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

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

Journal article

Peer reviewed

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 and G.E Marai

IEEE transactions on visualization and computer graphics, Vol.26(1), pp.949-959

01/2020

DOI: 10.1109/TVCG.2019.2934546

PMCID: PMC7253296

PMID: 31442988

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url

https://arxiv.org/pdf/1907.05919View

Open Access

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.

Tumors

Biological systems

Biomedical and Medical Visualization

Biomedical applications of radiation

Data visualization

High-Dimensional Data

Planning

Prediction algorithms

Spatial Techniques

Visual Design

Visualization

Details

Title: Subtitle: Cohort-based T-SSIM Visual Computing for Radiation Therapy Prediction and Exploration
Creators: A Wentzel - University of Illinois Chicago
P Hanula - University of Illinois Chicago
T Luciani - University of Illinois Chicago
B Elgohari - University of Texas MD Anderson Cancer Center
H Elhalawani - University of Texas MD Anderson Cancer Center
G Canahuate - University of Iowa
D Vock - University of Minnesota
C.D Fuller - University of Texas MD Anderson Cancer Center
G.E Marai - University of Illinois Chicago
Resource Type: Journal article
Publication Details: IEEE transactions on visualization and computer graphics, Vol.26(1), pp.949-959
DOI: 10.1109/TVCG.2019.2934546
PMID: 31442988
PMCID: PMC7253296
NLM abbreviation: IEEE Trans Vis Comput Graph
ISSN: 1077-2626
eISSN: 1941-0506
Publisher: IEEE
Grant note: NCI-R01-CA214825; NCI-R01CA225190 / National Institutes of Health (10.13039/100000002) CNS-1625941; CNS-1828265 / National Science Foundation
Language: English
Date published: 01/2020
Academic Unit: Electrical and Computer Engineering
Record Identifier: 9984197114802771

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