Afshar-Oromieh Ali, Prosch Helmut, Schaefer-Prokop Cornelia, Bohn Karl Peter, Alberts Ian, Mingels Clemens, Thurnher Majda, Cumming Paul, Shi Kuangyu, Peters Alan, Geleff Silvana, Lan Xiaoli, Wang Feng, Huber Adrian, Gräni Christoph, Heverhagen Johannes T, Rominger Axel, Fontanellaz Matthias, Schöder Heiko, Christe Andreas, Mougiakakou Stavroula, Ebner Lukas
Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstr. 18, CH-3010, Bern, Switzerland.
Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Vienna, Austria.
Eur J Nucl Med Mol Imaging. 2021 Jul;48(8):2500-2524. doi: 10.1007/s00259-021-05375-3. Epub 2021 May 1.
Medical imaging methods are assuming a greater role in the workup of patients with COVID-19, mainly in relation to the primary manifestation of pulmonary disease and the tissue distribution of the angiotensin-converting-enzyme 2 (ACE 2) receptor. However, the field is so new that no consensus view has emerged guiding clinical decisions to employ imaging procedures such as radiography, computer tomography (CT), positron emission tomography (PET), and magnetic resonance imaging, and in what measure the risk of exposure of staff to possible infection could be justified by the knowledge gained. The insensitivity of current RT-PCR methods for positive diagnosis is part of the rationale for resorting to imaging procedures. While CT is more sensitive than genetic testing in hospitalized patients, positive findings of ground glass opacities depend on the disease stage. There is sparse reporting on PET/CT with [F]-FDG in COVID-19, but available results are congruent with the earlier literature on viral pneumonias. There is a high incidence of cerebral findings in COVID-19, and likewise evidence of gastrointestinal involvement. Artificial intelligence, notably machine learning is emerging as an effective method for diagnostic image analysis, with performance in the discriminative diagnosis of diagnosis of COVID-19 pneumonia comparable to that of human practitioners.
医学成像方法在新型冠状病毒肺炎（COVID-19）患者的检查中发挥着越来越重要的作用，主要涉及肺部疾病的主要表现以及血管紧张素转换酶2（ACE 2）受体的组织分布。然而，该领域非常新，尚未形成共识观点来指导诸如放射照相、计算机断层扫描（CT）、正电子发射断层扫描（PET）和磁共振成像等成像程序的临床决策，以及通过所获得的知识来证明工作人员接触可能感染的风险在何种程度上是合理的。当前逆转录聚合酶链反应（RT-PCR）方法对阳性诊断的不敏感性是采用成像程序的部分理由。虽然CT在住院患者中比基因检测更敏感，但磨玻璃影的阳性结果取决于疾病阶段。关于COVID-19中[F]-FDG PET/CT的报道很少，但现有结果与早期关于病毒性肺炎的文献一致。COVID-19中脑部发现的发生率很高，同样也有胃肠道受累的证据。人工智能，尤其是机器学习正在成为诊断图像分析的有效方法，在COVID-19肺炎的鉴别诊断中的表现与人类从业者相当。
