Habitat-radiomics combining multichannel 2.5D deep learning for differentiating adrenal adenomas from metastases using automatic segmentation: a multicenter study

Background The qualitative diagnosis of lipid-poor adrenal adenomas and metastases presents challenges, yet there is a significant difference in their treatment principles and prognosis. Materials and methods A total of 390 patients from two hospitals were divided into training, internal validation, and external test sets. Lesion segmentation was performed automatically using the large segmentation model Medical SAM. The 2.5D deep learning model was constructed using the DenseNet-121 architectur