Automated target placement for VMAT lattice radiation therapy: enhancing efficiency and consistency

Abstract

Objective.
An algorithm was developed for automated positioning of lattice points within volumetric modulated arc lattice radiation therapy (VMATLRT) planning. These points are strategically placed within the gross tumor volume(GTV) to receive high doses, adhering to specific separation rules from adjacent organs at risk(OARs). The study goals included enhancing planning safety, consistency, and efficiency while emulating human performance. Approach. A Monte Carlo-based algorithm was designed to optimize the number and arrangement of lattice points within the GTV while considering placement constraints and objectives. These constraints encompassed minimum spacing between points, distance from OARs, and longitudinal separation along the z-axis. Additionally, the algorithm included an objective to permit, at the user’s discretion, solutions with more centrally placed lattice points within the GTV. To validate its effectiveness, the automated approach was compared with manually planned treatments for 24 previous patients. Prior to clinical implementation, a failure mode and effects analysis (FMEA) was conducted to identify potential shortcomings.
Main results.
The automated program successfully met all placement constraints with an average execution time of 0.29 ±0.07 minutes per lattice point. The average lattice point density(# points per cc of GTV) was similar for automated(0.00725) compared to manual placement(0.00704). The dosimetric differences between the automated and manual plans were minimal, with statistically significant differences in certain metrics like minimum dose(1.9% vs. 1.4%), D5%(52.8% vs. 49.4%), and Body-GTV V30%(20.7 cc vs. 19.7 cc). Significance. This study underscores the feasibility of employing a straightforward Monte Carlo-based algorithm to automate the creation of spherical target structures for VMAT LRT planning. The automated method yields similar dose metrics, enhances inter-planner consistency for larger targets, and requires fewer resources and less time compared to manual placement. This approach holds promise for standardizing treatment planning in prospective patient trials and facilitating its adoption across centers seeking to implement VMATLRT techniques.