ESTRO 37 Abstract book

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ESTRO 37

OC-0087 Clinical validation of Monte Carlo dose calculation for pencil beam scanning proton therapy. L. Widesott 1 , S. Lorentini 1 , F. Fracchiolla 1 , P. Farace 1 , M. Schwarz 1 1 Ospedale Santa Chiara di Trento, Centro di protonterapia, Trento, Italy Purpose or Objective Validation of a commercial Monte Carlo (MC) algorithm (RayStation (version 6.0.024) ) for the treatment of head and neck (H&N) tumours with pencil beam scanning (PBS) proton therapy, comparing it via measurements and analytical calculations in clinically realistic scenarios. Material and Methods For the measurements a 2D ion chamber array detector (MatriXX PT, IBA Dosimetry GmbH) was placed underneath the following targets: 1) anthropomorphic head phantom (with two different thickness) and 2) a biological sample (i.e. half lamb’s head). In addition, we compared the MC dose engine vs. the RayStation pencil beam (PB) algorithm clinically implemented so far, in critical conditions such as superficial targets (i.e. in need of range shifter), different air gaps and different gantry angles to simulate both orthogonal and tangential beam arrangements. For every plan the PB and MC dose calculation were compared to measurements using a gamma analysis metrics with passing criteria of 3% of maximum dose, 3mm distance-to-agreement, global approach, and dose threshold of 5%. Results For both the configurations of the head phantom (i.e. one and two slabs) the gamma passing rate (GPR) was always >96% and on average > 99% for the MC algorithm; PB algorithm had a GPR ≤90% for all the delivery configurations with single slab (apart 95 % GPR from gantry 0° and small air gap) and in case of two slabs of the head phantom the GPR was >95% only in case of small air gaps for all the three (0°, 45°,and 70°) simulated beam gantry angles. Overall the PB algorithm tends to overestimate the dose to the target (up to 25%) and underestimate the dose to the organ at risk (up to 30%) (Figure 1). We found similar results (but a bit worse for PB algorithm) for the two targets of the lamb’s head where only two beam gantry angles were simulated (Table1).

Conclusion Our results suggest that in PBS proton therapy Range Shifter need to be used with extreme caution when planning the treatment with an analytical algorithm due to potentially great discrepancies between the planned dose and the dose delivered to the patients, also in case of brain tumours where this issue could be underestimated. Our results also suggest that a MC evaluation of the dose has to be performed every time the RS is used and, mostly, when it is used with large air gaps and beam directions tangential to the patient surface. OC-0088 Simultaneous consideration of biologyical and physical uncertainties in robust ion therapy planning H.P. Wieser 1,2,3 , N. Wahl 1,3,4 , P. Hennig 5 , M. Bangert 1,3 1 German Cancer Research Center DKFZ, Medical Physics in Radiation Oncology, Heidelberg, Germany 2 University of Heidelberg, Medical Faculty, Heidelberg, Germany 3 Heidelberg Institute of Radiation Oncology, HIRO, Heidelberg, Germany 4 University of Heidelberg, Physics Faculty, Heidelberg, Germany Purpose or Objective Particle therapy is particularly prone to uncertainties. While this issue is commonly addressed for range and setup errors with robust optimization, uncertainties in the relative biological effectiveness (RBE) models are usually not considered. Especially for carbon ion therapy, where RBE induces pronounced non-linear modulation within the RBE-weighted dose (RBExD), biological uncertainties may be of particular importance. Here, we present a computational pipeline that combines physical and biological uncertainties simultaneously into a joint probabilistic optimization process. Our work builds upon analytical probabilistic modeling (APM) that uses closed form expressions for the computation of the expectation value of the RBExD and its standard deviation. Material and Methods APM builds upon a Gaussian parameterization of the RBExD calculation directly operating on α x dose (shown in figure 1) and sqrt(β) x dose (not shown) profiles. With such a formulation, uncertainties in range and setup can be modeled as positional uncertainties in the individual Gaussian components (range uncertainties are indicated by horizontal error bars in figure 1 (setup uncertainties are not shown). Uncertainties in the biological model, i.e., uncertainties in depth dependent dose averaged α c and β c can be modeled as uncertainty in the weight (i.e. the height) of the individual Gaussian components (indicated by vertical error bars in figure 1). Assuming a joint Gaussian probability distribution over component 5 Max Planck Institute for Intelligent Systems, Probabilistic Numerics, Tuebingen, Germany