Chest Chest Computed Tomography Images Research Articles

Clinical characteristics of rehospitalized patients with COVID-19 in China.

This study aims to observe the clinical characteristics of recovered patients from Coronavirus Disease 2019 (COVID‐19) with positive in reverse transcription‐polymerase chain reaction (RT‐PCR) or serum antibody. The profile, clinical symptoms, laboratory outcomes, and radiologic assessments were extracted on 11 patients, who tested positive for COVID‐19 with RT‐PCR or serum antibody after discharged and was admitted to Hubei No. 3 People's Hospital of Jianghan University for a second treatment in March 2020. The average interval time between the first discharge and the second admission measured 16.00 ± 7.14 days, ranging from 6 to 27 days. In the second hospitalization, one patient was positive for RT‐PCR and serum antibody immunoglobulin M (IgM)‐immunoglobulin G (IgG), five patients were positive for both IgM and IgG but negative for RT‐PCR. Three patients were positive for both RT‐PCR and IgG but negative for IgM. The main symptoms were cough (54.55%), fever (27.27%), and feeble (27.27%) in the second hospitalization. Compared with the first hospitalization, there were significant decreases in gastrointestinal symptoms (5 vs 0, P = .035), elevated levels of both white blood cell count (P = .036) and lymphocyte count (P = .002), remarkedly decreases in C‐reactive protein and serum amyloid A (P < .05) in the second hospitalization. Additionally, six patients' chest computed tomography (CT) exhibited notable improvements in acute exudative lesions. There could be positive results for RT‐PCR analysis or serum IgM‐IgG in discharged patients, even with mild clinical symptoms, however, their laboratory outcomes and chest CT images would not indicate the on‐going development in those patients.

Evaluation of antiviral therapies for coronavirus disease 2019 pneumonia in Shanghai, China.

The aim of our study was to evaluate the therapeutic effect of antiviral drugs on coronavirus disease 2019 (COVID‐19) pneumonia. Patients confirmed with COVID‐19 pneumonia were enrolled and divided into seven groups according to the treatment option. Information including age, sex, and duration from illness onset to admission, clinical manifestations, and laboratory data at admission, and length of hospital stay were evaluated. The chest computed tomography (CT) imaging obtained at admission and after a 5‐day treatment cycle were assessed. The clinical symptoms and laboratory tests at discharge were also assessed. At admission, no significant differences were found among the groups, including the duration from illness onset to admission, clinical symptoms, and main laboratory results. No significant differences were found among the groups in terms of the proportion of patients with pneumonia resolution (P = .151) after treatment or the length of hospital stay (P = .116). At discharge, 7 of 184 (4%) patients had a mild cough while their other symptoms had disappeared, and the proportion of patients with abnormal liver function and with increased leukocytes, neutrophils or erythrocyte sedimentation rate among the 184 patients were close to those at admission. According to the results, the inclusion of antiviral drugs in therapeutic regimens based on symptomatic treatment had no significant additional impact on the improvement in COVID‐19 patients. In addition, the results of chest CT imaging, clinical manifestations, and laboratory tests at discharge were not completely consistent.

Clinical characteristics and chest CT imaging features of critically ill COVID-19 patients.

ObjectivesTo compare clinical, laboratory, and chest computed tomography (CT) findings in critically ill patients diagnosed with coronavirus disease 2019 (COVID-19) who survived and who died.MethodsThis retrospective study reviewed 60 critically ill patients (43 males and 17 females, mean age 64.4 ± 11.0 years) with COVID-19 pneumonia who were admitted to two different clinical centers. Their clinical and medical records were analyzed, and the chest CT images were assessed to determine the involvement of lobes and the distribution of lesions in the lungs between the patients who recovered from the illness and those who died.ResultsCompared with recovered patients (50/60, 83%), deceased patients (10/60, 17%) were older (mean age, 70.6 vs. 62.6 years, p = 0.044). C-reactive protein (CRP) (110.8 ± 26.3 mg/L vs 63.0 ± 50.4 mg/L, p < 0.001) and neutrophil-to-lymphocyte ratio (NLR) (18.7 ± 16.6 vs 8.4 ± 7.5, p = 0.030) were significantly elevated in the deceased as opposed to the recovered. Medial or parahilar area involvement was observed in all the deceased patients (10/10, 100%), when compared to only 54% (27/50) in the recovered. Ground-glass opacities (97%), crazy-paving pattern (92%), and air bronchogram (93%) were the most common radiological findings. There was significant difference in diabetes (p = 0.025) and emphysema (p = 0.013), and the odds ratio on a deceased patient having diabetes and emphysema was 6 times and 21 times the odds ratio on a recovered patient having diabetes and emphysema, respectively.ConclusionsOlder patients with comorbidities such as diabetes and emphysema, and higher CRP and NLRs with diffuse lung involvement were more likely to die of COVID-19.Key Points• Almost all patients critically ill with COVID-19 pneumonia had five lung lobes involved.• Medial or parahilar area involvement and degree of lung involvement were more serious in the deceased patients when compared with those who recovered from treatment.• Chronic lung disease, e.g., emphysema, diabetes, and higher serum CRP and NLR characterized patients who died of COVID-19.

Computed tomography manifestations in super early stage 2019 novel coronavirus pneumonia.

BackgroundThe recent outbreak of pneumonia cases in Wuhan, PR China, was caused by a novel beta coronavirus, the 2019 novel coronavirus (COVID-19).PurposeTo summarize chest computed tomography (CT) manifestations of the early stage of COVID-19 infection and provide a piece of reliable imaging evidence for initial screening and diagnosis.Material and MethodsFrom 10 January 2020 to 10 February 2020, we continuously observed chest CT imaging of 14 patients with clinically suspected new coronavirus infection in the two weeks after onset of symptoms. Ground-glass opacity (GGO), consolidation, reticular pattern, and ground-glass mimic nodules in each patient’s chest CT image were recorded.ResultsWe enrolled 14 patients, of which nine patients had the infection confirmed by reverse transcription polymerase chain reaction (RT-PCR). Five patients were highly suspected of infection. All cases had epidemiological evidence. GGO was a dominant imaging manifestation in the initial days of infection. GGO performance accounts for 40% in 1– 2 days, 90% in 3– 6 days, and 85% in 7– 10 days. With disease progression, consolidation appeared on follow-up CT. Consolidation performance accounts for 0% in 1– 2 days, 40% in 3– 6 days, and 71% in 7– 10 days. The lesions are mostly near the pleura. The number of lesions and the extent of the lesions increased as the disease progressed.ConclusionPatients with novel coronavirus pneumonia have characteristic CT features in the initial stage of infection, which can be used as an essential supplement for nucleic acid examination.

Deep Transfer Learning Based Classification Model for COVID-19 Disease

The COVID-19 infection is increasing at a rapid rate, with the availability of limited number of testing kits. Therefore, the development of COVID-19 testing kits is still an open area of research. Recently, many studies have shown that chest Computed Tomography (CT) images can be used for COVID-19 testing, as chest CT images show a bilateral change in COVID-19 infected patients. However, the classification of COVID-19 patients from chest CT images is not an easy task as predicting the bilateral change is defined as an ill-posed problem. Therefore, in this paper, a deep transfer learning technique is used to classify COVID-19 infected patients. Additionally, a top-2 smooth loss function with cost-sensitive attributes is also utilized to handle noisy and imbalanced COVID-19 dataset kind of problems. Experimental results reveal that the proposed deep transfer learning-based COVID-19 classification model provides efficient results as compared to the other supervised learning models.

Need for Caution in the Diagnosis of Radiation Pneumonitis During the COVID-19 Pandemic

PurposePatients with cancer are at high risk for mortality from coronavirus disease 2019 (COVID-19). Radiation pneumonitis (RP) is a common toxicity of thoracic radiation therapy with clinical and imaging features that overlap with those of COVID-19; however, RP is treated with high-dose corticosteroids, which may exacerbate COVID-19–associated lung injury. We reviewed patients who presented with symptoms of RP during the intensification of a regional COVID-19 epidemic to report on their clinical course and COVID-19 testing results. Methods and MaterialsThe clinical course and chest computed tomography (CT) imaging findings of consecutive patients who presented with symptoms of RP in March 2020 were reviewed. The first regional COVID-19 case was diagnosed on March 1, 2020. All patients underwent COVID-19 qualitative RNA testing. ResultsFour patients with clinical suspicion for RP were assessed. Three out of 4 patients tested positive for COVID-19. All patients presented with symptoms of cough and dyspnea. Two patients had a fever, of whom only 1 tested positive for COVID-19. Two patients started on an empirical high-dose corticosteroid taper for presumed RP, but both had clinical deterioration and ultimately tested positive for COVID-19 and required hospitalization. Chest CT findings in patients suspected of RP but ultimately diagnosed with COVID-19 showed ground-glass opacities mostly pronounced outside the radiation field. ConclusionsAs this pandemic continues, patients with symptoms of RP require diagnostic attention. We recommend that patients suspected of RP be tested for COVID-19 before starting empirical corticosteroids and for careful attention to be paid to chest CT imaging to prevent potential exacerbation of COVID-19 in these high-risk patients.

Radiographic and Clinical Features of Children With Coronavirus Disease (COVID-19) Pneumonia

Objective: The purpose of this study was to investigate chest computed tomography (CT) findings in children with coronavirus disease-19 (COVID-19) pneumonia in our hospital. Methods: This study included 22 pediatric patients with confirmed COVID-19 from January to March, 2020. The chest CT images and clinical data were reviewed. Results: The most prevalent presenting symptoms were fever (64%) and cough (59%), and a mildly elevated mean (SD) C-reactive protein (CRP) level of 11.22(11.06) and erythrocyte sedimentation rateof 18.8(15.17) were detected. The major CT abnormalities observed were mixed ground-glass opacity and consolidation lesions (36%), consolidations (32%), and groundglass opacities (14%). Peripheral distribution (45%) of lung lesions was predominant. Most of the lesions were multilobar(68%), with an average of three lung segments involved. Conclusion: Children with COVID-19 had relatively milder symptoms and less severe lung inflammation than adults.Chest CT plays an important role in the management of children with COVID-19 pneumonia.

Implications of Infection Prevention and Control Measures From a Patient With Atypical 2019 Novel Coronavirus Pneumonia

The 2019 novel coronavirus (2019-nCoV) pneumonia broke out in Wuhan, China, at the end of December 2019.1Wang D. Hu B. Hu C. et al.Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China [published online ahead of print February 7, 2020].https://doi.org/10.1001/jama.2020.1585Google Scholar,2Lu R. Zhao X. Li J. et al.Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding.Lancet. 2020; 395: 565-574Abstract Full Text Full Text PDF PubMed Scopus (6913) Google Scholar A 50-year-old man presented to the hospital with a 6-day history of fever and fatigue without chills, runny nose, dyspnea, cough, and diarrhea. His temperature rose slightly to 38.3°C after admission, and a physical examination of his chest revealed coarse breath sounds in both lungs. Laboratory studies found that the white blood cell count values (5.35×109/L) and the serum concentration of procalcitonin (0.08 ng/mL; normal range, <0.15 ng/mL) were normal. The percentage of lymphocytes decreased slightly to 15.26% (normal range, 20%-40%). The erythrocyte sedimentation rate increased significantly to 54 mm/h (normal range, 0-20 mm/h). On admission, a swab test and chest computed tomography (CT) scanning were performed. Real-time fluorescence polymerase chain reaction (RT-PCR) of the patient’s pharyngeal swab was negative for the 2019-nCoV nucleic acid assay. However, unenhanced chest CT revealed multiple bilateral and peripheral cloudlike high-density shadow opacities in both lungs (Figure A- C). Therefore, the patient was hospitalized in a general ward with a diagnosis of “community-acquired pneumonia.” Three days after admission, the patient still had fever, with a body temperature of up to 39.1°C, and had new symptoms such as dyspnea, cough, and myalgia. Repeat chest CT revealed that the multiple outer bands of the lungs and the subpleural patchy ground-glass opacities were obviously enlarged as compared with previous chest CT (Figure D-F). The second RT-PCR 2019-nCoV nucleic acid assay was positive. Finally, the patient received a diagnosis of “2019-nCoV pneumonia.” A detailed epidemiological investigation revealed that the patient had a history of visiting relatives in Wuhan, China, and a history of close contact with many people. A total of 8 people with whom he was in close contact have received a diagnosis of 2019-nCoV pneumonia. In addition, the groups of close contacts have implemented centralized isolation medical observations, including clinicians and caregivers. Therefore, we may obtain some implications for infection prevention and control measures from this case. First, detailed and objective epidemiological investigations are extremely important for the prevention and control of infectious diseases. Second, reasonable and standardized management, isolation, and treatment of patients with suspected 2019-nCoV pneumonia can effectively reduce cross infection. Third, we should pay attention to the significance of chest CT imaging in the diagnosis of 2019-nCoV pneumonia. Although this patient’s first swab test presented with negative results of RT-PCR for 2019-nCoV, the first chest CT revealed the characteristics of viral pneumonia, such as bifocal extrazonal distribution, bilateral, multifocal.3Lin X. Gong Z. Xiao Z. Xiong J. Fan B. Liu J. Novel coronavirus pneumonia outbreak in 2019: computed tomographic findings in two cases.Korean J Radiol. 2020; 21: 365-368Crossref PubMed Scopus (78) Google Scholar,4Pan Y. Guan H. Zhou S. et al.Initial CT findings and temporal changes in patients with the novel coronavirus pneumonia (2019-nCoV): a study of 63 patients in Wuhan, China [published online ahead of print February 13, 2020].https://doi.org/10.1007/s00330-020-06731-xGoogle Scholar Repeat chest CT revealed a great progression of cloudlike high-density shadow opacities in both lungs as compared with previous chest CT. It may not be extremely reasonable to rely on only 1 nucleic acid test results to diagnose 2019-nCoV pneumonia. In these cases, repeat pharyngeal swab testing and patient isolation should be carefully considered.

A systematic review of chest imaging findings in COVID-19.

Chest computed tomography (CT) is frequently used in diagnosing coronavirus disease 2019 (COVID-19) for detecting abnormal changes in the lungs and monitoring disease progression during the treatment process. Furthermore, CT imaging appearances are correlated with patients presenting with different clinical scenarios, such as early versus advanced stages, asymptomatic versus symptomatic patients, and severe versus nonsevere situations. However, its role as a screening and diagnostic tool in COVID-19 remains to be clarified. This article provides a systematic review and meta-analysis of the current literature on chest CT imaging findings with the aim of highlighting the contribution and judicious use of CT in the diagnosis of COVID-19. A search of PubMed/Medline, Web of Science, ScienceDirect, Google Scholar and Scopus was performed to identify studies reporting chest imaging findings in COVID-19. Chest imaging abnormalities associated with COVID-19 were extracted from the eligible studies and diagnostic value of CT in detecting these abnormal changes was compared between studies consisting of both COVID-19 and non-COVID-19 patients. A random-effects model was used to perform meta-analysis for calculation of pooled mean values and 95% confidence intervals (95% CI) of abnormal imaging findings. Fifty-five studies met the selection criteria and were included in the analysis. Pulmonary lesions more often involved bilateral lungs (78%, 95% CI: 45-100%) and were more likely to have a peripheral (65.35%, 95% CI: 25.93-100%) and peripheral plus central distribution (31.12%, 95% CI: 1.96-74.07%), but less likely to have a central distribution (3.57%, 95% CI: 0.99-9.80%). Ground glass opacities (GGO) (58.05%, 95% CI: 16.67-100%), consolidation (44.18%, 95% CI: 1.61-71.46%) and GGO plus consolidation (52.99%, 95% CI: 19.05-76.79%) were the most common findings reported in 94.5% (52/55) of the studies, followed by air bronchogram (42.50%, 95% CI: 7.78-80.39%), linear opacities (41.29%, 95% CI: 7.44-65.06%), crazy-paving pattern (23.57%, 95% CI: 3.13-91.67%) and interlobular septal thickening (22.91%, 95% CI: 0.90-80.49%). CT has low specificity in differentiating pneumonia-related lung changes due to significant overlap between COVID-19 and non-COVID-19 patients with no significant differences in most of the imaging findings between these two groups (P>0.05). Furthermore, normal CT (13.31%, 95% CI: 0.74-38.36%) was reported in 26 (47.3%) studies. Despite widespread use of CT in the diagnosis of COVID-19 patients based on the current literature, CT findings are not pathognomonic as it lacks specificity in differentiating imaging appearances caused by different types of pneumonia. Further, there is a relatively high percentage of normal CT scans. Use of CT as a first-line diagnostic or screening tool in COVID-19 is not recommended.

Quantitative analysis of chest CT imaging findings with the risk of ARDS in COVID-19 patients: a preliminary study

BackgroundThe coronavirus disease 2019 (COVID-19) has rapidly become a pandemic worldwide. The value of chest computed tomography (CT) is debatable during the treatment of COVID-19 patients. Compared with traditional chest X-ray radiography, quantitative CT may supply more information, but its value on COVID-19 patients was still not proven.MethodsAn automatic quantitative analysis model based on a deep network called VB-Net for infection region segmentation was developed. A quantitative analysis was performed for patients diagnosed as severe COVID 19. The quantitative assessment included volume and density among the infectious area. The primary clinical outcome was the existence of acute respiratory distress syndrome (ARDS). A univariable and multivariable logistic analysis was done to explore the relationship between the quantitative results and ARDS existence.ResultsThe VB-Ne model was sensitive and stable for pulmonary lesion segmentation, and quantitative analysis indicated that the total volume and average density of the lung lesions were not related to ARDS. However, lesions with specific density changes showed some influence on the risk of ARDS. The proportion of lesion density from −549 to −450 Hounsfield unit (HU) was associated with increased risk of ARDS, while the density was ranging from −149 to −50 HU was related to a lowered risk of ARDS.ConclusionsThe automatic quantitative model based on VB-Ne can supply useful information for ARDS risk stratification in COVID-19 patients during treatment.

Classification of COVID-19 patients from chest CT images using multi-objective differential evolution-based convolutional neural networks.

Early classification of 2019 novel coronavirus disease (COVID-19) is essential for disease cure and control. Compared with reverse-transcription polymerase chain reaction (RT-PCR), chest computed tomography (CT) imaging may be a significantly more trustworthy, useful, and rapid technique to classify and evaluate COVID-19, specifically in the epidemic region. Almost all hospitals have CT imaging machines; therefore, the chest CT images can be utilized for early classification of COVID-19 patients. However, the chest CT-based COVID-19 classification involves a radiology expert and considerable time, which is valuable when COVID-19 infection is growing at rapid rate. Therefore, an automated analysis of chest CT images is desirable to save the medical professionals’ precious time. In this paper, a convolutional neural networks (CNN) is used to classify the COVID-19-infected patients as infected (+ve) or not (−ve). Additionally, the initial parameters of CNN are tuned using multi-objective differential evolution (MODE). Extensive experiments are performed by considering the proposed and the competitive machine learning techniques on the chest CT images. Extensive analysis shows that the proposed model can classify the chest CT images at a good accuracy rate.

CT characteristics of patients infected with 2019 novel coronavirus: association with clinical type

AIMTo summarise the features of chest computed tomography (CT) of a series of patients infected with 2019 novel coronavirus (2019-nCov) to speed up recognition and have a better understanding of COVID-19 disease.MATERIALS AND METHODSThe clinical information and chest CT images of 93 patients infected with 2019-nCov from multiple centres were reviewed.RESULTSOf the 93 cases, abnormalities in 91 cases were located at the subpleural level, presenting with ground-glass opacity (GGO; n=69, 74.2%) and consolidation (n=56, 60.2%) in multiple lobes. Other CT features included vascular dilatation (n=83, 89.2%), interlobular septal thickening (n=29, 31.2%), bronchodilatation (n=44, 47.3%), the crazy-paving sign (n=34, 36.6%), the sieve-hole sign (n=12, 12.9%), pleural thickening (n=21, 22.6%), and pleural effusion (n=8, 8.6%). Multiple lobe involvement, including the presence of consolidation, the crazy-paving sign, interlobular septal thickening, pleural thickening and pleural effusion, was more common in critical patients with heavy/critical infection (p<0.05), whereas the presence of GGO, involvement of one or two lobes, and the halo sign were more common in patients with mild/common-type infections (p<0.05). Moreover, older age, higher body temperature, complaints of chest tightness and breathlessness, and lymphopenia was associated with heavy/critical infections.CONCLUSIONThe CT and clinical appearances of COVID-19 are variable and reflect the severity of COVID-19 to some extent.

The characteristics and clinical value of chest CT images of novel coronavirus pneumonia

AIMTo investigate the characteristics and clinical value of chest computed tomography (CT) images of novel coronavirus pneumonia (NCP).MATERIALS AND METHODSClinical data and CT images of 80 cases of NCP were collected. The clinical manifestations and laboratory test results of the patients were analysed. The lesions in each lung segment of the patient's chest CT images were characterised. Lesions were scored according to length and diffusivity.RESULTSThe main clinical manifestations were fever, dry cough, fatigue, a little white sputum, or diarrhoea. A total of 1,702 scored lesions were found in the first chest CT images of 80 patients. The lesions were located mainly in the subpleural area of the lungs (92.4%). Most of the lesions were ground-glass opacity, and subsequent fusions could increase in range and spread mainly in the subpleural area. Pulmonary consolidation accounted for 44.1% of all of the lesions. Of the 80 cases, 76 patients (95%) had bilateral lung disease, four (5%) patients had unilateral lung disease, and eight (10%) patients had cord shadow.CONCLUSIONThe chest CT of NCP patients is characterised by the onset of bilateral ground-glass lesions located in the subpleural area of the lung, and progressive lesions that result in consolidation with no migratory lesions. Pleural effusions and mediastinal lymphadenopathy are rare. As patients can have inflammatory changes in the lungs alongside a negative early nucleic acid test, chest CT, in combination with epidemiological and laboratory tests, is a useful examination to evaluate the disease and curative effect.

Clinical diagnostic value of computed tomography features of corona virus disease 2019 in 17 cases

Objective To investigate the specific chest computed tomography (CT) features of corona virus disease 2019(COVID-19) and to evaluate its clinical diagnostic value. Methods The clinical data of 35 cases with suspected novel coronavirus pneumonia diagnosed from Shuguang Hospital affiliated to Shanghai University of Traditional Chinese Medicine and Oriental Hospital affiliated to Shanghai Tongji University School of Medicine from January 1, 2020 to February 14, 2020 were retrospectively analyzed. A total of 17 cases with positive results of two times of real time reverse-transcription–polymerase chain-reaction (RT-PCR) for 2019 novel coronavirus (2019-nCoV) were evaluated as the case group, and the remaining 18 cases with negative results of two times of RT-PCR for 2019-nCoV were evaluated as the control group. The features of chest CT images of 35 cases were obtained. The frequencies of four CT imaging indicators including ground-glass opacities (GGO), crazy-paving , heterogeneous consolidation and bilateral subpleural distribution were analyzed. The sensitivity, specificity, positive predictive values (PPV) and negative predictive value (NPV) for COVID-19 were calculated. Results In the case group, there were 11 cases (64.71%) with GGO, 7 cases (41.18%) with crazy-paving, 6 cases (35.29%) with heterogeneous consolidation, and 16 cases (94.12%) with bilateral subpleural distribution, while in the control group, there were 7 cases (27.28%) with GGO, 1 case (5.56%) with crazy-paving, 6 cases (33.33%) with heterogeneous consolidation, and 5 cases (29.41%) with bilateral subpleural distribution. When multiple subpleural lesions or any two of the CT indicators were used as the characteristic indicator, the diagnosis efficiencies were better, with the sensitivity, specificity, PPV, NPV and Youden index of 94.12%, 72.22%, 76.19%, 98.86% and 0.66, respectively, and 88.24%, 77.78%, 78.95%, 87.5% and 0.66, respectively. Conclusions Chest CT indictors are of high clinical diagnostic value for COVID-19. Any two of the four CT indicators (GGO, crazy-paving, heterogeneous consolidation and bilateral subpleural distribution) or the single characteristics (bilateral subpleural distribution) are of high diagnostic efficacy. Key words: COVID-19; Computed tomography

Imaging and clinical features of patients with 2019 novel coronavirus SARS-CoV-2.

BackgroundThe pneumonia caused by the 2019 novel coronavirus (SARS-CoV-2, also called 2019-nCoV) recently break out in Wuhan, China, and was named as COVID-19. With the spread of the disease, similar cases have also been confirmed in other regions of China. We aimed to report the imaging and clinical characteristics of these patients infected with SARS-CoV-2 in Guangzhou, China.MethodsAll patients with laboratory-identified SARS-CoV-2 infection by real-time polymerase chain reaction (PCR) were collected between January 23, 2020, and February 4, 2020, in a designated hospital (Guangzhou Eighth People’s Hospital). This analysis included 90 patients (39 men and 51 women; median age, 50 years (age range, 18–86 years). All the included SARS-CoV-2-infected patients underwent non-contrast enhanced chest computed tomography (CT). We analyzed the clinical characteristics of the patients, as well as the distribution characteristics, pattern, morphology, and accompanying manifestations of lung lesions. In addition, after 1–6 days (mean 3.5 days), follow-up chest CT images were evaluated to assess radiological evolution.FindingsThe majority of infected patients had a history of exposure in Wuhan or to infected patients and mostly presented with fever and cough. More than half of the patients presented bilateral, multifocal lung lesions, with peripheral distribution, and 53 (59%) patients had more than two lobes involved. Of all included patients, COVID-19 pneumonia presented with ground glass opacities in 65 (72%), consolidation in 12 (13%), crazy paving pattern in 11 (12%), interlobular thickening in 33 (37%), adjacent pleura thickening in 50 (56%), and linear opacities combined in 55 (61%). Pleural effusion, pericardial effusion, and lymphadenopathy were uncommon findings. In addition, baseline chest CT did not show any abnormalities in 21 patients (23%), but 3 patients presented bilateral ground glass opacities on the second CT after 3–4 days.ConclusionSARS-CoV-2 infection can be confirmed based on the patient’s history, clinical manifestations, imaging characteristics, and laboratory tests. Chest CT examination plays an important role in the initial diagnosis of the novel coronavirus pneumonia. Multiple patchy ground glass opacities in bilateral multiple lobular with periphery distribution are typical chest CT imaging features of the COVID-19 pneumonia.

Chest CT Findings in Patients With Coronavirus Disease 2019 and Its Relationship With Clinical Features

ObjectivesThe aim of this study was to investigate the chest computed tomography (CT) findings in patients with confirmed coronavirus disease 2019 (COVID-19) and to evaluate its relationship with clinical features.Materials and MethodsStudy sample consisted of 80 patients diagnosed as COVID-19 from January to February 2020. The chest CT images and clinical data were reviewed, and the relationship between them was analyzed.ResultsTotally, 80 patients diagnosed with COVID-19 were included. With regards to the clinical manifestations, 58 (73%) of the 80 patients had cough, and 61 (76%) of the 80 patients had high temperature levels. The most frequent CT abnormalities observed were ground glass opacity (73/80 cases, 91%), consolidation (50/80 cases, 63%), and interlobular septal thickening (47/80, 59%). Most of the lesions were multiple, with an average of 12 ± 6 lung segments involved. The most common involved lung segments were the dorsal segment of the right lower lobe (69/80, 86%), the posterior basal segment of the right lower lobe (68/80, 85%), the lateral basal segment of the right lower lobe (64/80, 80%), the dorsal segment of the left lower lobe (61/80, 76%), and the posterior basal segment of the left lower lobe (65/80, 81%). The average pulmonary inflammation index value was (34% ± 20%) for all the patients. Correlation analysis showed that the pulmonary inflammation index value was significantly correlated with the values of lymphocyte count, monocyte count, C-reactive protein, procalcitonin, days from illness onset, and body temperature (P < 0.05).ConclusionsThe common chest CT findings of COVID-19 are multiple ground glass opacity, consolidation, and interlobular septal thickening in both lungs, which are mostly distributed under the pleura. There are significant correlations between the degree of pulmonary inflammation and the main clinical symptoms and laboratory results. Computed tomography plays an important role in the diagnosis and evaluation of this emerging global health emergency.

Pulmonary function evaluation in pediatric patients with primary immunodeficiency complicated by bronchiectasis.

Primary immunodeficiency (PID) accompanying with recurrent respiratory infections is thought to have a devastating effect on lung function. However, the associations between the airway structural abnormalities on chest computed tomography (CT), severity of dyspnea, and deterioration of pulmonary function test (PFT) have not been fully addressed. Children diagnosed with PID in a tertiary referred center in northern Taiwan were enrolled. Demographic and clinical data including age, sex, age at diagnosis of PID, and follow-up period were collected. Chest CT images (modified Reiff scores), parameters of PFT, and life quality questionnaires (mMRC dyspnea scale) were analyzed and correlated using Spearman's rank correlation test. A total of nineteen children with PID were enrolled and thirteen patients were diagnosed as having bronchiectasis based on chest CT scans. Modified Reiff scores of chest CT scan were negatively correlated with FEV1 (% predicted) and FEV1/FVC ratio (P<0.05). A strongly negative correlation was found between the mMRC dyspnea scale and FEV1 (% predicted) and FVC (% predicted), but positively correlated with RV (% predicted) and RV/TLC ratio (P<0.05). Furthermore, there was a negative correlation between FVC (% predicted) with increasing follow-up period (P<0.05). In pediatric patients with PID, chest CT scan appears to be a good tool for not only the diagnosis of bronchiectasis, but also the degree of pulmonary function impairment. Further quality of life impairments could be particularly due to the airflow obstruction and air trapping related to bronchiectasis.

The relationship between diagnostic value of chest computed tomography imaging and symptom duration in COVID infection.

OBJECTIVES:Severe acute respiratory syndrome-coronovirus-2 is a global public health problem, in which early diagnosis is required to prevent the spread of infection. In this study, we aimed to reveal the diagnostic value of chest computed tomography (CT) imaging with respect to symptom duration.METHODS:This retrospective study involved patients from five centers, who were admitted with typical COVID-19 symptoms and found to be positive for COVID-19 real-time reverse transcription-polymerase chain reaction (rtRT-PCR) test.RESULTS:One hundred and five patients with positive COVID-19 rtRT-PCR test were involved in the study. Sixty percent of these patients had chest CT imaging findings consistent with COVID-19 pneumonia. The most common chest CT finding was bilateral and subpleural ground-glass opacity in middle-lower lobes of the lungs. Chest CT findings were detected in 85.1% of the patients with a symptom duration of more than 2 days. In receiver operating characteristic analysis of this parameter, area under the curve (AUC) was 0.869, while sensitivity and specificity were 90.5% and 76.2%, respectively. It was notable that chest CT findings were 7.17 times more common among the patients aged 60 years and older, with AUC, specificity, and positive predictive value of 0.768, 88.1%, and 84.8%, respectively.CONCLUSION:Chest CT imaging is a quite valuable tool in patients with longer than 2 days' duration of symptoms, in whom clinical and epidemiological data support the diagnosis of COVID-19 infection. We suggest that the diagnosis of COVID-19 pneumonia should be made with chest CT imaging when rtRT-PCR test cannot be performed or gives a negative result, which is important for public health and to prevent the spread of infection.

Appraisal of Deep-Learning Techniques on Computer-Aided Lung Cancer Diagnosis with Computed Tomography Screening.

Aims:Deep-learning methods are becoming versatile in the field of medical image analysis. The hand-operated examination of smaller nodules from computed tomography scans becomes a challenging and time-consuming task due to the limitation of human vision. A standardized computer-aided diagnosis (CAD) framework is required for rapid and accurate lung cancer diagnosis. The National Lung Screening Trial recommends routine screening with low-dose computed tomography among high-risk patients to reduce the risk of dying from lung cancer by early cancer detection. The evolvement of clinically acceptable CAD system for lung cancer diagnosis demands perfect prototypes for segmenting lung region, followed by identifying nodules with reduced false positives. Recently, deep-learning methods are increasingly adopted in medical image diagnosis applications.Subjects and Methods:In this study, a deep-learning-based CAD framework for lung cancer diagnosis with chest computed tomography (CT) images is built using dilated SegNet and convolutional neural networks (CNNs). A dilated SegNet model is employed to segment lung from chest CT images, and a CNN model with batch normalization is developed to identify the true nodules from all possible nodules. The dilated SegNet and CNN models have been trained on the sample cases taken from the LUNA16 dataset. The performance of the segmentation model is measured in terms of Dice coefficient, and the nodule classifier is evaluated with sensitivity. The discriminant ability of the features learned by a CNN classifier is further confirmed with principal component analysis.Results:Experimental results confirm that the dilated SegNet model segments the lung with an average Dice coefficient of 0.89 ± 0.23 and the customized CNN model yields a sensitivity of 94.8 on categorizing cancerous and noncancerous nodules.Conclusions:Thus, the proposed CNN models achieve efficient lung segmentation and two-dimensional nodule patch classification in CAD system for lung cancer diagnosis with CT screening.

Accuracy of noncontrast chest computed tomography in COVID-19 infection: An observational and retrospective study

Objective: During the novel coronavirus pandemic, great challenges are becoming prominent, such as early and rapid diagnoses using simple and readily available tests. Chest computed tomography (CT) scan plays a major role in the diagnosis and monitoring of the complications with high specificity. The purpose of this study is to assess the accuracy of nonenhanced chest CT scan in the diagnosis of coronavirus disease 2019 (COVID-19) disease in suspected patients. Materials and Methods: Eighty four suspected patients were included in this retrospective study from March 1, 2020, to April 1, 2020, who underwent both nonenhanced chest CT scan and real-time polymerase chain reaction (PCR) test for COVID-19 disease. Results: The total number of initial reverse transcriptase (RT)-PCR-positive results was 52 (61.9%) patients and that of negative results was 32 (38%) patients. Out of the total number of positive RT-PCR tests, only fifty (96%) patients showed positive CT findings. Of the total number of negative RT-PCR tests (32 [38%] patients), only 8 (25%) patients showed negative CT results. With RT-PCR results as the reference standard, the sensitivity, specificity, and the accuracy of chest CT in indicating the COVID-19 infection were 96.15%, 25%, and 69.04%, respectively. Ground-glass opacity (GGO) was seen in all positive CT patients (74 [100%]), mixed GGO was seen in 27 (36.4%) patients, and consolidation was seen in 36 (48.6%) patients which was subsegmental in 28 (37.8%) patients. Vascular dilatation sign was seen in 61 (82.4%) patients, crazy paving sign was seen in 31 (41.9%) patients, reverse halo signs were seen in 23 (31%) patients, and air bronchogram was seen in 39 (52.7%) patients. Conclusions: Highly characteristic and specific bilateral multifocal GGOs were reported as the classical/typical chest CT features of COVID-19 infection with high confidence rate, although it may vary in different patients' parameters and stages so that chest CT imaging has very high sensitivity (96.15%) for the diagnosis of COVID-19 in epidemic areas with high pretest probability for disease, and it is recommended to be included as a decision-making diagnostic test in suspected patients with RT-PCR-negative results.