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Combining multiple models to generate consensus: application to radiation-induced pneumonitis prediction.

Publication ,  Journal Article
Das, SK; Chen, S; Deasy, JO; Zhou, S; Yin, F-F; Marks, LB
Published in: Med Phys
November 2008

The fusion of predictions from disparate models has been used in several fields to obtain a more realistic and robust estimate of the "ground truth" by allowing the models to reinforce each other when consensus exists, or, conversely, negate each other when there is no consensus. Fusion has been shown to be most effective when the models have some complementary strengths arising from different approaches. In this work, we fuse the results from four common but methodologically different nonlinear multivariate models (Decision Trees, Neural Networks, Support Vector Machines, Self-Organizing Maps) that were trained to predict radiation-induced pneumonitis risk on a database of 219 lung cancer patients treated with radiotherapy (34 with Grade 2+ postradiotherapy pneumonitis). Each model independently incorporated a small number of features from the available set of dose and nondose patient variables to predict pneumonitis; no two models had all features in common. Fusion was achieved by simple averaging of the predictions for each patient from all four models. Since a model's prediction for a patient can be dependent on the patient training set used to build the model, the average of several different predictions from each model was used in the fusion (predictions were made by repeatedly testing each patient with a model built from different cross-validation training sets that excluded the patient being tested). The area under the receiver operating characteristics curve for the fused cross-validated results was 0.79, with lower variance than the individual component models. From the fusion, five features were extracted as the consensus among all four models in predicting radiation pneumonitis. Arranged in order of importance, the features are (1) chemotherapy; (2) equivalent uniform dose (EUD) for exponent a=1.2 to 3; (3) EUD for a=0.5 to 1.2, lung volume receiving >20-30 Gy; (4) female sex; and (5) squamous cell histology. To facilitate ease of interpretation and prospective use, the fused outcome results for the patients were fitted to a logistic probability function.

Duke Scholars

Published In

Med Phys

DOI

ISSN

0094-2405

Publication Date

November 2008

Volume

35

Issue

11

Start / End Page

5098 / 5109

Location

United States

Related Subject Headings

  • Risk
  • Regression Analysis
  • Radiotherapy Dosage
  • Radiation Pneumonitis
  • ROC Curve
  • Probability
  • Nuclear Medicine & Medical Imaging
  • Neural Networks, Computer
  • Models, Biological
  • Middle Aged
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Das, S. K., Chen, S., Deasy, J. O., Zhou, S., Yin, F.-F., & Marks, L. B. (2008). Combining multiple models to generate consensus: application to radiation-induced pneumonitis prediction. Med Phys, 35(11), 5098–5109. https://doi.org/10.1118/1.2996012
Das, Shiva K., Shifeng Chen, Joseph O. Deasy, Sumin Zhou, Fang-Fang Yin, and Lawrence B. Marks. “Combining multiple models to generate consensus: application to radiation-induced pneumonitis prediction.Med Phys 35, no. 11 (November 2008): 5098–5109. https://doi.org/10.1118/1.2996012.
Das SK, Chen S, Deasy JO, Zhou S, Yin F-F, Marks LB. Combining multiple models to generate consensus: application to radiation-induced pneumonitis prediction. Med Phys. 2008 Nov;35(11):5098–109.
Das, Shiva K., et al. “Combining multiple models to generate consensus: application to radiation-induced pneumonitis prediction.Med Phys, vol. 35, no. 11, Nov. 2008, pp. 5098–109. Pubmed, doi:10.1118/1.2996012.
Das SK, Chen S, Deasy JO, Zhou S, Yin F-F, Marks LB. Combining multiple models to generate consensus: application to radiation-induced pneumonitis prediction. Med Phys. 2008 Nov;35(11):5098–5109.

Published In

Med Phys

DOI

ISSN

0094-2405

Publication Date

November 2008

Volume

35

Issue

11

Start / End Page

5098 / 5109

Location

United States

Related Subject Headings

  • Risk
  • Regression Analysis
  • Radiotherapy Dosage
  • Radiation Pneumonitis
  • ROC Curve
  • Probability
  • Nuclear Medicine & Medical Imaging
  • Neural Networks, Computer
  • Models, Biological
  • Middle Aged