ESTRO 2020 Abstract Book

S416 ESTRO 2020

we will discuss the basic structure of the data found within the log files and how one can interpret them. We will then discuss the pros and cons of using log files in a QA program. Finally, we will present a log file based analysis that can be used to automate record-based delivery checks between the treatment planning system, the record-verify system and the linac. Those checks can be done for every patient and every fraction. Learning Objectives: -Understand the data found in Log Files -Review of log file data evaluation methodologies -Provide an overview of the log file-based QA process including its benefits and potential challenges SP-0746 Log file based QA in practice – proton beam therapy F. Albertini 1 1 Paul Scherrer Institute PSI, Center for Proton Therapy CPT, Villigen PSI, Switzerland Abstract text The clinical standard for patient specific quality assurance (PSQA) in proton therapy still consists in time-intensive measurement of the planned dose distribution. Additionally, these measurements do not consider the influence of density heterogeneities in the patient and are insensitive to daily delivery errors. In the last years it has been proposed to use the measured beam parameters, recorded during the delivery in log-files , to reconstruct the delivered dose in the patient [1–8]. This approach has been suggested to be a faster alternative to the standard PSQA. In this talk, a review of the current literature will be presented. Besides, it will be shown that the log-file based QA is not only a valid strategy but also that it is more sensitive to detect delivery errors (e.g. unplanned systematic shift of the Bragg peak position) than the PSQA based on measurements. Additionally, it will be shown that by reconstructing the dose with a Monte Carlo algorithm is the most accurate way to verify the daily delivered dose in the patient anatomy. With this approach indeed both calculational and delivery errors are considered. [1] Meier G, et al Phys Med Biol 2015 [2] Zhu X, et al. Cancers (Basel) 2015 [3] Matter M, et al. Phys Med Biol 2018 [4] Meijers A, et al. Med Phys 2019 [5] Scandurra D, et al. Phys Med Biol 2016 [6] Belosi MF, et al Radiother Oncol 2017 [7] Winterhalter C, et al Phys Med Biol 2019 [8] Toscano S, et al. Phys Med Biol 2019 SP-0747 What log file based QA can do and what it cannot solve M. Pasler 1 1 Lake Constance Radiation Oncology Center, Department for Medical Physics, Friedrichshafen, Germany Abstract text Log files provide versatile applications as complementary methods to typical QA measurements, especially with regard to complex and adaptive radiotherapy techniques. For most radiotherapy treatment units, log files are freely accessible, for others however, log file access is limited. Studies reporting on log file applications are therefore unevenly published, depending on access to log file data. In-house developed software tools or commercially available products process and evaluate log files for multiple purposes including data transfer testing,

visualising dynamic parameters, localizing machine malfunctions, machine performance monitoring, inter- institutional comparison, prediction of treatment plan deliverability/ robustness and dose recalculation. However, log files cannot replace every component of the dosimetric and technical QA chain: for instance, log file evaluation does not provide any check on aspects such as TPS beam modelling issues, absolute dose output calibration, patient changes during treatment or IGRT issues. Moreover, log files are generated by the delivery system and rely on feedback data from the linac and hence they are not strictly an independent measure. This presentation gives an overview of the potentials and limitations of log file analysis with a focus on state-of-the- art linear accelerators. SP-0748 The worst-case scenario survival guide: uncertainties in proton therapy. M. Lowe 1 1 The Christie NHS Foundation Trust, CMPE, Manchester, United Kingdom Abstract text The finite range and sharp distal dose fall-off of proton beams can confer an advantage compared to photons when it comes to producing dose distributions within a patient but these same properties make proton plans more sensitive to various uncertainties. This talk will address the main sources of uncertainty in proton therapy and their effects on the delivered dose distribution. We will look at how plans can be evaluated in the face of uncertainty and what we should look at when what we are used to seeing on screen in the treatment planning system may be unreliable. We will discuss what can be done to mitigate the effects of inevitable uncertainties, what should be anticipated upfront and whether it is always worth the cost of being robust to uncertainty. SP-0749 A systematic, large-scale planning comparison for patient selection in proton therapy S. Habraken 1,2 1 Erasmus Medical Center Cancer Institute, Radiation Oncology, Rotterdam, The Netherlands ; 2 holland Proton Therapy Center, Radiation Oncology, Delft, The Netherlands Abstract text In the Netherlands, the indication for proton therapy is model-based in a majority of cases. A photon treatment plan from the referring institute is compared to a proton plan on the same dataset (planning CT scan and delineation), made by the proton therapy facility. Differences in normal tissue complication probability (NTCP) are calculated and evaluated. For head-and-neck cancer, proton therapy is reimbursed if the model risk of xerostomia, dysphagia, and tube feeding dependence can be reduced by 10%, 10%, and 5%, respectively. Proton therapy is indicated for neurological tumors with a favorable prognosis (10-year survival 50% or more) if the mean dose to the supratentorial brain outside the target and/or the hippocampi can be reduced by 5% or more. For breast cancer, proton therapy is reimbursed if the model risk of (late) heart toxicity can be reduced by 2% or more. In this talk, I will review and discuss clinical experience with large-scale plan comparisons for head-and-neck, neurological and breast cases. The following topics will be Symposium: PBT planning: lessons learned