Differentiation between benign and malignant orbital tumors using deep transfer learning features and hand-crafted radiomics features from traditional CT imaging

Objective: To evaluate the diagnostic performance of deep learning-based radiomics (DL) and hand-crafted radiomics (HCR) in differentiating benign from malignant orbital tumors. Methods: A retrospective analysis was performed on CT data from 145 patients (48 benign, 97 malignant) diagnosed between December 2014 and March 2024. Two radiologists independently assessed conventional CT semantic features (e.g., lesion location, margin definition, internal density homogeneity, calcification, necrosis, and enhancement pattern). Deep transfer learning (DTL) extracted DL features, while traditional methods were used to obtain HCR features. Feature fusion, selection, and modeling were performed using the Least Absolute Shrinkage and Selection Operator (LASSO). Pathological diagnosis served as the gold standard. Model performance was evaluated using receiver operating characteristic (ROC) curves. A nomogram integrating clinical data and significant semantic features was constructed for visualization. The DeLong test and decision curve analysis (DCA) assessed model effectiveness. Results: < 0.05). DCA indicated that the nomogram provided higher clinical utility. Conclusion: The fused model outperformed single radiomics approaches in accuracy. The nomogram, which integrates clinical data and semantic features, demonstrated superior predictive performance and may support clinical decision-making, particularly for patients who cannot undergo invasive procedures.