ESTRO 36 Abstract Book

S234 ESTRO 36 2017 _______________________________________________________________________________________________

the EPDC constraint tolerance is adjusted gradually until a desired PDC is met. We have tested our probabilistic planning method based on datasets containing multiple imaging for four cervical cancer patients treated with VMAT (2 Gy, 23fx). The datasets formed the basis for a statistical shape model (SSM) that provided the scenario specific sampled deformations. A set of 100 scenarios sampled from the SSM was included in the probabilistic optimization. A final iteration using 400 scenarios was performed to increase the resulting precision. A set of 1000 independent scenarios not part of the optimization was used to verify that the requested PDC was met. Results For all patients in this work, the iterative process of finding the EPDC tolerance to fulfil the requested PDC converged in less than 10 iterations to within 0.1 Gy of the requested PDC (95% of 46 Gy = 43.7 Gy), see figure 1. The verification calculations showed that the requested PDC was met within 1.3%, see table 1.

Vesicles (PSV), Prostate & Pelvic Nodes (PPN) and Head & Neck (HN). Material and Methods A fully automated VMAT planning system has been developed using the scripting functionality of RayStation (RaySearch Laboratories, Stockholm, Sweden). For each treatment site, a set of clinical priorities is determined as a list of constraints and ‘tradeoffs’. The system is designed to ensure constraints are met while optimization of the prioritized tradeoffs is guided by an a priori calibration process. This process involves optimizing for the first priority trade-off with a range of objective weights. A GUI allows the user to navigate through these plans using the DVH and dose-distribution to determine the optimal weight. This weight is then stored and the process is repeated for the next priority. When all trade-offs have been optimized, the whole process is repeated to refine the set of objective weights. As the underlying automated-planning algorithm tailors the base plan to individual patient anatomy, a single set of configuration data can be used for all patients of a given site and plan can be generated with no user interaction. Ten patients of each configured site were planned using the automated system and compared against the clinically approved manual plan. Quantitative comparisons were made using relevant DVH metrics and qualitative comparisons of dose distributions. Results A selection of the DVH metrics, averaged across all patients in each site, is listed in Table 1. A set of representative dose distributions is provided in Figure 1. The tabulated data shows that the automated plans tend towards improved OAR sparing. For PSV and PPN, this was at the expense of target coverage. For HN plans, the automated plans improved coverage while also reducing OAR doses. Visual comparisons of dose distributions showed that, for all three sites, the automated plans were of equal or better quality relative to manually optimized plans. All plans met local clinical DVH constraints and were deemed to be clinically acceptable.

Figure 1. The convergence of D 98%,90% per iteration towards the requested indicated by the dashed line. A full probabilistic optimization is performed per iteration. Table 1. The D 98%,90% after optimization and verification calculations. Patient 1 PDC optimization [Gy] PDC verification [Gy] 1 43.4 43.2 2 43.8 43.8 3 43.4 43.4 4 43.1 43.1 Conclusion We proved that a probabilistic planning algorithm can be formulated such that the dose planner can request a PDC which the algorithm attempts to fulfil. Results for datasets of four cervical cancer patients indicate that the requested PDC was fulfilled within 1.3%. OC-0446 A Fully Automated VMAT Planning System with Site-Configurable Algorithm M. Chu 1 , R. Maggs 1 , M. Smyth 1 , R. Holmes 1 , D.G. Lewis 1 , J. Staffurth 2 , E. Spezi 3 , A.E. Millin 1 , P.A. Wheeler 1 1 Velindre Cancer Centre, Medical Physics, Cardiff, United Kingdom 2 Cardiff University, School of Medicine, Cardiff, United Kingdom 3 Cardiff University, School of Engineering, Cardiff, United Kingdom Purpose or Objective One of the key benefits to automation of the treatment planning process is that consistency in plan quality can be maintained, regardless of user experience. To ensure that the plans are fully optimal, however, the system should allow incorporation of clinical experience and knowledge of the oncologist. This work presents an automated planning system that can be configured via a novel Pareto navigation process. A retrospective study was performed with thirty patients across three sites: Prostate & Seminal