# Treatment planning for lung cancer reirradiation accounting for previously delivered dose

## TL;DR

This study introduces an automated method for lung cancer reirradiation planning that improves efficiency and plan quality compared to manual approaches.

## Contribution

The novel approach integrates deformable image registration and radiobiologically consistent dose accumulation into automated planning.

## Key findings

- Automated planning avoided 96 manual dose calculations and checks across six cases.
- The automated method produced clinically acceptable dose distributions for 5 out of 6 patients.
- Blinded clinicians preferred the automated equieffective dose optimization in 5 out of 6 cases.

## Abstract

•Lung reirradiation planning was automated using cumulative equieffective dose.•For six cases, automated planning avoided 96 manual dose calculations and checks.•Change of PTV D99% ranged from −2.1 Gy to 16.7 Gy (median 2.1 Gy).•Blinded clinician review picked equieffective dose optimisation in 5/6 cases.

Lung reirradiation planning was automated using cumulative equieffective dose.

For six cases, automated planning avoided 96 manual dose calculations and checks.

Change of PTV D99% ranged from −2.1 Gy to 16.7 Gy (median 2.1 Gy).

Blinded clinician review picked equieffective dose optimisation in 5/6 cases.

Reirradiation is increasingly used for recurrent and new primary cancers, but dedicated planning tools are limited. We integrated deformable image registration and radiobiologically consistent dose accumulation into plan optimisation to improve plan quality and robustness, applying this approach to six lung cancer cases and comparing it with a manual, maximum point-dose method.

The integrated automated approach delivered higher target doses within organ-at-risk limits, maintained robustness, and was qualitatively preferable in blinded comparison. It produced clinically acceptable dose distributions for 5/6 patients, versus 3/6 with the manual method, suggesting enhanced and potentially more efficient reirradiation planning.