Gillies Robert J, Kinahan Paul E, Hricak Hedvig
From the Department of Cancer Imaging, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Dr, Tampa, FL 33612 (R.J.G.); Department of Radiology, University of Washington, Seattle, Wash (P.E.K.); and Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York NY 10065 (H.H.).
Radiology. 2016 Feb;278(2):563-77. doi: 10.1148/radiol.2015151169. Epub 2015 Nov 18.
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.
在过去十年中,医学图像分析领域呈指数级增长,模式识别工具数量增加,数据集规模也不断扩大。这些进展推动了高通量提取定量特征流程的发展,从而将图像转化为可挖掘的数据,并对这些数据进行后续分析以提供决策支持;这种做法被称为放射组学。这与将医学图像仅视为用于视觉解读的图片的传统做法形成了对比。放射组学数据包含一阶、二阶和高阶统计量。这些数据与其他患者数据相结合,并使用复杂的生物信息学工具进行挖掘,以开发可能提高诊断、预后和预测准确性的模型。由于放射组学分析旨在使用标准护理图像进行,因此可以想象将数字图像转化为可挖掘数据最终将成为常规做法。本报告描述了放射组学的过程、其面临的挑战以及它在促进更好的临床决策,特别是在癌症患者护理方面的潜在作用。