深度学习在胶质母细胞瘤和单发脑转移瘤鉴别诊断中的研究进展OA

Glioblastoma(GBM)and solitary brain metastasis(SBM)exhibit similar conventional imaging features;however,their clinical treatment strategies differ significantly.Accurate differentiation between the two is therefore crucial for subsequent diagnosis and treatment.Deep learning,a branch of machine learning,can optimize multiple key steps in the image analysis workflow,including improving the efficiency of region-of-interest segmentation,accurately extracting imaging features,and constructing efficient fusion models,thus providing new solutions for differentiating GBM from SBM.Compared with traditional radiomic and machine learning,deep learning represents a more powerful and effective approach.This review systematically summarizes the current applications,technical progress,and challenges of deep learning in the differential diagnosis between GBM and SBM.