{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T05:17:57Z","timestamp":1788499077133,"version":"build-2803163510"},"reference-count":34,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,8,30]],"date-time":"2026-08-30T00:00:00Z","timestamp":1788048000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>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\u2013Anchor 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.<\/jats:p>","DOI":"10.3390\/jimaging12090408","type":"journal-article","created":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T05:55:56Z","timestamp":1788155756000},"page":"408","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Fusion of Radiomics and Gated Graph Attention Network for Pulmonary Nodule Malignancy Classification"],"prefix":"10.3390","volume":"12","author":[{"given":"Xinying","family":"Guo","sequence":"first","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zirong","family":"Yu","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1278-671X","authenticated-orcid":false,"given":"Jibin","family":"Yin","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"The National Lung Screening Trial Research Team (2011). 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