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Methods are described which relate the uncertainty in relative electron density derived from CT numbers to the uncertainty in treatment plan calculation for both photon and electron beams. These relationships are used to generate tolerance levels for electron density quality assurance measurements. These tolerance levels are dependent on treatment beam energy and tissue thickness, and are generally broader than current recommendations. The predicted treatment plan errors associated with these tolerance levels are shown to be consistent with calculations made using two commercial treatment planning systems. The tolerance levels are also shown to be practical by comparison against quality assurance measurements made on a conventional CT scanner and a treatment simulator CT system over a 12-month period. The results demonstrate that broader tolerances than are currently recommended can be justified in all situations except for electron beam treatments in which the therapeutic range falls within lung tissue.

BackgroundA knowledge based planning tool has been developed and implemented for prostate VMAT radiotherapy plans providing a target average rectum dose value based on previously achievable values for similar rectum/PTV overlap. The purpose of this planning tool is to highlight sub-optimal clinical plans and to improve plan quality and consistency.MethodsA historical cohort of 97 VMAT prostate plans was interrogated using a RayStation script and used to develop a local model for predicting optimum average rectum dose based on individual anatomy. A preliminary validation study was performed whereby historical plans identified as “optimal” and “sub-optimal” by the local model were replanned in a blinded study by four experienced planners and compared to the original clinical plan to assess whether any improvement in rectum dose was observed. The predictive model was then incorporated into a RayStation script and used as part of the clinical planning process. Planners were asked to use the script during planning to provide a patient specific prediction for optimum average rectum dose and to optimise the plan accordingly.ResultsPlans identified as “sub-optimal” in the validation study observed a statistically significant improvement in average rectum dose compared to the clinical plan when replanned whereas plans that were identified as “optimal” observed no improvement when replanned. This provided confidence that the local model can identify plans that were suboptimal in terms of rectal sparing. Clinical implementation of the knowledge based planning tool reduced the population-averaged mean rectum dose by 5.6Gy. There was a small but statistically significant increase in total MU and femoral head dose and a reduction in conformity index. These did not affect the clinical acceptability of the plans and no significant changes to other plan quality metrics were observed.ConclusionsThe knowledge-based planning tool has enabled substantial reductions in population-averaged mean rectum dose for prostate VMAT patients. This suggests plans are improved when planners receive quantitative feedback on plan quality against historical data.