Cohort-based T-SSIM Visual Computing for Radiation Therapy Prediction and Exploration

A. Wentzel; P. Hanula; T. Luciani; B. Elgohari; H. Elhalawani; G. Canahuate; D. Vock; C. D. Fuller; G. E. Marai

doi:10.1109/TVCG.2019.2934546

Cohort-based T-SSIM Visual Computing for Radiation Therapy Prediction and Exploration

A. Wentzel, P. Hanula, T. Luciani, B. Elgohari, H. Elhalawani, G. Canahuate, D. Vock, C. D. Fuller, G. E. Marai

Biostatistics

Research output: Contribution to journal › Article › peer-review

21 Scopus citations

Abstract

We describe a visual computing approach to radiation therapy (RT) planning, based on spatial similarity within a patient cohort. In radiotherapy for head and neck cancer treatment, dosage to organs at risk surrounding a tumor is a large cause of treatment toxicity. Along with the availability of patient repositories, this situation has lead to clinician interest in understanding and predicting RT outcomes based on previously treated similar patients. To enable this type of analysis, we introduce a novel topology-based spatial similarity measure, T-SSIM, and a predictive algorithm based on this similarity measure. We couple the algorithm with a visual steering interface that intertwines visual encodings for the spatial data and statistical results, including a novel parallel-marker encoding that is spatially aware. We report quantitative results on a cohort of 165 patients, as well as a qualitative evaluation with domain experts in radiation oncology, data management, biostatistics, and medical imaging, who are collaborating remotely.

Original language	English (US)
Pages (from-to)	949-959
Number of pages	11
Journal	IEEE Transactions on Visualization and Computer Graphics
Volume	26
Issue number	1
DOIs	https://doi.org/10.1109/TVCG.2019.2934546
State	Published - Jan 2020

Bibliographical note

Funding Information:
This work was supported by the National Institutes of Health [NCI-R01-CA214825, NCI-R01CA225190] and the National Science Foundation [CNS-1625941, CNS-1828265]. We thank all members of the Electronic Visualization Laboratory, members of the MD Anderson Head and Neck Cancer Quantitative Imaging Collaborative Group, and our collaborators at the University of Iowa and University of Minnesota.

Publisher Copyright:
© 2020 IEEE.

Keywords

Biomedical and Medical Visualization
High-Dimensional Data
Spatial Techniques
Visual Design

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/TVCG.2019.2934546

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abstract = "We describe a visual computing approach to radiation therapy (RT) planning, based on spatial similarity within a patient cohort. In radiotherapy for head and neck cancer treatment, dosage to organs at risk surrounding a tumor is a large cause of treatment toxicity. Along with the availability of patient repositories, this situation has lead to clinician interest in understanding and predicting RT outcomes based on previously treated similar patients. To enable this type of analysis, we introduce a novel topology-based spatial similarity measure, T-SSIM, and a predictive algorithm based on this similarity measure. We couple the algorithm with a visual steering interface that intertwines visual encodings for the spatial data and statistical results, including a novel parallel-marker encoding that is spatially aware. We report quantitative results on a cohort of 165 patients, as well as a qualitative evaluation with domain experts in radiation oncology, data management, biostatistics, and medical imaging, who are collaborating remotely.",

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AU - Vock, D.

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Cohort-based T-SSIM Visual Computing for Radiation Therapy Prediction and Exploration

Abstract

Bibliographical note

Keywords

UN SDGs

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