Construction of a multimodal artificial intelligence model for differentiating benign and malignant anterior mediastinal tumors

The logistic regression-based multimodal model demonstrated superior performance in differentiating benign and malignant anterior mediastinal tumors compared to models based on clinical data alone, imaging data alone, or the random forest approach. This method holds promise for aiding preoperative diagnosis in clinical settings. Notably, features such as tumor cross-sectional diameter, short-axis length on CT, history of myasthenia gravis, lactate dehydrogenase, and carcinoembryonic antigen showed significant clinical value in differentiating tumor types. These findings suggest that clinicians should pay closer attention to these indicators during differential diagnosis of anterior mediastinal tumors.