Information
- Publication Type: Conference Paper
- Workgroup(s)/Project(s):
- Date: May 2020
- Booktitle: The Gap between Visualization Research and Visualization Software (VisGap) (2020)
- Call for Papers: Call for Paper
- Event: EGEV2020 - VisGap Workshop
- Lecturer: Renata Raidou
- Open Access: yes
- Pages (from): 1
- Pages (to): 8
- 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.
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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 Meister 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,
booktitle = "The Gap between Visualization Research and Visualization
Software (VisGap) (2020)",
event = "EGEV2020 - VisGap Workshop",
pages = "1--8",
keywords = "Visual Analytics, Life and Medical Sciences",
URL = "https://www.cg.tuwien.ac.at/research/publications/2020/raidou_visgap2020/",
}