ESTRO 2020 Abstract book

S760 ESTRO 2020

5 mW/cm³ and the total power loss of TTFields in our simulated cases added up to 20-40 W. This corresponds to 412-825 kcal per day, an amount similar to the resting metabolic rate of the brain constituting about 20% of the whole body’s metabolic rate. Conclusion Our study demonstrated power loss density as a reasonable physical measure for quantitative determination of TTFields dose in treatment planning. In addition, we presented a comparison of the power delivered by TTFields to cells and the metabolic rate of the cells resulting in equivalent values. Our findings could lead to more insight into the mechanism of action of TTFields. PO‐1346 In‐vivo surface dose measurements in breast cancer patient treated with helical radiation therapy M. Valenti 1 , M. Parisotto 1 , F. Angeletti 2 , F. Cucciarelli 3 , G. Mantello 3 , S. Maggi 1 1 Azienda Ospedaliero Universitaria Ospedali Riuniti, Medical Physics, Ancona, Italy ; 2 Polytechnic University of Marche, Degree course for medical radiology technician, Ancona, Italy ; 3 Azienda Ospedaliero Universitaria Ospedali Riuniti, Radiotherapy, Ancona, Italy Purpose or Objective In-vivo assessment of Thomotherapy Treatment Planning System (TPS) dose estimation accuracy in breast cancer patient treated with helical volumetric radiation therapy when skin is part of the target. Material and Methods In vivo dosimetry was performed weekly, as part of our quality assurance program, on 7 breast patient where skin is part of the volume to treat. For superficial dose measurements we used EBT3 gaf chromic. Small strips (1,5X4 cm 2 ) of film were positioned on patient skin in a specific point, individuated on CT scan images, where TPS estimated dose in near to 200 cGy (fraction prescription dose). EBT3 film strips were scanned 24 hour after irradiation in RGB mode using an EPSON 1000 scanner with a resolution of 72 dpi. Red channel was used and, for optical density to dose conversion, we used the procedure described in [1]. In each measurement day, to check dosimetric system stability, a reference strip of EBT3 was irradiated in virtual water phantom with a known dose. Results In figure 1 are reported the in vivo measured dose value for each patient. The blue dots represent the single fraction measures and the red squares the patient average value. Near each average dose point is reported the indication of single patient percentage deviation from calculated dose. All the measurements are summarized in the box plot of figure 2. Measured surface dose is always lower than prescribed dose (ranging from -45% to -6%, with a media value of -31%). Another work [2], performed on 17 patients, reports e higher surface dose value (-10% respect to prescription dose) but in this case TLD dosimeters have been used and the surface dose is referred to a deeper point (about 0,5mm) and a “virtual bolus” like approach is used for planning. Several factors concur to the observed dose deviation: TPS surface dose overestimation [3], patient breathing and patient set up inaccuracy. The latter factor is highlighted by the observed inter-fraction dose variation.

0.37%±2.4 & 1.16%±2.3 respectively. Figure 1 illustrates the comparison of computed / reconstructed and calculated dose to PTVs for all cases. Likewise, the reconstructed dose for individual OARs correlated well with TPS planned dose except for small structures in head and neck cases. The average percentage of passed gamma values achieved was above 95% for all cases. However, no correlation was observed between gamma passing rates and DVH difference (%) for PTVs ( r 2 =0.11).

Figure 1: Comparison of TPS calculated dose for PTV (mean) to: (a) Computed dose (CC algorithm) and (b) Compass reconstructed (measured using Dolphin

Detector). Conclusion

DVH analysis of treatment plan using a model-based verification system and transmission detector provided better clinical relevance for all cases for complex VMAT plans as compared to traditional gamma based analysis. PO‐1345 Defining Tumor Treating Fields (TTFields) dosimetry based on power loss density and related measures Z. Bomzon 1 , A. Kinzel 2 , U. Noa 1 , H.S. Hershkovich 1 , A. Naveh 1 , S. Levi 1 1 Novocure Ltd., Research and Development, Haifa, Israel ; 2 Novocure GmbH, Medical, Munich, Germany Purpose or Objective Alternating electric fields known as Tumor Treating Fields (TTFields) are used to treat glioblastoma multiforme (GBM). The intensity of the electric field has formerly been used to describe TTFields dose, but for this physical modality, the energy transferred by the treatment modality to the tissue is to consider to comprehensively capture the dose. This is because this measure quantifies the extent of alteration the modality can cause on the state of the objects on which it operates. The power (energy per time unit) the electric field transfers to the tissue is quantified as power loss density, which can be used as a measure of TTFields dose. In this study, we analyzed this new measure as a basis for further studies and its use in treatment planning. Material and Methods With σ denoting tissue conductivity and E the magnitude of the electric field, the power loss density ( L ) of TTFields is defined as L [mW/cm 3 ]=½ σE 2 . We numerically simulated TTFields delivery to realistic head models of GBM patients to analyze the power loss density distribution of TTFields released to the brain. Color maps showing the field intensity and power loss density distribution within the models were generated and compared in a qualitative manner. Calculation of the total power loss within the models yielded a measure of the power that TTFields released to the brain during treatment. Results In regions of low conductivity, e.g. white matter, we detected an increase in electric field intensity, whereas in regions of high conductivity, e.g. the resection cavities or ventricles, field intensity was lowest. Power loss density, on the contrary, was found to be enhanced in regions of higher conductivity, even up to values as they are found in other tissue types within regions of high conductivity such as ventricles or resection cavity. Within the gross tumor volumes of all patients, the average power loss density was