Technical note: Optimization functions for re‐irradiation treatment planning

Background Although re-irradiation is increasingly used in clinical practice, almost no dedicated planning software exists.

Purpose Standard dose-based optimization functions were adjusted for re-irradiation planning using accumulated equivalent dose in 2-Gy fractions (EQD2) with rigid or deformable dose mapping, tissue-specific α/β, treatment-specific recovery coefficients, and voxelwise adjusted EQD2 penalization levels based on the estimated previously delivered EQD2 (EQD2deliv).

Methods To demonstrate proof-of-concept, 35 Gy in 5 fractions was planned to a fictitious spherical relapse planning target volume (PTV) in three separate locations following previous prostate treatment on a virtual human phantom. The PTV locations represented one repeated irradiation scenario and two re-irradiation scenarios. For each scenario, three re-planning strategies with identical PTV dose-functions but various organ at risk (OAR) EQD2-functions was used: 1) reRTregular: Regular functions with fixed EQD2 penalization levels larger than EQD2deliv for all OAR voxels.

2) reRTreduce: As reRTregular, but with lower fixed EQD2 penalization levels aiming to reduce OAR EQD2.

3) reRTvoxelwise: As reRTregular and reRTreduce, but with voxelwise adjusted EQD2 penalization levels based on EQD2deliv.

PTV near-minimum and near-maximum dose (D98%/D2%), homogeneity index (HI), conformity index (CI) and accumulated OAR EQD2 (α/β = 3 Gy) were evaluated. Results For the repeated irradiation scenario, all strategies resulted in similar dose distributions. For the re-irradiation scenarios, reRTreduce and reRTvoxelwise reduced accumulated average and near-maximum EQD2 by ˜1–10 Gy for all relevant OARs compared to reRTregular. The reduced OAR doses for reRTreduce came at the cost of distorted dose distributions with D98% = 92.3%, HI = 12.0%, CI = 73.7% and normal tissue hot spots ≥150% for the most complex scenario, while reRTregular (D98% = 98.1%, HI = 3.2%, CI = 94.2%) and reRTvoxelwise (D98% = 96.9%, HI = 6.1%, CI = 93.7%) fulfilled PTV coverage without hot spots.

Conclusions The proposed re-irradiation-specific EQD2-based optimization functions introduce novel planning possibilities with flexible options to guide the trade-off between target coverage and OAR sparing with voxelwise adapted penalization levels based on EQD2deliv.