WE‐D‐M100J‐04: Prediction of Lung Radiation‐Induced Pneumonitis Using the Support Vector Machine Algorithm

S. Chen, S. Zhou, J. Zhang, L. Marks, S. Das

Research output: Contribution to journalArticle

Abstract

Purpose: To build and test a Support Vector Machine (SVM) model to predict the occurrence of lung radiation‐induced Grade 2+ pneumonitis. SVM is a sophisticated statistical technique that is capable of using complex hypersurfaces to separate the cases with and without pneumonitis. Method and Materials: Two SVM models were built using data from 235 patients with lung cancer treated using radiotherapy (34 diagnosed with pneumonitis). One model (SVMall) selected input features from all dose‐volume and non‐dose factors. For comparison, the other model (SVMdose) selected input features only from lung dose‐volume factors. The models were built with in‐house developed software that employed a unique strategy to sequentially add/remove/substitute features. The SVM models were tested using ten‐fold cross‐validation, wherein 1/10th of the data were tested, in turn, using the model built with the remaining 9/10th of the data. Results: The input features selected to build SVMall were the lung generalized equivalent uniform dose (EUD) with exponents a=1.2, 1.3, 1.4, chemotherapy prior to radiotherapy (yes/no), tumor location (central/peripheral), gender, and histology (adenocarcinoma/other; small cell/other). The input features for SVMdose were EUD a = 1.1, 1.3, 1.4, lung volume receiving > 48 Gy (V48), and V50. Both models selected EUD a ≈ 1 (EUD a=1 is the mean lung dose, which frequently appears as a strong predictor of radiation pneumonitis in literature). The area under the cross‐validated SVMall Receiver Operating Characteristics curve was 0.76 (sensitivity/specificity = 74%/75%), compared to the corresponding SVMdose area of 0.71 (sensitivity/specificity = 68%/68%). SVMall was statistically superior (p=0.01), indicating that non‐dose features significantly contribute to separating patients with and without pneumonitis. Conclusions: The SVM model constructed from dose and non‐dose input factors is a valuable prospective tool for predicting the occurrence of radiation‐induced lung pneumonitis.

Original languageEnglish (US)
Pages (from-to)2602-2603
Number of pages2
JournalMedical physics
Volume34
Issue number6
DOIs
StatePublished - Jun 2007

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ASJC Scopus subject areas

  • Biophysics
  • Radiology Nuclear Medicine and imaging

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