ESTRO 2021 Abstract Book

S742

ESTRO 2021

Conclusion Octavius 1600SRS outperformed 729 and 1500 because of its higher spatial resolution. Sub-optimal deliveries can be predicted from plan complexity metrics using SPC tools. However, correlations depended on the QA device and on the complexity metric used and must be considered institution-specific.

Poster discussions: Poster discussion 30: Challenges and technology of proton-therapy

PD-0902 Do we need a precise proton machine-specific delivery sequence to assess the interplay effect? X. Ding 1 , L. Zhao 1 , G. Liu 1 , W. Zheng 1 , J. Shen 2 , A. Lee 3 , Y. Di 1 , R. Deraniyagala 1 , C. Stevens 1 , X. Li 1 , S. Tang 3 1 Beaumont Health, Radiation Oncology, Royal Oak, USA; 2 Mayo Clinic, Radiation Oncology, Scottsdale, USA; 3 Texas Center for Proton Therapy, Radiation Oncology, Irving, USA Purpose or Objective Proton spot delivery sequence plays a critical role in evaluating the motion interplay effect in pencil beam scanning technique. The proton vendor may offer a generic tool to simulate the delivery time for a specific machine. There also have been some efforts to model the clinical proton system, such as an IBA ProteusPLUS® from West German Proton Therapy Essen(WPE). However, whether or not other proton therapy institutions could use these models directly in clinic remains unanswered. This study proposed an experimental approach to build a precise machine-specific beam delivery time(BDT) prediction and delivery sequence model and compared the result with the generic tool and WPE model. Materials and Methods Test fields and clinical treatment plans were used to drive each beam delivery parameters that impacted BDT experimentally. The machine delivery log files were retrospectively analyzed to quantitatively model energy layer switching time (ELST), spot switching time (SSWT), and spot drill time (SDT) for standard and volumetric repainting delivery. Additionally, the minimum MU threshold per spot was derived for layer repainting deliver sequence model. A total of 103 clinical treatment fields (including standard delivery, volumetric, and layer repainting delivery) were used to validate the machine-specific model and compared to the BDT prediction from the treatment console, the generic tool. To evaluate the interplay effect, digital thoracic target 4DCT image sets were used to compare the results between the machine-specific model and the WPE model published previously. Results The study found that ELST is not stochastic; instead, it depends on the file size from the two kinds of data files transmitted between two sequential radiation energy layers. In other words, the ELST is also related to the network transmission speed of the specific proton therapy center. The validation showed that the accuracy of each component of the BDT matches well between machine log files and machine-specific model. More specifically, the difference of ELST, SSWT, and SDT were -0.99+3.18%, 4.66+10.88%, and -3.89%+10.30%, respectively. The average total BDT was about -0.68+3.41% difference compared to the actual treatment log files, which is significantly improved from the machine's generic model (67.22+26.19%). The delivery sequence and interplay effect results could be very different between the machine-specific model and the WPE model due to the different machine parameters and modeling (figure 1&2).