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Coronavirus Disease 2019 (COVID-19) in Molecular Imaging: A Systematic Review of Incidental Detection of SARS-CoV-2 Pneumonia on PET Studies.

ABSTRACT: There have been several reports of the incidental detection of severe acute respiratory syndrome coronavirus 2 pneumonia on positron emission tomography/computed tomography (PET/CT) studies, which represent the potential role of molecular imaging in the detection and management of coronavirus disease 2019. Here, we systematically review the value of PET/CT in this setting. We conducted a systematic search on June 23, 2020, for PET studies with findings suggestive of coronavirus disease 2019. Web of Science, PubMed, Scopus, EMBASE, and Google Scholar databases were used. Patients with at least one PET/CT imaging evaluation were included in the study. Fifty-two patients in 30 publications with a mean age of 60 ± 12.74 (age range; 27-87) were included in this study, of which 28 (53.8%) were male, and 19 (36.5%) were female. In 5 (9.7%) patients, gender was not reported. PET/CT was performed with ¹⁸F-fluorodeoxyglucose for 48 (92.3%), ¹⁸F-choline for 3 (5.8%), and ⁶⁸Ga-PSMA for 1 (1.9%) patients. The mean SUV max of pulmonary lesions with ¹⁸F-fluorodeoxyglucose uptake was 4.9 ± 2.3. Moreover, 39 (75%) cases had an underlying malignancy, including 18 different type of primary cancers and 6 (11.5%) patients with metastatic disease. The most common pulmonary findings in PET/CT were bilateral hypermetabolic ground-glass opacities in 39 (75%), consolidation in 18 (34.6%), and interlobular thickening in 4 (7.6%). In addition, mediastinal 14 (27%) and hilar 10 (19.2%) lymph node involvement with increased metabolic activity was frequently identified. Early diagnosis of severe acute respiratory syndrome coronavirus 2 pneumonia is not only crucial for both appropriate patient management but also helps to ensure appropriate postexposure precautions are implemented for the department and hospital staff and those who have been in contact with the patient.

SUBMITTER: Rafiee F

PROVIDER: S-EPMC7598766 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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Coronavirus Disease 2019 (COVID-19) in Molecular Imaging: A Systematic Review of Incidental Detection of SARS-CoV-2 Pneumonia on PET Studies.

Rafiee Faranak F Keshavarz Pedram P Katal Sanaz S Assadi Majid M Nejati Seyed Faraz SF Ebrahimian Sadabad Faranak F Gholamrezanezhad Ali A

Seminars in nuclear medicine 20201028 2

There have been several reports of the incidental detection of severe acute respiratory syndrome coronavirus 2 pneumonia on positron emission tomography/computed tomography (PET/CT) studies, which represent the potential role of molecular imaging in the detection and management of coronavirus disease 2019. Here, we systematically review the value of PET/CT in this setting. We conducted a systematic search on June 23, 2020, for PET studies with findings suggestive of coronavirus disease 2019. Web ...[more]

PMID: 33509374

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Project description:ObjectivesTo develop and validate a radiomics model for predicting 2019 novel coronavirus (COVID-19) pneumonia.MethodsFor this retrospective study, a radiomics model was developed on the basis of a training set consisting of 136 patients with COVID-19 pneumonia and 103 patients with other types of viral pneumonia. Radiomics features were extracted from the lung parenchyma window. A radiomics signature was built on the basis of reproducible features, using the least absolute shrinkage and selection operator method (LASSO). Multivariable logistic regression model was adopted to establish a radiomics nomogram. Nomogram performance was determined by its discrimination, calibration, and clinical usefulness. The model was validated in 90 consecutive patients, of which 56 patients had COVID-19 pneumonia and 34 patients had other types of viral pneumonia.ResultsThe radiomics signature, consisting of 3 selected features, was significantly associated with COVID-19 pneumonia (p < 0.05) in both training and validation sets. The multivariable logistic regression model included the radiomics signature and distribution; maximum lesion, hilar, and mediastinal lymph node enlargement; and pleural effusion. The individualized prediction nomogram showed good discrimination in the training sample (area under the receiver operating characteristic curve [AUC], 0.959; 95% confidence interval [CI], 0.933-0.985) and in the validation sample (AUC, 0.955; 95% CI, 0.899-0.995) and good calibration. The mixed model achieved better predictive efficacy than the clinical model. Decision curve analysis demonstrated that the radiomics nomogram was clinically useful.ConclusionsThe radiomics model derived has good performance for predicting COVID-19 pneumonia and may help in clinical decision-making.Key points• A radiomics model showed good performance for prediction 2019 novel coronavirus pneumonia and favorable discrimination for other types of pneumonia on CT images. • A central or peripheral distribution, a maximum lesion range > 10 cm, the involvement of all five lobes, hilar and mediastinal lymph node enlargement, and no pleural effusion is associated with an increased risk of 2019 novel coronavirus pneumonia. • A radiomics model was superior to a clinical model in predicting 2019 novel coronavirus pneumonia.

Project description:The emergence of 2019 novel coronavirus (2019-nCoV) is of global concern and might have emerged from RNA recombination among existing coronaviruses. CoV spike (S) protein which is crucial for receptor binding, membrane fusion via conformational changes, internalization of the virus, host tissue tropism and comprises crucial targets for vaccine development, remain largely uncharacterized. Therefore, the present study has been planned to determine the sequence variation, structural and antigenic divergence of S glycoprotein which may be helpful for the management of 2019-nCoV infection. The sequences of spike glycoprotein of 2019-nCoV and SARS coronavirus (SARS-CoV) were used for the comparison. The sequence variations were determined using EMBOSS Needle pairwise sequence alignment tools. The variation in glycosylation sites was predicted by NetNGlyc 1.0 and validated by N-GlyDE server. Antigenicity was predicted by NetCTL 1.2 and validated by IEDB Analysis Resource server. The structural divergence was determined by using SuperPose Version 1.0 based on cryo-EM structure of the SARS coronavirus spike glycoprotein. Our data suggests that 2019-nCoV is newly spilled coronavirus into humans in China is closely related to SARS-CoV, which has only 12.8% of difference with SARS-CoV in S protein and has 83.9% similarity in minimal receptor-binding domain with SARS-CoV. Addition of a novel glycosylation sites were observed in 2019-nCoV. In addition, antigenic analysis proposes that great antigenic differences exist between both the viral strains, but some of the epitopes were found to be similar between both the S proteins. In spite of the variation in S protein amino acid composition, we found no significant difference in their structures. Collectively, for the first time our results exhibit the emergence of human 2019-nCoV is closely related to predecessor SARS-CoV and provide the evidence that 2019-nCoV uses various novel glycosylation sites as SARS-CoV and may have a potential to become pandemic owing its antigenic discrepancy. Further, demonstration of novel Cytotoxic T lymphocyte epitopes may impart opportunities for the development of peptide based vaccine for the prevention of 2019-nCoV.

Project description:Almost the entire world, not only China, is currently experiencing the outbreak of a novel coronavirus that causes respiratory disease, severe pneumonia, and even death. The outbreak began in Wuhan, China, in December of 2019 and is currently still ongoing. This novel coronavirus is highly contagious and has resulted in a continuously increasing number of infections and deaths that have already surpassed the SARS-CoV outbreak that occurred in China between 2002 and 2003. It is now officially a pandemic, announced by WHO on the 11th of March. Currently, the 2019 novel coronavirus (SARS-CoV-2) can be identified by virus isolation or viral nucleic acid detection; however, false negatives associated with the nucleic acid detection provide a clinical challenge and thus make the imaging examination crucial. Imaging exams have been a main clinical diagnostic criteria for the 2019 novel coronavirus disease (COVID-19) in China. Imaging features of multiple patchy areas of ground glass opacity and consolidation predominately in the periphery of the lungs are characteristic manifestations on chest CT and extremely helpful in the early detection and diagnosis of this disease, which aids prompt diagnosis and the eventual control of this emerging global health emergency. Key Points • In December 2019, China, an outbreak of pneumonia caused by a novel, highly contagious coronavirus raised grave concerns and posed a huge threat to global public health. • Among the infected patients, characteristic findings on CT imaging include multiple, patchy, ground-glass opacity, crazy-paving pattern, and consolidation shadows, mainly distributed in the peripheral and subpleural areas of both lungs, which are very helpful for the frontline clinicians. • Imaging examination has become the indispensable means not only in the early detection and diagnosis but also in monitoring the clinical course, evaluating the disease severity, and may be presented as an important warning signal preceding the negative RT-PCR test results.

Dataset Information

Coronavirus Disease 2019 (COVID-19) in Molecular Imaging: A Systematic Review of Incidental Detection of SARS-CoV-2 Pneumonia on PET Studies.

Publications

Coronavirus Disease 2019 (COVID-19) in Molecular Imaging: A Systematic Review of Incidental Detection of SARS-CoV-2 Pneumonia on PET Studies.

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