ESTRO 36 Abstract Book

S813 ESTRO 36 2017 _______________________________________________________________________________________________

C. Cases 1 , A. Latorre-Musoll 1 , P. Carrasco 1 , N. Jornet 1 , T. Eudaldo 1 , A. Ruiz-Martínez 1 , M. Lizondo 1 , P. Delgado- Tapia 1 , M. Ribas 1 1 Hospital de la Santa Creu i Sant Pau, Radiofisica i Radioprotecció, Barcelona, Spain Purpose or Objective For SBRT lung treatments accurate 4D dose calculations, accounting for heterogeneities and respiratory motion, are crucial to determine an optimal ITV beyond a purely geometric ITV. We propose a model to predict an optimal ITV from a single figure computed from the Probability Density Function (PDF) of the breathing waveform. Material and Methods We used the QUASAR Respiratory Motion Phantom (Modus Medical Devices) with a cylindrical mobile wood insert as lung substitute and an inner 30mm diameter sphere as tumour substitute (GTV). We acquired 21 independent scans (CT z ) by axially shifting the mobile insert from z = – 10 to z = 10mm in 2mm steps. We generated 6 ITV: from ITV 0mm (static case, equal to the GTV of CT 0mm ) to ITV 10mm (overlap of all GTV from CT –10mm to CT 10mm ). We planned a SBRT treatment collimating to each ITV (from PLAN 0mm to PLAN 10mm ) in Varian Eclipse (AAA v13.5) using a 6MV non- coplanar 3DCRT technique. Due to the in silico nature of the study we added no extra margins to the ITV. We considered 3 breathing patterns: sinusoidal (provided by QUASAR software), free and trained (obtained by the Varian RPM from real patients). We rescaled every waveform to amplitudes from 2 to 10mm in 2mm steps. We built the actual 4D dose distribution for every PLAN z considering all combinations of breathing patterns/amplitudes. We first copied the original treatment (planned at CT 0mm ) to the remaining CT z scans and recalculated them by using fixed MU. Then we copied the resulting dose matrices back to the CT 0mm scan and we shifted them axially by -z. Later, we summed the dose matrices using weights derived from the PDF of the underlying waveforms. We defined a Quality Index which balances GTV coverage and healthy tissue-sparing as QI = (V 100%,GTV ) 2 /V PD , where V 100%,GTV and V PD stand for the percentage of GTV covered with the Prescribed Dose (PD) and the volume of the PD isodose respectively. The optimal plan, PLAN opt , was the highest QI scoring plan for each breathing pattern/amplitude. Finally, we assessed the PDF’s measure of central tendency that best predicts PLAN opt irrespective of the breathing pattern/amplitude. Results Figure 1a shows the QI for the sinusoidal movement. Every breathing pattern’s maximum QI scores project an optimal curve to the x-y plane (figure 1b). For any breathing pattern/amplitude, PLAN opt was found to be conformed to an optimal ITV smaller than the purely geometric ITV. We found that the integral of the PDF between ±x, i.e., the time fraction on which the GTV is on the central part of the respiratory excursion, was the best predictor of PLAN opt. Irrespective of the breathing pattern/amplitude all PDF’s integrals collapsed to a unique curve for x = 3mm (figure 2). Conclusion Based on 4D dose calculations, we propose a QI to reduce the ITV while maintaining the GTV coverage for SBRT lung treatments. We provide a model to predict the optimal ITV from the integral of the PDF of the breathing waveform. Partially financed by FIS PI15-00788 grant. EP-1533 Modulation complexity assessment in VMAT plans from different treatment planning systems. P. Winkler 1 , A. Trausnitz 2 , J. Schroettner 2 , A. Apfolter 1 , K. Kapp 1 1 Medical University of Graz, Department of Therapeutic Radiology and Oncology, Graz, Austria 2 University of Technology, Institute of Health Care Engineering, Graz, Austria

Purpose or Objective Modulation complexity (MC) in Linac-based VMAT plans might influence the accuracy of dose calculation and dose delivery as well as the precision of dose delivery. However, MC is not a single-parametric property, but rather consists of several different influencing factors, e.g. average leave speed (ALS), leave sequence variability (LSV), mean field aperture area (FAA), aperture area variability (AAV), gantry acceleration (gantry-speed variation, GSV) and dose rate variability (DRV), which might predominantly be correlated with uncertainties in either dose calculation or delivery. In our clinical treatment plans we observed, that different TPS accomplish strong modulation in a noticeably different way, forcing either ALS, LSV and AAV whilst retaining moderate GSV and DRV, or vice versa. The aim of this study is to present several distinct modulation complexity indices (MCI), describing the different aspects of modulation complexity in VMAT plans, and to assess the characteristic ranges of these MCI for different TPS. Material and Methods We established six MCI, parameterising the magnitude of ALS, LSV, FAA, AAV, GSV and DRV in a VMAT-arc, and implemented their calculation in our automated plan-QA software tool (in-house development). The MCI for 200 randomly selected clinical beams were calculated for the TPS Eclipse (Varian) and Pinnacle (Philips), respectively. Additionally 20 phantom plans (37 arcs) with increasing modulation complexity were generated for both of the two TPS using best possible matching of optimization criteria, and were subsequently analysed. Results In the phantom plans, the Pinnacle-optimized arcs showed significantly higher average leaf speed compared to Eclipse-optimized arcs (10.9 and 6.7 mm/sec, respectively). Aperture – opening was 40% larger for the Eclipse-arcs. Consequently, the number of monitor units was smaller in the Eclipse plans (-32%). Whereas the differences for LSV and AAV were rather small (figure 1), DRV and GSV differed significantly, revealing a more pronounced modulation in the Pinnacle plans as far as dose rate and gantry acceleration are concerned. Findings for retrospectively analysed clinical plans and (non-biased) phantom plans were similar.

Figure 1: Modulation complexity scores (LSV: leave sequence variability, AAV: aperture area variability, GSV: gantry-speed variation, DRV: dose rate variability) for 200 VMAT plans, calculated with Eclipse and Pinnacle TPS, respectively. Box-plots showing median, first and third quartile and range. Conclusion Modulation complexity in VMAT plans has a potential impact on dose-calculation and –delivery accuracy. We found considerable differences for two different TPS in multi-parametric assessment of MC features, indicating the diverging algorithms of the different optimizers. A