PDF(Pubmed)
Abstract：
Objectives: This study aimed to build a comprehensive deep-learning model for the prediction of radiation pneumonitis using chest computed tomography (CT), clinical, dosimetric, and laboratory data. Introduction: Radiation therapy is an effective tool for treating patients with lung cancer. Despite its effectiveness, the risk of radiation pneumonitis limits its application. Although several studies have demonstrated models to predict radiation pneumonitis, no reliable model has been developed yet. Herein, we developed prediction models using pretreatment chest CT and various clinical data to assess the likelihood of radiation pneumonitis in lung cancer patients. Methods: This retrospective study analyzed 3-dimensional (3D) lung volume data from chest CT scans and 27 features including dosimetric, clinical, and laboratory data from 548 patients who were treated at our institution between 2010 and 2021. We developed a neural network, named MergeNet, which processes lung 3D CT, clinical, dosimetric, and laboratory data. The MergeNet integrates a convolutional neural network with subsequent fully connected layers. A support vector machine (SVM) and light gradient boosting machine (LGBM) model were also implemented for comparison. For comparison, the convolution-only neural network was implemented as well. Three-dimensional Resnet-10 network and 4-fold cross-validation were used. Results: Classification performance was quantified by using the area under the receiver operative characteristic curve (AUC) metrics. MergeNet showed the AUC of 0.689. SVM, LGBM, and convolution-only networks showed AUCs of 0.525, 0.541, and 0.550, respectively. Application of DeLong test to pairs of receiver operating characteristic curves respectively yielded P values of .001 for the MergeNet-SVM pair and 0.001 for the MergeNet-LGBM pair. Conclusion: The MergeNet model, which incorporates chest CT, clinical, dosimetric, and laboratory data, demonstrated superior performance compared to other models. However, since its prediction performance has not yet reached an efficient level for clinical application, further research is required. Contribution: This study showed that MergeNet may be an effective means to predict radiation pneumonitis. Various predictive factors can be used together for the radiation pneumonitis prediction task via the MergeNet.
摘要：
目的：本研究旨在建立一个综合的深度学习模型，用于使用胸部计算机断层扫描（CT）预测放射性肺炎。临床,剂量测定,实验室数据。简介：放射治疗是治疗肺癌患者的有效工具。尽管有效,放射性肺炎的风险限制了其应用。尽管一些研究已经证明了预测放射性肺炎的模型，尚未开发出可靠的模型。在这里，我们使用治疗前胸部CT和各种临床数据建立了预测模型,以评估肺癌患者发生放射性肺炎的可能性.方法：这项回顾性研究分析了胸部CT扫描的三维（3D）肺容积数据和27个特征，包括剂量学，临床,以及2010年至2021年间在我们机构接受治疗的548例患者的实验室数据。我们开发了一个神经网络，名为MergeNet，处理肺部3DCT，临床,剂量测定,实验室数据。MergeNet将卷积神经网络与后续的完全连接层集成在一起。还实现了支持向量机（SVM）和光梯度增强机（LGBM）模型以进行比较。为了比较，也实现了仅卷积神经网络。使用三维Resnet-10网络和4折交叉验证。结果：通过使用接受者操作特征曲线（AUC）度量下的面积来量化分类性能。MergeNet显示AUC为0.689。SVM,LGBM,和仅卷积网络的AUC分别为0.525、0.541和0.550。DeLong测试对接收器工作特性曲线对的应用分别得出MergeNet-SVM对的P值为.001，MergeNet-LGBM对的P值为0.001。结论：MergeNet模型，结合了胸部CT，临床,剂量测定,和实验室数据，与其他型号相比，表现出卓越的性能。然而,由于其预测性能尚未达到临床应用的有效水平，需要进一步的研究。贡献：本研究表明MergeNet可能是预测放射性肺炎的有效手段。各种预测因子可以一起用于经由MergeNet的放射性肺炎预测任务。
