ESTRO 36 Abstract Book

S236 ESTRO 36 _______________________________________________________________________________________________

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.

Conclusion Rapidplan DVH prediction is a useful tool to guide treatment plan generation, although significant prediction inaccuracies may occur, even when the training database consists of as many as 114 treatment plans. Since all plans to train and validate RapidPlan DVH prediction were Pareto optimal, prediction errors for a database of manually optimized treatment plans are likely larger than presented here. For models based on a small number of plans (N=20), prediction performance depends strongly on the selected training patients, therefore larger models are recommended. OC-0445 Probabilistic optimization of the dose coverage – applied to treatment planning of cervical cancer D. Tilly 1,2 , A. Holm 2 , E. Grusell 1 , A. Ahnesjö 1 1 Uppsala University Hospital, Department of Immunology- Pathology and Genetics, Uppsala, Sweden 2 Elekta, R&D, Stockholm, Sweden Purpose or Objective Probabilistic optimization is an alternative to margins for handling geometrical uncertainties in treatment planning of radiotherapy where the uncertainties are explicitly incorporated into the plan optimization through sampling of treatment scenarios and thereby better exploit patient specific geometry. In this work, a probabilistic method is presented based on statistical measures of dose coverage, similar to the basis for margin based planning. The idea is that the dose planner requests a dose coverage to a specified probability, which the algorithm then fulfils. Material and Methods The van Herk margin recipe is designed to deliver sufficient target dose coverage in 90% of the treatments. The probability is however rarely specified. We generalize this prescription approach to include the probability explicitly through the concept of Percentile Dose Coverage (PDC), i.e. the dose coverage that is at least fulfilled to a specified probability. The PDC used in this work for target minimum dose criterion is the probability for the dose-volume criteria D 98% to be fulfilled with 90% probability which we denote as D 98%,90% . For optimization, we make use of the Expected Percentile Dose Coverage (EPDC), defined as the average dose coverage below a given PDC. The EPDC is, in contrast to PDC, a convex measure which allows for standard optimization techniques to be used for finding an optimal treatment plan. We propose an iterative method where a treatment optimization is performed at each iteration and

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