Development of an Automated Algorithm for Lung Cancer Lattice Radiation Therapy Which Meets Clinical Recommendations

Abstract

Purpose/Objectives: Spatially fractionated radiation therapy (SFRT) improves the therapeutic effect of radiotherapy (RT) likely through bystander and abscopal effects in tumors and by reducing normal tissue toxicity. This research seeks to develop an algorithm to intelligently place target vertices within a planning treatment volume (PTV) for a form of SFRT known as Lattice Radiation Therapy (LRT). Materials/Methods: The tumor volume was first exported from the treatment planning system as a shape file. Mathematically optimal packing of dose targets was accomplished through automated fitting of hexagonally close-packed (HCP) dose island vertices. An HCP matrix of vertices was grown to a size sufficient for the PTV. In a first, packing algorithm, the vertices matrix was manipulated to obtain geometrically optimal packing through the acceptance and rejection of vertices to maximize targets within the PTV. A second, placement algorithm modified the rigid HCP matrix spacing to intelligently place targets in vacant regions of the PTV. 4 late-stage lung cancer patients were simulated with various parameters using the proposed algorithm. Results: The developed algorithm intelligently packs targets within a minute with efficient PTV coverage and minimal dead space. Automated plans on several clinical cases were developed with average maximum target doses ranging from 22.4 to 26.8 Gy, valley doses ranging from 2.6 to 5.0 Gy, and peak to valley ratios ranging from 4.5:1 to 9.7:1. Conclusions: In this study, a packing algorithm was developed for LRT which meets clinical recommendations. Coupled with a previously developed auto planning tool, it is an important step towards a fully automated LRT treatment planning workflow.