Lessons Learnt from Developing Visual Analytics Applications for Adaptive Prostate Cancer Radiotherapy

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dc.contributor.authorRaidou, Renata Georgiaen_US
dc.contributor.authorFurmanová, Katarínaen_US
dc.contributor.authorGrossmann, Nicolasen_US
dc.contributor.authorCasares-Magaz, Oscaren_US
dc.contributor.authorMoiseenko, Vitalien_US
dc.contributor.authorEinck, John P.en_US
dc.contributor.authorGröller, Eduarden_US
dc.contributor.authorMuren, Ludvig P.en_US
dc.contributor.editorGillmann, Christina and Krone, Michael and Reina, Guido and Wischgoll, Thomasen_US
dc.date.accessioned2020-05-24T13:35:10Z
dc.date.available2020-05-24T13:35:10Z
dc.date.issued2020
dc.description.abstractIn 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.en_US
dc.description.sectionheadersApplication Retrospectives
dc.description.seriesinformationVisGap - The Gap between Visualization Research and Visualization Software
dc.identifier.doi10.2312/visgap.20201110
dc.identifier.isbn978-3-03868-125-0
dc.identifier.pages51-58
dc.identifier.urihttps://doi.org/10.2312/visgap.20201110
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/visgap20201110
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectHuman centered computing → Visual analytics
dc.subjectApplied computing → Life and medical sciences"
dc.titleLessons Learnt from Developing Visual Analytics Applications for Adaptive Prostate Cancer Radiotherapyen_US
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