Treatment planning methods for dose painting by numbers treatment in Gamma Knife radiosurgery

PURPOSE Dose painting radiotherapy delivers a non-uniform dose to tumours to account for heterogeneous radiosensitivity. With recent and ongoing development of Gamma Knife (GK) machines making large-volume brain tumour treatments more practical, it is increasingly feasible to deliver dose painting treatments. The increased prescription complexity means automated treatment planning is greatly beneficial, and the impact of dose painting on SRS plan quality has not yet been studied. This research investigates the plan quality achievable for GK SRS dose painting treatments when using optimisation techniques and automated isocentre placement in treatment planning. METHODS & MATERIALS Dose painting prescription functions with varying parameters are applied to convert voxel image intensities to prescriptions for ten sample cases. To study achievable plan quality and optimisation, clinically-placed isocentres are used with each dose painting prescription, and optimised using a semi-infinite linear programming formulation. To study automated isocentre placement, a grassfire sphere-packing algorithm and a clinically available Leksell Gamma Plan isocentre fill algorithm are used. Plan quality for each optimised treatment plan is measured with dose painting SRS metrics. RESULTS Optimisation can be used to find high quality dose painting plans, and plan quality is affected by the dose painting prescription method. Polynomial function prescriptions show better achievable plan quality than sigmoid function prescriptions even with high mean dose boost. Automated isocentre placement is shown as a feasible method for dose painting SRS treatment, and increasing the number of isocentres improves plan quality. The computational solve time for optimisation is within 5 minutes in most cases, which is suitable for clinical planning. CONCLUSIONS The impact of dose painting prescription method on achievable plan quality is quantified in this study. Optimisation and automated isocentre placement are shown as possible treatment planning methods to obtain high quality plans.