S2339
Physics - Quality assurance and auditing
ESTRO 2026
Purdie TG. A new metric for assessing IMRT modulation complexity and plan deliverability. Med Phys. 2010;37(2):505-515. doi:10.1118/1.3276775. Keywords: Complexity metrics, virtual PSQA, intra- plan
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Statistical Process Control to evaluate process capability indices and control limits in helical TomoTherapy quality assurance Aspasia Evgeneia 1 , Vasiliki Peppa 1,2 , Argyris Moutsatsos 3 , Liana Sideri 3 , Panagiotis Archontakis 3 , Anastasia Stergioula 3 , Evaggelos Pantelis 1,3 1 Medical Physics Lab, Medical School, National and Kapodistrian University of Athens, Athens, Greece. 2 Radiotherapy Department, General Hospital of Athens Alexandra, Athens, Greece. 3 Radiotherapy Department, Iatropolis Clinic, Athens, Greece Purpose/Objective: This study employs Statistical Process Control (SPC) methods to evaluate process capability indices (PCIs) and control limits for TomoTherapy machine parameters obtained with the TomoTherapy Quality Assurance (TQA) tool, aiming to characterize process variability and assess the alignment of QA process output with specified performance requirements. Material/Methods: Measurements of fourteen parameters related to output, energy, field width, multileaf collimator (MLC) position and timing, and couch offset, were collected using the “Daily QA” and “Step Wedge Helical” TQA modules of a TomoTherapy® HDA platform (Accuray Inc, CA, USA), between August 2024 to July 2025 (total 239 measurements per parameter). For each parameter, time-ordered individual (I) chart plots were constructed, and normality was assessed using the Anderson–Darling test. Parameters with normally distributed data were analyzed using the conventional Shewhart method, whereas the heuristic skewness correction approach was applied to non-normally distributed parameters. Lower (LCL) and upper (UCL) control limits were determined from in-control datasets obtained through an “Identify–Eliminate– Recalculate” iterative procedure. Corresponding PCIs (Cpk, Cpmk) were calculated based on vendor defined or literature-recommended target and specification limits. Results: SPC analysis results are presented in Table 1. As can be seen, most parameters demonstrated high process capability (Cpk, Cpmk > 1) with mean values well centered within specification limits, indicating stable and reproducible machine performance. Parameters such as Exit Detector Flatness, Field Width Constancy,
Conclusion: Beam-level complexity analysis reveals significant intra-plan variability in most class solutions. This is particularly the case for mixed techniques (VMAT, IMRT, and RAD). This supports shifting from plan-level to beam-level assessment to define more accurate thresholds and improve QA strategies, especially in adaptive workflows where measurement-based QA is impractical. A prospective study will aim to create such tighter thresholds for adaptive workflows. References: [1] Chiavassa S, Bessieres I, Edouard M, Mathot M, Moignier A. Complexity metrics for IMRT and VMAT plans: a review of current literature and applications. Br J Radiol. 2019;92(1102):20190270. doi:10.1259/bjr.20190270. [2] Schuring D, Westendorp H, Van der Bijl A, et al. NCS Report 35: Quality Assurance of Treatment Planning Systems. 2022. doi:10.25030/ncs-035. [3] Crijns W, Defraene G, Van Herck H, et al. Online adaptation and verification of VMAT. Med Phys. 2015;42(7):3877-3891. doi:10.1118/1.4921615. [4] McNiven AL, Sharpe MB,
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