Radiomics: a new application from established techniques.
Radiomics: a new application from established techniques.
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DOI：
10.1080/23808993.2016.1164013
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发表时间：
2016
影响因子：
1.2
通讯作者：
Jacobs MA
中科院分区：
文献类型：
--
作者：
Parekh V;Jacobs MA
越来越多的生物标志物在癌症中的使用导致了为患者提供个性化药物的概念。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提供关于不同类型组织的独特数据的机会。然而，在大数据时代，从所有放射数据中获取有用信息是具有挑战性的。在计算能力和基因组学应用方面的最新进展已经产生了一个新的研究领域，称为放射组学。放射组学的定义是从成像中高通量提取定量成像特征或纹理(放射组学)，以解码组织病理并创建用于特征提取的高维数据集。放射学特征提供了有关灰度级图案、像素间关系的信息。此外，还可以在放射图像上的相同感兴趣区域内提取形状和光谱属性。此外，这些功能还可以进一步用于使用先进的机器学习算法开发计算模型，这些算法可以作为个性化诊断和治疗指导的工具。
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.