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Lung cancer, particularly in its advanced stages, remains a leading cause ofdeath globally. Though early detection via low-dose computed tomography (CT) ispromising, the identification of high-risk factors crucial for surgical modeselection remains a challenge. Addressing this, our study introduces anAttention-Enhanced Graph Convolutional Network (AE-GCN) model to classifywhether there are high-risk factors in stage I lung cancer based on thepreoperative CT images. This will aid surgeons in determining the optimalsurgical method before the operation. Unlike previous studies that relied on 3Dpatch techniques to represent nodule spatial features, our method employs a GCNmodel to capture the spatial characteristics of pulmonary nodules.Specifically, we regard each slice of the nodule as a graph vertex, and theinherent spatial relationships between slices form the edges. Then, to enhancethe expression of nodule features, we integrated both channel and spatialattention mechanisms with a pre-trained VGG model for adaptive featureextraction from pulmonary nodules. Lastly, the effectiveness of the proposedmethod is demonstrated using real-world data collected from the hospitals,thereby emphasizing its potential utility in the clinical practice.

High-risk Factor Prediction in Lung Cancer Using Thin CT Scans: An Attention-Enhanced Graph Convolutional Network Approach