摘要
深度学习作为人工智能的一种方法,近几年才应用于胶质母细胞瘤(glioblastoma, GBM)的研究。大体上,该方法在GBM的应用主要分临床、病理及自身效果比较这三个方面。进一步细分,深度学习在GBM的临床应用又分为患者的预后判断、病变的诊断与鉴别诊断以及肿瘤的放疗三个方向;在GBM的病理应用分为分子及基因表达状态的判断、病理组织的识别。在深度学习方法上,用的最多的是卷积神经网络,其他的研究尚包括不同深度学习模型间的效果比较,源自不同MRI序列的深度模型建立等。本文就深度学习在GBM的应用展开综述。
Deep learning, a method of artificial intelligence, has been used for glioblastoma(GBM) in recent years. This method is mainly used in the clinical, pathological, and methodological aspects of GBM. The clinical usage includes the prediction of patient prognosis, differential diagnosis, and tumor radiotherapy. The pathological studies includes the prediction of tumor molecular and genetic expression status, and the identification of pathological tissue. As for the method itself, the most frequently used algorithm is convolutional neural network. Other studies included the comparison among different deep learning models and establishment of deep learning models based on different MRI sequences. This paper is to review the application of deep learning in GBM in detail.
作者
郑飞
陈绪珠
ZHENG Fei;CHEN Xuzhu(Department of Radiology,Beijing Tiantan Hospital,Capital Medical University,Beijing 100070,China)
出处
《磁共振成像》
CAS
CSCD
北大核心
2022年第3期115-117,共3页
Chinese Journal of Magnetic Resonance Imaging
基金
国家自然科学基金面上项目(编号:81772005)
北京市科委协同创新重大专项子课题(编号:Z191100006619088)。
关键词
胶质母细胞瘤
人工智能
深度学习
磁共振成像
影像组学
glioblastoma
artificial intelligence
deep learning
magnetic resonance imaging
machine learning
