Computed tomography findings and clinical manifestations in different clinical types of coronavirus disease 2019

OBJECTIVE: Since the coronavirus disease 2019 (COVID-19) outbreak in Wuhan in 2019, the virus has spread rapidly. We investigated the clinical and computed tomography (CT) characteristics of different clinical types of COVID-19. MATERIALS AND METHODS: We retrospectively analyzed clinical and chest CT findings of 89 reverse transcription polymerase chain reaction confirmed cases from five medical centers in China. All the patients were classified into the common (n = 65), severe (n = 18), or fatal (n = 6) type. CT features included lesion distribution, location, size, shape, edge, density, and the ratio of lung lesions to extra-pulmonary lesions. A COVID-19 chest CT analysis tool (uAI-discover-COVID-19) was used to calculate the number of infections from the chest CT images. RESULTS: Fatal type COVID-19 is more common in older men, with a median age of 65 years. Fever was more common in the severe and fatal type COVID-19 patients than in the common type patients. Patients with fatal type COVID-19 were more likely to have underlying diseases. On CT examination, common type COVID-19 showed bilateral (68%), patchy (83%), ground-glass opacity (48%), or mixed (46%) lesions. Severe and fatal type COVID-19 showed bilateral multiple mixed density lesions (56%). The infection ratio (IR) increased in the common type (2.4 [4.3]), severe type (15.7 [14.3]), and fatal type (36.9 [14.2]). The IR in the inferior lobe of both lungs was statistically different from that of other lobes in common and severe type patients (P < 0.05). However, in the fatal type group, only the IR in the right inferior lung (RIL) was statistically different from that in the right superior lung(RUL), right middle lung (RML), and the left superior lung (LSL) (P < 0.05). CONCLUSION: The CT findings and clinical features of the various clinical types of COVID-19 pneumonia are different. Chest CT findings have unique characteristics in the different clinical types, which can facilitate an early diagnosis and evaluate the clinical course and severity of COVID-19.

Clustering cases of Chlamydia psittaci pneumonia mimicking COVID-19 pneumonia.

BACKGROUND: The onset symptoms of people infected by Chlamydia psittaci can mimic the coronavirus disease 2019 (COVID-19). However, the differences in laboratory tests and imaging features between psittacosis and COVID-19 remain unknown. AIM: To better understand the two diseases and then make an early diagnosis and treatment. METHODS: Six patients from two institutions confirmed as psittacosis by high-throughput genetic testing and 31 patients confirmed as COVID-19 were retrospectively included. The epidemiology, clinical characteristics, laboratory tests and computed tomography (CT) imaging features were collected and compared between the two groups. The follow-up CT imaging findings of patients with psittacosis were also investigated. RESULTS: The white blood cell count (WBC), neutrophil count and calcium were more likely to be decreased in patients with COVID-19 but were increased in patients with psittacosis (all P = 0.000). Lymphocyte count and platelet count were higher in patients with psittacosis than in those with COVID-19 (P = 0.044, P = 0.035, respectively). Lesions in patients with psittacosis were more likely to be unilateral (P = 0.001), involve fewer lung lobes (P = 0.006) and have pleural effusions (P = 0.002). Vascular enlargement was more common in patients with COVID-19 (P = 0.003). Consolidation in lung CT images was absorbed in all 6 patients. CONCLUSION: Psittacosis has the potential for human-to-human transmission. Patients with psittacosis present increased WBC count and neutrophil count and have specific CT imaging findings, including unilateral distribution, less involvement of lung lobes and pleural effusions, which might help us to differentiate it from COVID-19.

Persistent white matter changes in recovered COVID-19 patients at the 1-year follow-up.

There is growing evidence that severe acute respiratory syndrome coronavirus 2 can affect the CNS. However, data on white matter and cognitive sequelae at the 1-year follow-up are lacking. Therefore, we explored these characteristics in this study. We investigated 22 recovered coronavirus disease 2019 (COVID-19) patients and 21 matched healthy controls. Diffusion tensor imaging, diffusion kurtosis imaging and neurite orientation dispersion and density imaging were performed to identify white matter changes, and the subscales of the Wechsler Intelligence scale were used to assess cognitive function. Correlations between diffusion metrics, cognitive function and other clinical characteristics were then examined. We also conducted subgroup analysis based on patient admission to the intensive care unit. The corona radiata, corpus callosum and superior longitudinal fasciculus had a lower volume fraction of intracellular water in the recovered COVID-19 group than in the healthy control group. Patients who had been admitted to the intensive care unit had lower fractional anisotropy in the body of the corpus callosum than those who had not. Compared with the healthy controls, the recovered COVID-19 patients demonstrated no significant decline in cognitive function. White matter tended to present with fewer abnormalities for shorter hospital stays and longer follow-up times. Lower axonal density was detected in clinically recovered COVID-19 patients after 1 year. Patients who had been admitted to the intensive care unit had slightly more white matter abnormalities. No significant decline in cognitive function was found in recovered COVID-19 patients. The duration of hospital stay may be a predictor for white matter changes at the 1-year follow-up.

Survivors of COVID-19 exhibit altered amplitudes of low frequency fluctuation in the brain: a resting-state functional magnetic resonance imaging study at 1-year follow-up.

Although some short-term follow-up studies have found that individuals recovering from coronavirus disease 2019 (COVID-19) exhibit anxiety, depression, and altered brain microstructure, their long-term physical problems, neuropsychiatric sequelae, and changes in brain function remain unknown. This observational cohort study collected 1-year follow-up data from 22 patients who had been hospitalized with COVID-19 (8 males and 11 females, aged 54.2 ± 8.7 years). Fatigue and myalgia were persistent symptoms at the 1-year follow-up. The resting state functional magnetic resonance imaging revealed that compared with 29 healthy controls (7 males and 18 females, aged 50.5 ± 11.6 years), COVID-19 survivors had greatly increased amplitude of low-frequency fluctuation (ALFF) values in the left precentral gyrus, middle frontal gyrus, inferior frontal gyrus of operculum, inferior frontal gyrus of triangle, insula, hippocampus, parahippocampal gyrus, fusiform gyrus, postcentral gyrus, inferior parietal angular gyrus, supramarginal gyrus, angular gyrus, thalamus, middle temporal gyrus, inferior temporal gyrus, caudate, and putamen. ALFF values in the left caudate of the COVID-19 survivors were positively correlated with their Athens Insomnia Scale scores, and those in the left precentral gyrus were positively correlated with neutrophil count during hospitalization. The long-term follow-up results suggest that the ALFF in brain regions related to mood and sleep regulation were altered in COVID-19 survivors. This can help us understand the neurobiological mechanisms of COVID-19-related neuropsychiatric sequelae. This study was approved by the Ethics Committee of the Second Xiangya Hospital of Central South University (approval No. 2020S004) on March 19, 2020.

The Differences and Changes of Semi-Quantitative and Quantitative CT Features of Coronavirus Disease 2019 Pneumonia in Patients With or Without Smoking History.

Objective: To assess CT features of COVID-19 patients with different smoking status using quantitative and semi-quantitative technologies and to investigate changes of CT features in different disease states between the two groups. Methods: 30 COVID-19 patients with current smoking status (29 men, 1 woman) admitted in our database were enrolled as smoking group and 56 COVID-19 patients without smoking history (24 men, 32 women) admitted during the same period were enrolled as a control group. Twenty-seven smoking cases and 55 control cases reached recovery standard and were discharged. Initial and follow-up CT during hospitalization and follow-up CT after discharge were acquired. Thirty quantitative features, including the ratio of infection volume and visual-assessed interstitial changes score including total score, score of ground glass opacity, consolidation, septal thickening, reticulation and honeycombing sign, were analyzed. Results: Initial CT images of the smoking group showed higher scores of septal thickening [4.5 (0-5) vs. 0 (0-4), p = 0.001] and reticulation [0 (0-5.25) vs 0 (0-0), p = 0.001] as well as higher total score [7 (5-12.25) vs. 6 (5-7), p = 0.008] with statistical significance than in the control group. The score of reticulation was higher in the smoking group than in the control group when discharged [0.89 (0-0) vs. 0.09 (0-0), p = 0.02]. The score of septal thickening tended to be higher in the smoking group than the control group [4 (0-4) vs. 0 (0-4), p = 0.007] after being discharged. Quantitative CT features including infection ratio of whole lung and left lung as well as infection ratio within HU (-750, -300) and within HU (-300, 49) were higher in the control group of initial CT with statistical differences. The infection ratio of whole lung and left lung, infection ratio within HU (-750), and within HU (-750, -300) were higher in the control group with statistical differences when discharged. This trend turned adverse after discharge and the values of quantitative features were generally higher in the smoking group than in the control group without statistical differences. Conclusions: Patients with a history of smoking presented more severe interstitial manifestations and more residual lesion after being discharged. More support should be given for COVID-19 patients with a smoking history during hospitalization and after discharge.

An AI-based radiomics nomogram for disease prognosis in patients with COVID-19 pneumonia using initial CT images and clinical indicators.

BACKGROUND: This study utilized a comprehensive nomogram to evaluate the prognosis of patients with COVID-19 pneumonia. METHODS: COVID-19 pneumonia data was divided into training set (256 of 321, 80%), internal validation set (65 of 321, 20%) and independent external validation set (n = 188). After image processing, lesion segmentation, feature extraction and feature selection, radiomics signatures and clinical indicators were used to develop a radiomics model and a clinical model respectively. Combining radiomics signatures and clinical indicators, a radiomics nomogram was built. The performance of proposed models was evaluated by the receiver operating characteristic curve (AUC). Calibration curves and decision curve analysis were used to assess the performance of the radiomics nomogram. RESULTS: Two clinical indicators that were age and chronic lung disease or asthma and 21 radiomics features were selected to build the radiomics nomogram. The radiomics nomogram yielded an Area Under The Curve1 (AUC) of 0.88 and accuracy of 0.80 in the training set, an AUC of 0.85 and accuracy of 0.77 in internal testing validation set and an AUC of 0.84 and accuracy of 0.75 in independent external validation set. The performance of radiomics nomogram was better than clinical model (AUC = 0.77, p < 0.001) and radiomics model (AUC = 0.72, p = 0.025) in independent external validation set. CONCLUSIONS: The radiomics nomogram may be used to assess the deterioration of COVID-19 pneumonia.

Automated Diagnosis of COVID-19 Using Deep Supervised Autoencoder With Multi-View Features From CT Images.

Accurate and rapid diagnosis of coronavirus disease 2019 (COVID-19) from chest CT scans is of great importance and urgency during the worldwide outbreak. However, radiologists have to distinguish COVID-19 pneumonia from other pneumonia in a large number of CT scans, which is tedious and inefficient. Thus, it is urgently and clinically needed to develop an efficient and accurate diagnostic tool to help radiologists to fulfill the difficult task. In this study, we proposed a deep supervised autoencoder (DSAE) framework to automatically identify COVID-19 using multi-view features extracted from CT images. To fully explore features characterizing CT images from different frequency domains, DSAE was proposed to learn the latent representation by multi-task learning. The proposal was designed to both encode valuable information from different frequency features and construct a compact class structure for separability. To achieve this, we designed a multi-task loss function, which consists of a supervised loss and a reconstruction loss. Our proposed method was evaluated on a newly collected dataset of 787 subjects including COVID-19 pneumonia patients, other pneumonia patients, and normal subjects without abnormal CT findings. Extensive experimental results demonstrated that our proposed method achieved encouraging diagnostic performance and may have potential clinical application for the diagnosis of COVID-19.

Health Protection of CT Radiographers During the Outbreak of COVID-19: Application of Automatic Positioning Technology for Relocatable CT in the Fang Cang Hospital.

Background: To investigate the value of automatic positioning technology in improving the protection of radiographers in the relocatable CT room of a Fang Cang hospital during the outbreak of coronavirus disease 2019 (COVID-19). Methods: The National Emergency Medical Team of our hospital assumed command of Wuchang Fang Cang Hospital and treated confirmed COVID-19 patients with mild symptoms. Relocatable CT was used to examine patients in this hospital. Automatic positioning technology was applied to avoid close contact between medical staff and patients and to protect medical staff more effectively. Results: Seven hundred lung CT scans acquired from 269 patients were completed from February 17 to 26, 2020 with automatic positioning technology for relocatable CT in a Fang Cang hospital. All scans were conducted successfully using automatic positioning technology. All patients entered the scanning room from a separate door. All the position lines were accurate, and all images met the requirement for diagnosis of COVID-19, with satisfied quality. None of our medical staff had any close contact with patients. Conclusion: Automatic positioning technology applied to relocatable CT can minimize the close contact between technologists and patients and effectively improve the protection of medical staff without sacrificing image quality.

SCOAT-Net: A novel network for segmenting COVID-19 lung opacification from CT images.

Automatic segmentation of lung opacification from computed tomography (CT) images shows excellent potential for quickly and accurately quantifying the infection of Coronavirus disease 2019 (COVID-19) and judging the disease development and treatment response. However, some challenges still exist, including the complexity and variability features of the opacity regions, the small difference between the infected and healthy tissues, and the noise of CT images. Due to limited medical resources, it is impractical to obtain a large amount of data in a short time, which further hinders the training of deep learning models. To answer these challenges, we proposed a novel spatial- and channel-wise coarse-to-fine attention network (SCOAT-Net), inspired by the biological vision mechanism, for the segmentation of COVID-19 lung opacification from CT images. With the UNet++ as basic structure, our SCOAT-Net introduces the specially designed spatial-wise and channel-wise attention modules, which serve to collaboratively boost the attention learning of the network and extract the efficient features of the infected opacification regions at the pixel and channel levels. Experiments show that our proposed SCOAT-Net achieves better results compared to several state-of-the-art image segmentation networks and has acceptable generalization ability.

A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning.

How to fast and accurately assess the severity level of COVID-19 is an essential problem, when millions of people are suffering from the pandemic around the world. Currently, the chest CT is regarded as a popular and informative imaging tool for COVID-19 diagnosis. However, we observe that there are two issues - weak annotation and insufficient data that may obstruct automatic COVID-19 severity assessment with CT images. To address these challenges, we propose a novel three-component method, i.e., 1) a deep multiple instance learning component with instance-level attention to jointly classify the bag and also weigh the instances, 2) a bag-level data augmentation component to generate virtual bags by reorganizing high confidential instances, and 3) a self-supervised pretext component to aid the learning process. We have systematically evaluated our method on the CT images of 229 COVID-19 cases, including 50 severe and 179 non-severe cases. Our method could obtain an average accuracy of 95.8%, with 93.6% sensitivity and 96.4% specificity, which outperformed previous works.

Synergistic learning of lung lobe segmentation and hierarchical multi-instance classification for automated severity assessment of COVID-19 in CT images.

Understanding chest CT imaging of the coronavirus disease 2019 (COVID-19) will help detect infections early and assess the disease progression. Especially, automated severity assessment of COVID-19 in CT images plays an essential role in identifying cases that are in great need of intensive clinical care. However, it is often challenging to accurately assess the severity of this disease in CT images, due to variable infection regions in the lungs, similar imaging biomarkers, and large inter-case variations. To this end, we propose a synergistic learning framework for automated severity assessment of COVID-19 in 3D CT images, by jointly performing lung lobe segmentation and multi-instance classification. Considering that only a few infection regions in a CT image are related to the severity assessment, we first represent each input image by a bag that contains a set of 2D image patches (with each cropped from a specific slice). A multi-task multi-instance deep network (called M 2 UNet) is then developed to assess the severity of COVID-19 patients and also segment the lung lobe simultaneously. Our M 2 UNet consists of a patch-level encoder, a segmentation sub-network for lung lobe segmentation, and a classification sub-network for severity assessment (with a unique hierarchical multi-instance learning strategy). Here, the context information provided by segmentation can be implicitly employed to improve the performance of severity assessment. Extensive experiments were performed on a real COVID-19 CT image dataset consisting of 666 chest CT images, with results suggesting the effectiveness of our proposed method compared to several state-of-the-art methods.

Severity assessment of COVID-19 using CT image features and laboratory indices.

The coronavirus disease 2019 (COVID-19) is now a global pandemic. Tens of millions of people have been confirmed with infection, and also more people are suspected. Chest computed tomography (CT) is recognized as an important tool for COVID-19 severity assessment. As the number of chest CT images increases rapidly, manual severity assessment becomes a labor-intensive task, delaying appropriate isolation and treatment. In this paper, a study of automatic severity assessment for COVID-19 is presented. Specifically, chest CT images of 118 patients (age 46.5 ± 16.5 years, 64 male and 54 female) with confirmed COVID-19 infection are used, from which 63 quantitative features and 110 radiomics features are derived. Besides the chest CT image features, 36 laboratory indices of each patient are also used, which can provide complementary information from a different view. A random forest (RF) model is trained to assess the severity (non-severe or severe) according to the chest CT image features and laboratory indices. Importance of each chest CT image feature and laboratory index, which reflects the correlation to the severity of COVID-19, is also calculated from the RF model. Using three-fold cross-validation, the RF model shows promising results: 0.910 (true positive ratio), 0.858 (true negative ratio) and 0.890 (accuracy), along with AUC of 0.98. Moreover, several chest CT image features and laboratory indices are found to be highly related to COVID-19 severity, which could be valuable for the clinical diagnosis of COVID-19.

The Relationship Between Chest Imaging Findings and the Viral Load of COVID-19.

Purpose: We aimed to investigate the relationship between clinical characteristics, radiographic features, and the viral load of patients with coronavirus disease 2019 (COVID-19). Methods and Materials: We retrospectively collected 56 COVID-19 cases from two institutions in Hunan province, China. The basal clinical characteristics, detail imaging features and follow-up CT changes were evaluated and the relationship with the viral load was analyzed. Results: GGO (48, 85.7%) and vascular enlargement (44, 78.6%) were the most frequent signs in COVID-19 patients. Of the lesions, 64.3% of the margins were uneasily differentiated. However, no significant correlations were found in terms of leucocytes, neutrophils, lymphocytes, platelets, and C-reactive protein (all P > 0.05). In contrast, the uneasily differentiated margin was negatively correlated with the Ct value (r = -0.283, P = 0.042), that is, an uneasily differentiated margin indicated a lower Ct value (P = 0.043). Patients with a lower Ct value were likely to present a progress follow-up change (P = 0.022). The Ct value at baseline could predict a progress follow-up change with an AUC of 0.685 (Cut-off value = 29.48). All four patients with normal CT findings presented new lesion(s) on follow-up CT scans. Conclusion: The viral load of COVID-19 is negatively correlated with an uneasily differentiated lesion margin on initial CT scan images and the Ct value should noted when making a diagnosis. In addition, following-up CT scans are necessary for patients who presented a normal CT at the initial diagnosis, especially for those with a low Ct value.

The importance of distinguishing COVID-19 from more common respiratory illnesses.

We recruited 1591 patients who presented to our fever clinics from 23 January 2020 to 16 February 2020. The different imaging findings between COVID-19 pneumonia and influenza A viruses, influenza B virus pneumonia were also investigated. Most patients were infected by influenza A and B viruses in the flu-season. A laboratory kit is urgently needed to test different viruses simultaneously. Computed tomography can help early screen suspected patients with COVID-19 and differentiate different virus-related pneumonia.

Chest CT for Typical Coronavirus Disease 2019 (COVID-19) Pneumonia: Relationship to Negative RT-PCR Testing.

Some patients with positive chest CT findings may present with negative results of real-time reverse-transcription polymerase chain reaction (RT-PCR) tests for coronavirus disease 2019 (COVID-19). In this study, the authors present chest CT findings from five patients with COVID-19 infection who had initial negative RT-PCR results. All five patients had typical imaging findings, including ground-glass opacity (five patients) and/or mixed ground-glass opacity and mixed consolidation (two patients). After isolation for presumed COVID-19 pneumonia, all patients were eventually confirmed to have COVID-19 infection by means of repeated swab tests. A combination of repeated swab tests and CT scanning may be helpful for individuals with a high clinical suspicion of COVID-19 infection but negative findings at RT-PCR screening.

Multistage CT features of coronavirus disease 2019. / 2019å† çŠ¶ç— æ¯’ç— å¤šé˜¶æ®µCT影像学特征.

OBJECTIVES: To determine imaging features of coronavirus disease 2019 (COVID-19) in different stages, and to provide foundations for early diagnosis and treatment. METHODS: CT image data of 187 COVID-19 patients were analyzed in the period of hospitalization. CT scanning was performed on admission and repeated every 3 days. The improvement time of clinical symptoms and the image changes of follow-up CT were statistically analyzed. RESULTS: All 187 patients' nucleic acid test were positive to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The early CT images of lung in 187 cases (100%) showed multiple patchy and ground-glass opacities with fine mesh and consolidation shade, which mainly distributed in pulmonary band or under the pleura. In the progressive stage, the pulmonary lesions in 146 cases (78.1%) were mainly consolidation, accompanied by air bronchogram, thickening of blood vessels, and interstitial changes. Severe pulmonary CT images in 15 cases (8%) showed diffuse lesions in both lungs, displaying consolidation, or "white lung". The CT imaging features in 185 cases (98.9%) at the absorptive period showed that the lesions diminished and fibrogenesis. The imaging features of 6 times of lung CT examination in one case showed continuous progress. The original lesion in one case was obviously absorbed, but new lesions appeared under the pleura of both lungs at the third review of CT scanning. The changes of CT imaging lesions during follow-up were significantly different in different clinical symptoms improvement time (P< 0.01). CONCLUSIONS: Images of COVID-19 in various stages have special characteristics. The change of clinical symptoms is synchronous with the change of reexamination CT. Follow-up CT can reflect the trend of clinical changes. Repeat CT examination plays an important role in the early clinical diagnosis and the evaluation for the therapeutic effect on COVID-19 patient.

Chest CT findings and clinical features of coronavirus disease 2019 in children. / 儿童2019å† çŠ¶ç— æ¯’ç— æ‚£è€ çš„èƒ¸éƒ¨CT表现和临床特征.

OBJECTIVES: To describe the CT features and clinical characteristics of pediatric patients with coronavirus disease 2019 (COVID-19). METHODS: A total of 9 COVID-19 infected pediatric patients were included in this study. Clinical history, laboratory examination, and detailed CT imaging features were analyzed. All patients underwent the first CT scanning on the same day of being diagnosed by real-time reverse-transcription polymerase chain reaction (rRT-PCR). A low-dose CT scan was performed during follow-up. RESULTS: All the child patients had positive results. Four patients had cough and one patient had fever. One patient presented both cough and fever. Two children presented other symptoms like sore throat and stuffy nose. One child showed no clinical symptom. Five patients had positive initial CT findings with subtle lesions like ground-glass opacity (GGO) or spot-like mixed consolidation. Three patients were reported with negative results in the initial and follow-up CT examination. One patient was reported with initial negative CT findings but turning positive during the first follow-up. All patients had absorbed lesions on follow-up CT images after treatment. CONCLUSIONS: Pediatric COVID-19 patients have certain imaging and clinical features as well as disease prognosis. Children with COVID-19 tend to have normal or subtle CT findings and relatively better outcome.

CT Scans of Patients with 2019 Novel Coronavirus (COVID-19) Pneumonia.

Rationale: The increasing speed of confirmed 2019 novel coronavirus (COVID-19) cases is striking in China. The purpose of this study is to summarize the outcomes of patients with novel COVID-19 pneumonia (NCP) at our institution. Methods: In this single-center study, we retrospectively included 118 cases of NCP, from January 16, 2020 to February 4, 2020. The clinical outcomes were monitored up to February 11, 2020. The outcomes of NCP patients were phase summarized at our institution. Three kinds of responses to clinical treatment were defined and evaluated: 1) good, symptoms continually improved; 2) fair, symptoms not improved or relapsed; 3) poor, symptoms aggravated. The risk factors, including basal clinical characteristics, CT imaging features, and follow-up CT changes (no change, progress, and improvement) related to poor/fair outcomes, were also investigated. Results: Six patients were improved to no-emergency type, 2 remained the same, and 2 progressed to fatal type. Besides, 13 patients progressed from the common type group to the emergency group (3 in fatal type and 10 in severe type). Forty-two (35.6%) patients were discharged with a median hospital stay of 9.5 days (range, 4.0-15.0 days). Thus, the numbers in different responses were, 73 patients in good response group (4 emergency cases, 69 no-emergency cases), 28 in fair response group (3 emergency cases, 25 no-emergency cases), and 17 in poor response group (3 emergency cases, 14 no-emergency cases). No patient has died in our hospital to date. The median duration of progress observed from CT scans was 6 days (range, 2-14 days). The progression in abnormal imaging findings indicate a poor/fair response, whereas the alleviated symptoms seen from CT suggest a good response. Conclusion: Most cases are no-emergency type and have a favorable response to clinical treatment. Follow-up CT changes during the treatment can help evaluate the treatment response of patients with NCP.

Relation Between Chest CT Findings and Clinical Conditions of Coronavirus Disease (COVID-19) Pneumonia: A Multicenter Study.

OBJECTIVE. The increasing number of cases of confirmed coronavirus disease (COVID-19) in China is striking. The purpose of this study was to investigate the relation between chest CT findings and the clinical conditions of COVID-19 pneumonia. MATERIALS AND METHODS. Data on 101 cases of COVID-19 pneumonia were retrospectively collected from four institutions in Hunan, China. Basic clinical characteristics and detailed imaging features were evaluated and compared between two groups on the basis of clinical status: nonemergency (mild or common disease) and emergency (severe or fatal disease). RESULTS. Patients 21-50 years old accounted for most (70.2%) of the cohort, and five (5.0%) patients had disease associated with a family outbreak. Most patients (78.2%) had fever as the onset symptom. Most patients with COVID-19 pneumonia had typical imaging features, such as ground-glass opacities (GGO) (87 [86.1%]) or mixed GGO and consolidation (65 [64.4%]), vascular enlargement in the lesion (72 [71.3%]), and traction bronchiectasis (53 [52.5%]). Lesions present on CT images were more likely to have a peripheral distribution (88 [87.1%]) and bilateral involvement (83 [82.2%]) and be lower lung predominant (55 [54.5%]) and multifocal (55 [54.5%]). Patients in the emergency group were older than those in the non-emergency group. Architectural distortion, traction bronchiectasis, and CT involvement score aided in evaluation of the severity and extent of the disease. CONCLUSION. Patients with confirmed COVID-19 pneumonia have typical imaging features that can be helpful in early screening of highly suspected cases and in evaluation of the severity and extent of disease. Most patients with COVID-19 pneumonia have GGO or mixed GGO and consolidation and vascular enlargement in the lesion. Lesions are more likely to have peripheral distribution and bilateral involvement and be lower lung predominant and multifocal. CT involvement score can help in evaluation of the severity and extent of the disease.