Optimization of isocenter position for multiple targets with nonuniform‐margin expansion

Abstract

The single isocenter for multiple-target (SIMT) technique has become a popular treatment technique for multiple brain metastases. We have implemented a method to obtain a nonuniform margin for SIMT technique. In this study, we further propose a method to determine the isocenter position so that the total expanded margin volume is minimal. Materials and method:Based on a statistical model, the relationship between nonuniform margin and the distance d (from isocenter to target point), setup uncertainties, and significance level was established. Due to the existence of rotational error, there is a nonlinear relationship between the margin volume and the isocenter position. Using numerical simulation, we study the relationship between optimal isocenter position and translational error, rotational error, and target size. In order to find the optimal isocenter position quickly, adaptive simulated annealing (ASA) algorithm was used. This method was implemented in the Pinnacle 3 treatment planning system and compared with isocenter at center-ofgeometric (COG), center-of -volume (COV), and center-of -surface (COS). Ten patients with multiple brain metastasis targets treated with the SIMT technique was selected for evaluation. Results: When the size of tumors is equal, the optimal isocenter obtained by ASA and numerical simulation coincides with COG, COV, and COS. When the size of tumors is different, the optimal isocenter is close to the large tumor. The position of COS point is closer to the optimal point than the COV point for nearly all cases.Moreover,in some cases the COS point can be approximately selected as the optimal point. The ASA algorithm can reduce the calculating time from several hours to tens of seconds for three or more tumors. Using multiple brain metastases targets, a series of volume difference and calculating time were obtained for various tumor number, tumor size, and separation distances. Compared with the margin volume with isocenter at COG, the margin volume for optimal point can be reduced by up to 27.7%. Conclusion: Optimal treatment isocenter selection of multiple targets with large differences could reduce the total margin volume. ASA algorithm can significantly improve the speed of finding the optimal isocenter. This method can be used for clinical isocenter selection and is useful for the protection of normal tissue nearby.

Innovative margin design and optimized isocenter to minimize the normal tissue in target volumes for single-isocenter multi-target stereotactic radiosurgery

“…While other works evaluate targets and even optimize the position of isocenters based on maximizing target coverage (Nakano et al 2020, Cui et al 2021, Nakano et al 2021, this work constructed PTVs that provided complete target coverage, and then within that constraint, sought to minimize the normal tissue included. This is similar to Slagowski and Wen (2020) and Miao et al (2022) but features the use of non-isotropic PTVs.…”

“…Miao et al present a more complex use of isotropic margins by applying margins of varying magnitude to small target subregions (Miao et al 2022). This method accounts for the varying effects of rotations across the target that increase with the increasing distance of target subregions from the isocenter.…”

“…Some works have taken approaches that model the probability of possible GTV positions when subject to translations and rotations (Chang 2017, 2018, Slagowski and Wen 2020, Cui et al 2021, Miao et al 2022. Although this approach is more computationally sophisticated than the work presented here, it requires additional information or assumptions to fully characterize the motion.…”

Evaluating effectiveness of clustering algorithms in multiple target stereotactic radiosurgery

“…It is important to acknowledge that the brute force algorithms utilized in this study for the unweighted objectives were specifically designed for uniform GTV to PTV margin expansion in all dimensions and targets. Consequently, the findings of this study may not be applicable to non-uniform margin expansion as proposed in some recent studies (Miao et al 2023, Shen et al 2023. However, non-uniform expansion is not commonly adopted and will require entirely different minimization objectives for clustering, rendering it impractical for our purposes.…”