Generating amorphous target margins in radiation therapy to promote maximal target coverage with minimal target size

Abstract

Background and significance: This work provides proof-of-principle for two versions of a heuristic approach that automatically creates amorphous radiation therapy planning target volume (PTV) margins considering local effects of tumor shape and motion to ensure adequate voxel coverage with while striving to minimize PTV size. The resulting target thereby promotes disease control while minimizing the risk of normal tissue toxicity.

Methods: This work describes the mixed-PDF algorithm and the independent-PDF algorithm which generate amorphous margins around a radiation therapy target by incorporating user-defined models of target motion. Both algorithms were applied to example targets - one circular and one "cashew-shaped." Target motion was modeled by four probability density functions applied to the target quadrants. The spatially variant motion model illustrates the application of the algorithms even with tissue deformation. Performance of the margins was evaluated in silico with respect to voxelized target coverage and PTV size, and was compared to conventional techniques: a threshold-based probabilistic technique and an (an)isotropic expansion technique. To demonstrate the algorithm's clinical utility, a lung cancer patient was analyzed retrospectively. For this case, 4D CT measurements were combined with setup uncertainty to compare the PTV from the mixed-PDF algorithm with a PTV equivalent to the one used clinically.

Results: For both targets, the mixed-PDF algorithm performed best, followed by the independent-PDF algorithm, the threshold algorithm, and lastly, the (an)isotropic algorithm. Superior coverage was always achieved by the amorphous margin algorithms for a given PTV size. Alternatively, the margin required for a particular level of coverage was always smaller (8-15%) when created with the amorphous algorithms. For the lung cancer patient, the mixed-PDF algorithm resulted in a PTV that was 13% smaller than the clinical PTV while still achieving ≥99.9% coverage.

Conclusions: The amorphous margin algorithms are better suited for the local effects of target shape and positional uncertainties than conventional margins. As a result, they provide superior target coverage with smaller PTVs, ensuring dose delivered to the target while decreasing the risk of normal tissue toxicity.

Keywords: Margins; Motion management; Planning target volume; Uncertainty.