Purpose: To develop an automated class-solution treatment-planning workflow for biologically guided dosepainting based on combined FDG-and FMISO-PET in head and neck cancer (HNC), and to compare its performance with manual planning.Material and Methods: 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).Results: 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.Conclusions: 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.
Ureba et al. (Thu,) studied this question.