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

Information

Publication Type: Conference Paper
Workgroup(s)/Project(s): not specified
Date: September 2023
ISBN: 978-3-03868-177-9
Publisher: The Eurographics Association
Lecturer: Sarah El-Sherbiny
Event: EG VCBM 2023
DOI: 10.2312/vcbm.20231220
Booktitle: VCBM 2023: Eurographics Workshop on Visual Computing for Biology and Medicine
Pages: 13
Conference date: 20. September 2023 – 22. September 2023
Pages: 121 – 133
Keywords: Visual Analytics, Human-centered computing, Applied computing, Life and medical sciences

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.

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BibTeX

@inproceedings{el-sherbiny-2023-vai,
  title =      "Visual Analytics for the Integrated Exploration and
               Sensemaking of Cancer Cohort Radiogenomics and Clinical
               Information",
  author =     "Sarah El-Sherbiny and Jing Ning and Brigitte Hantusch and
               Lukas Kenner and Renata Raidou",
  year =       "2023",
  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.",
  month =      sep,
  isbn =       "978-3-03868-177-9",
  publisher =  "The Eurographics Association",
  event =      "EG VCBM 2023",
  doi =        "10.2312/vcbm.20231220",
  booktitle =  "VCBM 2023: Eurographics Workshop on Visual Computing for
               Biology and Medicine",
  pages =      "13",
  pages =      "121--133",
  keywords =   "Visual Analytics, Human-centered computing, Applied
               computing, Life and medical sciences",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2023/el-sherbiny-2023-vai/",
}