ESTRO 36 Abstract Book

S439 ESTRO 36 2017 _______________________________________________________________________________________________

here several sampling strategies. We will also show that current robust optimizers sample scenarios in a statistically inconsistent way. Material and Methods Sampling must optimize the trade-off between clinical optimality and robustness. Both were assessed by computing the volume of the BSPTV and a confidence interval (CI), respectively. The latter is defined as the percentage of all possible ranges and beam positions that the BSPTV encompasses. The findings can then be applied later to robust optimizers. We have designed a simulation phantom to model uncertainties in lung tumors (Figure 1). Standard deviations of the Gaussian distributions for (systematic) setup errors, baseline shifts, and CT conversion errors were 5 mm, 5 mm, and 2%, respectively. The errors were sampled following three different methods: 1. M1 (conventional approach): sampling of setup errors and baseline shifts within conventional lateral PTV margin for systematic errors (encompassing 90% of possible beam positions). The distal and proximal margins encompass 98% of possible proton ranges scaled by a flat CT conversion error (±3.3% to include 90% of possible CT conversion errors). M2: same as M1 with random sampling of the CT conversion error. M3: all errors are simulated within an iso- likelihood hypersurface including 90% of all possible scenarios. A fixed breathing-induced motion amplitude of 1 cm has been considered for every scenario. 2. 3.

errors and to blindness relative to their potential degeneracies. The CI for M2 equals 88%, but 90% CI can be achieved for M2 by extending slightly the lateral PTV margin to encompass 92% of possible beam positions and 98% of possible ranges, leading to a 425% volume, thus still better than M3.

Conclusion The best tradeoff between robustness and optimality was achieved through random sampling of all errors limited by the lateral conventional PTV margin and a large margin for the possible proton ranges. PO-0826 Evaluation of the new InCise MLC for Cyberknife stereotactic radiotherapy C. Limoges 1 , J. Bellec 1 , N. Delaby 1 , M. Perdrieux 1 , F. Jouyaux 1 , E. Nouhaud 2 , I. Lecouillard 2 , E. Chajon 2 , R. De Crevoisier 2,3,4 , E. Le Prisé 2 , C. Lafond 1,3,4 1 Centre Eugène Marquis, Medical Physics Department, Rennes, France 2 Centre Eugène Marquis, Radiation Oncology Department, Rennes, France 3 INSERM, U 1099, Rennes, France 4 University of Rennes1, LTSI, Rennes, France Purpose or Objective The aim of this study was to evaluate treatment planning performances of the new InCise multileaf collimator (MLC) with reference to the Iris variable circular aperture collimator for intracranial and extracranial Cyberknife stereotactic radiotherapy. Material and Methods The study was performed on a Cyberknife M6 v10.6 (Accuray). A total of 50 cases including 10 brain metastases, 10 acoustic neuromas, 10 liver targets, 10 spinal metastases and 10 prostate cases were investigated. For each case, two treatment plans were generated with TPS Multiplan v5.3 (Accuray): one plan using the InCise MLC v2 associated with the Finite Size Pencil Beam (FSPB) dose calculation algorithm and one plan using the Iris collimator associated with RayTracing (RT) or MonteCarlo (MC) dose calculation algorithm. Dose was prescribed near the 80 % isodose and normalized to obtain the same PTV coverage at ± 0.5 % for both plans.

Results BSPTVs equaled 430, 420 and 564% of the CTV volume for the three methods, respectively (see figure 2 that illustrates the range margins). M1 does not ensure statistical consistency because of the flat CT conversion error, which overemphasizes unlikely scenarios (large geometrical AND large CT conversion errors) and makes non-trivial the computation of the CI. M3 guarantees at least 90% CI, but with a 34% increase of the irradiated volume. The latter is due to the non-prioritization of