Artificial Neural Network With Composite Architectures for Prediction of Local Control in Radiotherapy

In this paper, we investigated the application of artificial neural networks with composite architectures into the prediction of local control (LC) of lung cancer patients after radiotherapy. The motivation of this paper was to take advantage of the temporal associations among longitudinal (sequential) data to improve the predictive performance of outcome models under the circumstance of limited sample sizes. Two composite architectures: 1) a 1-D convolutional + fully connected and 2) a locally connected + fully connected architectures were implemented for this purpose. Compared with the fully connected architecture [multilayer perceptron (MLP)], our composite architectures yielded better predictive performance of LC in lung cancer patients who received radiotherapy. Specifically, in a cohort of 98 patients (29 patients failed locally), the composite architecture of 1-D convolutional layers and fully connected layers achieved an area under receiver operating characteristic curve (AUC) of 0.83 [95% confidence interval (CI): 0.807-0.841] with 18 features (14 features are longitudinal data). Whereas, the composite architecture of locally connected layers and fully connected layers achieved an AUC of 0.80 (95% CI: 0.775-0.811). Both outperformed an MLP in the prediction performance with the same set of features, which achieved an AUC of 0.78 (95% CI: 0.751-0.790); (P-values for differences in AUC using the DeLong tests were 1.609 x 10(-14) and 1.407 x 10(-4), respectively).