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A neural network to predict symptomatic lung injury.

Publication ,  Journal Article
Munley, MT; Lo, JY; Sibley, GS; Bentel, GC; Anscher, MS; Marks, LB
Published in: Phys Med Biol
September 1999

A nonlinear neural network that simultaneously uses pre-radiotherapy (RT) biological and physical data was developed to predict symptomatic lung injury. The input data were pre-RT pulmonary function, three-dimensional treatment plan doses and demographics. The output was a single value between 0 (asymptomatic) and 1 (symptomatic) to predict the likelihood that a particular patient would become symptomatic. The network was trained on data from 97 patients for 400 iterations with the goal to minimize the mean-squared error. Statistical analysis was performed on the resulting network to determine the model's accuracy. Results from the neural network were compared with those given by traditional linear discriminate analysis and the dose-volume histogram reduction (DVHR) scheme of Kutcher. Receiver-operator characteristic (ROC) analysis was performed on the resulting network which had Az = 0.833 +/- 0.04. (Az is the area under the ROC curve.) Linear discriminate multivariate analysis yielded an Az = 0.813 +/- 0.06. The DVHR method had Az = 0.521 +/- 0.08. The network was also used to rank the significance of the input variables. Future studies will be conducted to improve network accuracy and to include functional imaging data.

Duke Scholars

Published In

Phys Med Biol

DOI

ISSN

0031-9155

Publication Date

September 1999

Volume

44

Issue

9

Start / End Page

2241 / 2249

Location

England

Related Subject Headings

  • Radiotherapy
  • Radiation Injuries
  • Radiation Dosage
  • ROC Curve
  • Nuclear Medicine & Medical Imaging
  • Neural Networks, Computer
  • Models, Biological
  • Middle Aged
  • Male
  • Lymphoma
 

Citation

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Munley, M. T., Lo, J. Y., Sibley, G. S., Bentel, G. C., Anscher, M. S., & Marks, L. B. (1999). A neural network to predict symptomatic lung injury. Phys Med Biol, 44(9), 2241–2249. https://doi.org/10.1088/0031-9155/44/9/311
Munley, M. T., J. Y. Lo, G. S. Sibley, G. C. Bentel, M. S. Anscher, and L. B. Marks. “A neural network to predict symptomatic lung injury.Phys Med Biol 44, no. 9 (September 1999): 2241–49. https://doi.org/10.1088/0031-9155/44/9/311.
Munley MT, Lo JY, Sibley GS, Bentel GC, Anscher MS, Marks LB. A neural network to predict symptomatic lung injury. Phys Med Biol. 1999 Sep;44(9):2241–9.
Munley, M. T., et al. “A neural network to predict symptomatic lung injury.Phys Med Biol, vol. 44, no. 9, Sept. 1999, pp. 2241–49. Pubmed, doi:10.1088/0031-9155/44/9/311.
Munley MT, Lo JY, Sibley GS, Bentel GC, Anscher MS, Marks LB. A neural network to predict symptomatic lung injury. Phys Med Biol. 1999 Sep;44(9):2241–2249.
Journal cover image

Published In

Phys Med Biol

DOI

ISSN

0031-9155

Publication Date

September 1999

Volume

44

Issue

9

Start / End Page

2241 / 2249

Location

England

Related Subject Headings

  • Radiotherapy
  • Radiation Injuries
  • Radiation Dosage
  • ROC Curve
  • Nuclear Medicine & Medical Imaging
  • Neural Networks, Computer
  • Models, Biological
  • Middle Aged
  • Male
  • Lymphoma