ESTRO 38 Abstract book

S1041 ESTRO 38

A modelling framework was developed and implemented in a research version of a treatment planning system allowing for different radiobiology-based strategies for dose prescription at voxel level targeting hypoxia and enhanced density of tumour clonogens. This modelling framework could be used for further development of individualized radiation therapy dose prescription approaches. Further validation based on patient treatment outcome is however warranted and it is pursued in an ongoing study. EP-1916 Predictive model of the dose to the heart based on geometry evaluation in left breast radiotherapy S.M. Tomatis 1 , E. Esposito 1,2 , A. Gaudino 1 , F. Lobefalo 1 , P. Mancosu 1 , L. Paganini 1 , V. Palumbo 1 , A. Stravato 1 , E. Tomatis 3 , M. Rossi 3 , E. Merati 3 , F. De rose 1 , G. Reggiori 1 , M. Scorsetti 1,4 1 Humanitas Research Hospital, Medical Physics of Radiotherapy, Rozzano Milan, Italy ; 2 Università degli studi di Milano, facoltà di medicina, Milano, Italy ; 3 liceo classico giosuè carducci, alternanza scuola lavoro, Milano, Italy ; 4 Humanitas University, Department of Biomedical Sciences, Rozzano MI, Italy Purpose or Objective Radiotherapy treatment of the left breast can be difficult due to heart proximity. Conservative dose constraint with mean heart dose (MHD)<4 Gy was suggested for minimizing heart late effects. Deep Inspiration Breath Hold (DIBH) was demonstrated to help in reducing MHD. In this study a predictive model was developed to correlate MHD to the patient geometry aiming to select the best patients that Nineteen patients treated in our facility for left breast cancer were randomly selected and considered in this study. All cases were subjected to a treatment course of 15 fractions for a total dose of 48 Gy to the surgical bed and 40.5 Gy to the whole breast. Patients were treated with volumetric modulated arc therapy (VMAT) with two or four arcs. The model was developed by target expansion and overlap procedure following the Expansion Intersection Histogram (EIH) method (1). This procedure operates by progressive target isotropic expansions and mapping the corresponding intersection with the critical organ into the EIH graph. From this graph the distance (max non zero overlap expansion) and slope (mean EIH derivative) are extracted and added to the target volume as input variables for a simple linear model. All variables are subjected to function transforms in order to approach a gaussian shaped distribution. All calculations and tests were made with stata software and the “ladder” function was used to generate the proper variable transform. Results Parameter distribution and overall EIH graph (mean ± SD) for the 19 patients are represented in figure 1. would benefit to DIBH. Material and Methods

EP-1915 Modelling framework for FMISO and FDG PET imaging tailored dose prescription M. Lazzeroni 1 , A. Ureba 2 , D. Baltas 3 , A.L. Grosu 3 , I. Toma-Dasu 1 1 Stockholm University, Medical Radiation Physics- Dept. Physics, Stockholm, Sweden ; 2 Skandionkliniken, Skandionkliniken, Uppsala, Sweden ; 3 Medical Center- Medical Faculty Freiburg- German Cancer Consortium DKTK Partner Site Freiburg, Department of Radiation Oncology-, Freiburg, Germany Purpose or Objective Positron emission tomography (PET) may play a central role in personalized radiation therapy when aiming at a dose prescription based on the functional and biological properties of the tumor, such as hypoxia. Hypoxia is well known for increasing cell radioresistance and it is considered as one of the main determinants of locoregional failure. This study aims at setting the modelling framework for combining the metabolic tumor information derived from fluorodeoxyglucose (FDG) PET images together with tumor hypoxia imaging with fluoromisonidazole (FMISO) PET to quantify the tumor clonogenic cell and oxygen distributions for a tailored dose prescription. Material and Methods A modelling approach for assessing the dose to be prescribed in order to achieve a predefined level of tumour control probability accounting for hypoxia and density of clonogens was proposed. Two scenarios were considered regarding the initial distribution of clonogens in the clinical target volume, a homogeneous density of the clonogens and a heterogeneous one. The heterogeneous distribution of clonogens was hypothesized that could be derived based on the FDG avidity extracted from PET images. Linear and non-linear conversion functions of FDG uptake into number of clonogenic cells were considered. For each of these scenarios, the required radiation doses to counteract the increased tumor cell radioresistance at voxel level were calculated based on maps of oxygen partial pressure derived from FMISO PET images by the use of conversion functions of radiotracer The framework for dose calculation was implemented as a scripted module to a research version of a treatment planning system, RayStation (RaySearch Laboratories AB, Stockholm). For exemplifying the results of applying the proposed formalism for personalised treatment planning, the distribution of oxygen partial pressure in one of the clinical target volumes for a head and neck cancer patient, the clonogenic cell distribution obtained when a linear and a sigmoidal conversion function of uptake into clonogenic cell number and the corresponding dose distributions to counteract the radiation resistance at voxel level are shown in Figure 1. uptake. Results

After data extraction, variables were selected by the ladder command as follows: 1) square root of breast volume. 2) distance (not modified). 3) 1/average slope. All variables resulted to be Gaussian (p>0.05).

Conclusion