Partitioning of multiple brain metastases improves dose gradients in single-isocenter radiosurgery

A growing number of cancer patients with brain metastases can benefit from stereotactic radiosurgery (SRS) thanks to recent advances in systemic therapies which have led to improved survival. Meanwhile, selection criteria for SRS treatments are evolving to include patients with increasingly many metastases. With an increasing patient load, single-isocenter treatments on widely available C-arm linear accelerators are an attractive option. However, the planning of such treatments is challenging for multi-target cases due to the island blocking problem, which occurs when the multi-leaf collimator cannot conform to all targets simultaneously.

We propose a multi-target partitioning algorithm that mitigates excessive exposure of normal tissue caused by the island blocking problem.

The proposed algorithm considers an initial set of arc trajectories and divides (partitions) the set of targets per trajectory into smaller subsets to treat with separate back-and-forth arc passes, simultaneously optimizing both the target subsets and collimator angles to minimize island blocking. We incorporated this algorithm into a fully automated treatment planning script and evaluated it on 20 simulated patient cases, each with 10 brain metastases and 21 Gy prescriptions. For each case, the script generated a series of volumetric modulated arc therapy (VMAT) plans with increasingly many arcs along the three trajectories. Each such plan was compared to four baseline plans generated with alternative heuristics for distributing targets across arcs. We also evaluated the algorithm retrospectively on six clinical cases.

Partitioning significantly improved the gradient index (GI), global efficiency index ( $G\eta$ ) and brain $V_{12\mathrm{Gy}}$ compared to simultaneous treatment of all metastases. For example, the average GI improved from 5.9 to 3.3, $G\eta$ from 0.32 to 0.46, and normal brain $V_{12\mathrm{Gy}}$ from 49  $c{m}^3$ to 26  $c{m}^3$ between 3 and 9 arcs. The baseline plans improved similarly, but the proposed algorithm was significantly better at utilizing a limited budget of arcs. All target partitioning strategies increased the total number of monitor units (MUs).

The dose gradient in single-isocenter VMAT plans can be substantially improved by treating a smaller subset of metastases at a time. This requires more MUs and arcs, implying a trade-off between delivery time and plan quality, which can be explored using the algorithm proposed in this paper.