ESTRO 2025 - Abstract Book

S4252

RTT - Education, training, advanced practice and role developments

ESTRO 2025

The learning needs assessment revealed moderate overall awareness (3.44/5), with lower confidence regarding the correct steps for patient alignment (2.8/5) and diode placement on the patient’s skin (3.1/5). Higher confidence levels were noted in the use and execution of electron compensators (4.03/5) and the knowledge of criteria for evaluating the accuracy of radiographic images (3.73/5). Based on these responses, the VR training was designed to enable RTTs to practice all phases of the TBI procedure, with particular emphasis on specific steps such as patient alignment and diode placement.

Figure 1. Two-dimensional visualizer of some training scenarios viewable through Virtual reality (VR)

Conclusion: The questionnaire revealed moderate learning needs, despite the presence of a highly experienced staff. The highest demand was observed in the complex preparatory phases of TBI, when compared to other procedures. Considering the specificity of this technique and the significant benefits of VR in enhancing safety and optimizing treatment time, the identified needs will be addressed by focusing on the areas deemed most critical.

Keywords: Virtual Reality, Education, Total Body Irradiation

References: - A. Lastrucci, C. Votta, E. Serventi et al., The application of virtual environment radiotherapy for RTT training: A scoping review, Journal of Medical Imaging and Radiation Sciences, https://doi.org/10.1016/j.jmir.2024.02.013 - Chamunyonga C, Burbery J, Caldwell P, Rutledge P, Fielding A, Crowe S. Utilising the Virtual Environment for Radiotherapy Training System to Support Undergraduate Teaching of IMRT, VMAT, DCAT Treatment Planning, and QA Concepts. J Med Imaging Radiat Sci . 2018;49(1):31–38. doi: 10.1016/j.jmir.2017.11.002 .

2895

Digital Poster Artificial Intelligence in Radiotherapy: RTTs’ Attitudes and Competencies in Two Italian Regions Nicola Iosca 1,2 , Ilaria Pia Monaco 3 , Alessia Chieppa 4 , Marialuisa Doronzo Corvascio 5 , Andrea Lastrucci 1,2 , Yannick Wandael 1 , Renzo Ricci 1 1 Allied Health Professions, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy. 2 Radiation Oncology Unit, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy. 3 Radiotheraphy Unit, AOUC Policlinico-Givoanni XXIII, Bari, Italy. 4 BSc Radiography, University of Bari, Bari, Italy. 5 Radiotherapy Unit, ASL BAT, Barletta, Italy Purpose/Objective: The integration of Artificial Intelligence (AI) has driven significant advancements in the field of radiation oncology, particularly in the optimization of treatment planning and delivery. AI-powered software enhances precision,