放射组学的出现:识别疾病诊断、反应和进展中的成像生物标志物的新方法。

SM journal of clinical and medical imaging Pub Date : 2018-01-01 Epub Date: 2018-03-15
Edward Florez, Ali Fatemi, Pier Paolo Claudio, Candace M Howard
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引用次数: 0

摘要

放射组学是临床放射学研究中的一个新兴领域。它寻求充分利用多种医学成像模式中包含的所有信息。使用放射组学方法,医学图像不仅限于提供定性评估,还可以通过参数化图像特征提供定量数据。这些参数可用于识别感兴趣的区域和体积,并区分正常健康组织与异常或病变组织。放射组学是一系列相互关联的至关重要的过程,从医学图像的获取和选择开始,涉及采集协议的标准化和设备间的规范化。随后,放射科专家通过使用计算工具对感兴趣的区域或体积进行识别和分割,这些计算工具提供了速度,同时减少了可变性和偏差。分割过程是放射组学中最关键的阶段。这有时需要结合由先进技术(重建过程,滤波等)组成的预处理阶段。然后,通过基于统计、形态特征和基于变换的变量的方法提取感兴趣的区域或体积的代表性特征。接下来,对提供与感兴趣的临床状况高度关联和相关性的参数进行统计选择。最后,可以根据需要将数据集成、标准化、分类和挖掘过程等过程应用于特定应用程序。放射组学正在进行的研究旨在减少解释医学图像所涉及的时间和成本,同时提高诊断和监测的质量以及治疗策略的选择。许多将放射组学与标准医学技术相结合的研究结果非常令人鼓舞,这些新方法正在越来越多地使用。这篇综述文章详细介绍了放射组学的组成部分,并讨论了它的应用、挑战和这个令人兴奋的新研究领域的未来方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Emergence of Radiomics: Novel Methodology Identifying Imaging Biomarkers of Disease in Diagnosis, Response, and Progression.

Emergence of Radiomics: Novel Methodology Identifying Imaging Biomarkers of Disease in Diagnosis, Response, and Progression.

Emergence of Radiomics: Novel Methodology Identifying Imaging Biomarkers of Disease in Diagnosis, Response, and Progression.

Radiomics is an emerging area within clinical radiology research. It seeks to take full advantage of all the information contained in multiple medical imaging modalities. With a radiomics approach, medical images are not limited to providing only a qualitative assessment but can also provide quantitative data by parameterizing image features. These parameters can be used to identify regions and volumes of interest and discriminate normal healthy tissue from abnormal or diseased tissue. Radiomics is an interlinked sequence of processes of vital importance that begins with the acquisition and selection of medical images that involve standardization of acquisition protocols and inter-equipment normalization. This is followed by the identification and segmentation of regions or volumes of interest by expert radiologists through the use of computational tools that offer speed while reducing variability and bias. The segmentation process is the most critical stage in radiomics. This sometimes requires the incorporation of a pre-processing stage consisting of advanced techniques (reconstruction processes, filtering, etc.). Thereafter, representative characteristics of the region or volume of interest are extracted by approaches based on statistics, morphological features, and transform-based variables. Next, a statistical selection of the parameters that provide a high association and correlation with the clinical condition of interest is performed. Finally, processes such as data integration, standardization, classification, and mining processes can be applied as needed for particular applications. Ongoing research in radiomics aims to reduce the time and costs involved in interpreting medical images while simultaneously increasing the quality of diagnoses and monitoring of as well as the selection of treatment strategies. The results of many studies combining radiomics with standard medical techniques are highly encouraging, and these new approaches are increasingly used. This review article details the components of radiomics and discusses its applications, challenges, and future directions for this exciting new field of study.

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