放射基因组学
人工智能
学习迁移
无线电技术
机器学习
磁共振成像
神经影像学
个性化医疗
信息抽取
计算机科学
医学
数据科学
深度学习
生物信息学
神经科学
放射科
生物
出处
期刊:IEEE Reviews in Biomedical Engineering
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:16: 579-593
被引量:3
标识
DOI:10.1109/rbme.2021.3075500
摘要
A state-of-the-art interdisciplinary survey on multi-modal radiogenomic approaches is presented involving applications to the diagnosis and personalized management of gliomas a common kind of brain tumors through noninvasive imaging integrated with genomic information. It encompasses mining tumor radioimages employing deep learning for the automated extraction of relevant features from the segmented volume of interest (VOI). Gene expression values from surgically extracted tumor tissues are often simultaneously analyzed to determine patient specific features. Association between genomic and radiomic features are also explored in some cases to determine the imaging surrogates. Deep learning and transfer learning are typically exploited for efficient knowledge discovery and decision-making. Some studies on survival prediction ensemble learning and interactive learning are also included. The literature mainly focuses on magnetic resonance imaging (MRI) data of the brain for learning and validation and generally involves the NIH TCIA and TCGA repositories as well as the BraTS Challenge databases.
科研通智能强力驱动
Strongly Powered by AbleSci AI