期刊
EUROPEAN JOURNAL OF RADIOLOGY
卷 86, 期 -, 页码 297-307出版社
ELSEVIER IRELAND LTD
DOI: 10.1016/j.ejrad.2016.09.005
关键词
Positron emission tomography; Computed tomography; Image processing; Biomarkers; Lung cancer; Outcomes assessment
资金
- Medical Research Council [G0701127] Funding Source: Medline
- Medical Research Council [G0701127] Funding Source: researchfish
- MRC [G0701127] Funding Source: UKRI
- National Research Foundation of Korea [10Z20130000013] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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. (C) 2016 Elsevier Ireland Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据