Vol 26, No 5 (2021)
Research paper
Published online: 2021-08-13

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Evaluation of the ability of three commercially available dosimeters to detect systematic delivery errors in Step-and-Shoot IMRT plans

Alison Gray123, Omemh Bawazeer4, Sankar Arumugam123, Philip Vial123, Joseph Descallar2, David Thwaites5, Lois Holloway12356
Rep Pract Oncol Radiother 2021;26(5):793-803.


Background: There is limited data on error detectability for step-and-shoot intensity modulated radiotherapy (sIMRT) plans, despite significant work on dynamic methods. However, sIMRT treatments have an ongoing role in clinical practice.

This study aimed to evaluate variations in the sensitivity of three patient-specific quality assurance (QA) devices to systematic delivery errors in sIMRT plans.

Materials and methods: Four clinical sIMRT plans (prostate and head and neck) were edited to introduce errors in: Multi-Leaf Collimator (MLC) position (increasing field size, leaf pairs offset (1–3 mm) in opposite directions; and field shift, all leaves offset (1–3 mm) in one direction); collimator rotation (1–3 degrees) and gantry rotation (0.5–2 degrees). The total dose for each plan was measured using an ArcCHECK diode array. Each field, excluding those with gantry offsets, was also measured using an Electronic Portal Imager and a MatriXX Evolution 2D ionisation chamber array. 132 plans (858 fields) were delivered, producing 572 measured dose distributions. Measured doses were compared to calculated doses for the no-error plan using Gamma analysis with 3%/3 mm, 3%/2 mm, and 2%/2 mm criteria (1716 analyses).

Results: Generally, pass rates decreased with increasing errors and/or stricter gamma criteria. Pass rate variations with detector and plan type were also observed. For a 3%/3 mm gamma criteria, none of the devices could reliably detect 1 mm MLC position errors or 1 degree collimator rotation errors. 

Conclusions: This work has highlighted the need to adapt QA based on treatment plan type and the need for detector specific assessment criteria to detect clinically significant errors.

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