| Literature DB >> 32971906 |
Manuel Scimeca1,2,3, Nicoletta Urbano4, Rita Bonfiglio5,6, Manuela Montanaro5, Elena Bonanno5,7, Orazio Schillaci1,8, Alessandro Mauriello5,9.
Abstract
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.Entities:
Keywords: SARS-CoV-2; artificial intelligence; imaging diagnostic; pandemic; pathology
Mesh:
Year: 2020 PMID: 32971906 PMCID: PMC7554796 DOI: 10.3390/ijms21186960
Source DB: PubMed Journal: Int J Mol Sci ISSN: 1422-0067 Impact factor: 5.923
Figure 1Scheme of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection in respiratory epithelium. Image shows infection of SARS-CoV-2 in respiratory epithelium, virus spreading, and the antibody response (Martina Gioia Simeca, 5-year-old).
Histopathological characteristics of lungs infected by SARS-CoV-2.
| Characteristics | SARS-CoV-2 Infection |
|---|---|
| Gross Pathology |
multiple areas of congestion edematous lungs >gross weights pulmonary embolism |
| Microscopic examination |
diffuse alveolar damage severe capillary congestion interstitial mononuclear cell infiltrates multinucleated syncytial cells with atypical enlarged pneumocytes microthrombosisa |
| Pathogenesis | Combination of direct virus-induced cytopathic effects, immunologic injury, and microvascular damage induced by cytokines |
Most important histological lesions observed during autopsy of patients who died from SARS-CoV-2 infection.
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| Diffuse alveolar damage (histological hallmark of SARS-CoV-2 infection) | [ |
| Focal vasculitis and capillaritis associated to microthrombosis as direct viral effect | |
| Thrombosis of large and medium-size pulmonary, related to SARS-COV-2-associated coagulopathy (likely secondary to an endothelial damage related to direct viral infection of the endothelial cells) or deriving from the deep veins of the lower extremities. Superimposed bronchopneumonia as result of bacterial superinfection | |
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| Myocardial damage and myocarditis associated with increase in troponin levels, related to (a) direct myocardial infection by SARS-CoV-2 (b) hypoxemia due to respiratory failure and (c) inflammatory response correlated to the severe systemic inflammation status. Acute vasculitis of the intramyocardial vessels | [ |
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| Acute tubular injury involving mainly the proximal tubules, probably related to direct infection of kidney by SARS-CoV-2 | [ |
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| Urticarial rashes and papulovesicular exanthems (cause not yet known) | [ |
| Livedoid purple lesions and acrocyanosis | |
| Kawasaki disease | |
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| Aspecific acute hypoxic damage in the brain and cerebellum (molecular test in sections of brain tissue were positive for the virus, but not immunohistochemistry) | [ |
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| Sinusoidal dilatation with lymphocytic infiltration and steatosis (cause not yet known) | [ |
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| Acute fibrinoid necrosis of arterioles (cause not yet known) | [ |
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| Seminiferous tubular injury, mild lymphocytic inflammation (cause not yet known) | [ |
Table reported the main studies with the computerized tomography (CT) characterization of SARS-CoV-2 patients.
| Patients | Sex | Age (mean) | Type of Study | References | |
|---|---|---|---|---|---|
| Bernheim A et al. | 121 | 61 M; 60 W | 45 ± 16 | R | [ |
| Pan F et al. | 21 | 6 M; 15 W | 40 ± 9 | R | [ |
| Shi H et al. | 81 | 42 M; 39 W | 49.5 ± 11 | R | [ |
| Fang Y et al. | 51 | 29 M; 22 W | 45 | R | [ |
| Yoon SH et al. | 9 | 4 M; 5 W | 54 | R | [ |
| Li Y et al. | 53 | 29 M; 24 W | 58 ± 17 | R | [ |
| Wei J et al. | 1 | 1 W | 40 | CR | [ |
| Hu Z et al. | 24 | / | / | R | [ |
| Chen Z et al. | 98 | M 52; W 46 | 43 ± 17.2 | R | [ |
| Chen N et al. | 99 | M 67; W 32 | 55.5 ± 13.1 | R | [ |
| Huang C et al. | 41 | M 30; W 11 | 49 | R | [ |
| Wang D et al. | 138 | M 75; 63 W | / | R | [ |
| Chung M et al. | 21 | M 13: W 8 | 51 ± 14 | R | [ |
| Song F et al. | 51 | M 25; W 26 | 49 ± 16 | R | [ |
| Ai T et al. | 1014 | M 467; W 547 | 51 ± 15 | R | [ |
| Ng MY et al. | 18 | M 13; W 8 | 56 | R | [ |
R: retrospective CR: case report.
CT imaging features of patients affected by SARS-CoV-2.
| CT Findings | Number of Studies | Number of Patients (%) |
|---|---|---|
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| Ground-glass opacity with consolidation | 60 | 768 (18%) |
| Ground-glass opacity | 60 | 2482 (65%) |
| Consolidation | 60 | 1259 (22%) |
| Crazy paving pattern | 24 | 575 (12%) |
| Reversed halo sign | 24 | 146 (1%) |
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| Interlobular septal thickening | 23 | 691 (27%) |
| Air bronchogram sign | 23 | 531 (18%) |
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| Bilateral | 48 | 3952 (80%) |
| Unilateral | 48 | 641 (20%) |
| Right lung | 8 | 48 (62%) |
| Left lung | 8 | 29 (38%) |
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| One lobe | 13 | 278 (14%) |
| Two lobes | 13 | 299 (11%) |
| Three lobes | 13 | 250 (13%) |
| Four lobes | 13 | 212 (15%) |
| Five lobes | 14 | 384 (34%) |
| More than one lobe | 14 | 1145 (76%) |
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| Left upper lobe | 14 | 731 (74%) |
| Left lower lobe | 20 | 504 (46%) |
| Right upper lobe | 19 | 455 (40%) |
| Right middle lobe | 15 | 326 (38%) |
| Right lower lobe | 17 | 784 (74%) |
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| Pleural effusion | 60 | 94 (1.6%) |
| Lymphadenopathy | 60 | 21 (0.7%) |
| Pulmonary nodules | 22 | 262 (9%) |
Note—data are from reference [44].
Table reported the main nuclear medicine studies about SARS-CoV-2 pandemic.
| SARS-CoV-2 Positive | Imaging Analysis | Type of Study | Reference | |
|---|---|---|---|---|
| Qin et al. | 4 | 18F-FDG PET/CT | R | [ |
| Setti et al. | 13 | 18F-FDG PET/CT | P | [ |
| Polverari et al. | 1 | 18F-FDG PET/CT | CR | [ |
| Colandrea et al. | 5 | 18F-FDG PET/CT | CS | [ |
| Habouzit et al. | 1 | 18F-FDG PET/CT | CR | [ |
| Zou et al. | 1 | 18F-FDG PET/CT | CR | [ |
R: retrospective P: prospective CR: case report CS: case series.