论著摘要 |【综述】成像生物标志物的发展和大数据的产生(双语版)

2017-12-29 11:00:02 admin 0

Development of imaging biomarkers and generation of big data.

发表日期: 2017.02.21   来源:Radiol Med. 2017 Jun;122(6):444-448.

作者:

Alberich-Bayarri Á1,2, Hernández-Navarro R3 , Ruiz-Martínez E4, García-Castro F3, García-Juan D3, Martí-Bonmatí L4.

作者介绍:

1. Biomedical Imaging Research Group (GIBI230), La Fe Health Research Institute and La Fe Polytechnics and University Hospital, Tower E, Floor 0, GIBI230 Office, Av. Fernando Abril Martorell 106, 46026, Valencia, Spain. alberich_ang@gva.es.

2. QUIBIM SL, Valencia, Spain. alberich_ang@gva.es.

3. QUIBIM SL, Valencia, Spain.

4. Biomedical Imaging Research Group (GIBI230), La Fe Health Research Institute and La Fe Polytechnics and University Hospital, Tower E, Floor 0, GIBI230 Office, Av. Fernando Abril Martorell 106, 46026, Valencia, Spain.

摘要

目前已经有几种图像处理算法来弥补未满足的临床需求,但是将他们应用在临床中时,我们还不清楚他们在放射学进程中能否产生明显的临床影响力。从地方到大型基础设施,如医学影像生物库(数百万的研究),甚至更多的医学影像生物库联合会(在某些情况下,总共达数亿的研究)需要整合自动化传输途径以快速分析合并数据来提取临床相关结论,不单单是唯一地只与医学影像相关,而是结合如遗传图谱等其他信息。本文介绍了利用成像生物标志物的开发及其在云端的整合来进行大型数据库的定量管理和开发的总体策略。拟建的平台已经成功推出,并且在放射科医师,临床医师和医学影像研究人员这些早期采用者的社区中得到验证。

Abstact

Several image processing algorithms have emerged to cover unmet clinical needs but their application to radiological routine with a clear clinical impact is still not straightforward. Moving from local to big infrastructures, such as Medical Imaging Biobanks (millions of studies), or even more, Federations of Medical Imaging Biobanks (in some cases totaling to hundreds of millions of studies) require the integration of automated pipelines for fast analysis of pooled data to extract clinically relevant conclusions, not uniquely linked to medical imaging, but in combination to other information such as genetic profiling. A general strategy for the development of imaging biomarkers and their integration in the cloud for the quantitative management and exploitation in large databases is herein presented. The proposed platform has been successfully launched and is being validated nowadays among the early adopters' community of radiologists, clinicians, and medical imaging researchers.

关键词:

大数据,云,图像处理,图像生物标记物,放射学,影像组学

Keywords:

Big data; Cloud; Image processing; Imaging biomarkers; Radiology; Radiomics

阅读原文:PMID: 28224398  DOI: 10.1007/s11547-017-0742-x


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