ESTRO 36 Abstract Book

S436 ESTRO 36 2017 _______________________________________________________________________________________________

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.

A radiation oncologist found 96% of cervical cancer beam apertures were clinically acceptable, with all failures caused by a slight error in the position of the superior border. The primary and secondary aperture calculations agreed with average DICE and mean absolute distance of 0.93 and 5.5mm, respectively. An example is shown below. Automated beam weighting reduced hotspots by 1.5% on average.

Conclusion Normal tissue segmentation for head/neck cancer patients and determination of the jaw/MLC for cervical cancer patients are very successful. Both have been introduced into use in our clinic. Next steps include full evaluation of the resulting dose distributions, and assessing the use of these techniques for a prototype linac with flattening- filter-free beam and novel MLC design. PO-0821 Automatic re-planning of VMAT plans in prostate and HN patients using constrained optimization L. Künzel 1 , O. Dohm 2 , M. Alber 3 , D. Thorwarth 1 1 University Hospital Tübingen Eberhard Karls University Tübingen, Section for Biomedical Physics, Tübingen, Germany 2 University Hospital Tübingen Eberhard Karls University Tübingen, Radiation Oncology Division of Medical Physics, Tübingen, Germany

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