# Towards faster plan adaptation for proton arc therapy using initial treatment plan information

## TL;DR

This paper introduces a faster method for adapting proton arc therapy plans without sacrificing quality, reducing planning time by 3.4 times.

## Contribution

A novel proton arc therapy plan adaptation workflow that preserves energy layer patterns and uses partial spot reoptimization.

## Key findings

- Smart-adapted plans achieved similar organ dose levels as reference plans, with minimal increases in mean dose to the mandible.
- The proposed method reduced average planning time by 3.4 times while meeting clinical objectives for target coverage.
- Partial spot reoptimization based on initial weights and impact on the objective function maintained plan quality.

## Abstract

•Proposed arc energy layer pattern preservation did not impair plan quality.•Partial spot reoptimization yields similar plan quality to full reoptimization.•Proposed plan adaptation was 3.4 times faster than conventional full reoptimization.

Proposed arc energy layer pattern preservation did not impair plan quality.

Partial spot reoptimization yields similar plan quality to full reoptimization.

Proposed plan adaptation was 3.4 times faster than conventional full reoptimization.

Proton arc therapy (PAT) is an emerging modality delivering continuously rotating proton beams. Current PAT planning approaches are time-consuming, making them unsuitable for online adaptation. This study proposes an accelerated workflow for adapting PAT plans.

The proposed workflow transfers spots from initial computed tomography (CT) to the CT of the day, updates energy layers considering the initial pattern, and re-optimizes selected transferred spots based on their initial weights and impact on the objective function.

A retrospective study was conducted on five head and neck patients who underwent plan adaptation on a repeated CT. PAT plans were generated with two different methods on the repeated CT: reference, created de novo, and smart-adapted, generated with the proposed adaptive workflow. Robust optimization was performed for all plans.

Smart-adapted plans achieved similar mean dose to organs at risk as the reference: the largest median increase of mean dose was 1.9 Gy to the mandible; the median of maximum dose to spinal cord was 0.5 Gy lower for the smart-adapted plans. The median target coverage, i.e. D98, to primary tumor and nodes of smart-adapted plans decreased by 0.2 and 0.4 Gy for the nominal case, and 0.4 and 0.6 Gy for the worst-case scenario; all smart-adapted plans met clinical objectives. The smart-adaptation method reduced average planning time from 19184 s to 5626 s, a 3.4-fold improvement.

Smart-adapted plans achieve similar plan quality to the reference method, while significantly reducing plan generation time for new patient anatomy.