Visual Analytics for the Integrated Exploration and Sensemaking of Cancer Cohort Radiogenomics and Clinical Information
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Date
2023
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
We 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.
Description
CCS Concepts: Human-centered computing -> Visual analytics; Applied computing -> Life and medical sciences
@inproceedings{10.2312:vcbm.20231220,
booktitle = {Eurographics Workshop on Visual Computing for Biology and Medicine},
editor = {Hansen, Christian and Procter, James and Renata G. Raidou and Jönsson, Daniel and Höllt, Thomas},
title = {{Visual Analytics for the Integrated Exploration and Sensemaking of Cancer Cohort Radiogenomics and Clinical Information}},
author = {El-Sherbiny, Sarah and Ning, Jing and Hantusch, Brigitte and Kenner, Lukas and Raidou, Renata Georgia},
year = {2023},
publisher = {The Eurographics Association},
ISSN = {2070-5786},
ISBN = {978-3-03868-216-5},
DOI = {10.2312/vcbm.20231220}
}