论著摘要 |【综述】放射组学及其在肺癌研究,成像生物标志物和临床管理中的新兴作用:最先进的技术(双语版)

2017-08-20 17:06:17 admin 14

Radiomics and its emerging role in lung cancer research, imaging biomarkers and clinicalmanagement: State of the art.

发表日期:2017.6.6     来源:Eur J Radiol. 

作者:Lee G1Lee HY2Park H3Schiebler ML4van Beek EJR5Ohno Y6Seo JB7Leung A8.

作者介绍

    1.Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea; Department of Radiology and Medical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Busan, Republic of Korea.

    2.Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea. Electronic address: hoyunlee96@gmail.com.

    3.School of Electronic and Electrical Engineering and Center for Neuroscience Imaging Research, Sungkyunkwan University, Suwon, Republic of Korea.

    4.Department of Radiology, UW-Madison School of Medicine and Public Health, Madison, WI, United States.

    5.Clinical Research Imaging Centre, Edinburgh Imaging, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, United Kingdom.

    6.Division of Functional and Diagnostic Imaging Research, Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-cho, Chuo-ku, Kobe-shi 650-0017, Japan; Advanced Biomedical Imaging Research Center, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-cho, Chuo-ku, Kobe-shi 650-0017, Japan.

    7.Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.

    8.Department of Radiology, Stanford University, Palo Alto, CA, United States.

摘要

随着功能成像模式的发展,我们现在有能力研究肺癌的微环境及其基因组不稳定性。放射组学定义为使用自动或半自动后处理和分析大量可从医学图像中导出的定量成像特征。这些分析特征的自动生成有助于量化肺部恶性肿瘤成像评估中的一些变量。这些成像特征包括:肿瘤空间复杂性,肿瘤基因组异质性和组成的阐明,肿瘤活力或侵袭性的分区域鉴定以及对化学疗法和/或放疗的反应。因此,放射组学方法可以帮助揭示关于肿瘤行为的独特信息。目前可用的放射组学特征可以分为四个主要类别:(a)形态学,(b)统计学,(c)区域和(d)基于模型的。每个类别产生反映肿瘤特定方面的定量参数。主要的挑战是将放射组学数据与临床,病理和基因组信息相结合,以解码不同类型的组织生物学。目前有许多关于肺癌的放射组学研究,有必要总结当前的现状。

With the development of functional imaging modalities we now have the ability to study the microenvironment of lung cancer and its genomic instability. Radiomics is defined as the use of automated or semi-automated post-processing and analysis of large amounts of quantitative imaging features that can be derived from medical images. The automated generation of these analytical features helps to quantify a number of variables in the imaging assessment of lung malignancy. These imaging features include: tumor spatial complexity, elucidation of the tumor genomic heterogeneity and composition, subregional identification in terms of tumor viability or aggressiveness, and response to chemotherapy and/or radiation. Therefore, a radiomic approach can help to reveal unique information about tumor behavior. Currently available radiomic features can be divided into four major classes: (a) morphological, (b) statistical, (c) regional, and (d) model-based. Each category yields quantitative parameters that reflect specific aspects of a tumor. The major challenge is to integrate radiomic data with clinical, pathological, and genomic information to decode the different types of tissue biology. There are many currently available radiomic studies on lung cancer for which there is a need to summarize the current state of the art.

关键词

生物标志,CT检查,图像处理,肺癌,结果评估,正电子发射断层扫描成

Biomarkers; Computed tomography; Image processing; Lung cancer; Outcomes assessment; Positron emission tomography


阅读原文:10.1016/j.ejrad.2016.09.005


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