ESTRO 2023 - Abstract Book

S1284

Digital Posters

ESTRO 2023

about 75% of cases excluded due to the intrinsic limitations of this therapy. Therefore, the implementation of this technique could be justified only in a few centres with a high degree of expertise.

PO-1582 AI for Radiotherapy Auto-Contouring: Current Use, Perceptions of, and Barriers to Implementation

S. Hindocha 1 , K. Zucker 2 , R. Jena 2 , K. Banfill 2 , K. Mackay 2 , G. Price 3 , D. Pudney 2 , J. Wang 2 , A. Taylor 2

1 The Royal College of Radiologists, AI in Clinical Oncology (AICO), London, United Kingdom; 2 Royal College of Radiologists, AI in Clinical Oncology (AICO), London, United Kingdom; 3 Royal College of Radiologists , AI in Clinical Oncology (AICO), London, United Kingdom Purpose or Objective Manual contouring of tumour and OARs is laborious but crucial a component of radiotherapy (RT). Errors here can lead to underdosing of the tumour or increased toxicity impacting on survival and quality of life. Commercial AI auto-contouring tools are becoming increasingly available. Their clinical use raises important considerations including quality assurance, validation, education, training and job planning. Despite this, there is little in the literature capturing the views of Clinical Oncologists regarding these factors. The Royal College of Radiologists (RCR) surveyed UK Clinical Oncologists on their perceptions, current use of and barriers to using AI-based auto contouring for RT. Materials and Methods Survey questions consisted of close-ended questions and open-ended statements with options to select. Free-text comments were invited where necessary. The survey was conducted through the RCR’s Insights Panel and was sent to trainees, SAS doctors and consultants in the Faculty of Clinical Oncology. Results The survey was sent to 236 subscribers of the Insights Panel. 51 responded (response rate = 22%, considered acceptable by the Insights Panel). 40 respondents (78%) were consultants, 7 (14%) were trainees and 3 (6%) were of another grade. 40 (78%) felt the impact of AI on RT would be positive. None felt that AI would replace their role. 25 (49%) felt that use of AI in RT would decrease risk for patients and 11 (22%) felt there would be no change in risk to patients. 2 (4%) felt AI would increase the risk to patients. We asked about the percentage of OAR contouring that was performed by AI or various staff (Fig 1). 19 (37%) reported that consultants undertook ≥ 60% of all OAR contouring. 2 reported that AI auto-contouring was used for the majority of OAR contouring. 23 respondents (45%) reported that AI auto-contouring is being used clinically in their departments. This is predominantly for OAR contouring for head and neck, brain, thorax and prostate radiotherapy (Table 1). 25 respondents replied to the question on how much time AI auto-contouring saved in a typical week. 15 (60%) reported a time saving, with 9 (36%) reporting this to be >1 hour per week.

Respondents were also asked what the key priorities regarding AI should be for the RCR and to share their views or experience with AI for auto-contouring. 5 categories emerged:

1) Validation and Quality Assurance 2) Education, Training and Guidance 3) Understanding the Impact on Clinical Oncologists 4) Patient Engagement 5) Rolling Out Clinical AI tools for RT

Conclusion AI auto-contouring tools are in current clinical use and their use expected to grow. There is consensus on validation and quality assurance and there is a marked appetite for urgent guidance, education and training. Careful coordination is required to ensure that all RT departments and the patients they serve, may enjoy the benefits of AI in RT. Professional organisations such as the RCR have a key role to play in delivering this.

PO-1583 Need for relaxation techniques in patients undergoing radiotherapy - a single center interview

R. Asadpour 1 , S.T. Klusen 2 , N.A. Mayr 3 , S.E. Combs 4,5,6 , K.J. Borm 4

1 Technische Universität München (TUM), Klinikum rechts der Isar, Department of Radiation Oncology, Munich, Germany; 2 Technische Universität München (TUM) Klinikum rechts der Isar, 1. Department of Radiation Oncology, Munich, Germany; 3 UW School of Medicine., Department of Radiation Oncology, Seattle, USA; 4 Technische Universität München (TUM) Klinikum rechts der Isar, Department of Radiation Oncology, Munich, Germany; 5 Institute of Innovative Radiotherapy (iRT), Helmholtz Zentrum München, Department of Radiation Sciences (DRS), Munich, Germany; 6 German Consortium for Translational Cancer Research (dktk), Partner Site Munich , Munich, Germany

Purpose or Objective