Abstract
Purpose
Dose volume histogram (DVH)‐based analysis is utilized as a pretreatment quality assurance tool to determine clinical relevance from measured dose which is difficult in conventional gamma‐based analysis. In this study, we report our clinical experience with an ionization‐based transmission detector and model‐based verification system, using DVH analysis, as a comprehensive pretreatment QA tool for complex volumetric modulated arc therapy plans.
Methods and Materials
Seventy‐three subsequent treatment plans categorized into four clinical sites (Head and Neck, Thorax, Abdomen, and Pelvis) were evaluated. The average dose (Dmean) and dose received by 1% (D1) of the planning target volumes (PTVs) and organs at risks (OARs) calculated using the treatment planning system (TPS) were compared to a computed (model‐based) and reconstructed dose, from the measured fluence, using DVH analysis. The correlation between gamma (3% 3 mm) and DVH‐based analysis for targets was evaluated. Furthermore, confidence and action limits for detector and verification systems were established.
Results
Linear regression confirmed an excellent correlation between TPS planned and computed dose using a model‐based verification system (r2 = 1). The average percentage difference between TPS calculated and reconstructed dose for PTVs achieved using DVH analysis for each site is as follows: Head and Neck — 0.57 ± 2.8% (Dmean) and 2.6 ± 2.7% (D1), Abdomen — 0.19 ± 2.8% and 1.64 ± 2.2%, Thorax — 0.24 ± 2.1% and 3.12 ± 2.8%, Pelvis 0.37 ± 2.4% and 1.16 ± 2.3%, respectively. The average percentage of passed gamma values achieved was above 95% for all cases. However, no correlation was observed between gamma passing rates and DVH difference (%) for PTVs (r2 = 0.11). The results demonstrate a confidence limit of 5% (Dmean and D1) for PTVs using DVH analysis for both computed and reconstructed dose distribution.
Conclusion
DVH analysis of treatment plan using a model‐based verification system and transmission detector provided useful information on clinical relevance for all cases and could be used as a comprehensive pretreatment patient‐specific QA tool.