ESTRO 38 Abstract book

S960 ESTRO 38

to develop a fast, automated and accurate patient specific QA. Material and Methods The QA platform is composed of a web interface, servers and computation scripts, and is capable to autonomously launch simulations, identify and report dosimetric inconsistencies. After plan approval in TPS, data are exported to a dedicated DICOM server, that triggers the QA workflow for the TPS-based plan. A preliminary dose recalculation automatically takes place and gamma analysis results, comparing original and recalculated dose maps, become available at the website. After review, the dry-run of the plan is performed to generate machine log files and, upload of the logs, triggers the log-based plan QA workflow. A log-based plan is reconstructed from the retrieved log files, and after the dose calculation, results for the 2 nd workflow are available for review. For validation purposes of the MC beam model, a set of standardized plans (SOBPs) were designed and in-water calculations were compared between TPS and QA platform. Also, for a set of 10 clinical plans the QA results obtained from the QA platform were compared to the QA results from standard measurement-based approach. Results To perform patient QA according to the proposed methodology 0-15 min of in-treatment-room time are required and additional 5-10 min for data export from TPS and retrieval of the log files. Independent MC calculations require 15-20 minutes. In-water simulations over 30 SOBP plans with varying range and modulation showed a 99% ± 0.5% gamma pass ratio (2 mm, 2%) when compared to the TPS calculated dose maps. Comparison between QA platform and measurement-based results for 10 clinical cases, including the following indications: craniospinal axis, intracranial and head and neck, is summarized in Fig. 1. An example of calculated dose distributions for case #10 is shown in Fig. 2.

Note that this LINAC is resilient to leaf calibration changes of 200 item part value numbers, which equates to 0.2 mm, on each bank. This process was repeated on 7 linear accelerators in total.

By determining the SWOF at extremities of the leaf calibration range of each LINAC it is possible to determine the tolerance range for the SWOF. Figure 2 shows the SWOF tolerances for seven linear accelerators. The figure shows that sufficient overlap in the results to allow a single tolerance range for the whole fleet. The fleet wide SWOF tolerance range is 0.1169 to 0.1211, or 0.119 +/- 1.8 %. Conclusion The sliding window output test can be used to monitor the MLC calibration. The sliding window output test has been benchmarked against standard verification measurements using two complex VMAT deliveries across seven linear accelerators. A single tolerance range for the sliding window output factor has been established that can be used across the fleet. EP-1776 Automated proton plan QA via independent Monte Carlo simulations G. Guterres Marmitt 1 , A. Pin 2 , J.A. Langendijk 1 , S. Both 1 , A. Knopf 1 , A. Meijers 1 1 University Medical Center of Groningen, Department of Radiation Oncology, Groningen, The Netherlands ; 2 Ion Beam Applications, Reasearch Group, Louvain-la-Neuve, Belgium Purpose or Objective For radiation therapy, it is crucial to guarantee that the delivered dose matches the planned dose. Uncertainties in the dose calculations done in the treatment planning system (TPS), delivery errors or data corruption during transfer might lead to significant differences between predicted and delivered doses. As such, patient specific quality assurance (QA) of dose distributions, through experimental validation of individual fields, is currently necessary. In this work, we investigate the potential to replace the measurement-based patient specific QA with a simulation-based patient specific QA using a Monte Carlo (MC) code as independent dose calculation engine in order

Conclusion A new patient specific QA workflow was developed inside an automated web platform. The clinical application of this method has the potential to greatly reduce QA time. Validation with calculations in water shows great consistency of the MC engine over a wide range of beam- sets. The retrospective application of the approach for a set of 10 clinical plans implicate that the same decisions