ESTRO 2025 - Abstract Book

S39

Invited Speaker

ESTRO 2025

presentation explores efficiency as the balance between resource inputs and intermediate outputs (such as the number of patients treated/waiting times) 3 . By optimising resource use, we can enable broader clinical implementation and increase patient access to advanced treatments. OART treatments remain longer than conventional radiotherapy, which is less favourable for patient experience, reduces patient throughput and increasing the risk of intra-fractional anatomical variation. While established immobilisation and patient preparation approaches remain valuable, examples such as optimising image acquisition, plan robustness, utilising margin recipes to aid contouring and scripting tools may aid timeliness. Nonetheless, speed of the online workflow is subject to software efficiency, as well as other hardware capabilities such as availability of VMAT versus IMRT, treatment gating and intra-fractional adaption; developments of which remain responsibility of vendors. Artificial Intelligence (AI) has a role to play in resource consumption and revolutionising efficiency in oART. Auto segmentation and auto-planning can streamline workflows by eliminating the need for timely manual contouring, and user-modified plan optimisation. However, these technologies require robust system validation. Equally, this rapidly developing field highlights the need for workforce training to verify automation and guarantee high-quality treatment 4 . Emerging evidence continues to advocate for training therapeutic radiographers (RTTs) in online contouring and dosimetry 4 . Where two RTTs deliver conventional radiotherapy, oART typically requires a multi-disciplinary team. The upskilling of RTTs has been demonstrated to release radiation oncologists and medical physicists from workflows, proven to be cost saving and increase patient throughput due to increased RTT availability 5 . However, challenges remain, particularly where resources for training may be limited. Addressing these barriers, including attitudes to role reallocation, or governing body restrictions on practice, are essential for widespread implementation. Notably, the precision of oART has facilitated further adoption of hypo-fractionation and ultra-hypo-fractionation, reducing the number of treatment sessions without compromising clinical outcomes 6 . Simulation-free radiotherapy will also reduce resource input by eliminating the need for pre-treatment imaging. Despite both approaches decreasing the number of patient visits, enhancing patient experience, throughput and waiting times, it is unclear that gains in efficiency are not offset by resource inputs 6 . Through continued innovation, optimisation of online workflows and crucially, international collaboration of healthcare services, policy makers and vendors, oART can become a more efficient, accessible, and effective treatment modality for cancer patients worldwide. References 1. Lievens, Y., & Grau, C. (2012). Health economics in radiation oncology: introducing the ESTRO HERO project. Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology , 103 (1), 109–112. https://doi-org.ezproxy.icr.ac.uk/10.1016/j.radonc.2011.12.026 2. Hehakaya, C., van der Voort van Zyp, J. R. N., Vanneste, B. G. L., Grutters, J. P. C., Grobbee, D. E., Verkooijen, H. M, et al. (2021). Early health economic analysis of 1.5 T MRI-guided radiotherapy for localized prostate cancer: Decision analytic modelling. Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology, 161, 74–82. https://doi.org/10.1016/j.radonc.2021.05.022 3. Palmer, S., & Torgerson, D. J. (1999). Economic notes: definitions of efficiency. BMJ (Clinical research ed.), 318(7191), 1136. https://doi-org.ezproxy.icr.ac.uk/10.1136/bmj.318.7191.1136 4. Shepherd, M., Joyce, E., Williams, B., Graham, S., Li, W., Booth, J, et al. (2025). Training for tomorrow: Establishing a worldwide curriculum in online adaptive radiation therapy. Technical Innovations & Patient Support in Radiation Oncology. Volume 33. 100304. ISSN 2405-6324. https://doi.org/10.1016/j.tipsro.2025.100304. 5. Xue, E., Williams, B., Tree, A., McNair, H., & Giorgakoudi, K. (2024). Cost-consequence analysis of RTT target volume online contouring for prostate MRIgRT. In ESTRO 2024 – Abstract Book (pp. 2852-2853). Glasgow. https://doi.org/10.1016/S0167-8140(24)02694-X