Abstract

Solid tumours may present hypoxic sub-regions of increased radioresistance. Hypoxia quantification requires of clinically implementable, non-invasive and reproducible techniques as positron emission tomography (PET). PET-based dose painting strategies aiming at targeting those sub-regions may be limited by the resolution gap between the PET imaging resolution and the smaller scale at which hypoxia occurs. The ultimate benefit of the usage of dose painting may be reached if the planned dose distribution can be performed and delivered consistently. This study aimed at assessing the feasibility of two PET-based dose painting strategies using two beam qualities (photon or proton beams) in terms of tumour control probability (TCP), accounting for underlying oxygen distribution at sub-millimetre scale.A tumour oxygenation model at submillimetre scale was created consisting of three regions with different oxygen partial pressure distributions, being hypoxia decreasing from core to periphery. A published relationship between uptake and oxygen partial pressure was used and a PET image of the tumour was simulated. The fundamental effects that limit the PET camera resolution were considered by processing the uptake distribution with a Gaussian 3D filter and re-binning to a PET image voxel size of 2 mm. Prescription doses to overcome tumour hypoxia were calculated based on the processed images, and planned using robust optimisation.Normal tissue complication probabilities and TCPs after the delivery of the planned doses were calculated for the nominal plan and the lowest bounds of the dose volume histograms resulting from the robust scenarios planned, taking into account the underlying oxygenation at submillimetre scale. Results were presented for the two beam qualities and the two dose painting strategies: by contours (DPBC) and by using a voxel grouping-based approach (DPBOX).In the studied case, DPBOX outperforms DPBC with respect to TCP regardless the beam quality, although both dose painting strategy plans demonstrated robust target coverage.

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