Purpose One of the important developments in brachytherapy in recent years has been the clinical implementation of complex modern technical procedures. Today, 3D-imaging has become the standard procedure and it is used for contouring and precise position determination and reconstruction of used...

Palliative patients receiving radiotherapy often experience significant pain. Nevertheless, they must be transported for a planning computed tomography (CT) scan and endure additional waiting times because treatment planning is time-consuming. New artificial intelligence (AI)-assisted radiotherapy devices enable simplified workflows. Using cone-beam CT data, an adaptive treatment plan based on the current patient anatomy can be generated within minutes. This study examines how technical innovations can shorten radiation treatment planning using diagnostic images instead of planning CT (pCT) scans. The entire treatment planning chain was evaluated using an Alderson phantom in an end-to-end test with dose measurements for an AI-supported adaptive workflow. Different diagnostic CT acquisitions were used, and the resulting dose calculations were compared with those obtained from a pCT calibrated for radiotherapy. In Report 24, the International Commission on Radiation Units and Measurements (ICRU) specifies a tolerance for radiotherapy dose delivery of ±5% relative to the prescribed dose. To evaluate if an adaptive workflow with diagnostic images on the Varian’s Ethos system (v1.1) meets these requirements, ionization chamber measurements in a phantom were compared to planned doses. Comparison of the results showed that the requirements of ICRU Report 24 were met. When diagnostic CT images were used instead of a dedicated treatment pCT, increased dose deviations of up to 2% were observed, although these remained within the ICRU tolerance. The end-to-end test presented here provides a practical approach to assessing the impact of using diagnostic CT data in adaptive treatment planning. The findings indicate that the observed dose deviations remain within the 5% limit defined by ICRU Report 24.

Purpose: DirectDensity enables tube voltage‐independent, density‐calibrated computed tomography (CT) images for treatment planning and is therefore increasingly used in radiotherapy facilities. In accelerated emergency workflows, diagnostic CT (dCT) data are often acquired without DirectDensity. It remains unclear whether using a DirectDensity calibration curve in this context leads to major dose deviations. This study therefore investigates the impact of different CT calibration curves on treatment planning for an artificial intelligence‐supported emergency workflow in a phantom‐based end‐to‐end test. To validate the transferability of phantom measurements to clinical scenarios, a clinically implemented emergency radiotherapy workflow using diagnostic CT data is retrospectively evaluated in four previously treated patients representing different electron density scenarios. Method: On Varian's Ethos 1.1 platform (Palo Alto, CA, USA), the entire treatment chain was simulated in an adaptive workflow using an Alderson‐Rando phantom scanned on three diagnostic CTs. This simulation, which lacks a dedicated calibration curve for the treatment planning software, was conducted as an end‐to‐end test and was compared with the results of a radiotherapy‐calibrated planning CT scan. The phantom featured a purpose‐built insert for a Farmer 30013 chamber from PTW (Freiburg, Germany). The end‐to‐end test was performed using three different calibration curves (DirectDensity, 120 kV, and a custom hybrid of DirectDensity and 120 kV). Gamma analysis was performed for the retrospectively evaluated patient plans (calculated with the three different CT curves) using three different gamma criteria to assess the robustness of dose agreement under clinically realistic conditions. Results: The point doses at the effective point of measurement of the ionization chamber, determined using the treatment planning software, were compared with the measured dose values. All tested approaches met the requirements of ICRU Report 24 (± 5% of the prescribed dose) and could be used in emergency workflows with dCTs. Despite minor differences (0.1%–0.7% better accuracy), the hybrid curve achieved the best results. Recalculations of four representative patients confirmed the trends observed in phantom measurements. 2D array evaluations showed that dose differences between calibration curves remained within clinically defined acceptance limits, with the hybrid curve consistently performing best. Conclusion: Using a DirectDensity calibration curve in emergency workflows with image data acquired without DirectDensity leads to clinically acceptable treatment plans. Dose deviations can be reduced by carefully evaluating the entire treatment chain. A self‐generated hybrid CT density curve shows the best results, although deviations remain larger (< 1.5%) than when planning directly on a calibrated planning CT. The combined phantom‐ and patient‐based analysis demonstrates that calibration curve selection can influence dose distributions under clinically realistic conditions, supporting the superior performance of the hybrid curve and highlighting clinical relevance beyond technical feasibility.

DirectDensity enables tube voltage-independent, density-calibrated computed tomography (CT) images for treatment planning and is therefore increasingly used in radiotherapy facilities. In accelerated emergency workflows, diagnostic CT (dCT) data are often acquired without DirectDensity. It remains unclear whether using a DirectDensity calibration curve in this context leads to major dose deviations. This study therefore investigates the impact of different CT calibration curves on treatment planning for an artificial intelligence-supported emergency workflow in a phantom-based end-to-end test. To validate the transferability of phantom measurements to clinical scenarios, a clinically implemented emergency radiotherapy workflow using diagnostic CT data is retrospectively evaluated in four previously treated patients representing different electron density scenarios. On Varian's Ethos 1.1 platform (Palo Alto, CA, USA), the entire treatment chain was simulated in an adaptive workflow using an Alderson-Rando phantom scanned on three diagnostic CTs. This simulation, which lacks a dedicated calibration curve for the treatment planning software, was conducted as an end-to-end test and was compared with the results of a radiotherapy-calibrated planning CT scan. The phantom featured a purpose-built insert for a Farmer 30013 chamber from PTW (Freiburg, Germany). The end-to-end test was performed using three different calibration curves (DirectDensity, 120 kV, and a custom hybrid of DirectDensity and 120 kV). Gamma analysis was performed for the retrospectively evaluated patient plans (calculated with the three different CT curves) using three different gamma criteria to assess the robustness of dose agreement under clinically realistic conditions. The point doses at the effective point of measurement of the ionization chamber, determined using the treatment planning software, were compared with the measured dose values. All tested approaches met the requirements of ICRU Report 24 (± 5% of the prescribed dose) and could be used in emergency workflows with dCTs. Despite minor differences (0.1%-0.7% better accuracy), the hybrid curve achieved the best results. Recalculations of four representative patients confirmed the trends observed in phantom measurements. 2D array evaluations showed that dose differences between calibration curves remained within clinically defined acceptance limits, with the hybrid curve consistently performing best. Using a DirectDensity calibration curve in emergency workflows with image data acquired without DirectDensity leads to clinically acceptable treatment plans. Dose deviations can be reduced by carefully evaluating the entire treatment chain. A self-generated hybrid CT density curve shows the best results, although deviations remain larger (< 1.5%) than when planning directly on a calibrated planning CT. The combined phantom- and patient-based analysis demonstrates that calibration curve selection can influence dose distributions under clinically realistic conditions, supporting the superior performance of the hybrid curve and highlighting clinical relevance beyond technical feasibility.

Background: Radiotherapy commonly requires a computed tomography (CT) dataset for treatment planning. Artificial intelligence-supported radiotherapy systems now enable online adaptive workflows that reduce uncertainties resulting from the use of diagnostic instead of planning CT images. The aim was to identify patient groups that may particularly benefit from adaptive emergency treatments. Methods: In this retrospective exploratory study, 16 patients comprising 21 planning target volumes (PTVs) were evaluated. PTVs were classified as predominantly soft-tissue or bony. To verify the geometric agreement between scheduled and adapted treatment plans, these subgroups were analyzed using absolute planning target volume change, the Dice similarity coefficient (DSC), and the 95th percentile Hausdorff distance, and their association with V95% PTV coverage was evaluated. Results: Bony targets showed higher geometric reproducibility than soft-tissue-dominant lesions, with higher median DSC values (0.96 vs. 0.88) and lower median HD95 values (2.6 mm vs. 5.5 mm). Online adaptation improved coverage in 20 of 21 PTVs, while predominantly soft-tissue PTVs showed potentially greater benefit from online adaptation. Conclusions: Soft-tissue-dominant PTVs showed greater geometric variability and potentially greater benefit from adaptation, whereas selected bone-dominant cases may be suitable for faster conventional workflows. Given the small exploratory cohort, these results require further confirmation.