RDINet: A novel dynamic mapping model integrating radiomics and deep learning for predicting treatment response in thyroid eye disease.

Objective: Artificial intelligence-based magnetic resonance imaging (MRI) analysis has shown promising potential in predicting the response to intravenous glucocorticoid (IVGC) treatment in thyroid eye disease (TED). We aimed to explore a novel model for multimodal feature fusion based on MRI data to enhance predictive effectiveness. Methods: We enrolled a total of 147 TED patients who underwent IVGC treatment. Pre-treatment orbital T2-weighted image was obtained for each subject, and individual segmentation of four extraocular muscles (EOMs) was performed. Radiomics analysis using different combinations of muscles as region of interests (ROIs) and machine learning algorithms were employed. Furthermore, we proposed a Residual Dynamic Integration Network (RDINet), which integrated image features extracted by convolutional neural network (ResNet-50), radiomics features, and clinical features using a dynamic mapping module. Results: Combining medial rectus (MR) and lateral rectus (LR) in the modeling exhibited marginally improved performance than using all four EOMs in radiomics analysis (AUC = 0.8981 vs. 0.8796). ResNet-50 models based on MR + LR and four EOMs yielded AUC values of 0.9074 and 0.8889, respectively. RDINet achieved AUC values of 0.9213 and 0.9028 for the above two ROI strategies, which increased to 0.9537 and 0.9259 after integrating clinical features into modeling. Conclusion: The focused combination of MR and LR in radiomics and deep learning analysis outperformed the inclusion of four EOMs for IVGC response prediction in TED patients. The proposed RDINet realizes effective multimodal feature fusion of TED, which serves as a potent dynamic mapping model for treatment response prediction.