Platform for automatic patient quality assurance via Monte Carlo simulations in proton therapy. (February 2020) - Guterres Marmitt, G.; Pin, A.; Ng Wei Siang, K.; Janssens, G.; Souris, K.; Cohilis, M.; Langendijk, J.A.; Both, S.; Knopf, A.; Meijers, A.
Highlights: Radiotherapy plan QA via Monte Carlo simulations available in Open Source platform. Monte Carlo dose engine validated with heterogeneous real animal tissue. Retrospective evaluation of clinical plans shows consistency with measurement QA. Automated workflows significantly reduced in-room time required for QA procedures. Abstract: For radiation therapy, it is crucial to ensure that the delivered dose matches the planned dose. Errors in the dose calculations done in the treatment planning system (TPS), treatment delivery errors, other software bugs 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 necessary. These measurement based approaches, however, are performed with 2D detectors, with limited resolution and in a water phantom. Moreover, they are work intensive and often impose a bottleneck to treatment efficiency. In this work, we investigated the potential to replace measurement-based approach with a simulation-based patient specific QA using a Monte Carlo (MC) code as independent dose calculation engine in combination with treatment log files. Our developed QA platform is composed of a web interface, servers and computation scripts, and is capable to autonomously launch simulations, identify and report dosimetric inconsistencies. To validate the beam model of independent MC engine, in-water simulations of mono-energetic layers and 30 SOBP-type dose distributions were performed. Average Gamma passing ratio 99 ± 0.5% for criteria 2%/2 mm was observed. To demonstrate feasibility of the proposed approach, 10 clinical cases such as head and neck, intracranial indications and craniospinal axis, were retrospectively evaluated via the QA platform. The results obtained via QA platform were compared to QA results obtained by measurement-based approach. This comparison demonstrated consistency between the methods, while the proposed approach significantly reduced in-room time required for QA procedures.