论著摘要 |【综述】影像组学:现有技术的新应用(双语版)

2017-09-20 10:32:04 admin 0

Radiomics: a new application from established techniques.

发表日期: 2016.03.31   来源: Expert review of precision medicine and drug development, 2016, 1(2): 207-226.

作者

Vishwa Parekh, Michael A. Jacobs.

作者介绍:

Vishwa Parekh

The Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging, The Johns Hopkins University School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA; Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21208, USA

Michael A. Jacobs

The Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging, The Johns Hopkins University School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA; Sidney Kimmel Comprehensive Cancer Center, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA.

摘要

癌症研究中越来越多的生物标志物的应用,引发了个性化医疗的概念。个性化的药物为临床医生提供了更好的诊断与治疗选择。放射学影像技术为不同类型组织提供了特定的影像学数据。然而,在“大数据”时代,从所有的影像学数据中获得有用的信息具有一定的挑战性。计算能力的最新进展和基因组学越来越多的应用催生了一个新的领域,这就是影像组学。影像组学被定义为将病理组织进行成像,解码,获得定量图像特征或纹理,进行高通量分析,为特征提取创建高维数据集。影像组学能够提供影像的灰度模式,内在像素之间关系的信息。同时,可以在影像图片中,实现对我们感兴趣的相同区域进行形状特征和光谱特征的提取。此外,这些特征可以进一步用于开发先进机器学习算法的计算模型,将他变成个性化诊断与治疗指导的工具。

 

Abstract

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.

 关键词:

放射组学,信息学,纹理,机器学习,乳腺,核磁共振成像,治疗反应,质子,弥散加权成像,ADC图,癌症,基因组学

KEYWORDS:

Radiomics, informatics, texture, machine learning, Breast, Magnetic Resonance Imaging, treatment response, proton, diffusion-weighted imaging, DWI, ADC map, cancer, genetics

·        阅读原文:http://dx.doi.org/10.1080/23808993.2016.1164013


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