Radiomics in Lung Diseases Imaging: State-of-the-Art for Clinicians.
Radiomics in Lung Diseases Imaging: State-of-the-Art for Clinicians.
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DOI：
10.3390/jpm11070602
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发表时间：
2021-06-25
影响因子：
--
通讯作者：
Guiot J
中科院分区：
文献类型：
--
作者：
Frix AN;Cousin F;Refaee T;Bottari F;Vaidyanathan A;Desir C;Vos W;Walsh S;Occhipinti M;Lovinfosse P;Leijenaar RTH;Hustinx R;Meunier P;Louis R;Lambin P;Guiot J
在过去的50年里，人工智能(AI)越来越多地服务于放射学领域。随着现代医学向精准医学发展，提供个性化的患者护理和治疗，对稳健的成像生物标志物的需求逐渐增加。放射组学是一种使用数据表征算法高通量提取海量定量成像数据的特定方法，在个性化成像生物标志物方面显示出巨大的潜力。放射体分析可以通过以下两种方法实现：手工提取放射体特征或深度学习算法。它在肺部疾病中的应用可用于临床决策支持系统，因为它具有在许多呼吸道病理中开发描述性和预测性模型的能力。本文从临床医生的角度，综述了近年来有关这一主题的文献，并简要总结了放射组学在胸部计算机断层扫描(CT)中的应用及其在肺部疾病领域的相关性。
Artificial intelligence (AI) has increasingly been serving the field of radiology over the last 50 years. As modern medicine is evolving towards precision medicine, offering personalized patient care and treatment, the requirement for robust imaging biomarkers has gradually increased. Radiomics, a specific method generating high-throughput extraction of a tremendous amount of quantitative imaging data using data-characterization algorithms, has shown great potential in individuating imaging biomarkers. Radiomic analysis can be implemented through the following two methods: hand-crafted radiomic features extraction or deep learning algorithm. Its application in lung diseases can be used in clinical decision support systems, regarding its ability to develop descriptive and predictive models in many respiratory pathologies. The aim of this article is to review the recent literature on the topic, and briefly summarize the interest of radiomics in chest Computed Tomography (CT) and its pertinence in the field of pulmonary diseases, from a clinician’s perspective.
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DOI：
10.1016/j.jaci.2016.11.053
发表时间：
2017-09
期刊：
The Journal of allergy and clinical immunology
影响因子：
--
作者：
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DOI：
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发表时间：
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期刊：
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影响因子：
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5
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影响因子：
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