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Radiomics for precision medicine: current challenges,future prospects, and the proposal of a new framework
Methods ( IF 4.2 ) Pub Date : 2020-06-01 , DOI: 10.1016/j.ymeth.2020.05.022
A Ibrahim 1 , S Primakov 2 , M Beuque 3 , H C Woodruff 4 , I Halilaj 3 , G Wu 3 , T Refaee 5 , R Granzier 6 , Y Widaatalla 3 , R Hustinx 7 , F M Mottaghy 8 , P Lambin 4
Affiliation  

The advancement of artificial intelligence concurrent with the development of medical imaging techniques provided a unique opportunity to turn medical imaging from mostly qualitative, to further quantitative and mineable data that can be explored for the development of clinical decision support systems (cDSS). Radiomics, a method for the high throughput extraction of hand-crafted features from medical images, and deep learning -the data driven modeling techniques based on the principles of simplified brain neuron interactions, are the most researched quantitative imaging techniques. Many studies reported on the potential of such techniques in the context of cDSS. Such techniques could be highly appealing due to the reuse of existing data, automation of clinical workflows, minimal invasiveness, three-dimensional volumetric characterization, and the promise of high accuracy and reproducibility of results and cost-effectiveness. Nevertheless, there are several challenges that quantitative imaging techniques face, and need to be addressed before the translation to clinical use. These challenges include, but are not limited to, the explainability of the models, the reproducibility of the quantitative imaging features, and their sensitivity to variations in image acquisition and reconstruction parameters. In this narrative review, we report on the status of quantitative medical image analysis using radiomics and deep learning, the challenges the field is facing, propose a framework for robust radiomics analysis, and discuss future prospects.

中文翻译:

精准医学放射组学:当前挑战、未来前景和新框架的提出

随着医学成像技术的发展,人工智能的进步提供了一个独特的机会,可以将医学成像从主要是定性的数据转变为可用于开发临床决策支持系统 (cDSS) 的进一步定量和可挖掘的数据。放射组学是一种从医学图像中高通量提取手工特征的方法,深度学习是基于简化脑神经元相互作用原理的数据驱动建模技术,是研究最多的定量成像技术。许多研究报告了此类技术在 cDSS 背景下的潜力。由于现有数据的重用、临床工作流程的自动化、微创、三维体积表征、以及结果的高精度和可重复性以及成本效益的承诺。然而,定量成像技术面临着一些挑战,需要在转化为临床应用之前加以解决。这些挑战包括但不限于模型的可解释性、定量成像特征的可重复性以及它们对图像采集和重建参数变化的敏感性。在这篇叙述性评论中,我们报告了使用放射组学和深度学习进行定量医学图像分析的现状,该领域面临的挑战,提出了一个稳健的放射组学分析框架,并讨论了未来的前景。定量成像技术面临一些挑战,需要在转化为临床应用之前加以解决。这些挑战包括但不限于模型的可解释性、定量成像特征的可重复性以及它们对图像采集和重建参数变化的敏感性。在这篇叙述性评论中,我们报告了使用放射组学和深度学习进行定量医学图像分析的现状,该领域面临的挑战,提出了一个稳健的放射组学分析框架,并讨论了未来的前景。定量成像技术面临一些挑战,需要在转化为临床应用之前加以解决。这些挑战包括但不限于模型的可解释性、定量成像特征的可重复性以及它们对图像采集和重建参数变化的敏感性。在这篇叙述性评论中,我们报告了使用放射组学和深度学习进行定量医学图像分析的现状,该领域面临的挑战,提出了一个稳健的放射组学分析框架,并讨论了未来的前景。以及它们对图像采集和重建参数变化的敏感性。在这篇叙述性评论中,我们报告了使用放射组学和深度学习进行定量医学图像分析的现状,该领域面临的挑战,提出了一个稳健的放射组学分析框架,并讨论了未来的前景。以及它们对图像采集和重建参数变化的敏感性。在这篇叙述性评论中,我们报告了使用放射组学和深度学习进行定量医学图像分析的现状,该领域面临的挑战,提出了一个稳健的放射组学分析框架,并讨论了未来的前景。
更新日期:2020-06-01
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