S2980
Invited Speaker
ESTRO 2026
space and time [3]. Without this understanding, predictive models risk remaining empirical and limited in generalizability. Emerging opportunities are rapidly expanding the scope of response modelling. These include spatially resolved modelling of dose–effect relationships, incorporation of temporal dynamics of response, and integration of imaging biomarkers reflecting functional and biological changes during and after treatment. New paradigms such as spatially fractionated radiotherapy (SFRT) or cardiac-gated stereotactic arrhythmia radioablation (STAR) further highlight the need for advanced modelling approaches [4]. In parallel, there is growing need in exploiting spatial information in dose distributions through voxel-based methods in radiation oncology[5]. A deeper understanding of radiation response will be essential to fully exploit modern radiotherapy technologies. It will support the development of knowledge-based and response-guided treatment strategies, ultimately enabling their integration into clinical treatment planning systems. In this future paradigm, optimisation will no longer focus solely on physical dose distributions, but on predicted biological outcomes. The next revolution in radiotherapy will therefore not come solely from delivering dose with greater precision, but from the ability to understand and predict how tissues respond to radiation in space and time. References: 1. Pacelli, R., et al., Technological evolution of radiation treatment: Implications for clinical applications. Semin Oncol, 2019. 46(3): p. 193-201. 2. Palma, G., et al. Normal tissue complication probability (NTCP) models for modern radiation therapy . in Seminars in oncology . 2019. Elsevier. 3. de Kermenguy, F., et al., Radiation-Induced Lymphopenia: From Mathematical Modeling Toward Mechanistic Learning. Int J Radiat Oncol Biol Phys, 2026. 124(2): p. 465-483. 4. Wall, P.D.H., et al., The State of Tomorrow's SBRT. Int J Radiat Oncol Biol Phys, 2026. 124(5): p. 1155-1158. 5. McWilliam, A., et al., Voxel-based analysis: Roadmap for clinical translation. Radiother Oncol, 2023. 188: p. 109868. 5255 Integrating imaging in radiation oncology Catarina Veiga Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
technologists, dosimetrists, radiation oncologists, and physicists) updated when necessary. Treatment workflow and control software should be interoperable between different external beam treatment modalities (photon, electron, hadrons, BNCT) like navigation, air traffic control and instrumentation display for aviation. Specialized treatment units to treat a minority of patient who require non-coplanar fields (SRS and SBRT) and/or very large fields for example for treatment of widespread (oligometastatic) disease should adopt a no-field-junctioning methodology using a continuously moving isocentre by moving the patient. New types of combination and even simultaneous radiotherapy beams like photon-proton (p[h,r]oton) beams to provide sharper penumbras at deeper depths, real-time image verification, and redundancy. Flexible platform for accepting imaging modalities for function, real-time motion detection, and dose verification (particle therapy). 5254 Response Modelling: What’s Next? Laura Cella Institute of Biostructures and Bioimaging, National Research Council, Napoli, Italy Radiotherapy has undergone remarkable technological progress over the past decades, including the development of advanced treatment units, integration of multimodal imaging, improvements in dose calculation algorithms, and increasingly precise dosimetry [1]. These innovations have substantially enhanced our ability to deliver radiation with high geometric accuracy and conformity. However, despite these advances, treatment planning remains largely grounded in physical dose metrics, such as dose–volume histograms, which do not fully capture the biological complexity of radiation effects [2]. This raises a fundamental question: what is the ultimate goal of these technological developments? The answer lies in improving radiation response. Response modelling represents the natural convergence point of the entire radiotherapy chain, linking delivered dose, spatial distribution, temporal dynamics, and biological outcomes. It provides a framework to move from describing what dose is delivered to understanding what that dose actually does to tissues. A key challenge for the field is to move beyond purely data-driven approaches. While artificial intelligence and large datasets offer powerful tools, medical physics must remain central in providing mechanistic insight into how tissues respond to radiation across
The integration of imaging in radiation oncology has been fundamental in improving its targeting abilities
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