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Modeling the impact of out-of-phase ventilation on normal lung tissue response to radiation dose.

Authors
  • Wallat, Eric M1
  • Flakus, Mattison J1
  • Wuschner, Antonia E1
  • Shao, Wei2
  • Christensen, Gary E2
  • Reinhardt, Joseph M3
  • Baschnagel, Andrew M1
  • Bayouth, John E1
  • 1 Department of Human Oncology, University of Wisconsin-Madison, Madison, WI, 53705, USA.
  • 2 Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA, 52242, USA.
  • 3 Department of Biomedical Engineering, University of Iowa, Iowa City, IA, 52242, USA.
Type
Published Article
Journal
Medical Physics
Publisher
Wiley (John Wiley & Sons)
Publication Date
Jul 01, 2020
Volume
47
Issue
7
Pages
3233–3242
Identifiers
DOI: 10.1002/mp.14146
PMID: 32187683
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

To create a dose-response model that predicts lung ventilation change following radiation therapy, and examine the effects of out-of-phase ventilation. The dose-response model was built using 27 human subjects who underwent radiation therapy (RT) from an IRB-approved trial. For each four-dimensional computed tomography, two ventilation maps were created by calculating the N-phase local expansion ratio (LERN ) using most or all breathing phases and the 2-phase LER (LER2 ) using only the end inspiration and end expiration breathing phases. A polynomial regression model was created using the LERN ventilation maps pre-RT and post-RT and dose distributions for each subject, and crossvalidated with a leave-one-out method. Further validation of the model was performed using 15 additional human subjects using common statistical operating characteristics and gamma pass rates. For voxels receiving 20 Gy or greater, there was a significant increase from 52% to 59% (P = 0.03) in the gamma pass rates of the LERN model predicted post-RT Jacobian maps to the actual post-RT Jacobian maps, relative to the LER2 model. Additionally, accuracy significantly increased (P = 0.03) from 68% to 75% using the LERN model, relative to the LER2 model. The LERN model was significantly more accurate than the LER2 model at predicting post-RT ventilation maps. More accurate post-RT ventilation maps will aid in producing a higher quality functional avoidance treatment plan, allowing for potentially better normal tissue sparing. © 2020 American Association of Physicists in Medicine.

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