Room: 301
Purpose: To investigate the impact of delivery characteristics on the dose delivery accuracy of volumetric modulated arc therapy (VMAT) for different treatment sites.
Methods: The pretreatment quality assurance (QA) results of 344 VMAT patients diagnosed with gynecological (GYN), head and neck (H&N), rectal, and prostate cancer, respectively, were randomly chosen in this study. Gamma passing rate (GPR) was used to assess the dose delivery accuracy of VMAT plans. Eight metrics reflecting aperture complexity, monitor units (MU), aperture area and leaf speed of VMAT delivery characteristics were extracted from the QA plans. The difference in metrics, the impact of metrics on GPR, and correlations among metrics were investigated.
Results: Compared to GYN and rectal plans, H&N and prostate plans had higher aperture complexity, MU and smaller aperture area. Prostate plans had the smallest aperture area and lowest leaf speed compared with other plans (P<.001). GPR of GYN, rectal, and H&N plans were inversely associated with union aperture area (UAA) and leaf speed (Pearson’s r: -0.39 to -0.68). GPR of prostate plans were inversely correlated with aperture complexity, MU and small aperture score (SAS) (Absolute Pearson’s r: 0.34 to 0.49) and were not associated with UAA and leaf speed. Leaf speed was more strongly associated with UAA than with SAS. Aperture complexity and MU were more strongly associated with SAS than with UAA and leaf speed. Significant differences in GPR between high SAS and low SAS subgroups were found only when leaf speed was lower than 0.42 cm/s (P<.001).
Conclusion: VMAT plans from different sites have distinct delivery characteristics. Affecting dose delivery accuracy, leaf speed is the key factor for GYN, rectal, and H&N plans while aperture complexity and small apertures have higher influence on prostate plans. The impact of small apertures on VMAT dose delivery accuracy is dependent on leaf speed.
Funding Support, Disclosures, and Conflict of Interest: This study was supported by National natural Science Foundation of China (No.81071237)
Quality Assurance, Feature Extraction, Linear Regression Analysis