Automated class-solution planning for biologically guided radiotherapy: a comparison with manual planning in head and...

To develop an automated class-solution treatment-planning workflow for biologically guided dose-painting based on combined FDG- and FMISO-PET in head and neck cancer (HNC), and to compare its performance with manual planning.

The workflow incorporating image-processing and treatment planning via a class-solution template was implemented in RayStation-10B-R and applied to patients imaged with FDG- and FMISO-PET/CT. The workflow converted FMISO- and FDG-PET uptake into oxygen partial pressure and clonogenic cell-density distributions, respectively. Accordingly, simultaneous integrated boost plans aiming at 95% tumour control probability (TCP) and using a dose-painting-by-contours approach for TV1, TV2, the GTV, and the hypoxic target volume (HTV), were created. For nine patients, automated and manual plans were compared using equivalent dose in 2-Gy fractions (EQD2)-based target metrics, organ-at-risk (OAR) doses, plan-complexity parameters, planning time, TCP and normal tissue complication probability (NTCP).

The automated workflow generated plans achieving target coverage; however not all plans met mandatory OAR constraints. In the nine-patient comparison, no statistically significant differences were found in OAR metrics or TCP/NTCP, except for the right parotid EQD2_mean, which favoured manual plans. Target results were mixed: template plans performed better for inner volumes, whereas manual plans showed higher EQD2_mean in the TV1-TV2 and HTV. Manual planning required ∼ 1 h, whereas automated planning required ∼ 5 h with no user interaction.

A scripting-based, biologically guided class-solution for dose-painting in HNC is feasible and achieves plan quality and radiobiological outcomes comparable to manual planning, providing a platform for standardised and adaptive radiotherapy workflows.