Department of Biomedicine and Prevention, University of Rome "Tor Vergata", Via Montpellier 1, 00133 Rome, Italy.
San Raffaele University, Via di Val Cannuta 247, 00166 Rome, Italy.
Int J Mol Sci. 2020 Sep 22;21(18):6960. doi: 10.3390/ijms21186960.
In December 2019, physicians reported numerous patients showing pneumonia of unknown origin in the Chinese region of Wuhan. Following the spreading of the infection over the world, The World Health Organization (WHO) on 11 March 2020 declared the novel severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) outbreak a global pandemic. The scientific community is exerting an extraordinary effort to elucidate all aspects related to SARS-CoV-2, such as the structure, ultrastructure, invasion mechanisms, replication mechanisms, or drugs for treatment, mainly through in vitro studies. Thus, the clinical in vivo data can provide a test bench for new discoveries in the field of SARS-CoV-2, finding new solutions to fight the current pandemic. During this dramatic situation, the normal scientific protocols for the development of new diagnostic procedures or drugs are frequently not completely applied in order to speed up these processes. In this context, interdisciplinarity is fundamental. Specifically, a great contribution can be provided by the association and interpretation of data derived from medical disciplines based on the study of images, such as radiology, nuclear medicine, and pathology. Therefore, here, we highlighted the most recent histopathological and imaging data concerning the SARS-CoV-2 infection in lung and other human organs such as the kidney, heart, and vascular system. In addition, we evaluated the possible matches among data of radiology, nuclear medicine, and pathology departments in order to support the intense scientific work to address the SARS-CoV-2 pandemic. In this regard, the development of artificial intelligence algorithms that are capable of correlating these clinical data with the new scientific discoveries concerning SARS-CoV-2 might be the keystone to get out of the pandemic.
2019 年 12 月,医生报告称在中国武汉地区有许多不明原因的肺炎患者。随着感染在全球范围内的传播,世界卫生组织(WHO)于 2020 年 3 月 11 日宣布新型严重急性呼吸综合征冠状病毒-2(SARS-CoV-2)爆发为全球大流行。科学界正在全力以赴阐明与 SARS-CoV-2 相关的各个方面,例如结构、超微结构、入侵机制、复制机制或治疗药物,主要通过体外研究。因此,临床体内数据可以为 SARS-CoV-2 领域的新发现提供一个测试平台,找到应对当前大流行的新解决方案。在这种戏剧性的情况下,为了加快这些进程,通常不会完全应用新诊断程序或药物开发的正常科学方案。在这种情况下,跨学科是基础。具体来说,基于对放射学、核医学和病理学等医学学科图像的研究,对来自这些学科的数据进行关联和解释,可以为解决当前大流行提供巨大的贡献。因此,在这里,我们强调了与 SARS-CoV-2 感染在肺和肾脏、心脏和血管系统等其他人体器官相关的最新组织病理学和影像学数据。此外,我们评估了放射科、核医学和病理科的数据之间可能存在的匹配关系,以支持解决 SARS-CoV-2 大流行的紧张科学工作。在这方面,开发能够将这些临床数据与有关 SARS-CoV-2 的新科学发现相关联的人工智能算法可能是摆脱大流行的关键。