RESUMO
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.
