Visual Analytics for the Integrated Exploration and Sensemaking of Cancer Cohort Radiogenomics and Clinical Information

Simple item page
dc.contributor.authorEl-Sherbiny, Sarahen_US
dc.contributor.authorNing, Jingen_US
dc.contributor.authorHantusch, Brigitteen_US
dc.contributor.authorKenner, Lukasen_US
dc.contributor.authorRaidou, Renata Georgiaen_US
dc.contributor.editorHansen, Christianen_US
dc.contributor.editorProcter, Jamesen_US
dc.contributor.editorRenata G. Raidouen_US
dc.contributor.editorJönsson, Danielen_US
dc.contributor.editorHöllt, Thomasen_US
dc.date.accessioned2023-09-19T11:31:56Z
dc.date.available2023-09-19T11:31:56Z
dc.date.issued2023
dc.description.abstractWe present a visual analytics (VA) framework for the comprehensive exploration and integrated analysis of radiogenomic and clinical data from a cancer cohort. Our framework aims to support the workflow of cancer experts and biomedical data scientists as they investigate cancer mechanisms. Challenges in the analysis of radiogenomic data, such as the heterogeneity and complexity of the data sets, hinder the exploration and sensemaking of the available patient information. These challenges can be answered through the field of VA, but approaches that bridge radiogenomic and clinical data in an interactive and flexible visual framework are still lacking. Our approach enables the integrated exploration and joint analysis of radiogenomic data and clinical information for knowledge discovery and hypothesis assessment through a flexible VA dashboard. We follow a user-centered design strategy, where we integrate domain knowledge into a semi-automated analytical workflow based on unsupervised machine learning to identify patterns in the patient data provided by our collaborating domain experts. An interactive visual interface further supports the exploratory and analytical process in a free and a hypothesis-driven manner. We evaluate the unsupervised machine learning models through similarity measures and assess the usability of the framework through use cases conducted with cancer experts. Expert feedback indicates that our framework provides suitable and flexible means for gaining insights into large and heterogeneous cancer cohort data, while also being easily extensible to other data sets.en_US
dc.description.sectionheadersNeuroanatomy and Omics
dc.description.seriesinformationEurographics Workshop on Visual Computing for Biology and Medicine
dc.identifier.doi10.2312/vcbm.20231220
dc.identifier.isbn978-3-03868-216-5
dc.identifier.issn2070-5786
dc.identifier.pages121-133
dc.identifier.pages13 pages
dc.identifier.urihttps://doi.org/10.2312/vcbm.20231220
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/vcbm20231220
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Human-centered computing -> Visual analytics; Applied computing -> Life and medical sciences
dc.subjectHuman centered computing
dc.subjectVisual analytics
dc.subjectApplied computing
dc.subjectLife and medical sciences
dc.titleVisual Analytics for the Integrated Exploration and Sensemaking of Cancer Cohort Radiogenomics and Clinical Informationen_US
Files
Original bundle
Now showing 1 - 5 of 7
No Thumbnail Available
Name:
121-133.pdf
Size:
4.92 MB
Format:
Adobe Portable Document Format
Download
No Thumbnail Available
Name:
data-clustering.pdf
Size:
2.45 MB
Format:
Adobe Portable Document Format
Download
No Thumbnail Available
Name:
data-imputation.pdf
Size:
229.5 KB
Format:
Adobe Portable Document Format
Download
No Thumbnail Available
Name:
readme.txt
Size:
334 B
Format:
Plain Text
Download
No Thumbnail Available
Name:
task-typology.pdf
Size:
281.67 KB
Format:
Adobe Portable Document Format
Download
Collections
VCBM 2023: Eurographics Workshop on Visual Computing for Biology and Medicine