ESTRO 36 Abstract Book

S235 ESTRO 36 _______________________________________________________________________________________________

OC-0444 Pareto-optimal plans as ground truth to validate a commercial knowledge-based DVH- prediction system E. Cagni 1 , A. Botti 1 , Y. Wang 2 , M. Iori 1 , S.F. Petit 2 , B.J. Heijmen 2 1 Arcispedale S. Maria Nuova - IRCCS, Medical Physics Unit, Reggio Emilia, Italy 2 Erasmus MC Cancer Institute, Department of Radiation Oncology, Rotterdam, The Netherlands Purpose or Objective The purpose of the current study was two fold. First, to evaluate the DVH prediction accuracy of RapidPlan (Varian Medical Systems, Palo Alto) using a large database of Pareto optimal treatment plans. These were consistently generated with automated prioritized multi-criterial treatment plan optimization, independent of RapidPlan, and therefore can be considered as an unbiased, ground truth of achievable plan quality. Second, to determine the importance of the size/variability of the plan database on the accuracy of the RapidPlan DVH predictions. Using the automatically generated plans, the prediction accuracy of RapidPlan could be investigated without an impact of unavoidable plan quality variations related to manual planning. Material and Methods A previously published database of 115 Pareto-optimal prostate VMAT plans, consistently generated with automatic prioritized planning, was used in this study [Wang et al, PMB, 2016]. Separate Rapidplan prediction models were generated for training groups consisting of 20, 30, 45, 55, or 114 randomly selected plans, the latter using a leave-one-out technique. In a second experiment, Model-20 was also built for 4 other groups of randomly selected training patients. Prediction accuracy of all models was assessed using a fixed, independent validation group of 60 plans and comparing predicted dose parameters (rectum Dmean, V65, and V75, anus Dmean, and bladder Dmean) with the achieved values of the Pareto optimal plans. Results For Model-114, the absolute (relative) prediction errors (mean±SD), for rectum Dmean, V65, and V75 were 1.8±1.4Gy (6.9±5.6%), 1.0±0.9% (8.9±13.4%), and 1.6±1.4% (36.3±62.2%), respectively. For anus and bladder Dmean, these errors were 2.2±1.7Gy (18.3±21.3%) and 1.8±1.3Gy (4.9±4.2%), respectively. For 63.3% of the validation plans, Model-114 predicted a lower rectum V65 than could actually be achieved. Because of the prioritized optimization used for generating the input Pareto-optimal plans, this can only be realized by underdosing the target. In 36.7% of plans, the predicted V65 was higher than obtained in the input plan, possibly losing an opportunity for lower rectum dose. Table 1 demonstrates equal prediction accuracies for model-114 and the smaller models (only first Model-20 included). Table 2 compares Model-114 with all 5 investigated Models-20, showing significant differences in performance of the Models-20.

Conclusion Robust CTV-based VMAT optimization in head and neck patients resulted in improved estimated actual given dose distributions with lower normal tissue dose and equal target coverage compared to non-robustly optimized plans. This is the first study to compare robustly optimized photon plans to non-robustly optimized photon plans in terms of dose accumulation using daily CBCT images. The differences in dose are deemed clinically relevant and are expected to lead to an improved method of patient selection for proton therapy.