Abrol Srishti, Kotrotsou Aikaterini, Salem Ahmed, Zinn Pascal O, Colen Rivka R
*Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center †Department of Neurosurgery, Baylor College of Medicine ‡Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX.
Top Magn Reson Imaging. 2017 Feb;26(1):43-53. doi: 10.1097/RMR.0000000000000117.
Radiomics is a new area of research in the field of imaging with tremendous potential to unravel the hidden information in digital images. The scope of radiology has grown exponentially over the last two decades; since the advent of radiomics, many quantitative imaging features can now be extracted from medical images through high-throughput computing, and these can be converted into mineable data that can help in linking imaging phenotypes with clinical data, genomics, proteomics, and other "omics" information. In cancer, radiomic imaging analysis aims at extracting imaging features embedded in the imaging data, which can act as a guide in the disease or cancer diagnosis, staging and planning interventions for treating patients, monitor patients on therapy, predict treatment response, and determine patient outcomes.
放射组学是成像领域的一个新研究领域，在揭示数字图像中的隐藏信息方面具有巨大潜力。在过去二十年中，放射学的范围呈指数级增长；自放射组学出现以来，现在可以通过高通量计算从医学图像中提取许多定量成像特征，并且这些特征可以转化为可挖掘的数据，有助于将成像表型与临床数据、基因组学、蛋白质组学和其他“组学”信息联系起来。在癌症方面，放射组学成像分析旨在提取嵌入成像数据中的成像特征，这些特征可作为疾病或癌症诊断、分期以及为患者制定治疗干预计划、监测患者治疗情况、预测治疗反应和确定患者预后的指南。
