Parekh Vishwa, Jacobs Michael A
The Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging, The Johns Hopkins University School of Medicine, Baltimore, MD 21205; Department of Computer Science, The Johns Hopkins University School of Medicine, Baltimore, MD 21205.
The Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging, The Johns Hopkins University School of Medicine, Baltimore, MD 21205; Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins University School of Medicine, Baltimore, MD 21205.
Expert Rev Precis Med Drug Dev. 2016;1(2):207-226. doi: 10.1080/23808993.2016.1164013. Epub 2016 Mar 31.
The increasing use of biomarkers in cancer have led to the concept of personalized medicine for patients. Personalized medicine provides better diagnosis and treatment options available to clinicians. Radiological imaging techniques provide an opportunity to deliver unique data on different types of tissue. However, obtaining useful information from all radiological data is challenging in the era of "big data". Recent advances in computational power and the use of genomics have generated a new area of research termed Radiomics. Radiomics is defined as the high throughput extraction of quantitative imaging features or texture (radiomics) from imaging to decode tissue pathology and creating a high dimensional data set for feature extraction. Radiomic features provide information about the gray-scale patterns, inter-pixel relationships. In addition, shape and spectral properties can be extracted within the same regions of interest on radiological images. Moreover, these features can be further used to develop computational models using advanced machine learning algorithms that may serve as a tool for personalized diagnosis and treatment guidance.
生物标志物在癌症中的使用日益增加，催生了针对患者的个性化医疗概念。个性化医疗为临床医生提供了更好的诊断和治疗选择。放射成像技术提供了一个机会，可提供有关不同类型组织的独特数据。然而，在“大数据”时代，从所有放射学数据中获取有用信息具有挑战性。计算能力的最新进展和基因组学的应用催生了一个新的研究领域——放射组学。放射组学被定义为从成像中高通量提取定量成像特征或纹理（放射组学），以解码组织病理学并创建用于特征提取的高维数据集。放射组学特征提供有关灰度模式、像素间关系的信息。此外，还可以在放射图像的相同感兴趣区域内提取形状和光谱特性。而且，这些特征可进一步用于使用先进的机器学习算法开发计算模型，这些模型可作为个性化诊断和治疗指导的工具。
