Investigation of the support vector machine algorithm to predict lung radiation-induced pneumonitis.

Published

Journal Article

The purpose of this study is to build and test a support vector machine (SVM) model to predict for the occurrence of lung radiation-induced Grade 2+ pneumonitis. SVM is a sophisticated statistical technique capable of separating the two categories of patients (with/without pneumonitis) using a boundary defined by a complex hypersurface. Despite the complexity, the SVM boundary is only minimally influenced by outliers that are difficult to separate. By contrast, the simple hyperplane boundary computed by the more commonly used and related linear discriminant analysis method is heavily influenced by outliers. Two SVM models were built using data from 219 patients with lung cancer treated using radiotherapy (34 diagnosed with pneumonitis). One model (SVM(all)) selected input features from all dose and non-dose factors. For comparison, the other model (SVM(dose)) selected input features only from lung dose-volume factors. Model predictive ability was evaluated using ten-fold cross-validation and receiver operating characteristics (ROC) analysis. For the model SVM(all), the area under the cross-validated ROC curve was 0.76 (sensitivity/specificity = 74%/75%). Compared to the corresponding SVM(dose) area of 0.71 (sensitivity/specificity = 68%/68%), the predictive ability of SVM(all) was improved, indicating that non-dose features are important contributors to separating patients with and without pneumonitis. Among the input features selected by model SVM(all), the two with highest importance for predicting lung pneumonitis were: (a) generalized equivalent uniform doses close to the mean lung dose, and (b) chemotherapy prior to radiotherapy. The model SVM(all) is publicly available via internet access.

Full Text

Duke Authors

Cited Authors

  • Chen, S; Zhou, S; Yin, F-F; Marks, LB; Das, SK

Published Date

  • October 2007

Published In

Volume / Issue

  • 34 / 10

Start / End Page

  • 3808 - 3814

PubMed ID

  • 17985626

Pubmed Central ID

  • 17985626

International Standard Serial Number (ISSN)

  • 0094-2405

Digital Object Identifier (DOI)

  • 10.1118/1.2776669

Language

  • eng

Conference Location

  • United States