肺癌是一种具有高致死性的全球性疾病，早期筛查对提高5年生存率有很大帮助。早期筛查影像的多模态特征是肺腺癌预测的重要部分，建立基于多模态特征的肺腺癌诊断模型是明显的临床需求。通过我们的实践和调查，我们发现图形神经网络（GNNs）是多模态特征融合的优秀平台，可以使用边缘生成网络完善数据。因此，我们提出了一种基于多模态特征和边缘生成网络的肺腺癌多分类模型。根据比率为80%和20%，将338例数据集通过5折交叉验证分为训练集和测试集，两个数据集的分布相同。首先，从计算机断层扫描（CT）影像中裁剪出感兴趣区域（ROIs），分别输入到卷积神经网络（CNNs）和放射学特征处理平台中。然后，将两部分的结果输入到图嵌入表示网络中，以获取融合的特征向量。随后，根据临床和语义特征建立了一个基于图数据库，并使用边缘生成网络补充数据，将融合的特征向量作为节点的输入。这使我们能够清楚地了解GNN的信息传输发生在哪里，并提高了模型的可解释性。最后，使用GNN将节点进行分类。在我们的数据集上，本文提出的方法相比传统方法取得了优越的结果，并且在肺结节分类方面与最先进的方法具有一定的可比性。我们的方法的结果如下：精度（ACC）= 66.26%（±4.46%），曲线下面积（AUC）= 75.86％（±1.79%），F1分数= 64.00％（±3.65%），Matthews相关系数（MCC）= 48.40％（±5.07%）。具有边缘生成网络的模型在各个方面始终优于没有边缘生成网络的模型。实验表明，在适当的数据构建方法下，GNNs在CT医学图像分类领域可以优于传统的图像处理方法。此外，我们的模型具有更高的解释性，因为它采用了主观的临床和语义特征作为数据构建方法，这将有助于医生更好地利用人机交互。版权所有，2023 Quantitative Imaging in Medicine and Surgery。

Lung cancer is a global disease with high lethality, with early screening being considerably helpful for improving the 5-year survival rate. Multimodality features in early screening imaging are an important part of the prediction for lung adenocarcinoma, and establishing a model for adenocarcinoma diagnosis based on multimodal features is an obvious clinical need. Through our practice and investigation, we found that graph neural networks (GNNs) are excellent platforms for multimodal feature fusion, and the data can be completed using the edge-generation network. Therefore, we propose a new lung adenocarcinoma multiclassification model based on multimodal features and an edge-generation network.According to a ratio of 80% to 20%, respectively, the dataset of 338 cases was divided into the training set and the test set through 5-fold cross-validation, and the distribution of the 2 sets was the same. First, the regions of interest (ROIs) cropped from computed tomography (CT) images were separately fed into convolutional neural networks (CNNs) and radiomics processing platforms. The results of the 2 parts were then input into a graph embedding representation network to obtain the fused feature vectors. Subsequently, a graph database based on the clinical and semantic features was established, and the data were supplemented by an edge-generation network, with the fused feature vectors being used as the input of the nodes. This enabled us to clearly understand where the information transmission of the GNN takes place and improves the interpretability of the model. Finally, the nodes were classified using GNNs.On our dataset, the proposed method presented in this paper achieved superior results compared to traditional methods and showed some comparability with state-of-the-art methods for lung nodule classification. The results of our method are as follows: accuracy (ACC) =66.26% (±4.46%), area under the curve (AUC) =75.86% (±1.79%), F1-score =64.00% (±3.65%), and Matthews correlation coefficient (MCC) =48.40% (±5.07%). The model with the edge-generating network consistently outperformed the model without it in all aspects.The experiments demonstrate that with appropriate data=construction methods GNNs can outperform traditional image processing methods in the field of CT-based medical image classification. Additionally, our model has higher interpretability, as it employs subjective clinical and semantic features as the data construction approach. This will help doctors better leverage human-computer interactions.2023 Quantitative Imaging in Medicine and Surgery. All rights reserved.