Artificial Neural Network With Composite Architectures for Prediction of Local Control in Radiotherapy
Artificial Neural Network With Composite Architectures for Prediction of Local Control in Radiotherapy
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DOI：
10.1109/trpms.2018.2884134
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发表时间：
2019-03-01
影响因子：
4.4
通讯作者：
El Naqa, Issam
中科院分区：
文献类型：
--
作者：
Cui, Sunan;Luo, Yi;El Naqa, Issam
本文研究了复合结构人工神经网络在肺癌放疗后局部控制预测中的应用。本文的动机是利用纵向（顺序）数据之间的时间关联，以提高在样本量有限的情况下的结果模型的预测性能。两种复合体系结构：1）1-D卷积+全连接和2）局部连接+全连接架构被实现用于此目的。与全连接架构[多层感知器（MLP）]相比，我们的复合架构在接受放疗的肺癌患者中获得了更好的LC预测性能。具体而言，在98名患者（29名患者局部失败）的队列中，一维卷积层和全连接层的复合架构实现了0.83 [95%置信区间（CI）：0.807-0.841]的受试者工作特征曲线下面积（AUC），具有18个特征（14个特征是纵向数据）。然而，局部连接层和完全连接层的复合结构实现了0.80的AUC（95% CI：0.775-0.811）。两者在预测性能方面均优于具有相同特征集的MLP，其AUC为0.78（95% CI：0.751-0.790）;（使用DeLong检验的AUC差异P值分别为1.609 x 10（-14）和1.407 x 10（-4））。
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).