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A neural network model to predict lung radiation-induced pneumonitis.

Publication ,  Journal Article
Chen, S; Zhou, S; Zhang, J; Yin, F-F; Marks, LB; Das, SK
Published in: Med Phys
September 2007

A feed-forward neural network was investigated to predict the occurrence of lung radiation-induced Grade 2+ pneumonitis. The database consisted of 235 patients with lung cancer treated using radiotherapy, of whom 34 were diagnosed with Grade 2+ pneumonitis at follow-up. The network was constructed using an algorithm that alternately grew and pruned it, starting from the smallest possible network, until a satisfactory solution was found. The weights and biases of the network were computed using the error back-propagation approach. Momentum and variable leaning techniques were used to speed convergence. Using the growing/pruning approach, the network selected features from 66 dose and 27 non-dose variables. During network training, the 235 patients were randomly split into ten groups of approximately equal size. Eight groups were used to train the network, one group was used for early stopping training to prevent overfitting, and the remaining group was used as a test to measure the generalization capability of the network (cross-validation). Using this methodology, each of the ten groups was considered, in turn, as the test group (ten-fold cross-validation). For the optimized network constructed with input features selected from dose and non-dose variables, the area under the receiver operating characteristics (ROC) curve for cross-validated testing was 0.76 (sensitivity: 0.68, specificity: 0.69). For the optimized network constructed with input features selected only from dose variables, the area under the ROC curve for cross-validation was 0.67 (sensitivity: 0.53, specificity: 0.69). The difference between these two areas was statistically significant (p = 0.020), indicating that the addition of non-dose features can significantly improve the generalization capability of the network. A network for prospective testing was constructed with input features selected from dose and non-dose variables (all data were used for training). The optimized network architecture consisted of six input nodes (features), four hidden nodes, and one output node. The six input features were: lung volume receiving > 16 Gy (V16), generalized equivalent uniform dose (gEUD) for the exponent a = 1 (mean lung dose), gEUD for the exponent a = 3.5, free expiratory volume in 1 s (FEV1), diffusion capacity of carbon monoxide (DLCO%), and whether or not the patient underwent chemotherapy prior to radiotherapy. The significance of each input feature was individually evaluated by omitting it during network training and gauging its impact by the consequent deterioration in cross-validated ROC area. With the exception of FEV1 and whether or not the patient underwent chemotherapy prior to radiotherapy, all input features were found to be individually significant (p < 0.05). The network for prospective testing is publicly available via internet access.

Duke Scholars

Published In

Med Phys

DOI

ISSN

0094-2405

Publication Date

September 2007

Volume

34

Issue

9

Start / End Page

3420 / 3427

Location

United States

Related Subject Headings

  • Radiation Pneumonitis
  • Nuclear Medicine & Medical Imaging
  • Neural Networks, Computer
  • Humans
  • Dose-Response Relationship, Radiation
  • 5105 Medical and biological physics
  • 4003 Biomedical engineering
  • 1112 Oncology and Carcinogenesis
  • 0903 Biomedical Engineering
  • 0299 Other Physical Sciences
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Chen, S., Zhou, S., Zhang, J., Yin, F.-F., Marks, L. B., & Das, S. K. (2007). A neural network model to predict lung radiation-induced pneumonitis. Med Phys, 34(9), 3420–3427. https://doi.org/10.1118/1.2759601
Chen, Shifeng, Sumin Zhou, Junan Zhang, Fang-Fang Yin, Lawrence B. Marks, and Shiva K. Das. “A neural network model to predict lung radiation-induced pneumonitis.Med Phys 34, no. 9 (September 2007): 3420–27. https://doi.org/10.1118/1.2759601.
Chen S, Zhou S, Zhang J, Yin F-F, Marks LB, Das SK. A neural network model to predict lung radiation-induced pneumonitis. Med Phys. 2007 Sep;34(9):3420–7.
Chen, Shifeng, et al. “A neural network model to predict lung radiation-induced pneumonitis.Med Phys, vol. 34, no. 9, Sept. 2007, pp. 3420–27. Pubmed, doi:10.1118/1.2759601.
Chen S, Zhou S, Zhang J, Yin F-F, Marks LB, Das SK. A neural network model to predict lung radiation-induced pneumonitis. Med Phys. 2007 Sep;34(9):3420–3427.

Published In

Med Phys

DOI

ISSN

0094-2405

Publication Date

September 2007

Volume

34

Issue

9

Start / End Page

3420 / 3427

Location

United States

Related Subject Headings

  • Radiation Pneumonitis
  • Nuclear Medicine & Medical Imaging
  • Neural Networks, Computer
  • Humans
  • Dose-Response Relationship, Radiation
  • 5105 Medical and biological physics
  • 4003 Biomedical engineering
  • 1112 Oncology and Carcinogenesis
  • 0903 Biomedical Engineering
  • 0299 Other Physical Sciences