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In the Danish Head and Neck Cancer Group (DAHANCA) 35 trial, patients are selected for proton treatment based on simulated reductions of Normal Tissue Complication Probability (NTCP) for proton compared to photon treatment at the referring departments. After inclusion in the trial, immobilization, scanning, contouring and planning are repeated at the national proton centre. The new contours could result in reduced expected NTCP gain of the proton plan, resulting in a loss of validity in the selection process. The present study evaluates if contour consistency can be improved by having access to AI (Artificial Intelligence) based contours. The 63 patients in the DAHANCA 35 pilot trial had a CT from the local DAHANCA centre and one from the proton centre. A nationally validated convolutional neural network, based on nnU-Net, was used to contour OARs on both scans for each patient. Using deformable image registration, local AI and oncologist contours were transferred to the proton centre scans for comparison. Consistency was calculated with the Dice Similarity Coefficient (DSC) and Mean Surface Distance (MSD), comparing contours from AI to AI and oncologist to oncologist, respectively. Two NTCP models were applied to calculate NTCP for xerostomia and dysphagia. The AI contours showed significantly better consistency than the contours by oncologists. The median and interquartile range of DSC was 0.85 [0.78 − 0.90] and 0.68 [0.51 − 0.80] for AI and oncologist contours, respectively. The median and interquartile range of MSD was 0.9 mm [0.7 − 1.1] mm and 1.9 mm [1.5 − 2.6] mm for AI and oncologist contours, respectively. There was no significant difference in Δ NTCP. The study showed that OAR contours made by the AI algorithm were more consistent than those made by oncologists. No significant impact on the Δ NTCP calculations could be discerned. [ABSTRACT FROM AUTHOR]

Background: Previously, many radiotherapy (RT) trials were based on a few selected dose measures. Many research questions, however, rely on access to the complete dose information. To support such access, a national RT plan database was created. The system focuses on data security, ease of use, and re-use of data. This article reports on the development and structure, and the functionality and experience of this national database. Methods and materials: A system based on the DICOM-RT standard, DcmCollab, was implemented with direct connections to all Danish RT centres. Data is segregated into any number of collaboration projects. User access to the system is provided through a web interface. The database has a finely defined access permission model to support legal requirements. Results: Currently, data for more than 14,000 patients have been submitted to the system, and more than 50 research projects are registered. The system is used for data collection, trial quality assurance, and audit data set generation. Results: Users reported that the process of submitting data, waiting for it to be processed, and then manually attaching it to a project was resource intensive. This was accommodated with the introduction of triggering features, eliminating much of the need for users to manage data manually. Many other features, including structure name mapping, RT plan viewer, and the Audit Tool were developed based on user input. Conclusion: The DcmCollab system has provided an efficient means to collect and access complete datasets for multi-centre RT research. This stands in contrast with previous methods of collecting RT data in multi-centre settings, where only singular data points were manually reported. To accommodate the evolving legal environment, DcmCollab has been defined as a 'data processor', meaning that it is a tool for other research projects to use rather than a research project in and of itself. [ABSTRACT FROM AUTHOR]

Abstract: Introduction: The aim of the present study was to test the feasibility of a Nickel–Titanium (Ni–Ti) stent technique (Memocore™) in a porcine model. The stent is intended as a new fiducial for gated image guided radiotherapy in the lung. The study included test of an improved insertion system and respiratory gated treatments with this new technique. Methods and materials: Tests were carried out in a porcine model using Göttingen mini-pigs. The study included 10 animals. Planning CT was performed as 4 dimensional CT (4DCT) using the Varian RPM system. Respiratory gated radiotherapy treatments were simulated using the Brainlab ExacTrac system. Reproducibility of stent position during treatment was analyzed off-line using an experimental version of the ExacTrac software. The experimental version has a dedicated algorithm for segmentation of the stent in the planning CT and subsequent registration to X-ray position images. Results: A total of 23 stents were inserted in the 10 animals. Stents could be placed in all parts of the lungs. No stent migrated within the four weeks the experiment lasted. Stent trajectories in the lung were not reproducible, even though respiration was highly standardized using a respirator. The best accuracy of stent position in the gating window was obtained using gating at the half_max amplitude as reference level. The smallest stent movement within the gating window was observed in the exhale phase. Further success of human application will depend on the possibility to insert the stent within or close to lung tumors. Conclusions: This new technique based on the Memocore™ lung stent used in connection with respiratory gated radiotherapy was demonstrated to be feasible in a porcine model. The study demonstrated lack of reproducibility in lung trajectories of inserted stents. The technique gave the best accuracy when applied to the exhale phase of respiration. [Copyright &y& Elsevier]