Evaluation Of 4-Hz Log Files And Secondary Monte Carlo Dose Calculation As Patient-Specific Quality Assurance For Vmat Prostate Plans

Purpose In this study, 4-Hz log files were evaluated with an independent secondary Monte Carlo dose calculation algorithm to reduce the workload for patient-specific quality assurance (QA) in clinical routine. Materials and Methods A total of 30 randomly selected clinical prostate VMAT plans were included. The used treatment planning system (TPS) was Monaco (Elekta, Crawley), and the secondary dose calculation software was SciMoCa (Scientific-RT, Munich). Monaco and SciMoCa work with a Monte Carlo algorithm. A plausibility check of Monaco and SciMoCa was performed using an ionization chamber in the BodyPhantom (BP). First, the original Monaco RT plans were verified with SciMoCa (pretreatment QA). Second, the corresponding 4-Hz log files were converted into RT log file plans and sent to SciMoCa as on-treatment QA. MLC shift errors were introduced for one prostate plan to determine the sensitivity of on-treatment QA. For pretreatment and on-treatment QA, a gamma analysis (2%/1mm/20%) was performed and dosimetric values of PTV and OARs were ascertained in SciMoCa. Results Plausibility check of TPS Monaco vs. BP measurement and SciMoCa vs. BP measurement showed valid accuracy for clinical VMAT QA. Using SciMoCa, there was no significant difference in PTV Dmean between RT plan and RT log file plan. Between pretreatment and on-treatment QA, PTV metrics, femur right and left showed no significant dosimetric differences as opposed to OARs rectum and bladder. The overall gamma passing rate (GPR) ranged from 96.10% to 100% in pretreatment QA and from 93.50% to 99.80% in on-treatment QA. MLC shift errors were identified for deviations larger than -0.50 mm and +0.75 mm using overall gamma criterion and PTV Dmean. Conclusion SciMoCa calculations of Monaco RT plans and RT log file plans are in excellent agreement to each other. Therefore, 4-Hz log files and SciMoCa can replace labor-intensive phantom-based measurements as patient-specific QA.