Information
- Publication Type: Conference Paper
- Workgroup(s)/Project(s):
- Date: May 2020
- Pages (to): 8
- Pages (from): 1
- Open Access: yes
- Lecturer: Renata Raidou
- Event: EGEV2020 - VisGap Workshop
- Call for Papers: Call for Paper
- Booktitle: The Gap between Visualization Research and Visualization Software (VisGap) (2020)
- Keywords: Visual Analytics, Life and Medical Sciences
Abstract
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.Additional Files and Images
Weblinks
No further information available.BibTeX
@inproceedings{raidou_visgap2020,
title = "Lessons Learnt from Developing Visual Analytics Applications
for Adaptive Prostate Cancer Radiotherapy",
author = "Renata Raidou and Katar\'{i}na Furmanov\'{a} and Nicolas
Grossmann and Oscar Casares-Magaz and Vitali Moiseenko and
John P. Einck and Eduard Gr\"{o}ller and Ludvig Paul Muren",
year = "2020",
abstract = "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.",
month = may,
event = "EGEV2020 - VisGap Workshop",
booktitle = "The Gap between Visualization Research and Visualization
Software (VisGap) (2020)",
pages = "1--8",
keywords = "Visual Analytics, Life and Medical Sciences",
URL = "https://www.cg.tuwien.ac.at/research/publications/2020/raidou_visgap2020/",
}