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

Research output: Contribution to journalArticle

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 languageEnglish (US)
Pages (from-to)949-959
Number of pages11
JournalIEEE Transactions on Visualization and Computer Graphics
Volume26
Issue number1
DOIs
StatePublished - Jan 2020

Fingerprint

Radiotherapy
Oncology
Medical imaging
Information management
Toxicity
Tumors
Topology
Availability
Radiation
Planning

Keywords

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

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

Cite this

Wentzel, A., Hanula, P., Luciani, T., Elgohari, B., Elhalawani, H., Canahuate, G., ... Marai, G. E. (2020). Cohort-based T-SSIM Visual Computing for Radiation Therapy Prediction and Exploration. IEEE Transactions on Visualization and Computer Graphics, 26(1), 949-959. https://doi.org/10.1109/TVCG.2019.2934546

Cohort-based T-SSIM Visual Computing for Radiation Therapy Prediction and Exploration. / Wentzel, A.; Hanula, P.; Luciani, T.; Elgohari, B.; Elhalawani, H.; Canahuate, G.; Vock, D.; Fuller, C. D.; Marai, G. E.

In: IEEE Transactions on Visualization and Computer Graphics, Vol. 26, No. 1, 01.2020, p. 949-959.

Research output: Contribution to journalArticle

Wentzel, A, Hanula, P, Luciani, T, Elgohari, B, Elhalawani, H, Canahuate, G, Vock, D, Fuller, CD & Marai, GE 2020, 'Cohort-based T-SSIM Visual Computing for Radiation Therapy Prediction and Exploration', IEEE Transactions on Visualization and Computer Graphics, vol. 26, no. 1, pp. 949-959. https://doi.org/10.1109/TVCG.2019.2934546
Wentzel, A. ; Hanula, P. ; Luciani, T. ; Elgohari, B. ; Elhalawani, H. ; Canahuate, G. ; Vock, D. ; Fuller, C. D. ; Marai, G. E. / Cohort-based T-SSIM Visual Computing for Radiation Therapy Prediction and Exploration. In: IEEE Transactions on Visualization and Computer Graphics. 2020 ; Vol. 26, No. 1. pp. 949-959.
@article{4212bfebdab241e59887df810d286e09,
title = "Cohort-based T-SSIM Visual Computing for Radiation Therapy Prediction and Exploration",
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.",
keywords = "Biomedical and Medical Visualization, High-Dimensional Data, Spatial Techniques, Visual Design",
author = "A. Wentzel and P. Hanula and T. Luciani and B. Elgohari and H. Elhalawani and G. Canahuate and D. Vock and Fuller, {C. D.} and Marai, {G. E.}",
year = "2020",
month = "1",
doi = "10.1109/TVCG.2019.2934546",
language = "English (US)",
volume = "26",
pages = "949--959",
journal = "IEEE Transactions on Visualization and Computer Graphics",
issn = "1077-2626",
publisher = "IEEE Computer Society",
number = "1",

}

TY - JOUR

T1 - Cohort-based T-SSIM Visual Computing for Radiation Therapy Prediction and Exploration

AU - Wentzel, A.

AU - Hanula, P.

AU - Luciani, T.

AU - Elgohari, B.

AU - Elhalawani, H.

AU - Canahuate, G.

AU - Vock, D.

AU - Fuller, C. D.

AU - Marai, G. E.

PY - 2020/1

Y1 - 2020/1

N2 - 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.

AB - 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.

KW - Biomedical and Medical Visualization

KW - High-Dimensional Data

KW - Spatial Techniques

KW - Visual Design

UR - http://www.scopus.com/inward/record.url?scp=85075613160&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85075613160&partnerID=8YFLogxK

U2 - 10.1109/TVCG.2019.2934546

DO - 10.1109/TVCG.2019.2934546

M3 - Article

C2 - 31442988

AN - SCOPUS:85075613160

VL - 26

SP - 949

EP - 959

JO - IEEE Transactions on Visualization and Computer Graphics

JF - IEEE Transactions on Visualization and Computer Graphics

SN - 1077-2626

IS - 1

ER -