An artificial neural network for predicting the incidence of radiation pneumonitis.
A method to predict radiation-induced pneumonitis (RP) using an artificial neural network (ANN) was investigated. A retrospective study was applied to the clinical data from 142 patients who have been treated with three-dimensional conformal radiotherapy for tumors in the thoracic region. These data were classified, based on their treatment outcome, into two patient clusters: with RP (Np=26) and without RP (Np= 116). An ANN was designed as a classifier. To perform the classification, a patient-treatment outcome with RP was assigned a value of 1, and a patient treatment outcome without RP was assigned a value of -1. The input of the ANN was limited to the patient lung dose-volume data only. A volume vector (VD) that describes patient lung subvolumes receiving more than a set of threshold doses was used as the network input variable. A zero value was used as the threshold to set the output value into -1 or 1. Three ANNs (ANN_1, ANN_2, and ANN_3), each with three layers, were trained to perform this classification function and to show the effect of training data on the ANN performance. Radial basis function was applied as the hidden layer neuron activation function and a sigmoid function was selected as the output layer neuron function. Backpropagation with a conjugate gradient algorithm was used to train the network. ANN_1 was trained and tested by using the leave-one-out method. ANN_2 was trained by randomly selecting 2/3 of the patient data, and tested by the remaining 1/3 of the data. ANN_3 was trained by a user selecting 2/3 of the patient data, and tested by the remaining 1/3 of the data. The predictive accuracy was verified as the area under a receiver operator characteristic (ROC) curve. The correct classification rates of 73% for RP cases, and 99% for non-RP cases were obtained from ANN_1. The corresponding correct classification rates of 44% for RP cases, and 89% for non-RP cases were obtained from ANN_2. From the ANN_3 test phase, the corresponding correct classification rates of 55% for RP cases, and 95% non-RP cases were achieved. The area under ROC curve was 0.85+/-0.05, 0.68+/-0.10, and 0.81+/-0.09 for ANN_1, ANN _2, and ANN_3, respectively, within its asymmetric 95% confidence interval. The sensitivity was 95%, 57%, and 71%, and the specificity was 94%, 88%, and 90% for ANN_1, ANN_2, and ANN_3, respectively. Preliminary results suggest that the ANN approach provides a useful tool for the prediction of radiation-induced lung pneumonitis, using the patient lung dose-volume information.
Su
Min
M
Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710, USA.
Miften
Moyed
M
Whiddon
Curtis
C
Sun
Xuejun
X
Light
Kim
K
Marks
Lawrence
L
D000465:Algorithms; D015897:Comorbidity; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D015994:Incidence; D008175:Lung Neoplasms / Q000453:epidemiology* / Q000532:radiotherapy; D016571:Neural Networks, Computer; D011379:Prognosis; D017564:Radiation Pneumonitis / Q000175:diagnosis* / Q000453:epidemiology*; D011835:Radiation Protection / Q000379:methods*; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods; D020266:Radiotherapy, Conformal / Q000706:statistics & numerical data*; D015203:Reproducibility of Results; D012189:Retrospective Studies; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D012680:Sensitivity and Specificity; D014481:United States / Q000453:epidemiology