Monte Carlo secondary plan check: validation and definition of the action limits for patient QA
PO-1341
Abstract
Monte Carlo secondary plan check: validation and definition of the action limits for patient QA
Authors: Fotina|, Irina(1)*[irina_fotina@hotmail.com];Siamkousky|, Stanislau (2);Zverava|, Alyona(2);Alber|, Markus(3);
(1)IBA Dosimetry GmbH, Physics Dept., Nuremberg, Germany;(2)Minsk City Clinical Oncology Center, Radiation Therapy Dept., Minsk, Belarus;(3)ScientificRT GmbH/ University of Heidelberg, Dept. of Radiation Oncology, Munich, Germany;
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Purpose or Objective
The purposes of this study were 1) to assess the accuracy of 3D dose calculation with Monte Carlo (MC) algorithm of the automated treatment plan verification software SciMoCa v1.4.2 (Radialogica, USA) and 2) to establish action limits for plan QA based on the gamma criteria taking into account the sensitivity of SciMoCa to specific plan errors.
Material and Methods
The MC model for 6MV VersaHD linac was commissioned by comparisons between measured data and calculations done with SciMoCa and a MC-based TPS (Monaco 5.11, Elekta). In case of square fields the verification with water phantom measurements was performed, whereas for complex fields and 20 IMRT/VMAT (head, lung, breast) cases SciMoCa was compared to TPS and measurements with 2D-array. The same plans were used to determine the sensitivity of the γ-analysis in SciMoCa to treatment plan errors, such as incorrect HU-ED curve, density overrides or a wrong MLC offset in TPS model. Results were correlated with plan complexity indices and used to derive action limits for plan QA with SciMoCa.
Results
The study shows good agreement between SciMoCa, TPS and measurements for PDDs, profiles and point doses. A maximum difference of 0.4% and 1.5% in output factors was found for 2x2cm2 field for SciMoCa vs. measurement and TPS, respectively. For clinical cases 3D γ-analysis (global, 2%-1mm) vs TPS showed mean values of 99,2% in phantom and 97,5% in patient geometry. Analysis of measured 2D doses with 3%-3mm criteria for all cases showed mean global γ-values of 97.2% and 97.3% and local γ of 94.2% and 94.7% for TPS and SciMoCa doses, respectively.
The γ-analysis for SciMoCa vs. TPS with 2%-2mm and 2%-1mm criteria showed moderate correlation with gamma pass rates of the TPS calculation vs. measurement QA (r=0.54, p<0.05). Moderate correlation was present between SciMoCa γ-pass rates (2%-2mm and 2%-1mm) and few plan complexity indices: Field Irregularity (FI) (r= -0.66, p<0.05) and product of the FI and Small Segment Contribution index (r=-0.48, p<0.05).
SciMoCa plan QA results applying different γ-criteria for all plans with two exemplary induced errors are shown in the Fig 1.
For 2%-1 mm γ criteria sensitivity for MLC offset error detection was above 80% and 90% if the plan is considered passing with y>90% and >95%, respectively. Errors in density overrides or HU-ED conversion were detected with γ-criteria of 2%-1mm. Control charts (QACC) were created for the QA with SciMoCa to be used together with measurement QACC (Fig. 2) and γ-values of 94.5% and 92% were derived as new warning and failed plan criteria.
Conclusion
The study shows that 3D MC plan verification with tight gamma criteria (2%-2mm and below) provides reliable method to discover clinically relevant errors in beam model and plan parameters, besides rigorous second check of the TPS algorithm. Institution-specific control charts are useful tool to define action limits for calculation-based QA and enhance safety in clinical process.