ESTRO 36 Abstract Book

S441 ESTRO 36 _______________________________________________________________________________________________

3 University Hospital Heidelberg, Radiation Oncology, Heidelberg, Germany Purpose or Objective To develop and evaluate a new concept for automatic re- planning of VMAT plans as failure concept for solitary treatment machines, e.g. MR-Linac. In contrast to previously published automatic planning approaches which replicate the planned dose distribution, we propose an automatic re-planning concept which uses constrained optimization to generate Pareto-optimal VMAT plans for different treatment machines. The scheme interprets a treatment plan as a point on the corresponding Pareto front, and creates the re-planned one by projecting this point onto the substitute´s Pareto front. Thereby, comparable biological effect and hence clinical outcome can be guaranteed. Material and Methods In this automatic re-planning study, n=16 prostate cancer and n=19 head and neck cancer (HNC) cases were included. All patients had previously planned clinical VMAT plans created with in-house TPS Hyperion. Hyperion uses constrained optimization where a Lagrange multiplier λi is associated to each cost-function constraint Ci, rating the effect of each organ-at-risk (OAR) constraint on the target objective. Automatic re-planning starts from the initially reached optimal constraints Ci for PTVs and OARs and adapted machine parameters. A full optimization was executed automatically, in order to generate a comparable Pareto- optimal plan. For prostate cases, Elekta BeamModulator plans were re-planned for Elekta Agility, whereas for HNC, Elekta Agility plans were re-planned for Elekta MLCi. For prostate cases we identified rectum and bladder as main OARs and for HNC contralateral parotid gland and spinal cord. For PTV we evaluated variations in EUD, D Mean , D 2% and D 98% and for OARs EUD and D 2% . Results Automatic re-planning using constrained optimization was successful in all cases. Auto-optimized plans never corrupted OAR constraints, in some cases re-planning even improved OAR sparing. The mean deviation (range) in rectum EUD was 0,30% (-1,04 – -0,27%), bladder EUD 0,44% (-1,08 – -0,13%), parotid EUD -0,34% (-14,79 – 8,23%) and spinal cord EUD -0,02% (-0,49 – 0,31%). For the prostate cases the mean EUD deviation in PTV was -0,15% (-0,57 – 0,56%) and for the HNC cases -0,60% for PTV_60 (-2,58 – -0,08%) and -0,79% (-3,44 – 0,20%) for PTV_54, respectively. Except of 3 HNC cases, all evaluated parameters for targets showed variations within ±1%. For 3 HN cases the target EUD is reduced by up to 3.44%, indicated by λ > 10 * λ avg . Consequently, if all λ < 10* λ avg , the original and the re-planned plan comply with the given constraints and therefore represent the same optimal point on the Pareto-front, which means they are equal in terms of biological effect for targets and OAR.

quality. Furthermore this approach enables the identification of problematic plans beforehand. PO-0822 An evolutionary model improvement strategy for knowledge-based planning Y. Zhang 1 , F. Jiang 1 , H. Yue 1 , S. Li 1 , Q. Hu 1 , M. Wang 2 , H. Wu 1 1 Key Laboratory of Carcinogenesis and Translational Research Ministry of Education/Beijing- Department of Radiation Oncology- Peking University Cancer Hospital & Institute, Department of Radiation Oncology, Beijing, China 2 National Institute for Radiological Protection- China CDC, National Institute for Radiological Protection-, Beijing, China Purpose or Objective It was reported that RapidPlan, a knowledge-based solution, can improve planning efficiency, quality and consistency. Should the performance of RapidPlan be dependent on the quality of the plans training the model, this study hypothesizes that RapidPlan can improve its constituent plans (closed-loop) and improve the model itself by incorporating the better knowledge (evolution). Moreover, the maximum number of iterations to exhaust the full potential was also tested. Material and Methods An initial RapidPlan model (M 0 ) for pre-surgical rectal cancer patients was configured using 81 best-effort manual VMAT plans (P 0 ) with SIB (50.6 Gy and 41.8 Gy to 95% PTV boost and PTV respectively). For simplification, decreased or increased mean dose to both femoral head (FH) and urinary bladder (UB) were considered as improved (P + ) or worsened plans (P - ) respectively. P ± denoted intertwined plans. The first closed-loop iteration of re-optimizing P 0 using M 0 yielded P 1 : 69 P 1+ , 12 P 1± and 0 P 1- . By substituting P 1+ for their corresponding P 0 , the library of model M 1+ consisted of 12 P 0 and 69 P 1+ . The second closed-loop iteration of re-optimizing P 0 using M 1+ produced 35 P 2+ that were superior to both P 0 and P 1 , hence the knowledge base of M 2+ composed 9 P 0 , 37 P 1+ and 35 P 2+ . As open-loop validation, 30 clinical plans (P v ) that were not included in the model were re-optimized using each model. Re-optimization maintained all parameters except the MLC sequences were redesigned using the objectives generated by the models. Renormalizations to target prescriptions were performed to make OAR dose comparable. Results Consistent with literature, knowledge-based so lution improved the plan quality in both closed- and open-loop validations than the conventional trial-and-error process. In the first closed-loop evolution, the mean±SD of D mean_FH and D mean_UB for 69 P 1+ were 12.88±1.38 Gy and 23.06±3.11 Gy respectively, which were significantly lower by 23.70% and 9.53% than the corresponding values of P 0 (P<0.05). In the second round of closed-loop re-optimization, the D mean_FH of the 35 P 2+ decreased to 11.12±1.48 Gy (corresponding P 0 : 17.13±2.06 Gy; P 1 : 13.08±1.36 Gy), and D mean_UB decreased to 22.80±3.72 Gy (corresponding P 0 : 25.79±3.34 Gy; P 1 : 23.41±3.59 Gy). Table 1 and figure 1 display the open-loop validation results for various models. The marginal disparities of HI and CI (magnitudes ≦ 0.04) and largely overlapped DVH lines indicated comparable target dose distribution. In line with the closed-loop test, RapidPlan reduced the dose to OARs massively than clinical plans in the open-loop validation (Fig 1). The first model evolution has greatly and significantly lowered the dose to FH at comparable dose to the UB and targets. The second iteration has made little difference though.

Conclusion This study showed that fully automatic re-planning by taking a prescription list from previously optimized VMAT plans is feasible and successful in terms of equal plan