Technical note: Optimization functions for re‐irradiation treatment planning
Jakob Ödén, Kjell Eriksson, Stina Svensson, J. Lilley, Christopher M. Thompson, Christopher Pagett, Ane Appelt, Louise Murray, Rasmus Bokrantz
Abstract
Abstract 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 (EQD2 deliv ). 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: reRT regular : Regular functions with fixed EQD2 penalization levels larger than EQD2 deliv for all OAR voxels. reRT reduce : As reRT regular , but with lower fixed EQD2 penalization levels aiming to reduce OAR EQD2. reRT voxelwise : As reRT regular and reRT reduce , but with voxelwise adjusted EQD2 penalization levels based on EQD2 deliv . PTV near‐minimum and near‐maximum dose (D 98% /D 2% ), 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, reRT reduce and reRT voxelwise reduced accumulated average and near‐maximum EQD2 by ˜1–10 Gy for all relevant OARs compared to reRT regular . The reduced OAR doses for reRT reduce came at the cost of distorted dose distributions with D 98% = 92.3%, HI = 12.0%, CI = 73.7% and normal tissue hot spots ≥150% for the most complex scenario, while reRT regular (D 98% = 98.1%, HI = 3.2%, CI = 94.2%) and reRT voxelwise (D 98% = 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 EQD2 deliv .