81 Background: Patient-specific quality assurance measurements are time consuming and Gamma pass/fail criteria are often picked based on typical criteria used for IMRT QA measurements in the past. The questions needs to be asked if with these criteria QA plans could still show clinically significant deviations from the treatment plan calculated and how we should be doing QA for treatment delivery of complex treatment plans. In our study DICOM files of clinical Rapidarc plans were modified with in-house developed software to mimic leaf alignment errors and gravitation shifts. The Octavius 2D-ARRAY (PTW-Freiburg) and the Delta4 device (Scandidos) were used to investigate the effect of the simulated errors on the passing rate of quality assurance results. The manipulated Rapidarc plans were recalculated on patient CT scans in Eclipse. Methods: Three different types of errors were simulated and applied to five prostate (two arcs), three 2-arc head and neck cases and three 3-arc head and neck cases. The MLC modifications were: (1) both MLC banks are opened by 0.25mm, 0.50mm and 1.00mm in opposing directions resulting in larger fields, (2) both MLC banks are closed by 0.10mm, 0.25mm and 0.50mm, (3) both MLC banks are shifted in the same direction for lateral gantry angles to simulate effects of gravitational forces onto the leaves by 1mm, 2mm and 3mm, (4) 25%, 50% 70% and 100% of all active leaves are shifted by 3mm as in (3). QA evaluations were performed according to a gamma-index criterion of 3mm and 3% as well as 2mm and 2%. Results: All unmodified plans and the majority of the plans with the smallest modification pass the gamma-index criterion of 2%/2mm with >90%. After that the passing rate drops below 90%. For the largest modifications passing rates were typically below 85%. The Delta4 is generally more sensitive and the passing rate for modified plans drops below 90% earlier and more drastically. With the standard criteria (3mm, 3%) even the largest modifications would satisfy a >90% passing rate. Conclusions: A stricter gamma-index (2mm, 2%) is necessary in order to detect MLC positional errors and a passing rate of >90% should be expected. Clinical pass/fail criteria need to be developed.
IMRT QA
using machine learning: A multi‐institutional validation
