ESTRO 2020 Abstract Book

S706 ESTRO 2020

machine are not present. For the uncertainties related to the patient, different strategies (image guidance, motion/setup uncertainty simulation) have found the way to the treatment process. From there we asked if and how typical machine delivery errors like gantry and MLC positions, isocenter and gantry sag uncertainties will have an effect on the dose distribution or if they can be neglected within the radiotherapy process as we classically do. Material and Methods Main delivery errors like gantry sag (.5, 1, 1.5, 2mm), isocenter uncertainty (.5, 1, 1.5, 2mm), MLC calibration errors (+/-.5, 1, 1.5, 2mm) and gantry angle errors (+/-.5, 1, 1.5, 2°) are tested for 3 patient cases (Prostate, BC, H+N). Original treatment plans were used to generate modified DICOM RT plan files using an editor tool designed in IDL (Harris Geospatial Solutions). Manipulated plans have been reimported into Raystation 8.0 (Raysearch) and the resulting dose was calculated. For evaluation a volume dose factor was calculated based on the DVHs. This was done by multiplying each DVH dose point and the corresponding volume of the organ or target for all modified plans. To get an idea of this factor, redosed original plans (+/- 2Gy) were calculated. From that, a linear fit for each volume (dose to dose volume) was established. This made it possible to calculate a rescale (offset) dose the plan would have to be rescaled with to show the same effect as the error related dose volume Gantry sag: Prostate and BC had higher doses for higher sag for all volumes while H+N had lower doses at targets only. Isocenter: H+N and BC had lower doses for all volumes while prostate only had some higher doses. MLC positioning: clear correlation: larger field is more dose (Fig 1). Gantry angle: H+N and BC mostly had lower and prostate had some higher doses. In general the errors resulted in higher dose offsets when volumes were small as for the H+N case. Therefor delivery errors might be of higher interest for such cases. change. Results

Figure 1. Novel 3D printed plastic scintillator, (A) ordinary state and (B) irradiated state, irradiated by 6 MV x-rays. Results Results indicate that the emission spectra of the 3D printed PSDs do not show significant differences when compared to the emission spectrum of the commercial sample. However, the magnitude of scintillation light output was found to be strongly dependent on the parameters of the fabrication process. The 3D printed PSDs showed an increase in the total relative intensity with increasing layer thickness. Additionally, lower extrusion temperatures produced greater output signal intensity. The 3D printed PSDs also demonstrated a strong directional dependence, with the bottom (facing build-plate) and top faces exhibiting approximately 15% and 8% decreases in total relative intensity, respectively, compared to the sides. Dosimetric testing indicates that the 3D printed PSDs share many desirable properties with current commercially available PSDs such as dose linearity, dose rate independence, energy independence in the MV range, repeatability and stability. Conclusion In this study, we performed the first comprehensive analysis of the various fabrication and dosimetric properties of 3D printed PSDs. Results demonstrate that not only does 3D printing offer a new avenue for the production and manufacturing of PSDs but also allows for further investigation into the application of 3D printing in dosimetry. Such investigations could include options for 3D printed, patient-specific scintillating dosimeters that may be used as standalone dosimeters or incorporated into existing 3D printed patient devices (e.g. bolus or immobilization) used during the delivery of radiation therapy.

Conclusion The limited number of cases in this study showed complex dependencies. Commonly delivery errors do vary the dose more than expected but the dose change is difficult to predict as it depends on the case and beam arrangement. Therefore new dose calculation models should include a kind of dose uncertainty display based on general or better individual machine QA results. This might even help with the decision on the QA procedures used for plan acceptance. PO-1337 Pre-treatment verification of multi-target SRS using a virtual water phantom J.F. Calvo Ortega 1 , P. Greer 2 , M. Hermida-López 3 , S. Moragues-Femenía 4 , J. Casals-Farran 4 1 1.Servicio de Oncología Radioterápica- Hospital

Poster: Physics track: Dose measurement and dose calculation

PO-1336 Should we include machine uncertainties in radiotherapy planning?

P. Haering 1 , M. Splinter 1 , C. Lang 1 1 DKFZ, e040, Heidelberg, Germany

Purpose or Objective Dose given by the treatment planning system (TPS) reflects an ideal situation as we classically assume the patient as a rigid object and delivery uncertainties from the treatment