scholarly journals Early clinical and CT features of COVID-19 and community-acquired pneumonia from a fever observation ward in Ningbo, China

2021 ◽  
Author(s):  
G Qian ◽  
Y Lin ◽  
AHY Ma ◽  
X Zhang ◽  
G Li ◽  
...  
Keyword(s):  
Early Stage ◽  
Laboratory Data ◽  
Clinical Manifestations ◽  
Lower Lobe ◽  
Ground Glass Opacity ◽  
Ct Images ◽  
Community Acquired Pneumonia ◽  
Ground Glass ◽  
Significant Difference ◽  
Eosinophil Counts

Introduction: We aimed to compare the early clinical manifestations, laboratory results and chest computed tomography (CT) images of coronavirus disease 2019 (COVID-19) patients with those of other community-acquired pneumonia (CAP) patients to differentiate COVID-19 before reverse transcription-polymerase chain reaction results are obtained. Methods: The clinical and laboratory data and chest CT images of 51 patients were assessed in a fever observation ward for evidence of COVID-19 between January and February 2020. Results: 24 patients had laboratory-confirmed COVID-19, whereas 27 individuals had negative results. No statistical difference in clinical features was found between COVID-19 and CAP patients except for diarrhoea. There was a significant difference in lymphocyte and eosinophil counts between COVID-19 and CAP patients. 22 (91.67%) COVID-19 patients had bilateral involvement and multiple lesions according to their lung CT images; the left lower lobe (87.50%) and right lower lobe (95.83%) were most often affected, and all lesions were located in peripheral zones of the lung. The most common CT feature of COVID-19 was ground-glass opacity, found in 95.83% of patients, compared to 66.67% of CAP patients. Conclusion: Diarrhoea, lymphocyte counts, eosinophil counts and CT findings (e.g. ground-glass opacity) could help to distinguish COVID-19 from CAP at an early stage of infection, based on findings from our fever observation ward.

scholarly journals Computing infection distributions and longitudinal evolution patterns in lung CT images

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Dongdong Gu ◽  
Liyun Chen ◽  
Fei Shan ◽  
Liming Xia ◽  
Jun Liu ◽  
...  
Keyword(s):  
Time Course ◽  
Lower Lobe ◽  
Ground Truth ◽  
Distribution Patterns ◽  
Lung Infection ◽  
Ct Images ◽  
Community Acquired Pneumonia ◽  
Disease Course ◽  
Distribution Maps ◽  
Significant Difference

Abstract Background Spatial and temporal lung infection distributions of coronavirus disease 2019 (COVID-19) and their changes could reveal important patterns to better understand the disease and its time course. This paper presents a pipeline to analyze statistically these patterns by automatically segmenting the infection regions and registering them onto a common template. Methods A VB-Net is designed to automatically segment infection regions in CT images. After training and validating the model, we segmented all the CT images in the study. The segmentation results are then warped onto a pre-defined template CT image using deformable registration based on lung fields. Then, the spatial distributions of infection regions and those during the course of the disease are calculated at the voxel level. Visualization and quantitative comparison can be performed between different groups. We compared the distribution maps between COVID-19 and community acquired pneumonia (CAP), between severe and critical COVID-19, and across the time course of the disease. Results For the performance of infection segmentation, comparing the segmentation results with manually annotated ground-truth, the average Dice is 91.6% ± 10.0%, which is close to the inter-rater difference between two radiologists (the Dice is 96.1% ± 3.5%). The distribution map of infection regions shows that high probability regions are in the peripheral subpleural (up to 35.1% in probability). COVID-19 GGO lesions are more widely spread than consolidations, and the latter are located more peripherally. Onset images of severe COVID-19 (inpatients) show similar lesion distributions but with smaller areas of significant difference in the right lower lobe compared to critical COVID-19 (intensive care unit patients). About the disease course, critical COVID-19 patients showed four subsequent patterns (progression, absorption, enlargement, and further absorption) in our collected dataset, with remarkable concurrent HU patterns for GGO and consolidations. Conclusions By segmenting the infection regions with a VB-Net and registering all the CT images and the segmentation results onto a template, spatial distribution patterns of infections can be computed automatically. The algorithm provides an effective tool to visualize and quantify the spatial patterns of lung infection diseases and their changes during the disease course. Our results demonstrate different patterns between COVID-19 and CAP, between severe and critical COVID-19, as well as four subsequent disease course patterns of the severe COVID-19 patients studied, with remarkable concurrent HU patterns for GGO and consolidations.

scholarly journals Development a quantitative segmentation model to assess the effect of comorbidity on patients with COVID-19

2020 ◽  
Author(s):  
Cui Zhang ◽  
Guangzhao Yang ◽  
Chunxian Cai ◽  
Zhihua Xu ◽  
Hai Wu ◽  
...  
Keyword(s):  
Automatic Segmentation ◽  
Ground Glass Opacity ◽  
Ct Images ◽  
Community Acquired Pneumonia ◽  
Ground Glass ◽  
Quantitative Ct ◽  
Laboratory Findings ◽  
Diabetes Mellitus Group ◽  
Test Dataset ◽  
Radiological Evidence

Abstract Background: The coronavirus disease 2019 (COVID-19) has brought a global disaster. Quantitative lesions may provide the radiological evidence of the severity of pneumonia and further to assess the effect of comorbidity on patients with COVID-19.Methods: 294 patients with COVID-19 were enrolled from February, 24, 2020 to June, 1, 2020 from six centers. Multi-task Unet network was used to segment the whole lung and lesions from chest CT images. This deep learning method was pre-trained in 650 CT images (550 in primary dataset and 100 in test dataset) with COVID-19 or community acquired pneumonia and Dice coefficients in test dataset were calculated. 50 CT scans of 50 patients (15 with comorbidity and 35 without comorbidity) were random selected to mark lesions manually. The results will be compared with the automatic segmentation model. Eight quantitative parameters were calculated based on the segmentation results to evaluate the effect of comorbidity on patients with COVID-19.Results: Quantitative segmentation model was proved to be effective and accurate with all Dice coefficients more than 0.85 and all accuracies more than 0.95. Of the 294 patients, 52 (17.7%) patients were reported having at least one comorbidity, 14 (4.8%) having more than one comorbidity. Patients with any comorbidity were older (P<0.001), had longer incubation period (P<0.001), were more likely to have abnormal laboratory findings (P<0.05) and be in severity status (P<0.001). More lesions (including larger volume of lesion, consolidation and ground-glass opacity) were shown in patients with any comorbidity than patients without comorbidity (all P<0.001). The more comorbidities patients have, the poorer CT manifestation is. The median volume of lesion, consolidation and ground-glass opacity in diabetes mellitus group was largest among the three prevalently single comorbidity groups.Conclusions: Multi-task Unet network can make quantitative CT analysis of lesions to assess the effect of comorbidity on patients with COVID-19, further to provide the radiological evidence of the severity of pneumonia. More lesions were found in CT images of cases with comorbidity. The more comorbidities patients have, the poorer CT manifestation is.

scholarly journals Development a quantitative segmentation model to assess the effect of comorbidity on patients with COVID-19

2020 ◽  
Author(s):  
Cui Zhang ◽  
Guangzhao Yang ◽  
Chunxian Cai ◽  
Zhihua Xu ◽  
Hai Wu ◽  
...  
Keyword(s):  
Automatic Segmentation ◽  
Ground Glass Opacity ◽  
Ct Images ◽  
Community Acquired Pneumonia ◽  
Ground Glass ◽  
Quantitative Ct ◽  
Laboratory Findings ◽  
Diabetes Mellitus Group ◽  
Test Dataset ◽  
Radiological Evidence

Abstract Background: The coronavirus disease 2019 (COVID-19) has brought a global disaster. Quantitative lesions may provide the radiological evidence of the severity of pneumonia and further to assess the effect of comorbidity on patients with COVID-19.Methods: 294 patients with COVID-19 were enrolled from February, 24, 2020 to June, 1, 2020 from six centers. Multi-task Unet network was used to segment the whole lung and lesions from chest CT images. This deep learning method was pre-trained in 650 CT images (550 in primary dataset and 100 in test dataset) with COVID-19 or community acquired pneumonia and Dice coefficients in test dataset were calculated. 50 CT scans of 50 patients (15 with comorbidity and 35 without comorbidity) were random selected to mark lesions manually. The results will be compared with the automatic segmentation model. Eight quantitative parameters were calculated based on the segmentation results to evaluate the effect of comorbidity on patients with COVID-19.Results: Quantitative segmentation model was proved to be effective and accurate with all Dice coefficients more than 0.85 and all accuracies more than 0.95. Of the 294 patients, 52 (17.7%) patients were reported having at least one comorbidity, 14 (4.8%) having more than one comorbidity. Patients with any comorbidity were older (P<0.001), had longer incubation period (P<0.001), were more likely to have abnormal laboratory findings (P<0.05) and be in severity status (P<0.001). More lesions (including larger volume of lesion, consolidation and ground-glass opacity) were shown in patients with any comorbidity than patients without comorbidity (all P<0.001). More lesions were found on CT images in patients with more comorbidities. The median volumes of lesion, consolidation and ground-glass opacity in diabetes mellitus group were largest among the groups with single comorbidity that had the incidence rate of top three.Conclusions: Multi-task Unet network can make quantitative CT analysis of lesions to assess the effect of comorbidity on patients with COVID-19, further to provide the radiological evidence of the severity of pneumonia. More lesions (including GGO and consolidation) were found in CT images of cases with comorbidity. The more comorbidities patients have, the more lesions CT images show.

scholarly journals Spectrum of Chest Computerised Tomography (CT) findings of Covid 19 in Indian Population

2021 ◽  
Vol 9 (1) ◽  
pp. 43-49
Author(s):  
Shravya Boini ◽  
Vikas Chennamaneni ◽  
Vamshi Kiran Diddy ◽  
Momin Sayed Kashif
Keyword(s):  
Lower Lobe ◽  
Ground Glass Opacity ◽  
Ct Images ◽  
Chest Ct ◽  
Ct Scans ◽  
Ground Glass ◽  
Study Cohort ◽  
Study Population ◽  
Ct Features

Background: To analyze the chest computed tomography (CT) features in patients with coronavirus disease 2019 (COVID-19) pneumonia. Methods: This was a prospective descriptive study comprising 202 consecutive reverse transcriptase polymerase chain reaction (RT-PCR) positive patients who underwent CT chest. For 25 patients, follow-up CT scans were obtained. The CT images were evaluated for the number, type and distribution of the opacity, and CT severity scoring was done Results: Among the total study cohort of 202 patients, 152 were males and 50 were females .From July 07, 2020, to september07, 2020, totally 202 laboratory-confirmed patients with COVID-19 underwent chest CT. For 25 patients, follow-up CT scans were obtained. The CT images were evaluated for the number, type and distribution of the opacity, and the affected lung lobes. Furthermore, the initial CT scan and the follow-up CT scans were compared. Results were patients (98.5%) had two or more opacities in the lung and 3 (1.5%) patients has negative chest CT. 183 (90.6%) patients had only ground-glass opacities; 13 patients (6.4%) had ground-glass and consolidative opacities; and 3 patients (1.5%) had only consolidation. A total 192 of patients (96.5%) showed two or more lobes involved. The opacities tended to be both in peripheral and central 7 (3.5%) or purely peripheral distribution 192 (96.5%). 177 patients (88.9%) had the lower lobe involved.8 patients showed complete resolution of lung findings. Conclusion: In this study population, the typical CT features of COVID 19 pneumonia are ground glass opacity with or without consolidation, which is patchy and peripheral, predominantly in lower lobes.

scholarly journals Development of a quantitative segmentation model to assess the effect of comorbidity on patients with COVID-19

2020 ◽  
Vol 25 (1) ◽  
Author(s):  
Cui Zhang ◽  
Guangzhao Yang ◽  
Chunxian Cai ◽  
Zhihua Xu ◽  
Hai Wu ◽  
...  
Keyword(s):  
Automatic Segmentation ◽  
Ground Glass Opacity ◽  
Ct Images ◽  
Community Acquired Pneumonia ◽  
Ground Glass ◽  
Quantitative Ct ◽  
Laboratory Findings ◽  
Diabetes Mellitus Group ◽  
Test Dataset ◽  
Radiological Evidence

Abstract Background The coronavirus disease 2019 (COVID-19) has brought a global disaster. Quantitative lesions may provide the radiological evidence of the severity of pneumonia and further to assess the effect of comorbidity on patients with COVID-19. Methods 294 patients with COVID-19 were enrolled from February, 24, 2020 to June, 1, 2020 from six centers. Multi-task Unet network was used to segment the whole lung and lesions from chest CT images. This deep learning method was pre-trained in 650 CT images (550 in primary dataset and 100 in test dataset) with COVID-19 or community-acquired pneumonia and Dice coefficients in test dataset were calculated. 50 CT scans of 50 patients (15 with comorbidity and 35 without comorbidity) were random selected to mark lesions manually. The results will be compared with the automatic segmentation model. Eight quantitative parameters were calculated based on the segmentation results to evaluate the effect of comorbidity on patients with COVID-19. Results Quantitative segmentation model was proved to be effective and accurate with all Dice coefficients more than 0.85 and all accuracies more than 0.95. Of the 294 patients, 52 (17.7%) patients were reported having at least one comorbidity; 14 (4.8%) having more than one comorbidity. Patients with any comorbidity were older (P < 0.001), had longer incubation period (P < 0.001), were more likely to have abnormal laboratory findings (P < 0.05), and be in severity status (P < 0.001). More lesions (including larger volume of lesion, consolidation, and ground-glass opacity) were shown in patients with any comorbidity than patients without comorbidity (all P < 0.001). More lesions were found on CT images in patients with more comorbidities. The median volumes of lesion, consolidation, and ground-glass opacity in diabetes mellitus group were largest among the groups with single comorbidity that had the incidence rate of top three. Conclusions Multi-task Unet network can make quantitative CT analysis of lesions to assess the effect of comorbidity on patients with COVID-19, further to provide the radiological evidence of the severity of pneumonia. More lesions (including GGO and consolidation) were found in CT images of cases with comorbidity. The more comorbidities patients have, the more lesions CT images show.

scholarly journals Clinical and epidemiological characteristics of children with community-acquired mycoplasma pneumonia in Nanjing

2019 ◽  
Author(s):  
Changdi Xu ◽  
Xiao Ma ◽  
Fengxia Zhang ◽  
Ying Bi ◽  
Qiangquan Rong ◽  
...  
Keyword(s):  
Pathogen Detection ◽  
Laboratory Data ◽  
Clinical Manifestations ◽  
Community Acquired Pneumonia ◽  
Hospitalized Children ◽  
Chain Reaction ◽  
Significant Difference ◽  
Mycoplasma Pneumonia ◽  
Polymerase Chain ◽  
Epidemiological Characteristics

Abstract Background Mycoplasma pneumoniae is a ubiquitous pathogen, causing various manifestations of community-acquired pneumonia (CAP). This study aimed to update the epidemiology and clinical manifestations of community-acquired mycoplasma pneumonia (CAMP) in hospitalized children in Nanjing and to investigate the association of age, sex, and season of onset with the prognosis of CAMP. Methods The clinical data of children <18 years old, hospitalized for CAP in 2016, were collected and reviewed. Blood and nasopharyngeal aspirates were obtained for pathogen detection, including cultivation, immunofluorescence, and polymerase chain reaction and acid tests. Demographic, clinical, radiographic, and laboratory data were analyzed using SPSS version 21.0 software. Results Of 3377 eligible children with radiographic confirmation of pneumonia, 1249 (36.99%) had M. pneumoniae infection. Although most children (614, 49.16%) with M. pneumoniae infection were ≤3 years old, CAMP occurred mostly in those aged 5-10 years (70.23%). The peak incidence was recorded between July and September (49.05%). Children aged 5-10 years had significantly longer hospitalization and more frequent atelectasis. No significant difference in CAMP was found between the sexes. Conclusions M. pneumoniae remains one of the leading pathogens in pediatric CAP .Particular care is necessary for children older than 5 years and during the peak periods of disease.

scholarly journals Clinical manifestations, CT features and outcome of 39 patients with COVID-19: retrospective case analysis

2020 ◽  
Author(s):  
Haohao Lu ◽  
Chuansheng Zheng ◽  
Qiaoxia Tong ◽  
Jin Tian
Keyword(s):  
Early Stage ◽  
Clinical Manifestations ◽  
Ground Glass Opacity ◽  
Ground Glass ◽  
Rt Pcr ◽  
Retrospective Case ◽  
Recovery Stage ◽  
Discharged Patients ◽  
D Dimer ◽  
Pulmonary Ct

Abstract 1. BackgroundTo explore the manifestations and evolution of the pulmonary CT in COVID-19, and to analyze the causes and countermeasures of “Recurrent positive” in discharged patients.2. MethodsData of 39 patients with COVID-19 were collected. RT-PCR was positive at admission.From onset to discharge, pulmonary CT was performed regularly.During the treatment,Blood-RT,CRP and D-dimer were detected.3.ResultFrom the onset to 14 days, the lesions in pulmonary CT increased significantly.After treatment, pulmonary CT before discharge showed that some patients' lesions were completely absorbed, and some residual strip like lesions or ground glass opacity with reduced density.Two weeks after discharge, there were 2 patients with new ground glass opacity.There were 20 patients with D-dimer increased.4.ConclusionIn the early stage of COVID-19, the pulmonary CT has the characteristic manifestations, which is helpful for early diagnosis.In the middle stage, pulmonary lesions changed rapidly.In the recovery stage, some of the patients remained strip like lesions.It is necessary to pay attention to the possibility of pulmonary fibrosis after recovery.The discharge standard of COVID-19 needs to be more strict to avoid “Recurrent positive”,the discharged patients should continue to be observed.D-dimer was increased in some patients, it is safe to use heparin in anticoagulation without contraindications.

scholarly journals Computing Infection Distributions and Longitudinal Evolution Patterns in Lung CT Images

2020 ◽  
Author(s):  
Dongdong Gu ◽  
Liyun Chen ◽  
Fei Shan ◽  
Liming Xia ◽  
Jun Liu ◽  
...  
Keyword(s):  
Time Course ◽  
Lower Lobe ◽  
Ground Truth ◽  
Distribution Patterns ◽  
Lung Infection ◽  
Ct Images ◽  
Community Acquired Pneumonia ◽  
Disease Course ◽  
Distribution Maps ◽  
Significant Difference

Abstract Background: Spatial and temporal lung infection distributions of coronavirus disease 2019 (COVID-19) and their changes could reveal important patterns to better understand the disease and its time course. This paper presents a pipeline to analyze statistically these patterns by automatically segmenting the infection regions and registering them onto a common template. Methods: A VB-Net is designed to automatically segment infection regions in CT images. After training and validating the model, we segmented all the CT images in the study. The segmentation results are then warped onto a pre-defined template CT image using deformable registration based on registering CT images within the lung fields. Then, the spatial distributions of infection regions and those during the course of the disease are calculated at the voxel level. Visualization and quantitative comparison can be performed between different groups. As a result, we compared the distribution maps between COVID-19 and community acquired pneumonia (CAP), between severe and critical COVID-19, and across different course of the disease. Results: For the performance of infection segmentation, comparing the segmentation results with manually annotated ground truth, the average Dice is 91.6%±10.0%, which is close to the inter-rater difference between two radiologists (the Dice is 96.1%±3.5%). The distribution map of infection regions shows that high probability regions are in the peripheral subpleural (up to 35.1% in probability). COVID-19 GGO lesions are more widely spread than consolidations, and the latter are located more peripherally. Onset images of severe COVID-19 (inpatients) show similar lesion distributions but with smaller areas of significant difference in the right lower lobe compared to critical COVID-19 (intensive care unit patients). About the disease course, critical COVID-19 patients showed four distinct patterns (progression, absorption, enlargement, and further absorption) with remarkable concurrent HU patterns for GGO and consolidations.Conclusions: By segmenting the infection regions with a VB-Net and registering all the CT images and the segmentation results onto a template, spatial distribution patterns of infections can be computed automatically. The algorithm provides an effective tool to visualize and quantify the spatial patterns of lung infection diseases and their changes during the disease course. Our results demonstrate different patterns between COVID-19 and CAP, between severe and critical COVID-19, as well as four distinct disease course patterns of the severe COVID-19 patients studied, with remarkable concurrent HU patterns for GGO and consolidations.

scholarly journals Early Chest CT Features of Patients with 2019 Novel Coronavirus (COVID-19) Pneumonia: Relationship to Diagnosis and Prognosis

2020 ◽  
Author(s):  
Hui Juan Chen ◽  
Jie Qiu ◽  
Biao Wu ◽  
Tao Huang ◽  
Yunsuo Gao ◽  
...  
Keyword(s):  
Early Stage ◽  
Clinical Manifestations ◽  
Ground Glass Opacity ◽  
Chest Ct ◽  
Ground Glass ◽  
Rt Pcr ◽  
Icu Patients ◽  
Clinical Prognosis ◽  
Discharged Patients ◽  
Ct Features

Abstract Objective: To elucidate the consistency between CT findings and real time reverse-transcription–polymerase chain- reaction (RT-PCR) results and investigate the relationship between CT features and clinical prognosis in COVID-19.Methods: The clinical manifestations, laboratory parameters and CT imaging findings were analyzed in thirty-four patients with COVID-19 confirmed by RT-PCR from January 20 to February 4 in Hainan province. CT score was compared between the discharged patients and ICU patients.Results: Fever (85%) and cough (79%) were most commonly seen. 10 (29%) patients demonstrated negative results on their first RT-PCR.22/34(65%) patients showed pure ground glass opacity (GGO). 17/34 (50%) patients had five lobes of lung involvement, while the 23(68%) patients had lower lobes were involved and 24/34 (71%) were subpleural. Lesions of 24 (71%) patients were distributed mainly in the subpleural. During follow-up, the initial CT lesions of ICU patients are distributed in both subpleural and parenchyma (80%) and the lesions are scattered. 60% of ICU patients had five lobes involved, while this was seen in only 25% discharged patients. Lesions of discharged patients are mainly in the subpleural (75%). 62.5% of discharged patients showed pure ground-glass opacity. 80% ICU demonstrated progressive stage on their first CT scan. 75 % discharged patients were at an early stage. CT score of ICU patients were significantly higher than that of the discharged patients.Conclusion: Chest CT plays a crucial role in the early diagnosis of COVID-19, particularly for those patients with negative RT-PCR. The initial features in CT may be associated with prognosis.Authors Hui Juan Chen and Jie Qiu contributed equally to this work.

scholarly journals Chest computed tomography imaging features in patients with coronavirus disease 2019 (COVID-19)

2021 ◽  
Vol 49 (5) ◽  
pp. 030006052110106
Author(s):  
Hoda Salah Darwish ◽  
Mohamed Yasser Habash ◽  
Waleed Yasser Habash
Keyword(s):  
Computed Tomography ◽  
Intermediate Phase ◽  
Early Stage ◽  
Lower Lobe ◽  
Ground Glass Opacity ◽  
Intermediate Stage ◽  
Mediastinal Lymphadenopathy ◽  
Imaging Features ◽  
Stage Disease ◽  
Bilateral Lesions

Objective To analyze computed tomography (CT) features of symptomatic patients with coronavirus disease 2019 (COVID-19). Methods Ninety-five symptomatic patients with COVID-19 confirmed by reverse-transcription polymerase chain reaction from 1 May to 14 July 2020 were retrospectively enrolled. Follow-up CT findings and their distributions were analyzed and compared from symptom onset to late-stage disease. Results Among all patients, 15.8% had unilateral lung disease and 84.2% had bilateral disease with slight right lower lobe predilection (47.4%). Regarding lesion density, 49.4% of patients had pure ground glass opacity (GGO) and 50.5% had GGO with consolidation. Typical early-stage patterns were bilateral lesions in 73.6% of patients, diffuse lesions (41.0%), and GGO (65.2%). Pleural effusion occurred in 13.6% and mediastinal lymphadenopathy in 11.5%. During intermediate-stage disease, 47.4% of patients showed GGO as the disease progressed; however, consolidation was the predominant finding (52.6%). Conclusion COVID-19 pneumonia manifested on lung CT scans with bilateral, peripheral, and right lower lobe predominance and was characterized by diffuse bilateral GGO progressing to or coexisting with consolidation within 1 to 3 weeks. The most frequent CT lesion in the early, intermediate, and late phases was GGO. Consolidation appeared in the intermediate phase and gradually increased, ending with reticular and lung fibrosis-like patterns.

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