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

Brain Re-Irradiation Robustly Accounting for Previously Delivered Dose

(1) Background: The STRIDeR (Support Tool for Re-Irradiation Decisions guided by Radiobiology) planning pathway aims to facilitate anatomically appropriate and radiobiologically meaningful re-irradiation (reRT). This work evaluated the STRIDeR pathway for robustness compared to a more conservative manual pathway. (2) Methods: For ten high-grade glioma reRT patient cases, uncertainties were applied and cumulative doses re-summed. Geometric uncertainties of 3, 6 and 9 mm were applied to the background dose, and LQ model robustness was tested using α/β variations (values 1, 2 and 5 Gy) and the linear quadratic linear (LQL) model δ variations (values 0.1 and 0.2). STRIDeR robust optimised plans, incorporating the geometric and α/β uncertainties during optimisation, were also generated. (3) Results: The STRIDeR and manual pathways both achieved clinically acceptable plans in 8/10 cases but with statistically significant improvements in the PTV D98% (p < 0.01) for STRIDeR. Geometric and LQ robustness tests showed comparable robustness within both pathways. STRIDeR plans generated to incorporate uncertainties during optimisation resulted in a superior plan robustness with a minimal impact on PTV dose benefits. (4) Conclusions: Our results indicate that STRIDeR pathway plans achieved a similar robustness to manual pathways with improved PTV doses. Geometric and LQ model uncertainties can be incorporated into the STRIDeR pathway to facilitate robust optimisation

Operator 4.0 - Emerging Job Categories in Manufacturing

With the trends of industry 4.0 and increased degree of digitalization in production plants, it is expected that production plants in future is much more adaptive where they can both self-optimize production parameters as well as self-maintain of standard activities. All though this would reduce manual operations, new work activities are expected in a cyber-physical production plant. For instance, the establishment of digital twins in cloud solutions enabled with Internet of Things (IoT) can result in crafts in maintenance analytics as well as more guided maintenance for the maintenance operator with augmented reality. In addition, more service from external personnel such as the machine builder is expected to be offered in Industry 4.0. In overall, it will be of interest to identify and recommend qualification criteria relevant for a cyber physical production plant that would be implemented in the organisation. The aim of this article is to evaluate the role of operator as well as other relevant job categories in a cyber physical production plant. The result in this paper is a recommended framework with qualification criteria of these job categories. Further research will require more case studies of this framework