WE‐D‐M100J‐03: A Neural Network Model to Predict Lung Radiation‐Induced Pneumonitis

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

Research output: Contribution to journalArticlepeer-review


Purpose: To build and test a feed‐forward neural network model to predict the occurrence of lung radiation‐induced Grade 2+ pneumonitis. Method and Materials: The database comprised 235 patients with lung cancer treated using radiotherapy (34 diagnosed with pneumonitis). The neural network was constructed using a unique 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. The network was 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 network was constructed with input features selected from dose and non‐dose variables. The selected input features were: lung volume receiving > 16 Gy (V16), mean lung dose, generalized equivalent uniform dose (gEUD) for the exponent a=3.5, free expiratory volume in 1s (FEV1), diffusion capacity of Carbon Monoxide (DLCO%), and whether or not the patient underwent chemotherapy prior to radiotherapy. With the exception of FEV1, all input features were found to be individually significant (p < 0.05). The area under the Receiver Operating Characteristics (ROC) curve for cross‐validated testing was 0.76 (sensitivity: 68%, specificity: 69%). To gauge the impact of non‐dose variables on model predictive capability, a second network was constructed with input features selected only from lung dose‐volume histogram variables. The area under the ROC curve for cross‐validation was 0.67 (sensitivity: 53%, specificity: 69%). The network constructed from dose and non‐dose variables was statistically superior (p=0.020), indicating that the addition of non‐dose features significantly improves the generalization capability of the network. Conclusions: The neural network constructed from dose and non‐dose variables can be used to prospectively predict radiotherapy‐induced pneumonitis and, thereby, appropriately alter radiotherapy plans to reduce this possibility.

Original languageEnglish (US)
Pages (from-to)2602
Number of pages1
JournalMedical physics
Issue number6
StatePublished - Jun 2007
Externally publishedYes

ASJC Scopus subject areas

  • Biophysics
  • Radiology Nuclear Medicine and imaging


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