国际医学放射学杂志2026，Vol.49Issue(2)：178-184,7.DOI:10.19300/j.2026.Z22490
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
Research progress of deep learning in the differential diagnosis between glioblastoma and solitary brain metastasis
摘要
Abstract
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.关键词
胶质母细胞瘤/脑转移瘤/磁共振成像/深度学习/影像组学Key words
Glioblastoma/Brain metastasis/Magnetic resonance imaging/Deep learning/Radiomics分类
医药卫生引用本文复制引用
唐旭梅,吴磊,黄飚..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[J].国际医学放射学杂志,2026,49(2):178-184,7.基金项目
国家自然科学基金面上项目(82071871) （82071871）
