Fusion of Radiomics and Gated Graph Attention Network for Pulmonary Nodule Malignancy Classification

Pulmonary nodule malignancy classification requires effective integration of heterogeneous imaging features and contextual information among nodules. Existing models mainly analyze nodules independently and may overlook inter-nodule relationships. We propose RGGA-Net, a radiomics-guided graph attention network that integrates deep imaging features and radiomics representations through cross-modal interaction and graph-based reasoning. A learned edge gate is introduced to adaptively modulate graph message passing, and an anchor regularization strategy is used to improve representation stability. RGGA-Net was evaluated on the LUNA25 dataset using patient-level splitting with an internal held-out test set and further assessed on the LIDC-IDRI cohort under cross-dataset evaluation. On the internal test set, RGGA-Net achieved an AUC of 0.8910 and a PR-AUC of 0.5173. External evaluation on LIDC-IDRI demonstrated moderate discrimination (AUC = 0.7023). Gate-Anchor ablation analysis showed a favorable interaction pattern between the learned gate and anchor regularization, although statistical superiority was not established. These findings suggest that radiomics-guided graph attention provides a feasible framework for incorporating inter-nodule information into malignancy classification, while further multi-center validation remains necessary.