无线电技术
医学
数据集
数据提取
临床实习
过程(计算)
医学影像学
领域(数学)
人工智能
集合(抽象数据类型)
医学物理学
数据挖掘
数据科学
机器学习
放射科
计算机科学
梅德林
程序设计语言
法学
纯数学
家庭医学
操作系统
数学
政治学
作者
Robert J. Gillies,Paul E. Kinahan,Hedvig Hricak
出处
期刊:Radiology
[Radiological Society of North America]
日期:2015-11-18
卷期号:278 (2): 563-577
被引量:6394
标识
DOI:10.1148/radiol.2015151169
摘要
In the past decade, the field of medical image analysis has grown exponentially, with an increased number of pattern recognition tools and an increase in data set sizes. These advances have facilitated the development of processes for high-throughput extraction of quantitative features that result in the conversion of images into mineable data and the subsequent analysis of these data for decision support; this practice is termed radiomics. This is in contrast to the traditional practice of treating medical images as pictures intended solely for visual interpretation. Radiomic data contain first-, second-, and higher-order statistics. These data are combined with other patient data and are mined with sophisticated bioinformatics tools to develop models that may potentially improve diagnostic, prognostic, and predictive accuracy. Because radiomics analyses are intended to be conducted with standard of care images, it is conceivable that conversion of digital images to mineable data will eventually become routine practice. This report describes the process of radiomics, its challenges, and its potential power to facilitate better clinical decision making, particularly in the care of patients with cancer.
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