates for organ-at-risk contouring (AI-assisted contouring) holds tremendous potential globally. With the ability to reduce contouring time and inter-observer variation, AI assisted contouring may contribute to a shortening of diagnosis-to-treatment time and with increasing the consistency of contouring practices. While these two effects are beneficial by themselves, it is unknown how AI assisted contouring affects the quality of contours. Teaching is currently the cornerstone in acquiring contouring competencies, and therefore this study investigated how contour quality was affected by (A) a virtual teaching session on contouring guidelines, and (B) AI-assisted contouring.

Material/Methods: