ESTRO 2024 - Abstract Book

S3698

Physics - Dose prediction, optimisation and applications of photon and electron planning

ESTRO 2024

References:

Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. Journal of Digital Imaging. 2013 Dec;26(6):1045-57. DOI: 10.1007/s10278-013-9622-7

Tewell MA, Adams R. The PLUNC 3D treatment planning system: a dynamic alternative to commercially available systems. Med Dosim. 2004 Summer;29(2):134-8. doi: 10.1016/j.meddos.2004.03.014. PMID: 15191763.

Wieser, H. P., Cisternas, E., Wahl, N., Ulrich, S., Stadler, A., Mescher, H., Müller, L. R., Klinge, T., Gabrys, H., Burigo, L., Mairani, A., Ecker, S., Ackermann, B., Ellerbrock, M., Parodi, K., Jäkel, O., & Bangert, M. (2017). Development of the open-source dose calculation and optimization toolkit matRad. Medical physics, 44(6), 2556–2568. https://doi.org/10.1002/mp.12251

2673

Proffered Paper

Biologically-guided radiotherapy: a clinical evaluation of automated vs manual planning

Ana Ureba 1,2 , Jakob Öden 3 , Iuliana Toma-Dasu 4,1 , Nils H. Nicolai 5,6 , Alexander Ruehle 5,6 , Dimos Baltas 6 , Michael Mix 7 , Anca L. Grosu 6 , Marta Lazzeroni 4,1 1 Karolinska Institute, Oncology and Pathology, Solna, Sweden. 2 University of Seville, Medical physiology and biophysics, Seville, Spain. 3 RaySearch AB Laboratories, -, Stockholm, Sweden. 4 Stockholm University, Physics, Stockholm, Sweden. 5 University of Leipzig Medical Center, Radiation Oncology, Leipzig, Germany. 6 University Medical Center Freiburg, Radiation Oncology, Freiburg, Germany. 7 University Medical Center Freiburg, Nuclear Medicine, Freiburg, Germany

Purpose/Objective:

Despite technical advancements, treatment planning still remains a time-consuming and often planner dependent step in the radiotherapy process. Automated treatment planning strategies (e.g. template-based, artificial intelligence-based) may help overcome these aspects, which may potentially impact on plan quality and, eventually, on treatment outcome. In this study, we present a template-based treatment planning (TP) pipeline underpinned by a biologically-guided dose-painting strategy. This strategy hinges on integrating essential datasets, such as the tumor clonogenic cell distribution and oxygen distribution, as, respectively, derived from FDG and FMISO positron emission tomography (PET) images. Integrating such biologically relevant information into the TP process may represent a significant step toward personalized radiation therapy. The automated TP pipeline is designed to optimize intensity modulated treatment plans that are tailored to the unique biological characteristics of individual tumors. This approach not only holds the promise of improving therapeutic outcomes but also serves to minimize the impact on healthy tissues, a critical consideration, particularly in the context of head and neck (H&N) cancer treatment, where the proximity of vital anatomical structures necessitates careful planning.