ESTRO 2022 - Abstract Book

S861

Abstract book

ESTRO 2022

The second part of the presentation will present initial work towards a preclinical validation of spatiotemporal fractionation in small animal models. While an end-to-end validation of the concept as a whole is impractical due to technical and biological limitations, individual underlying assumptions may be tested. One of the key assumptions of spatiotemporal fractionation is that different parts of the tumor can be treated to different doses in different fractions, and tumor control is maintained as long as the prescribed cumulative BED is delivered to all parts of the tumor by the end of the treatment. Ongoing work tests this assumption in subcutaneous tumors in mice using a high-precision image-guided small animal radiotherapy research platform [3]. [1] J. Unkelbach, M. Bussière, P. Chapman, J. Loeffler, H. Shih. Spatiotemporal Fractionation Schemes for Irradiating Large Cerebral Arteriovenous Malformations. Int. J. Rad. Onc. Biol. Phys., 2016;95(3):1067-1074 [2] J. Unkelbach, D. Papp, M. Gaddy, N. Andratschke, T. Hong, M. Guckenberger. Spatiotemporal fractionation schemes for liver stereotactic body radiotherapy. Radiother. Oncol. 125(2):357-364, 2017 [3] I. Telarovic, J. Krayenbuehl, I. Grgic, F. Tschanz, M. Guckenberger, M. Pruschy, J. Unkelbach. Probing spatiotemporal fractionation on the preclinical level. Phys. Med. Biol. 65(22):22NT02, 2020

Symposium: Modelling at the voxel level: Dose and image-data mining

SP-1015 Mining the radiotherapy dose: Report from the physics workshop

L. Cella 1 , A. McWilliam 2 , G. Palma 3

1 National Research Council, Institute of Biostructures and Bioimaging, Napoli, Italy; 2 The University of Manchester / The Christie NHS Foundation Trust, Division of Cancer Science, Manchester, Italy; 3 National Research Council, Institute of Nanotechnology, Lecce, Italy Abstract Text The conformality rush of the last decade has revealed the existence of radiobiological phenomena that were either concealed or ignored in the classical radiation therapy (RT) treatment strategy. On the one hand, the progressive sparing of healthy tissue permits a focus on toxicity outcomes that was impossible using historic RT delivery techniques. On the other hand, the increasing heterogeneity of dose distribution across organs-at-risks (OARs) highlights the existence of dose- response patterns and, as a result, emphasises the limits of the traditional dose-volume histogram (DVH)-based toxicity analysis and toxicity modeling philosophy. The high sparing capability of modern techniques at the same time demands for more and more accurate insights of possible avoidance region within a specific OAR for an effective plan optimisation. In recent years, a new methodology is emerging where the spatial information of the planned dose for every patient is maintained. No assumptions are made regarding anatomical regions, instead the dose in every voxel across many patients is analysed against a given outcome including survival or radiation-induced morbidity. This process identifies sub-regions within an anatomical location that are most strongly correlated against these outcomes and, therefore, can better define the anatomy driving these outcomes. This new approach for dose distribution analysis, commonly referred to as voxel-based analysis (VBA), consists of two main processes: spatial normalisation of the different anatomies in the analysed cohort of patients to a common reference and statistical inference on the spatial signature of dose response (Palma et al EJMP 2020). So far VBA has been successfully applied by a small number of research groups to several tumor types and to different toxicity endpoints (Acosta et al Phys. Med. Biol 2013, Palma et al, IJROBP 2016, Monti et al Sci Rep 2017, Beasley et al IJROBP 2018) or mortality (McWilliam et al EJC 2017, Green et al Frontiers Oncology, 2021) and to identify anatomical regions driving biochemical recurrence (Witte et al IJROBP 2010). Now, to fully empower such technique, we need robust image normalization approaches as well as statistical analysis. In addition, a crucial issue is the possibility to mine dose distributions across multiple institutions. This would ensure a wider heterogeneity of patient populations and treatment techniques, more robust and generalisable results, and opportunities for validation. Combining datasets can be achieved by pooling data in one location or via distributed learning networks. Given this scenario, the aim of our workshop (WS) was discussing the key aspects of VBA in RT to lay the basis for a common framework in which the methodology could be developed. The online WS was organized over two-days, during which invited speakers addressed the current methodologies adopted for VBA studies. These focused on, 1) different aspects of spatial