  Lung cancer, particularly in its advanced stages, remains a leading cause of
death globally. Though early detection via low-dose computed tomography (CT) is
promising, the identification of high-risk factors crucial for surgical mode
selection remains a challenge. Addressing this, our study introduces an
Attention-Enhanced Graph Convolutional Network (AE-GCN) model to classify
whether there are high-risk factors in stage I lung cancer based on the
preoperative CT images. This will aid surgeons in determining the optimal
surgical method before the operation. Unlike previous studies that relied on 3D
patch techniques to represent nodule spatial features, our method employs a GCN
model to capture the spatial characteristics of pulmonary nodules.
Specifically, we regard each slice of the nodule as a graph vertex, and the
inherent spatial relationships between slices form the edges. Then, to enhance
the expression of nodule features, we integrated both channel and spatial
attention mechanisms with a pre-trained VGG model for adaptive feature
extraction from pulmonary nodules. Lastly, the effectiveness of the proposed
method is demonstrated using real-world data collected from the hospitals,
thereby emphasizing its potential utility in the clinical practice.
