Automatic segmentation and quantification of ground-glass opacities on high-resolution CT by a neural network are sufficiently accurate to be implemented for the preinterpretation of images in a clinical environment; it is superior to a double-threshold density mask.

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Since 2007, children and adolescents with Hodgkin lymphomas are treated in the Europe-wide EuroNet-PHL trials. A real time central review process for stratification of the patients enhances quality control and efficient therapy management. This process includes reading of all cross-sectional-images. Since reference evaluation is time critical, a fast, easy to handle and safe data transfer is important. In addition, immediate and constant access to all the data has to be guaranteed in case of queries and for regulatory reasons. To meet the mentioned requirements the EuroNet Paediatric Hodgkin Data Network (funded by the European Union - Project Number: 2007108) was established between 2008 and 2011. A respective tailored data protection plan was formulated. The aim of this article is to describe the networks' mode of operation and the advantages for multi-centre trials that include centralized image review.

No abstract