Radiomics: the process and the challenges.
Radiomics: the process and the challenges.
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DOI：
10.1016/j.mri.2012.06.010
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发表时间：
2012-11
影响因子：
2.5
通讯作者：
Gillies RJ
中科院分区：
文献类型：
--
作者：
Kumar V;Gu Y;Basu S;Berglund A;Eschrich SA;Schabath MB;Forster K;Aerts HJ;Dekker A;Fenstermacher D;Goldgof DB;Hall LO;Lambin P;Balagurunathan Y;Gatenby RA;Gillies RJ
“放射组学”是指从利用计算机断层扫描（CT）、正电子发射断层扫描（PET）或磁共振成像（MRI）获得的医学图像中以高通量提取和分析大量高级定量成像特征。重要的是，这些数据被设计为从标准护理图像中提取，从而产生非常大的潜在受试者库。放射组学数据是可开采的形式，可用于建立描述性和预测性模型，将图像特征与表型或基因-蛋白质签名相关联。放射组学的核心假设是，这些模型可以包括生物或医学数据，可以提供有价值的诊断，预后或预测信息。放射组学企业可以分为不同的过程，每个过程都有自己的挑战需要克服：（i）图像采集和重建（ii）图像分割和渲染（iii）特征提取和特征鉴定（iv）数据库和数据共享，以最终（v）特别信息分析。这些进程中的每一个都提出了独特的挑战。例如，必须确定和协调图像采集和重建的最佳协议。此外，分割必须是鲁棒的，并且涉及最少的操作员输入。生成的特征必须能够稳健地反映各个卷的复杂性，但不能过于复杂或冗余。此外，还必须生成信息学数据库，该数据库允许将图像特征和图像注释与医学和遗传数据一起沿着。最后，分析这些数据的统计方法必须优化，因为放射组学还不是一个成熟的研究领域。将依次讨论这些过程中的每一个过程，以及它们的一些独特挑战和解决这些挑战的拟议方法。这篇文章的重点将是对图像的非小细胞肺癌，非小细胞肺癌。
“Radiomics” refers to the extraction and analysis of large amounts of advanced quantitative imaging features with high throughput from medical images obtained with computed tomography (CT), positron emission tomography (PET) or magnetic resonance imaging (MRI). Importantly, these data are designed to be extracted from standard-of-care images, leading to a very large potential subject pool. Radiomic data are in a mineable form that can be used to build descriptive and predictive models relating image features to phenotypes or gene-protein signatures. The core hypothesis of radiomics is that these models, which can include biological or medical data, can provide valuable diagnostic, prognostic or predictive information. The radiomics enterprise can be divided into distinct processes, each with its own challenges that need to be overcome: (i) image acquisition and reconstruction (ii) image segmentation and rendering (iii) feature extraction and feature qualification (iv) databases and data sharing for eventual (v) ad hoc informatic analyses. Each of these individual processes poses unique challenges. For example, optimum protocols for image acquisition and reconstruction have to be identified and harmonized. Also, segmentations have to be robust and involve minimal operator input. Features have to be generated that robustly reflect the complexity of the individual volumes, but cannot be overly complex or redundant. Furthermore, informatics databases that allow incorporation of image features and image annotations, along with medical and genetic data have to be generated. Finally, the statistical approaches to analyze these data have to be optimized, as radiomics is not a mature field of study. Each of these processes will be discussed in turn, as well as some of their unique challenges and proposed approaches to solve them. The focus of this article will be on images of non-small cell lung cancer, NSCLC.
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DOI：
10.1097/ppo.0b013e318238216e
发表时间：
2011-11
期刊：
Cancer journal (Sudbury, Mass.)
影响因子：
--
作者：
Fenstermacher DA;Wenham RM;Rollison DE;Dalton WS
通讯作者：
Dalton WS
DOI：
10.1016/j.compmedimag.2005.03.002
发表时间：
2005-09-01
影响因子：
5.7
作者：
Fu, JC;Lee, SK;Wu, HK
通讯作者：
Wu, HK
影响因子：
4.4
作者：
Freymann, John B.;Kirby, Justin S.;Jaffe, C. Carl
通讯作者：
Jaffe, C. Carl
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y
影响因子：
--
作者：
Dougherty, E R
通讯作者：
Dougherty, E R