ESTRO 2023 - Abstract Book

S467

Sunday 14 May 2023

ESTRO 2023

The MLC models implemented in RayStation and Monaco could not accurately reproduce the leaf tip effects for the Agility. Therefore, trade-offs are needed and the optimal MLC parameters are dependent on the specific characteristics of treatment plans. Refining the MLC models for the Agility to better approximate the measured leaf tip and tongue-and- groove effects is needed to extend the validity of the MLC model, reduce the variability in the MLC parameters used by the community, and facilitate the standardization of the MLC configuration process.

[1] Phys Med Biol. 2017 Aug 1;62(16):6688-6707 [2] Phys Med Biol. 2020 Jul 27;65(15):155006

PD-0584 Multicenter validation of gynaecological Knowledge-Based Planning to automate plan optimization A. Zawadzka 1 , D. Kopec 1 , B. Bekman 2 , T. Siudzinski 3 , J. Kaminska 4 , A. Ryczkowski 5 , M. Poltorak 6 , T. Piotrowski 5 1 Maria Sk ł odowska-Curie National Research Institute of Oncology in Warsaw, Medical Physics Department, Warsaw, Poland; 2 The Maria Sklodowska-Curie National Research Institute of Oncology – branch in Gliwice, Radiotherapy Department, Gliwice, Poland; 3 Lower Silesian Oncology, Pulmonology and Haematology Center, Medical Physics Department, Wroclaw, Poland; 4 Medical University of Gdansk, Department of Radiation Oncology, Gdansk, Poland; 5 Greater Poland Cancer Centre, Medical Physics Department, Poznan, Poland; 6 Oncology Center in Siedlce, Medical Physics Department, Siedlce, Poland Purpose or Objective The study aimed to evaluate the results of knowledge-based planning models clinically validated in 6 sites on a non- homogeneous group of gynaecological patients as part of the model quality control (QC) program. Materials and Methods Six centres created and clinically validated RapidPlan (VARIAN) models for their own group of gynecologic patients. Each site then selected ten randomised gynaecological patients, other than those used in the model, anonymised and exported them. Patients were delineated according to their institution’s internal protocols: CTV, PTV, rectum, bladder, left and right femoral heads (FH), bones and bowels. Each site then prepared plans for a group of all 60 patients following its procedures. The Eclipse TPS was used (AAA - 4 sites, Acuros XB - 2 sites). The VMAT technique, X 6MV photon beams and TrueBeam machine (Varian) with MLC120 were used. Based on the model predictions, one optimisation was performed without the planner’s intervention. Data from 6 treatment plans were collected and compared for each patient. For CTV and PTV: D98, D95, D2, Dmax, V95% were analysed. For OARs, the following statistics were compared: rectum - V10, V20, V30, V40, V50, D0.03cm ³ , bladder - V20, V45, V50, D0.03cm ³ , bowels - V45, FH L/R - V35, Dmax, bones - Dmean. The statistical significance of the differences was tested with the Kruskal-Wallis test (p<0.5). Results For CTV only in one model (S5), parameter D98% > 95% was violated in 4 cases. In the rest, it was always fulfilled. For PTV, the lowest coverage was achieved by the S2 model (on average D98% = 94.3%). For the S1, S3, S4, S5 and S6 models, the average was 95.7%, 97.3%, 95.6%, 95.5%, and 98.8%, respectively. For the rectum and clinically meaningful parameters D0.03cm ³ and V50Gy, the maximum difference between models was on average 0.8 cm ³ and 1.45 Gy, respectively. Larger differences occurred in the moderated doses, reaching on average 17.7% for V30. The same was true for the bladder, where the models’ results did not differ clinically in high doses. The maximum difference for D0.03cm ³ was 0.4 Gy. In contrast, in moderated doses, the differences reached on average 26.3% (V20). For the bowels, the parameter V45 > 195 cm ³ was most often exceeded in S3 (22 times) and the least frequently in S2 (11 times). All models failed to meet the bowels constraints for the same 11 patients (the anatomical reasons). For bones, the maximum difference in mean dose was on average 3.4 Gy. Dmean > 25 Gy less often was violated for S5 (24 times) and the most for S3 (55 times). Conclusion The differences obtained by the six models were statistically significant, but the greatest differences were found in parameters not clinically relevant (moderated doses). The results were not influenced by the contouring method but by the requirements for dose distribution adopted at the site and the quality of learning plans. It seems that the mutual validation of models is valuable for an arbitrary assessment of their quality. PD-0585 Characterization of 3D printed material for end-to-end test phantoms in proton therapy J. Brunner 1,2 , M. Stock 2,3 , D. Georg 1 , B. Knäusl 1,2 1 Medical University of Vienna, Department of Radiation Oncology, Vienna, Austria; 2 MedAustron Ion Therapy Center, Medical Physics, Wiener Neustadt, Austria; 3 Karl Landsteiner University of Health Sciences, Medical Physics, Wiener Neustadt, Austria Purpose or Objective End-to-end (E2E) tests in radiation oncology aim to mimic a complete patient workflow by employing a phantom suitable for both imaging and dosimetry. In proton therapy tissue equivalency of phantom materials is essential, i.e. the matching of the CT numbers (CTN) with the stopping power ratio (SPR) assigned by a heuristic calibration curve [1] is the basis for any accurate comparison of dose prediction and measurements. This work aims to characterize 3D printed materials for the design of a versatile E2E test phantom for proton therapy. Materials and Methods For nine samples (5x5 cm2, thickness t = 1 cm) the water equivalent thickness (WET) was measured via a water column (Peakfinder, PTW) in a 148.2 MeV proton beam. To assess print quality/homogeneity, all measurements were repeated at three different points of the samples. Additionally, a CIRS CT calibration phantom equipped with cylindrical plugs from the same 3D printed materials was scanned at the CT scanner (kVp = 120V, slice thickness = 2mm) to determine the CTN (Fig1a), which in turn were used to derive the predicted SPR from the treatment planning system [2]. The employed 3D printing techniques were fused deposition modeling (FDM) (FunmatPro410, Intamsys), stereolithography (STL) (Form3, Formlabs) and selective laser sintering (SLS) (EOS P396, EOS). FDM printed materials included: acrylonitrile butadiene styrene (ABS), polylactic acid (PLA), high impact polystyrene (HIPS) and Nylon (NY-FDM). STL materials were: Durable10K (DUR), Tough2000 (TGH) and WhiteResin (WR). Nylon-12 (NY-12) was produced using SLS.