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A review in radiomics: Making personalized medicine a reality via routine imaging
Medicinal Research Reviews ( IF 10.9 ) Pub Date : 2021-07-26 , DOI: 10.1002/med.21846
Julien Guiot 1 , Akshayaa Vaidyanathan 2, 3 , Louis Deprez 4 , Fadila Zerka 2, 3 , Denis Danthine 4 , Anne-Noelle Frix 1 , Philippe Lambin 3 , Fabio Bottari 2 , Nathan Tsoutzidis 2 , Benjamin Miraglio 2 , Sean Walsh 2 , Wim Vos 2 , Roland Hustinx 5, 6 , Marta Ferreira 6 , Pierre Lovinfosse 5 , Ralph T H Leijenaar 2
Affiliation  

Radiomics is the quantitative analysis of standard-of‑care medical imaging; the information obtained can be applied within clinical decision support systems to create diagnostic, prognostic, and/or predictive models. Radiomics analysis can be performed by extracting hand-crafted radiomics features or via deep learning algorithms. Radiomics has evolved tremendously in the last decade, becoming a bridge between imaging and precision medicine. Radiomics exploits sophisticated image analysis tools coupled with statistical elaboration to extract the wealth of information hidden inside medical images, such as computed tomography (CT), magnetic resonance (MR), and/or Positron emission tomography (PET) scans, routinely performed in the everyday clinical practice. Many efforts have been devoted in recent years to the standardization and validation of radiomics approaches, to demonstrate their usefulness and robustness beyond any reasonable doubts. However, the booming of publications and commercial applications of radiomics approaches warrant caution and proper understanding of all the factors involved to avoid “scientific pollution” and overly enthusiastic claims by researchers and clinicians alike. For these reasons the present review aims to be a guidebook of sorts, describing the process of radiomics, its pitfalls, challenges, and opportunities, along with its ability to improve clinical decision-making, from oncology and respiratory medicine to pharmacological and genotyping studies.

中文翻译:

放射组学综述:通过常规成像使个性化医疗成为现实

放射组学是标准护理医学成像的定量分析;获得的信息可以应用于临床决策支持系统,以创建诊断、预后和/或预测模型。放射组学分析可以通过提取手工制作的放射组学特征或通过深度学习算法来执行。放射组学在过去十年中发生了巨大的变化,成为成像和精准医学之间的桥梁。Radiomics 利用复杂的图像分析工具和统计分析来提取隐藏在医学图像中的大量信息,例如计算机断层扫描 (CT)、磁共振 (MR) 和/或正电子发射断层扫描 (PET) 扫描,这些扫描通常在日常临床实践。近年来,许多努力致力于放射组学方法的标准化和验证,以证明其有用性和鲁棒性超越任何合理怀疑。然而,放射组学方法的出版物和商业应用的蓬勃发展需要谨慎和正确理解所有相关因素,以避免“科学污染”和研究人员和临床医生等过于热情的主张。由于这些原因,本综述旨在成为一本指南,描述放射组学的过程、它的陷阱、挑战和机遇,以及它改进临床决策的能力,从肿瘤学和呼吸医学到药理学和基因分型研究。放射组学方法的出版物和商业应用的蓬勃发展需要谨慎和正确理解所涉及的所有因素,以避免“科学污染”和研究人员和临床医生等过于热情的主张。由于这些原因,本综述旨在成为一本指南,描述放射组学的过程、它的陷阱、挑战和机遇,以及它改进临床决策的能力,从肿瘤学和呼吸医学到药理学和基因分型研究。放射组学方法的出版物和商业应用的蓬勃发展需要谨慎和正确理解所涉及的所有因素,以避免“科学污染”和研究人员和临床医生等过于热情的主张。由于这些原因,本综述旨在成为一本指南,描述放射组学的过程、它的陷阱、挑战和机遇,以及它改进临床决策的能力,从肿瘤学和呼吸医学到药理学和基因分型研究。
更新日期:2021-07-26
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