title: Investigation of the support vector machine algorithm to predict lung radiation-induced pneumonitis.
abstract: 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.
authors.affiliation: Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710, USA. shifeng.chen@duke.edu
authors.forename: Shifeng
authors.initials: S
authors.lastname: Chen
authors.forename: Sumin
authors.initials: S
authors.lastname: Zhou
authors.forename: Fang-Fang
authors.initials: FF
authors.lastname: Yin
authors.forename: Lawrence B
authors.initials: LB
authors.lastname: Marks
authors.forename: Shiva K
authors.initials: SK
authors.lastname: Das
mesh_terms: D000465:Algorithms; D004307:Dose-Response Relationship, Radiation; D006801:Humans; D008168:Lung / Q000473:pathology / Q000528:radiation effects; D015233:Models, Statistical; D012372:ROC Curve; D017564:Radiation Pneumonitis / Q000175:diagnosis* / Q000209:etiology*; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted; D012680:Sensitivity and Specificity; D012984:Software