ESTRO 2024 - Abstract Book

S3683

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

ESTRO 2024

[1] Webb, S. “The physical basis of IMRT and inverse planning.” The British Journal of Radiology, 5 3 2003, pp. 678 689.

[2] Mynampati, et al. “Application of AAPM TG 119 to volumetric arc therapy (VMAT).” Journal of Applied Clinical Medical Physics, 06 09 2012, pp. 108-116.

[3] Low, et al. “Dosimetry tools and techniques for IMRT.” Medical Physics, 16 02 2011, pp. 1313-1338.

[4] Blondel, Vincent D., et al. “Fast unfolding of communities in large networks.” Journal of Statistical Mechanics: Theory and Experiment, 09 10 2008, p. P10008.

[5] Ebert, M. A., et al. “Comparison of DVH data from multiple radiotherapy treatment planning systems.” Physics in Medicine & Biology, 12 05 2010, p. N337.

[6] Ahnesjö, Anders. “Collapsed cone convolution of radiant energy for photon dose calculation in heterogeneous media.” Medical Physics, 01 07 1989, pp. 577-592.

[7] Yang, et al. “Quantitative comparison of automatic and manual IMRT optimization for prostate cancer: the benefits of DVH prediction.” Journal of Applied Clinical Medical Physics, vol. 16, 2015, pp. 241-250.

2627

Mini-Oral

An automatic tool for lattice optimisation of spatially fractionated stereotactic body radiotherapy

Andrea Botti 1 , Domenico Finocchiaro 2 , Nicola Panico 1 , Valeria Trojani 1 , Giulia Paolani 1 , Federico Iori 3 , Roberto Sghedoni 1 , Elisabetta Cagni 1 , Daniele Lambertini 1 , Patrizia Ciammella 3 , Cinzia Iotti 1 , Mauro Iori 1 1 Azienda USL-IRCCS di Reggio Emilia, Medical Physics Unit, Department of Advanced Technology, Reggio Emilia, Italy. 2 Azienda Ospedaliero-Universitaria di Modena, Medical Physics Unit, Modena, Italy. 3 Azienda USL-IRCCS di Reggio Emilia, Radiotherapy Unit, Department of Advanced Technologycal, Reggio Emilia, Italy

Purpose/Objective:

Lattice radiotherapy (LRT) is a 3D implementation of Spatially Fractionated Radiation Therapy and is based on a 3D-modulated spatial dose distribution of high ablative dose spheres (called vertices), located inside the target volume, and modelled as peak-valley dose rate (PVDR). Due to the vast heterogeneity of tumour shapes, the problem of finding the best lattice vertices arrangement is not trivial. Manually achieving a clinically acceptable vertex placement in LRT is a task subjected to non-optimal solutions, which is a part of expensive trial and error processes, involving both contouring and plan optimization. In addition to being time consuming, manual vertex arrangement can also produce not standardized solutions, hindering multicentric studies. Lately, several studies and clinical practice surveys showed that LRTs are gaining popularity [1-9], highlighting the need for standardized and reproducible protocols. The aim of this study is to develop a home-made tool (LatticeOpt) that automatically generates the optimal lattice structure in agreement with target lesion shape and volume. Performances of this tool are validated on real anatomy cases.