DAFNet: A multi-modal deep learning model based on whole-lung CT for the automated prediction of the air space spread of lung adenocarcinoma

Spread through air spaces (STAS) is a characteristic invasive pattern of lung adenocarcinoma (LUAD), which is associated with a high recurrence rate and poor prognosis. This research introduced the automatic deep learning multimodal detection network (DAFNet) for predicting STAS based on preoperative whole-lung CT scans. In contrast to conventional approaches necessitating manual tumor delineation, DAFNet performs comprehensive end-to-end analysis of pulmonary imaging data through integrated multimodal data fusion and multiscale feature extraction methodologies. A retrospective analysis was performed on 1164 patients with LUAD (511 STAS-positive and 653 negative) from two centers, with a training-to-test split ratio of 70:30 (814 in training set and 350 in test set). In the test set, DAFNet demonstrated an area under the receiver operating characteristic curve (AUROC) of 0.90 (95% confidence interval 0.86-0.94), significantly outperforming predictive models utilizing clinical examination numerical data alone (AUROC 0.72) and radiomics features independently (AUROC 0.65). The implementation of an adaptive gate fusion mechanism combined with a DINOv3-based pre-trained architecture substantially improved predictive accuracy for STAS status determination. These findings establish DAFNet as a promising fully automated, non-invasive diagnostic tool for preoperative STAS prediction in lung adenocarcinoma, thereby advancing personalized surgical planning and promoting AI-driven oncological applications through enhanced clinical translation potential.