Contributions to Proceedings:
R. Raidou, K. Furmanová, N. Grossmann, O. Casares-Magaz, V. Moiseenko, J. Einck, E. Gröller, L. Muren:
"Lessons Learnt from Developing Visual Analytics Applications for Adaptive Prostate Cancer Radiotherapy";
in: "The Gap between Visualization Research and Visualization Software (VisGap) (2020)",
C. Gillmann (ed.);
In radiotherapy (RT), changes in patient anatomy throughout the treatment period might lead to deviations between planned and delivered dose, resulting in inadequate tumor coverage and/or overradiation of healthy tissues. Adapting the treatment to account for anatomical changes is anticipated to enable higher precision and less toxicity to healthy tissues. Corresponding tools for the in-depth exploration and analysis of available clinical cohort data were not available before our work. In this paper, we discuss our on-going process of introducing visual analytics to the domain of adaptive RT for prostate cancer. This has been done through the design of three visual analytics applications, built for clinical researchers working on the deployment of robust RT treatment strategies. We focus on describing our iterative design process, and we discuss the lessons learnt from our fruitful collaboration with clinical domain experts and industry, interested in integrating our prototypes into their workflow.
"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
Electronic version of the publication:
Created from the Publication Database of the Vienna University of Technology.