Evaluation of the Spiral Chest CT Scan Findings in Patients with Multiple Trauma.

Objective: To evaluate the spiral chest computed tomography (CT) scan findings in patients with multiple trauma during the COVID-19 pandemic. Methods: This retrospective study was performed on multiple trauma patients admitted to a tertiary hospital in the north of Iran in 2020. All patients with multiple trauma who had undergone a chest spiral CT were included in this study. Furthermore, the data analysis was performed through descriptive and analytical statistics using SPSS software. Results: A total of 600 patients were included over the study period. The mean age of patients was 48.2±20.3 years. Of the total, 496 (65.3%) patients had blunt chest injuries, and 104 (34.7%) had penetrating chest injuries. Falling was the most common mechanical cause of chest trauma in 270 patients (45%). Surgical interventions were performed in 110 (18.3%) patients. A total of 276 (46%) patients had chest injuries identified by CT scans. Many patients (15.6%) had ground-glass lung opacity in the CT scan reports. Lung consolidation, pneumothorax, lung contusion, hemothorax, and rib fractures were the most common. Conclusion: Due to the high frequency of typical findings in spiral CT scan examinations, obtaining a reliable history of trauma severity, injury mechanism, and a detailed physical examination is recommended before prescribing a CT scan for patients.

Chest CT Features of Patients with COVID-19 Pneumonia Following Hospital Discharge.

OBJECTIVE: To evaluate chest computed tomographic (CT) findings in patients with coronavirus disease 2019 (COVID-19) pneumonia following hospital discharge. METHODS: 52 patients with confirmed COVID-19 pneumonia underwent follow-up chest CT. The scans were obtained on average 43.1 days after hospital admission and analyzed for parenchymal abnormality (e.g., ground-glass opacities, consolidation, or interstitial thickening) and evidence of fibrosis (e.g., assigned to one of three groups: Group 1 (normal lung), Group 2 (parenchymal abnormality but without evidence of fibrosis), and Group 3 (evidence of fibrosis)). Clinical data and CT manifestations of the patients were compared among the three groups. RESULTS: 30.8% (16/52) of patients with COVID-19 pneumonia showed normal lung and were designated as Group 1. 69.2% (36/52) of patients showed parenchymal abnormality ranging from residual ground-glass opacities, consolidation, or interstitial thickening in Group 2 (51.9%) to fibrosis in Group 3 (17.3%). All patients in Group3 had severe/critical COVID-19, while most patients in Group 2 and Group 1 had common COVID-19. Patients in Group 3 were older (60.9 vs. 40.8 and 36.8 years, p<0.001, there is a significant difference), had a longer hospitalization day (20.2 vs. 15.3 and 12.3 days, p<0.05, there is a significant difference), a higher ratio of patients with comorbidities (88.9%vs14.8% and 25%, p<0.001, there is a significant difference), and higher peak CT scores (13 vs. 6.2 and 3.2, p<0.001, there is a significant difference) than those patients in Group 2 and Group 1. CONCLUSIONS: Elderly severe/critical COVID-19 patients with comorbidities are more prone to develop fibrosis early on following hospital discharge. On the other hand, lung inflammation in younger patients with common COVID-19 can be resolved completely.

Diagnostic performance of chest CT findings of COVID-19 with RT-PCR negative.

COVID-19 presented with lung abnormalities on computed tomography (CT) scans in patient with false negative RT-PCR, which are helpful in diagnosis of this emerging global health emergency. It's a case report the young woman of 35-year-old patient with 2019-nCoV pneumonia confirmed with IgM-IgG serology underwent thin-section Chest CT. Our patient has the Chest CT with some lung abnormalities, the Ground-glass opacities, crazy paving pattern and smooth interlobular septal thickening. The clinical findings and with conspicuous ground grass opacity lesions in the peripheral and posterior lungs on CT are highly suspected of 2019-nCoV pneumonia.

Diagnostic value of ground-glass opacity in suspected coronavirus disease 2019 patients: A meta-analysis

OBJECTIVE: The aim of the study was to evaluate the diagnostic efficiency of ground-glass opacity (GGO) for coronavirus disease 2019 (COVID-19) in suspected patients. MATERIALS AND METHODS: In this systematic review and meta-analysis, PubMed, Embase, Cochrane Library, Scopus, Web of Science, CNKI, and Wanfang databases were searched from November 01, 2019 to November 29, 2020. Studies providing the diagnostic test accuracy of chest computed tomography (CT) and description of detailed CT features for COVID-19 were included. Data were extracted from the publications. The sensitivity, specificity, and summary receiver operating characteristic curves were pooled. Heterogeneity was detected across included studies. RESULTS: Eleven studies with 1618 cases were included. The pooled sensitivity, specificity and area under the curve were 0.74 (95% confidence interval [CI], 0.61–0.84), 0.52 (95% CI, 0.33–0.70), and 0.70 (95% CI, 0.66–0.74), respectively. There was obvious heterogeneity among included studies (P < 0.05). Differences in the study region, inclusion criteria, research quality, or research methods might have contributed to the heterogeneity. The included studies had no significant publication bias (P > 0.1). CONCLUSIONS: COVID-19 was diagnosed not only by GGO with a medium level of diagnostic accuracy but also by white blood cell counts, epidemic history, and revers transcription-polymerase chain reaction testing.

Dexamethasone Induced Sino-Orbital Mucormycosis In a Patient Infected With COVID-19.

Background: The most common causes of immunodeficiency are iatrogenic and the result of the widespread use of therapies which modulates the immune system, whether they are planned or haphazardly. Mucormycosis is an invasive fungal disease which is usually secondary to immunosuppression, diabetic ketoacidosis, and long-term use of antibiotics, corticosteroids, and cytotoxic drugs. There are researches which show patients with coronavirus disease 2019 (COVID-19), especially severely ill or immunocompromised, are more likely to suffer from invasive fungal infections. Patients with diabetes are at a higher risk for severe COVID-19 outcomes. However, there has been no clear evidence on the relationship between pre-diabetes state and mucormycosis as a complication of SARS-CoV-2 infection so far. Case Presentation: Here, we report a case of sino-orbital mucormycosis in a pre-diabetic 54-year-old female without any underlying diseases. The patient suffered from COVID-19 pneumonia. She received 8 mg dexamethasone for 12 days. Afterwards, she returned three days after her discharge with a complaint of pre-orbital cellulitis, unilateral facial numbness and decreased visual acuity. Therefore, after primary diagnostic imaging, she was regarded as a candidate for invasive surgical intervention and was consequently treated with a combination of liposomal amphotericin B, radical recurrent surgery and posaconazole. Conclusion: It is very important to consider patients who are in the pre-diabetic state or possibly immunocompromised before prescribing steroids. The patients should be examined for invasive fungal infections in post-discharge period.

An original deep learning model using limited data for COVID-19 discrimination: A multicenter study.

OBJECTIVES: Artificial intelligence (AI) has been proved to be a highly efficient tool for COVID-19 diagnosis, but the large data size and heavy label force required for algorithm development and the poor generalizability of AI algorithms, to some extent, limit the application of AI technology in clinical practice. The aim of this study is to develop an AI algorithm with high robustness using limited chest CT data for COVID-19 discrimination. METHODS: A three dimensional algorithm that combined multi-instance learning with the LSTM architecture (3DMTM) was developed for differentiating COVID-19 from community acquired pneumonia (CAP) while logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), and a three dimensional convolutional neural network set for comparison. Totally, 515 patients with or without COVID-19 between December 2019 and March 2020 from five different hospitals were recruited and divided into relatively large (150 COVID-19 and 183 CAP cases) and relatively small datasets (17 COVID-19 and 35 CAP cases) for either training or validation and another independent dataset (37 COVID-19 and 93 CAP cases) for external test. Area under the receiver operating characteristic curve (AUC), sensitivity, specificity, precision, accuracy, F1 score, and G-mean were utilized for performance evaluation. RESULTS: In the external test cohort, the relatively large data-based 3DMTM-LD achieved an AUC of 0.956 (95% confidence interval, 95% CI, 0.929∼0.982) with 86.2% and 98.0% for its sensitivity and specificity. 3DMTM-SD got an AUC of 0.937 (95% CI, 0.909∼0.965), while the AUC of 3DCM-SD decreased dramatically to 0.714 (95% CI, 0.649∼0.780) with training data reduction. KNN-MMSD, LR-MMSD, SVM-MMSD, and 3DCM-MMSD benefited significantly from the inclusion of clinical information while models trained with relatively large dataset got slight performance improvement in COVID-19 discrimination. 3DMTM, trained with either CT or multi-modal data, presented comparably excellent performance in COVID-19 discrimination. CONCLUSIONS: The 3DMTM algorithm presented excellent robustness for COVID-19 discrimination with limited CT data. 3DMTM based on CT data performed comparably in COVID-19 discrimination with that trained with multi-modal information. Clinical information could improve the performance of KNN, LR, SVM, and 3DCM in COVID-19 discrimination, especially in the scenario with limited data for training.

Evaluation of the Prognostic Value of Chest Computed Tomography Scan in COVID-19 Patients

Background: The world is facing the coronavirus 2 pandemic since 2019 (COVID-19 infection) and all countries have challenges in management of patients based on their facilities. Chest computed tomography (CT) scan can be valuable in early detection and estimation of the pulmonary involvement in these patients. Objectives: To evaluate the prognostic value of chest CT imaging features in patients with coronavirus disease 2019 (COVID-19) pneumonia. Patients and Methods: In this cross-sectional study, 201 patients with COVID-19 were enrolled consecutively. The patients’ chest CT scans were analyzed, and the disease severity was rated using two methods: (1) total lung involvement (TLI) in which each lobe is scored from 0 to 4 based on the percentage of involvement;and (2) modified TLI in which each lobe involvement score is multiplied by the number of its segments, and the sum is recorded as the modified TLI. The patients were categorized into four groups depending on their prognosis (patients admitted to hospital wards, patients admitted to intensive care units [ICUs], patients with intubation during hospitalization, and expired patients). The relationship between both scoring methods and the clinical outcomes of patients was examined in the four groups. Results: The receiver operating characteristic (ROC) curve analysis showed no significant difference between the two scoring methods (TLI and modified TLI) in predicting the patients’ prognosis. The average disease severity based on the two scoring methods was significantly different between the four groups. Patients who were intubated during hospitalization and patients who expired had significantly higher scores than patients admitted to the ICUs and hospital wards (P = 0.001). The area under the ROC curve for the prediction of mortality was 0.81 (95% CI: 0.72-0.90;P < 0.001). The TLI score of 18.5 could predict mortality with specificity of > 95%. Conclusion: The TLI scoring system can be used for predicting in-hospital mortality and ICU admission in COVID-19 patients. This scoring method can help us devise a better strategic healthcare plan during the COVID-19 pandemic. © 2021, Author(s).

COVID-19 CT Scan Lung Segmentation: How We Do It.

The National Health Systems have been severely stressed out by the COVID-19 pandemic because 14% of patients require hospitalization and oxygen support, and 5% require admission to an Intensive Care Unit (ICU). Relationship between COVID-19 prognosis and the extent of alterations on chest CT obtained by both visual and software-based quantification that expresses objective evaluations of the percentage of ventilated lung parenchyma compared to the affected one has been proven. While commercial applications for automatic medical image computing and visualization are expensive and limited in their spread, the open-source systems are characterized by not enough standardization and time-consuming troubles. We analyzed chest CT exams on 246 patients suspected of COVID-19 performed in the Emergency Department CT room. The lung parenchyma segmentation was obtained by a threshold-based method using the open-source 3D Slicer software and software tools called "Segment Editor" and "Segment Quantification." For the three main characteristics analyzed on lungs affected by COVID-19 pneumonia, a specifical densitometry value range was defined: from - 950 to - 700 HU for well-aerated parenchyma; from - 700 to - 250 HU for interstitial lung disease; from - 250 to 250 HU for parenchymal consolidation. For the well-aerated parenchyma and the interstitial alterations, the procedure was semi-automatic with low time consumption, whereas consolidations' analysis needed manual interventions by the operator. After the chest CT, 13% of the sample was admitted to intensive care, while 34% of them to the sub-intensive care. In patients moved to intensive care, the parenchyma analysis reported a higher crazy paving presentation. The quantitative analysis of the alterations affecting the lung parenchyma of patients with COVID-19 pneumonia can be performed by threshold method segmentation on 3D Slicer. The segmentation could have an important role in the quantification in different COVID-19 pneumonia presentations, allowing to help the clinician in the correct management of patients.

Heterogeneity Analysis of Chest CT Predict Individual Prognosis of COVID-19 Patients.

BACKGROUND: Ground-glass Opacity (GGO) and Consolidation Opacity (CLO) are the common CT lung opacities, and their heterogeneity may have potential for prognosis ofcoronavirus disease-19 (COVID-19) patients. OBJECTIVE: This study aimed to estimate clinical outcomes in individual COVID-19 patients using histogram heterogeneity analysis based on CT opacities. METHODS: 71 COVID-19 cases' medical records were retrospectively reviewed from a designated hospital in Wuhan, China, from January 24th to February 28th at the early stage of the pandemic. Two characteristic lung abnormity opacities, GGO and CLO, were drawn on CT images to identify the heterogeneity using quantitative histogram analysis. The parameters (mean, mode, kurtosis, and skewness) were derived from histograms to evaluate the accuracy of clinical classification and outcome prediction. Nomograms were built to predict the risk of death and median length of hospital stays (LOS), respectively. RESULTS: A total of 57 COVID-19 cases were eligible for the study cohort after excluding 14 cases. The highest lung abnormalities were GGO mixed with CLO in both the survival populations (26 in 42, 61.9%) and died population (10 in 15, 66.7%). The best performance heterogeneity parameters to discriminate severe type from mild/moderate counterparts were as follows: GGO_skewness: specificity= 66.67%, sensitivity=78.12%, AUC=0.706; CLO_mean: specificity=70.00%, sensitivity= 76.92%, and AUC=0.746. Nomogram based on histogram parameters can predict the individual risk of death and the prolonged median LOS of COVID-19 patients. C-indexes were 0.763 and 0.888 for risk of death and prolonged median LOS, respectively. CONCLUSION: Histogram analysis method based on GGO and CLO has the ability for individual risk prediction in COVID-19 patients.

Appearance of COVID-19 pneumonia on 1.5 T TrueFISP MRI.

OBJECTIVE: To evaluate the performance of 1.5 T true fast imaging with steady state precession (TrueFISP) magnetic resonance imaging (MRI) sequences for the detection and characterization of pulmonary abnormalities caused by coronavirus disease 2019 (COVID-19). MATERIALS AND METHODS: In this retrospective single-center study, computed tomography (CT) and MRI scans of 20 patients with COVID-19 pneumonia were evaluated with regard to the distribution, opacity, and appearance of pulmonary lesions, as well as bronchial changes, pleural effusion, and thoracic lymphadenopathy. McNemar's test was used in order to compare the COVID-19-associated alterations seen on CT with those seen on MRI. RESULTS: Ground-glass opacities were better visualized on CT than on MRI (p = 0.031). We found no statistically significant differences between CT and MRI regarding the visualization/characterization of the following: consolidations; interlobular/intralobular septal thickening; the distribution or appearance of pulmonary abnormalities; bronchial pathologies; pleural effusion; and thoracic lymphadenopathy. CONCLUSION: Pulmonary abnormalities caused by COVID-19 pneumonia can be detected on TrueFISP MRI sequences and correspond to the patterns known from CT. Especially during the current pandemic, the portions of the lungs imaged on cardiac or abdominal MRI should be carefully evaluated to promote the identification and isolation of unexpected cases of COVID-19, thereby curbing further spread of the disease.

OBJETIVO: Avaliar o desempenho da ressonância magnética (RM) de 1,5 T true fast imaging with steady state precession (TrueFISP) para detecção e caracterização de anormalidades pulmonares causadas por doença do coronavírus 2019 (COVID-19). MATERIAIS E MÉTODOS: Neste estudo retrospectivo unicêntrico, imagens de tomografia computadorizada (TC) e RM de 20 pacientes com pneumonia COVID-19 foram avaliadas em relação a distribuição, opacidade e forma das lesões pulmonares, anormalidades brônquicas, derrame pleural e linfadenopatia torácica. O teste de McNemar foi usado para comparar os achados associados à COVID-19 na TC e na RM. RESULTADOS: As opacidades em vidro fosco foram mais bem visualizadas na TC do que na RM (p = 0,031). Não foram encontradas diferenças estatisticamente significantes entre TC e RM em relação aos outros aspectos, ou seja, visualização de consolidações e espessamento septal interlobular/intralobular, distribuição ou forma de anormalidades pulmonares, doenças brônquicas, derrame pleural ou linfadenopatia torácica. CONCLUSÃO: As anomalias pulmonares causadas pela pneumonia por COVID-19 podem ser detectadas nas sequências TrueFISP e correspondem aos padrões conhecidos da TC. Especialmente em face da pandemia atual, as porções de imagem dos pulmões na RM cardíaca ou abdominal devem ser cuidadosamente avaliadas para apoiar a identificação e o isolamento de casos inesperados de COVID-19 e, assim, conter a disseminação.

Clinical and chest CT features as a predictive tool for COVID-19 clinical progress: introducing a novel semi-quantitative scoring system.

OBJECTIVE: Proposing a scoring tool to predict COVID-19 patients' outcomes based on initially assessed clinical and CT features. METHODS: All patients, who were referred to a tertiary-university hospital respiratory triage (March 27-April 26, 2020), were highly clinically suggestive for COVID-19 and had undergone a chest CT scan were included. Those with positive rRT-PCR or highly clinically suspicious patients with typical chest CT scan pulmonary manifestations were considered confirmed COVID-19 for additional analyses. Patients, based on outcome, were categorized into outpatient, ordinary-ward admitted, intensive care unit (ICU) admitted, and deceased; their demographic, clinical, and chest CT scan parameters were compared. The pulmonary chest CT scan features were scaled with a novel semi-quantitative scoring system to assess pulmonary involvement (PI). RESULTS: Chest CT scans of 739 patients (mean age = 49.2 ± 17.2 years old, 56.7% male) were reviewed; 491 (66.4%), 176 (23.8%), and 72 (9.7%) cases were managed outpatient, in an ordinary ward, and ICU, respectively. A total of 439 (59.6%) patients were confirmed COVID-19 cases; their most prevalent chest CT scan features were ground-glass opacity (GGO) (93.3%), pleural-based peripheral distribution (60.3%), and multi-lobar (79.7%), bilateral (76.6%), and lower lobes (RLL and/or LLL) (89.1%) involvement. Patients with lower SpO2, advanced age, RR, total PI score or PI density score, and diffuse distribution or involvement of multi-lobar, bilateral, or lower lobes were more likely to be ICU admitted/expired. After adjusting for confounders, predictive models found cutoffs of age ≥ 53, SpO2 ≤ 91, and PI score ≥ 8 (15) for ICU admission (death). A combination of all three factors showed 89.1% and 95% specificity and 81.9% and 91.4% accuracy for ICU admission and death outcomes, respectively. Solely evaluated high PI score had high sensitivity, specificity, and NPV in predicting the outcome as well. CONCLUSION: We strongly recommend patients with age ≥ 53, SpO2 ≤ 91, and PI score ≥ 8 or even only high PI score to be considered as high-risk patients for further managements and care plans. KEY POINTS: • Chest CT scan is a valuable tool in prioritizing the patients in hospital triage. • A more accurate and novel 35-scale semi-quantitative scoring system was designed to predict the COVID-19 patients' outcome. • Patients with age ≥ 53, SpO2 ≤ 91, and PI score ≥ 8 or even only high PI score should be considered high-risk patients.

COVID-19 pneumonia-ultrasound, radiographic, and computed tomography findings: a comprehensive pictorial essay.

Ultrasound, chest X-ray, and computed tomography (CT) have been used with excellent results in diagnosis, first assessment, and follow-up of COVID-19 confirmed and suspected patients. Ultrasound and chest X-ray have the advantages of the wide availability and acquisition at the patient's bed; CT showed high sensitivity in COVID-19 diagnosis. Ground-glass opacities and consolidation are the main CT and X-ray features; the distribution of lung abnormalities is typically bilateral and peripheral. Less typical findings, including pleural effusion, mediastinal lymphadenopathies, the bubble air sign, and cavitation, can also be visible on chest CT. Radiologists should be aware of the advantages and limitations of the available imaging techniques and of the different pulmonary aspects of COVID-19 infection.

Value of minimum intensity projections for chest CT in COVID-19 patients.

PURPOSE: To investigate whether minimum intensity projection (MinIP) reconstructions enable more accurate depiction of pulmonary ground-glass opacity (GGO) compared to standard transverse sections and multiplanar reformat (MPR) series in patients with suspected coronavirus disease 2019 (COVID-19). METHOD: In this multinational study, chest CT scans of 185 patients were retrospectively analyzed. Diagnostic accuracy, diagnostic confidence, image quality regarding the assessment of GGO, as well as subjective time-efficiency of MinIP and standard MPR series were analyzed based on the assessment of six radiologists. In addition, the suitability for COVID-19 evaluation, image quality regarding GGO and subjective time-efficiency in clinical routine was assessed by five clinicians. RESULTS: The reference standard revealed a total of 149 CT scans with pulmonary GGO. MinIP reconstructions yielded significantly higher sensitivity (99.9 % vs 95.6 %), specificity (95.8 % vs 86.1 %) and accuracy (99.1 % vs 93.8 %) for assessing of GGO compared with standard MPR series. MinIP reconstructions achieved significantly higher ratings by radiologists concerning diagnostic confidence (medians, 5.00 vs 4.00), image quality (medians, 4.00 vs 4.00), contrast between GGO and unaffected lung parenchyma (medians, 5.00 vs 4.00) as well as subjective time-efficiency (medians, 5.00 vs 4.00) compared with MPR-series (all P < .001). Clinicians preferred MinIP reconstructions for COVID-19 assessment (medians, 5.00 vs 3.00), image quality regarding GGO (medians, 5.00 vs 3.00) and subjective time-efficiency in clinical routine (medians, 5.00 vs 3.00). CONCLUSIONS: MinIP reconstructions improve the assessment of COVID-19 in chest CT compared to standard images and may be suitable for routine application.

Quantitative chest CT analysis in COVID-19 to predict the need for oxygenation support and intubation.

OBJECTIVE: Lombardy (Italy) was the epicentre of the COVID-19 pandemic in March 2020. The healthcare system suffered from a shortage of ICU beds and oxygenation support devices. In our Institution, most patients received chest CT at admission, only interpreted visually. Given the proven value of quantitative CT analysis (QCT) in the setting of ARDS, we tested QCT as an outcome predictor for COVID-19. METHODS: We performed a single-centre retrospective study on COVID-19 patients hospitalised from January 25, 2020, to April 28, 2020, who received CT at admission prompted by respiratory symptoms such as dyspnea or desaturation. QCT was performed using a semi-automated method (3D Slicer). Lungs were divided by Hounsfield unit intervals. Compromised lung (%CL) volume was the sum of poorly and non-aerated volumes (- 500, 100 HU). We collected patient's clinical data including oxygenation support throughout hospitalisation. RESULTS: Two hundred twenty-two patients (163 males, median age 66, IQR 54-6) were included; 75% received oxygenation support (20% intubation rate). Compromised lung volume was the most accurate outcome predictor (logistic regression, p < 0.001). %CL values in the 6-23% range increased risk of oxygenation support; values above 23% were at risk for intubation. %CL showed a negative correlation with PaO2/FiO2 ratio (p < 0.001) and was a risk factor for in-hospital mortality (p < 0.001). CONCLUSIONS: QCT provides new metrics of COVID-19. The compromised lung volume is accurate in predicting the need for oxygenation support and intubation and is a significant risk factor for in-hospital death. QCT may serve as a tool for the triaging process of COVID-19. KEY POINTS: • Quantitative computer-aided analysis of chest CT (QCT) provides new metrics of COVID-19. • The compromised lung volume measured in the - 500, 100 HU interval predicts oxygenation support and intubation and is a risk factor for in-hospital death. • Compromised lung values in the 6-23% range prompt oxygenation therapy; values above 23% increase the need for intubation.

Chest computed tomography findings of COVID-19 pneumonia: pictorial essay with literature review.

Available information on chest Computed Tomography (CT) findings of the 2019 novel coronavirus disease (COVID-19) is constantly evolving. Ground glass opacities and consolidation with bilateral and peripheral distribution were reported as the most common CT findings, but also less typical features could be identified. All radiologists should be aware of the imaging spectrum of the COVID-19 pneumonia and imaging changes in the course of the disease. Our aim is to display the chest CT findings at first assessment and follow-up through a pictorial essay, to help in the recognition of these features for an accurate diagnosis.

CT features of novel coronavirus pneumonia (COVID-19) in children.

A serious epidemic of COVID-19 broke out in Wuhan, Hubei Province, China, and spread to other Chinese cities and several countries now. As the majority of patients infected with COVID-19 had chest CT abnormality, chest CT has become an important tool for early diagnosis of COVID-19 and monitoring disease progression. There is growing evidence that children are also susceptible to COVID-19 and have atypical presentations compared with adults. This review is mainly about the differences in clinical symptom spectrum, diagnosis of COVID-19, and CT imaging findings between adults and children, while highlighting the value of radiology in prevention and control of COVID-19 in pediatric patients. KEY POINTS: • Compared with adults, pediatric patients with COVID-19 have the characteristics of lower incidence, slighter clinical symptoms, shorter course of disease, and fewer severe cases. • The chest CT characteristics of COVID-19 in pediatric patients were atypical, with more localized GGO extent, lower GGO attenuation, and relatively rare interlobular septal thickening. • Chest CT should be used with more caution in pediatric patients with COVID-19 to protect this vulnerable population from risking radiation.