Frix Anne-Noëlle, Cousin François, Refaee Turkey, Bottari Fabio, Vaidyanathan Akshayaa, Desir Colin, Vos Wim, Walsh Sean, Occhipinti Mariaelena, Lovinfosse Pierre, Leijenaar Ralph T H, Hustinx Roland, Meunier Paul, Louis Renaud, Lambin Philippe, Guiot Julien
Department of Respiratory Medicine, University Hospital of Liège, 4000 Liège, Belgium.
Department of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, 4000 Liège, Belgium.
J Pers Med. 2021 Jun 25;11(7):602. doi: 10.3390/jpm11070602.
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.
在过去的50年里，人工智能（AI）在放射学领域的应用越来越广泛。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，对强大的影像生物标志物的需求也在逐渐增加。放射组学是一种使用数据特征算法对大量定量影像数据进行高通量提取的特定方法，在个体化影像生物标志物方面显示出巨大潜力。放射组学分析可通过以下两种方法实现：手工制作的放射组学特征提取或深度学习算法。鉴于其在多种呼吸道疾病中开发描述性和预测性模型的能力，其在肺部疾病中的应用可用于临床决策支持系统。本文旨在回顾该主题的最新文献，并从临床医生的角度简要总结放射组学在胸部计算机断层扫描（CT）中的价值及其在肺部疾病领域的相关性。
