Determining tolerance levels for quality assurance of 3D printed bolus for modulated arc radiotherapy of the nose

Given the existing literature on the subject, there is obviously a need for specific advice on quality assurance (QA) tolerances for departments using or implementing 3D printed bolus for radiotherapy treatments. With a view to providing initial suggested QA tolerances for 3D printed bolus, this study evaluated the dosimetric effects of changes in bolus geometry and density, for a particularly common and challenging clinical situation: specifically, volumetric modulated arc therapy (VMAT) treatment of the nose. Film-based dose verification measurements demonstrated that both the AAA and the AXB algorithms used by the Varian Eclipse treatment planning system (Varian Medical Systems, Palo Alto, USA) were capable of providing sufficiently accurate dose calculations to allow this planning system to be used to evaluate the effects of bolus errors on dose distributions from VMAT treatments of the nose. Thereafter, the AAA and AXB algorithms were used to calculate the dosimetric effects of applying a range of simulated errors to the design of a virtual bolus, to identify QA tolerances that could be used to avoid clinically significant effects from common printing errors. Results were generally consistent, whether the treatment target was superficial and treated with counter-rotating coplanar arcs or more-penetrating and treated with noncoplanar arcs, and whether the dose was calculated using the AAA algorithm or the AXB algorithm. The results of this study suggest the following QA tolerances are advisable, when 3D printed bolus is fabricated for use in photon VMAT treatments of the nose: bolus relative electron density variation within [Formula: see text] (although an action level at [Formula: see text] may be permissible); bolus thickness variation within [Formula: see text] mm (or 0.5 mm variation on opposite sides); and air gap between bolus and skin [Formula: see text] mm. These tolerances should be investigated for validity with respect to other treatment modalities and anatomical sites. This study provides a set of baselines for future comparisons and a useful method for identifying additional or alternative 3D printed bolus QA tolerances.

Clinical implementation of a Monte Carlo based independent TPS dose checking system

The increase in complexity of treatment plans over time through modalities such as intensity modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT) has often not been met with an increase in capability of the secondary dose calculation checking systems typically used to verify the treatment planning system. Monte Carlo (MC) codes such as EGSnrc have become easily available and are capable of performing calculations of highly complex radiotherapy treatments. This educational note demonstrates a method for implementing and using a fully automated system for performing and analysing full MC calculations of conformal, IMRT and VMAT radiotherapy plans. Example calculations were based on BEAMnrc/DOSXYZnrc and are performed automatically after either uploading exported plan DICOM data through a Python-based web interface, or exporting DICOM data to a monitored network location. This note demonstrates how completed MC calculations can then be analysed using an automatically generated dose point comparison report, or easily re-imported back into the treatment planning system. Agreement between the TPS and MC calculation was an improvement on agreement between RadCalc and the TPS, with differences ranging from 1.2 to 5.5% between RadCalc and the treatment planning system (TPS), and 0.1-1.7% between MC and TPS. Comparison of the dose-volume histogram (DVH) parameters [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] for the example VMAT plans showed agreement for the mean planning target volume dose within [Formula: see text], [Formula: see text] and [Formula: see text] generally within [Formula: see text] with the exception of a brain case, and [Formula: see text] within [Formula: see text]. Overall, this note provides a demonstration of a system that has been integrated well into existing clinical workflow, and has been shown to be a valuable additional tool in the secondary checking of treatment plan calculations.