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1.
Eur J Radiol ; 150: 110259, 2022 May.
Article in English | MEDLINE | ID: covidwho-1748029

ABSTRACT

PURPOSE: It is known from histology studies that lung vessels are affected in viral pneumonia. However, their diagnostic potential as a chest CT imaging parameter has only rarely been exploited. The purpose of this study is to develop a robust method for automated lung vessel segmentation and morphology analysis and apply it to a large chest CT dataset. METHODS: In total, 509 non-enhanced chest CTs (NECTs) and 563 CT pulmonary angiograms (CTPAs) were included. Sub-groups were patients with healthy lungs (group_NORM, n = 634) and those RT-PCR-positive for Influenza A/B (group_INF, n = 159) and SARS-CoV-2 (group_COV, n = 279). A lung vessel segmentation algorithm (LVSA) based on traditional image processing was developed, validated with a point-of-interest approach, and applied to a large clinical dataset. Total blood vessel volume in lung (TBV) and the blood vessel volume percentage (BV%) of three blood vessel size types were calculated and compared between groups: small (BV5%, cross-sectional area < 5 mm2), medium (BV5-10%, 5-10 mm2) and large (BV10%, >10 mm2). RESULTS: Sensitivity of the LVSA was 84.6% (95 %CI: 73.9-95.3) for NECTs and 92.8% (95 %CI: 90.8-94.7) for CTPAs. In viral pneumonia, besides an increased TBV, the main finding was a significantly decreased BV5% in group_COV (n = 14%) and group_INF (n = 15%) compared to group_NORM (n = 18%) [p < 0.001]. At the same time, BV10% was increased (group_COV n = 15% and group_INF n = 14% vs. group_NORM n = 11%; p < 0.001). CONCLUSION: In COVID-19 and Influenza, the blood vessel volume is redistributed from small to large vessels in the lung. Automated LSVA allows researchers and clinicians to derive imaging parameters for large amounts of CTs. This can enhance the understanding of vascular changes, particularly in infectious lung diseases.


Subject(s)
COVID-19 , Influenza, Human , Pneumonia, Viral , Humans , Influenza, Human/diagnostic imaging , Lung/blood supply , Lung/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Retrospective Studies , SARS-CoV-2
2.
Contrast Media Mol Imaging ; 2022: 8549707, 2022.
Article in English | MEDLINE | ID: covidwho-1741728

ABSTRACT

Coronavirus (COVID-19) is a deadly virus that initially starts with flu-like symptoms. COVID-19 emerged in China and quickly spread around the globe, resulting in the coronavirus epidemic of 2019-22. As this virus is very similar to influenza in its early stages, its accurate detection is challenging. Several techniques for detecting the virus in its early stages are being developed. Deep learning techniques are a handy tool for detecting various diseases. For the classification of COVID-19 and influenza, we proposed tailored deep learning models. A publicly available dataset of X-ray images was used to develop proposed models. According to test results, deep learning models can accurately diagnose normal, influenza, and COVID-19 cases. Our proposed long short-term memory (LSTM) technique outperformed the CNN model in the evaluation phase on chest X-ray images, achieving 98% accuracy.


Subject(s)
COVID-19 , Deep Learning , Influenza, Human , SARS-CoV-2 , Tomography, X-Ray Computed , COVID-19/classification , COVID-19/diagnostic imaging , Female , Humans , Influenza, Human/classification , Influenza, Human/diagnostic imaging , Male
3.
J Med Virol ; 93(12): 6619-6627, 2021 Dec.
Article in English | MEDLINE | ID: covidwho-1544307

ABSTRACT

Both severe acute respiratory syndrome coronavirus 2 and influenza viruses cause similar clinical presentations. It is essential to assess severely ill patients presenting with a viral syndrome for diagnostic and prognostic purposes. We aimed to compare clinical and biochemical features between pneumonia patients with coronavirus disease 2019 (COVID-19) and H1N1. Sixty patients diagnosed with COVID-19 pneumonia and 61 patients diagnosed with influenza pneumonia were hospitalized between October 2020-January 2021 and October 2017-December 2019, respectively. All the clinical data and laboratory results, chest computed tomography scans, intensive care unit admission, invasive mechanical ventilation, and outcomes were retrospectively evaluated. The median age was 65 (range 32-96) years for patients with a COVID-19 diagnosis and 58 (range 18-83) years for patients with influenza (p = 0.002). The comorbidity index was significantly higher in patients with COVID-19 (p = 0.010). Diabetes mellitus and hypertension were statistically significantly more common in patients with COVID-19 (p = 0.019, p = 0.008, respectively). The distribution of severe disease and mortality was not significantly different among patients with COVID-19 than influenza patients (p = 0.096, p = 0.049).). In comparison with inflammation markers; C-reactive protein (CRP) levels were significantly higher in influenza patients than patients with COVID-19 (p = 0.033). The presence of sputum was predictive for influenza (odds ratio [OR] 0.342 [95% confidence interval [CI], 2.1.130-0.899]). CRP and platelet were also predictive for COVID-19 (OR 4.764 [95% CI, 1.003-1.012] and OR 0.991 [95% CI 0.984-0.998], respectively. We conclude that sputum symptoms by itself are much more detected in influenza patients. Besides that, lower CRP and higher PLT count would be discriminative for COVID-19.


Subject(s)
COVID-19/pathology , Influenza, Human/pathology , Adolescent , Adult , Aged , Aged, 80 and over , C-Reactive Protein/analysis , COVID-19/diagnostic imaging , COVID-19/therapy , Female , Hospitalization , Humans , Influenza A Virus, H1N1 Subtype , Influenza, Human/diagnostic imaging , Influenza, Human/therapy , Intensive Care Units/statistics & numerical data , Length of Stay/statistics & numerical data , Male , Middle Aged , Radiography, Thoracic , Respiration, Artificial/statistics & numerical data , Retrospective Studies , Tomography, X-Ray Computed , Young Adult
4.
Clin Neurol Neurosurg ; 210: 106956, 2021 11.
Article in English | MEDLINE | ID: covidwho-1525730

ABSTRACT

Influenza virus-associated encephalopathy/encephalitis is a rare entity in adults that can lead to severe neurological sequelae and even death. The clinical presentation can be quite diverse. This absence of a typical presentation along with the difficulty detecting the virus in the cerebrospinal fluid represents a diagnostic challenge. We present the case of a 79-year-old male with sudden onset of decreased consciousness and signs of right hemisphere damage. The presence of influenza A (H3N2) virus in respiratory sample along with compatible findings in cranial magnetic resonance led to the diagnosis. The patient died without responding to treatment with antivirals and immunomodulators and the anatomopathological study did not detect infectious agent. Early diagnostic suspicion is essential to establish adequate treatment and improve the prognosis.


Subject(s)
Cerebral Cortex/diagnostic imaging , Encephalitis, Viral/diagnostic imaging , Influenza A Virus, H3N2 Subtype/isolation & purification , Influenza, Human/diagnostic imaging , Aged , Cerebral Cortex/virology , Humans , Magnetic Resonance Imaging , Male
5.
Iran J Med Sci ; 46(6): 420-427, 2021 Nov.
Article in English | MEDLINE | ID: covidwho-1513426

ABSTRACT

BACKGROUND: Chest computed tomography (CT) plays an essential role in diagnosing coronavirus disease 2019 (COVID-19). However, CT findings are often nonspecific among different viral pneumonia conditions. The differentiation between COVID-19 and influenza can be challenging when seasonal influenza concurs with the COVID-19 pandemic. This study was conducted to test the ability of radiomics-artificial intelligence (AI) to perform this task. METHODS: In this retrospective study, chest CT images from 47 patients with COVID-19 (after February 2020) and 19 patients with H1N1 influenza (before September 2019) pneumonia were collected from three hospitals affiliated with Arak University of Medical Sciences, Arak, Iran. All pulmonary lesions were segmented on CT images. Multiple radiomics features were extracted from the lesions and used to develop support-vector machine (SVM), k-nearest neighbor (k-NN), decision tree, neural network, adaptive boosting (AdaBoost), and random forest. RESULTS: The patients with COVID-19 and H1N1 influenza were not significantly different in age and sex (P=0.13 and 0.99, respectively). Nonetheless, the average time between initial symptoms/hospitalization and chest CT was shorter in the patients with COVID-19 (P=0.001 and 0.01, respectively). After the implementation of the inclusion and exclusion criteria, 453 pulmonary lesions were included in this study. On the harmonized features, random forest yielded the highest performance (area under the curve=0.97, sensitivity=89%, precision=90%, F1 score=89%, and classification accuracy=89%). CONCLUSION: In our preliminary study, radiomics feature extraction, conjoined with AI, especially random forest and neural network, appeared to yield very promising results in the differentiation between COVID-19 and H1N1 influenza on chest CT.


Subject(s)
Artificial Intelligence , COVID-19 , Influenza A Virus, H1N1 Subtype , Influenza, Human , Pneumonia, Viral , COVID-19/diagnostic imaging , Diagnosis, Differential , Feasibility Studies , Female , Humans , Influenza, Human/diagnostic imaging , Male , Pneumonia, Viral/diagnostic imaging , Retrospective Studies , Tomography, X-Ray Computed
6.
J Clin Lab Anal ; 35(12): e24100, 2021 Dec.
Article in English | MEDLINE | ID: covidwho-1508785

ABSTRACT

OBJECTIVES: This study aimed to explore clinical indexes for management of severe/critically ill patients with COVID-19, influenza A H7N9, and H1N1 pneumonia by comparing hematological and radiological characteristics. METHODS: Severe/critically ill patients with COVID-19, H7N9, and H1N1 pneumonia were retrospectively enrolled. The demographic data, clinical manifestations, hematological parameters, and radiological characteristics were compared. RESULTS: In this study, 16 cases of COVID-19, 10 cases of H7N9, and 13 cases of H1N1 who met severe/critically ill criteria were included. Compared with COVID-19, H7N9 and H1N1 groups had more chronic diseases (80% and 92.3% vs. 25%, p < 0.05), higher APACHE Ⅱ scores (16.00 ± 8.63 and 15.08 ± 6.24, vs. 5.50 ± 2.58, p < 0.05), higher mortality rates (40% and 46.2% vs. 0%, p < 0.05), significant lymphocytopenia (0.59 ± 0.31 × 109 /L and 0.56 ± 0.35 × 109 /L vs. 0.97 ± 0.33 × 109 /L, p < 0.05), and elevated neutrophil-to-lymphocyte ratio (NLR; 14.67 ± 6.10 and 14.64 ± 10.36 vs. 6.29 ± 3.72, p < 0.05). Compared with the H7N9 group, ground-glass opacity (GGO) on chest CT was common in the COVID-19 group (p = 0.028), while pleural effusion was rare (p = 0.001). CONCLUSIONS: The NLR can be used as a clinical parameter for the predication of risk stratification and outcome in COVID-19 and influenza A pneumonia. Manifestations of pleural effusion or GGO in chest CT may be helpful for the identification of different viral pneumonia.


Subject(s)
COVID-19/blood , COVID-19/diagnostic imaging , Influenza, Human/blood , Influenza, Human/diagnostic imaging , Aged , Aged, 80 and over , Blood Cell Count , COVID-19/etiology , Chronic Disease , Critical Illness , Female , Humans , Influenza A Virus, H1N1 Subtype , Influenza A Virus, H7N9 Subtype , Influenza, Human/etiology , Influenza, Human/virology , Male , Middle Aged , Pneumonia, Viral/blood , Pneumonia, Viral/diagnostic imaging , Pneumonia, Viral/mortality , Pneumonia, Viral/virology , Retrospective Studies , Sex Factors
7.
AJR Am J Roentgenol ; 217(5): 1093-1102, 2021 Nov.
Article in English | MEDLINE | ID: covidwho-1484970

ABSTRACT

BACKGROUND. Previous studies compared CT findings of COVID-19 pneumonia with those of other infections; however, to our knowledge, no studies to date have included noninfectious organizing pneumonia (OP) for comparison. OBJECTIVE. The objectives of this study were to compare chest CT features of COVID-19, influenza, and OP using a multireader design and to assess the performance of radiologists in distinguishing between these conditions. METHODS. This retrospective study included 150 chest CT examinations in 150 patients (mean [± SD] age, 58 ± 16 years) with a diagnosis of COVID-19, influenza, or non-infectious OP (50 randomly selected abnormal CT examinations per diagnosis). Six thoracic radiologists independently assessed CT examinations for 14 individual CT findings and for Radiological Society of North America (RSNA) COVID-19 category and recorded a favored diagnosis. The CT characteristics of the three diagnoses were compared using random-effects models; the diagnostic performance of the readers was assessed. RESULTS. COVID-19 pneumonia was significantly different (p < .05) from influenza pneumonia for seven of 14 chest CT findings, although it was different (p < .05) from OP for four of 14 findings (central or diffuse distribution was seen in 10% and 7% of COVID-19 cases, respectively, vs 20% and 21% of OP cases, respectively; unilateral distribution was seen in 1% of COVID-19 cases vs 7% of OP cases; non-tree-in-bud nodules was seen in 32% of COVID-19 cases vs 53% of OP cases; tree-in-bud nodules were seen in 6% of COVID-19 cases vs 14% of OP cases). A total of 70% of cases of COVID-19, 33% of influenza cases, and 47% of OP cases had typical findings according to RSNA COVID-19 category assessment (p < .001). The mean percentage of correct favored diagnoses compared with actual diagnoses was 44% for COVID-19, 29% for influenza, and 39% for OP. The mean diagnostic accuracy of favored diagnoses was 70% for COVID-19 pneumonia and 68% for both influenza and OP. CONCLUSION. CT findings of COVID-19 substantially overlap with those of influenza and, to a greater extent, those of OP. The diagnostic accuracy of the radiologists was low in a study sample that contained equal proportions of these three types of pneumonia. CLINICAL IMPACT. Recognized challenges in diagnosing COVID-19 by CT are furthered by the strong overlap observed between the appearances of COVID-19 and OP on CT. This challenge may be particularly evident in clinical settings in which there are substantial proportions of patients with potential causes of OP such as ongoing cancer therapy or autoimmune conditions.


Subject(s)
COVID-19/diagnostic imaging , Cryptogenic Organizing Pneumonia/diagnostic imaging , Influenza, Human/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Tomography, X-Ray Computed , Diagnosis, Differential , Female , Humans , Influenza, Human/virology , Male , Massachusetts , Middle Aged , Observer Variation , Pneumonia, Viral/virology , Radiography, Thoracic , Retrospective Studies , SARS-CoV-2
8.
Acad Radiol ; 28(10): 1331-1338, 2021 10.
Article in English | MEDLINE | ID: covidwho-1225101

ABSTRACT

OBJECTIVES: To investigate the chest CT and clinical characteristics of COVID-19 pneumonia and H1N1 influenza, and explore the radiologist diagnosis differences between COVID-19 and influenza. MATERIALS AND METHODS: This cross-sectional study included a total of 43 COVID-19-confirmed patients (24 men and 19 women, 49.90 ± 18.70 years) and 41 influenza-confirmed patients (17 men and 24 women, 61.53 ± 19.50 years). Afterwards, the chest CT findings were recorded and 3 radiologists recorded their diagnoses of COVID-19 or of H1N1 influenza based on the CT findings. RESULTS: The most frequent clinical symptom in patients with COVID-19 and H1N1 pneumonia were dyspnea (96.6%) and cough (62.5%), respectively. The CT findings showed that the COVID-19 group was characterized by GGO (88.1%), while the influenza group had features such as GGO (68.4%) and consolidation (66.7%). Compared to the influenza group, the COVID-19 group was more likely to have GGO (88.1% vs. 68.4%, p = 0.032), subpleural sparing (69.0% vs. 7.7%, p <0.001) and subpleural band (50.0% vs. 20.5%, p = 0.006), but less likely to have pleural effusion (4.8% vs. 33.3%, p = 0.001). The agreement rate between the 3 radiologists was 65.8%. CONCLUSION: Considering similarities of respiratory infections especially H1N1 and COVID-19, it is essential to introduce some clinical and para clinical modalities to help differentiating them. In our study we extracted some lung CT scan findings from patients suspected to COVID-19 as a newly diagnosed infection comparing with influenza pneumonia patients.


Subject(s)
COVID-19 , Influenza A Virus, H1N1 Subtype , Influenza, Human , Cross-Sectional Studies , Female , Humans , Influenza, Human/diagnostic imaging , Influenza, Human/epidemiology , Lung , Male , Observer Variation , Radiologists , Retrospective Studies , SARS-CoV-2 , Tomography, X-Ray Computed
9.
Sci Rep ; 11(1): 6422, 2021 03 19.
Article in English | MEDLINE | ID: covidwho-1142463

ABSTRACT

Coronavirus disease 2019 (COVID-19) has spread in more than 100 countries and regions around the world, raising grave global concerns. COVID-19 has a similar pattern of infection, clinical symptoms, and chest imaging findings to influenza pneumonia. In this retrospective study, we analysed clinical and chest CT data of 24 patients with COVID-19 and 79 patients with influenza pneumonia. Univariate analysis demonstrated that the temperature, systolic pressure, cough and sputum production could distinguish COVID-19 from influenza pneumonia. The diagnostic sensitivity and specificity for the clinical features are 0.783 and 0.747, and the AUC value is 0.819. Univariate analysis demonstrates that nine CT features, central-peripheral distribution, superior-inferior distribution, anterior-posterior distribution, patches of GGO, GGO nodule, vascular enlargement in GGO, air bronchogram, bronchiectasis within focus, interlobular septal thickening, could distinguish COVID-19 from influenza pneumonia. The diagnostic sensitivity and specificity for the CT features are 0.750 and 0.962, and the AUC value is 0.927. Finally, a multivariate logistic regression model combined the variables from the clinical variables and CT features models was made. The combined model contained six features: systolic blood pressure, sputum production, vascular enlargement in the GGO, GGO nodule, central-peripheral distribution and bronchiectasis within focus. The diagnostic sensitivity and specificity for the combined features are 0.87 and 0.96, and the AUC value is 0.961. In conclusion, some CT features or clinical variables can differentiate COVID-19 from influenza pneumonia. Moreover, CT features combined with clinical variables had higher diagnostic performance.


Subject(s)
COVID-19/diagnosis , Influenza, Human/diagnosis , Pneumonia, Viral/diagnosis , Adult , COVID-19/diagnostic imaging , Diagnosis, Differential , Female , Humans , Influenza, Human/diagnostic imaging , Male , Middle Aged , Pneumonia, Viral/diagnostic imaging , Retrospective Studies , Thorax/diagnostic imaging , Tomography, X-Ray Computed , Young Adult
10.
Viruses ; 13(3)2021 03 10.
Article in English | MEDLINE | ID: covidwho-1124780

ABSTRACT

BACKGROUND: Both SARS-CoV-2 and influenza virus share similarities such as clinical features and outcome, laboratory, and radiological findings. METHODS: Literature search was done using PubMed to find MEDLINE indexed articles relevant to this study. As of 25 November 2020, the search has been conducted by combining the MeSH words "COVID-19" and "Influenza". RESULTS: Eighteen articles were finally selected in adult patients. Comorbidities such as cardiovascular diseases, diabetes, and obesity were significantly higher in COVID-19 patients, while pulmonary diseases and immunocompromised conditions were significantly more common in influenza patients. The incidence rates of fever, vomiting, ocular and otorhinolaryngological symptoms were found to be significantly higher in influenza patients when compared with COVID-19 patients. However, neurologic symptoms and diarrhea were statistically more frequent in COVID-19 patients. The level of white cell count and procalcitonin was significantly higher in influenza patients, whereas thrombopenia and elevated transaminases were significantly more common in COVID-19 patients. Ground-grass opacities, interlobular septal thickening, and a peripheral distribution were more common in COVID-19 patients than in influenza patients where consolidations and linear opacities were described instead. COVID-19 patients were significantly more often transferred to intensive care unit with a higher rate of mortality. CONCLUSIONS: This study estimated differences of COVID-19 and influenza patients which can help clinicians during the co-circulation of the two viruses.


Subject(s)
COVID-19/virology , Influenza, Human/virology , Orthomyxoviridae/physiology , SARS-CoV-2/physiology , Adolescent , Adult , Aged , COVID-19/diagnostic imaging , COVID-19/mortality , Child , Child, Preschool , Female , Humans , Influenza, Human/diagnostic imaging , Influenza, Human/mortality , Male , Middle Aged , Orthomyxoviridae/genetics , SARS-CoV-2/genetics , Young Adult
11.
Front Public Health ; 9: 616963, 2021.
Article in English | MEDLINE | ID: covidwho-1106063

ABSTRACT

Background: This study was to collect clinical features and computed tomography (CT) findings of Influenza-Like Illness (ILI) cases, and to evaluate the correlation between clinical data and the abnormal chest CT in patients with the Influenza-Like Illness symptoms. Methods: Patients with the Influenza-Like Illness symptoms who attended the emergency department of The Six Medical Center of The PLA General Hospital from February 10 to April 1, 2020 were enrolled. Clinical and imaging data of the enrolled patients were collected and analyzed. The association between clinical characteristics and abnormal chest CT was also analyzed. Results: A total of 148 cases were enrolled in this study. Abnormalities on chest CT were detected in 61/148 (41.2%) patients. The most common abnormal CT features were as follows: patchy consolidation 22/61(36.1%), ground-glass opacities 21/61(34.4%), multifocal consolidations 17/61(27.9%). The advanced age and underlying diseases were significantly associated with abnormal chest CT. Conclusions: Abnormal chest CT is a common condition in Influenza-Like Illness cases. The presence of advanced age and concurrent underlying diseases is significantly associated with abnormal chest CT findings in patients with ILI symptoms. The chest CT characteristic of ILI is different from the manifestation of COVID-19 infection, which is helpful for differential diagnosis.


Subject(s)
COVID-19 , Diagnosis, Differential , Influenza, Human/diagnostic imaging , Tomography, X-Ray Computed , Adult , Aged , Aged, 80 and over , China , Female , Humans , Image Interpretation, Computer-Assisted , Influenza, Human/physiopathology , Male , Middle Aged , Multivariate Analysis , Radiography, Thoracic , Retrospective Studies , SARS-CoV-2
12.
Int J Biol Sci ; 17(2): 539-548, 2021.
Article in English | MEDLINE | ID: covidwho-1090199

ABSTRACT

Rationale: Coronavirus disease 2019 (COVID-19) has caused a global pandemic. A classifier combining chest X-ray (CXR) with clinical features may serve as a rapid screening approach. Methods: The study included 512 patients with COVID-19 and 106 with influenza A/B pneumonia. A deep neural network (DNN) was applied, and deep features derived from CXR and clinical findings formed fused features for diagnosis prediction. Results: The clinical features of COVID-19 and influenza showed different patterns. Patients with COVID-19 experienced less fever, more diarrhea, and more salient hypercoagulability. Classifiers constructed using the clinical features or CXR had an area under the receiver operating curve (AUC) of 0.909 and 0.919, respectively. The diagnostic efficacy of the classifier combining the clinical features and CXR was dramatically improved and the AUC was 0.952 with 91.5% sensitivity and 81.2% specificity. Moreover, combined classifier was functional in both severe and non-serve COVID-19, with an AUC of 0.971 with 96.9% sensitivity in non-severe cases, which was on par with the computed tomography (CT)-based classifier, but had relatively inferior efficacy in severe cases compared to CT. In extension, we performed a reader study involving three experienced pulmonary physicians, artificial intelligence (AI) system demonstrated superiority in turn-around time and diagnostic accuracy compared with experienced pulmonary physicians. Conclusions: The classifier constructed using clinical and CXR features is efficient, economical, and radiation safe for distinguishing COVID-19 from influenza A/B pneumonia, serving as an ideal rapid screening tool during the COVID-19 pandemic.


Subject(s)
COVID-19 Testing/methods , COVID-19/diagnostic imaging , Influenza, Human/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Radiography, Thoracic , Aged , COVID-19/epidemiology , COVID-19/physiopathology , COVID-19/virology , Deep Learning , Diagnosis, Differential , Humans , Influenza A virus/isolation & purification , Influenza B virus/isolation & purification , Influenza, Human/physiopathology , Influenza, Human/virology , Male , Middle Aged , Pandemics , Pneumonia , Pneumonia, Viral/physiopathology , Pneumonia, Viral/virology , ROC Curve , Retrospective Studies , SARS-CoV-2/isolation & purification , Sensitivity and Specificity
13.
BMC Med Imaging ; 21(1): 31, 2021 02 17.
Article in English | MEDLINE | ID: covidwho-1088584

ABSTRACT

BACKGROUND: In this COVID-19 pandemic, the differential diagnosis of viral pneumonia is still challenging. We aimed to assess the classification performance of computed tomography (CT)-based CT signs and radiomics features for discriminating COVID-19 and influenza pneumonia. METHODS: A total of 154 patients with confirmed viral pneumonia (COVID-19: 89 cases, influenza pneumonia: 65 cases) were collected retrospectively in this study. Pneumonia signs and radiomics features were extracted from the initial unenhanced chest CT images to build independent and combined models. The predictive performance of the radiomics model, CT sign model, the combined model was constructed based on the whole dataset and internally invalidated by using 1000-times bootstrap. Diagnostic performance of the models was assessed via receiver operating characteristic (ROC) analysis. RESULTS: The combined models consisted of 4 significant CT signs and 7 selected features and demonstrated better discrimination performance between COVID-19 and influenza pneumonia than the single radiomics model. For the radiomics model, the area under the ROC curve (AUC) was 0.888 (sensitivity, 86.5%; specificity, 78.4%; accuracy, 83.1%), and the AUC was 0.906 (sensitivity, 86.5%; specificity, 81.5%; accuracy, 84.4%) in the CT signs model. After combining CT signs and radiomics features, AUC of the combined model was 0.959 (sensitivity, 89.9%; specificity, 90.7%; accuracy, 90.3%). CONCLUSIONS: CT-based radiomics combined with signs might be a potential method for distinguishing COVID-19 and influenza pneumonia with satisfactory performance.


Subject(s)
COVID-19/diagnostic imaging , Influenza, Human/diagnostic imaging , Pneumonia, Viral/etiology , Radiographic Image Interpretation, Computer-Assisted/methods , Tomography, X-Ray Computed/methods , Adult , Area Under Curve , Diagnosis, Differential , Female , Humans , Male , Middle Aged , Models, Theoretical , Pneumonia, Viral/diagnostic imaging , Predictive Value of Tests , Retrospective Studies
14.
Eur Rev Med Pharmacol Sci ; 25(2): 1135-1145, 2021 Jan.
Article in English | MEDLINE | ID: covidwho-1082411

ABSTRACT

OBJECTIVE: To explore the different clinical and CT features distinguishing COVID-19 from H1N1 influenza pneumonia. PATIENTS AND METHODS: We compared two independent cohorts of COVID-19 pneumonia (n=405) and H1N1 influenza pneumonia (n=78), retrospectively. All patients were confirmed by RT-PCR. Four hundred and five cases of COVID-19 pneumonia were confirmed in nine hospitals of Zhejiang province, China from January 21 to February 20, 2020. Seventy-eight cases of H1N1 influenza pneumonia were confirmed in our hospital from January 1, 2017 to February 29, 2020. Their clinical manifestations, laboratory test results, and CT imaging characteristics were compared. RESULTS: COVID-19 pneumonia patients showed less proportions of underlying diseases, fever and respiratory symptoms than those of H1N1 pneumonia patients (p<0.01). White blood cell count, neutrophilic granulocyte percentage, C-reactive protein, procalcitonin, D-Dimer, and lactate dehydrogenase in H1N1 pneumonia patients were higher than those of COVID-19 pneumonia patients (p<0.05). H1N1 pneumonia was often symmetrically located in the dorsal part of inferior lung lobes, while COVID-19 pneumonia was unusually showed as a peripheral but non-specific lobe distribution. Ground glass opacity was more common in COVID-19 pneumonia and consolidation lesions were more common in H1N1 pneumonia (p<0.01). COVID-19 pneumonia lesions showed a relatively clear margin compared with H1N1 pneumonia. Crazy-paving pattern, thickening vessels, reversed halo sign and early fibrotic lesions were more common in COVID-19 pneumonia than H1N1 pneumonia (p<0.05). Pleural effusion in COVID-19 pneumonia was significantly less common than H1N1 pneumonia (p<0.01). CONCLUSIONS: Compared with H1N1 pneumonia in Zhejiang, China, the clinical manifestations of COVID-19 pneumonia were more concealed with less underlying diseases and slighter respiratory symptoms. The more common CT manifestations of COVID-19 pneumonia included ground-glass opacity with a relatively clear margin, crazy-paving pattern, thickening vessels, reversed halo sign, and early fibrotic lesions, while the less common CT manifestations of COVID-19 pneumonia included consolidation and pleural effusion.


Subject(s)
COVID-19/diagnostic imaging , COVID-19/epidemiology , Influenza A Virus, H1N1 Subtype/isolation & purification , Influenza, Human/diagnostic imaging , Influenza, Human/epidemiology , Tomography, X-Ray Computed/methods , Adult , Aged , Case-Control Studies , China/epidemiology , Cohort Studies , Female , Humans , Male , Middle Aged , Retrospective Studies
15.
BMC Infect Dis ; 21(1): 68, 2021 Jan 13.
Article in English | MEDLINE | ID: covidwho-1067191

ABSTRACT

BACKGROUND: Coronavirus disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel coronavirus that was first discovered in December 2019 in Wuhan, China. With the growing numbers of community spread cases worldwide, the World Health Organization (WHO) declared the COVID-19 outbreak as a pandemic on March 11, 2020. Like influenza viruses, SARS-CoV-2 is thought to be mainly transmitted by droplets and direct contact, and COVID-19 has a similar disease presentation to influenza. Here we present a case of influenza A and COVID-19 co-infection in a 60-year-old man with end-stage renal disease (ESRD) on hemodialysis. CASE PRESENTATION: A 60-year-old man with ESRD on hemodialysis presented for worsening cough, shortness of breath, and diarrhea. The patient first developed a mild fever (37.8 °C) during hemodialysis 3 days prior to presentation and has been experiencing worsening flu-like symptoms, including fever of up to 38.6 °C, non-productive cough, generalized abdominal pain, nausea, vomiting, and liquid green diarrhea. He lives alone at home with no known sick contacts and denies any recent travel or visits to healthcare facilities other than the local dialysis center. Rapid flu test was positive for influenza A. Procalcitonin was elevated at 5.21 ng/mL with a normal white blood cell (WBC) count. Computed tomography (CT) chest demonstrated multifocal areas of consolidation and extensive mediastinal and hilar adenopathy concerning for pneumonia. He was admitted to the biocontainment unit of Nebraska Medicine for concerns of possible COVID-19 and was started on oseltamivir for influenza and vancomycin/cefepime for the probable bacterial cause of his pneumonia and diarrhea. Gastrointestinal (GI) pathogen panel and Clostridioides difficile toxin assay were negative. On the second day of admission, initial nasopharyngeal swab came back positive for SARS-CoV-2 by real-time reverse-transcriptase polymerase chain reaction (RT-PCR). The patient received supportive care and resumed bedside hemodialysis in strict isolation, and eventually fully recovered from COVID-19. CONCLUSIONS: We presented a case of co-infection of influenza and SARS-CoV-2 in a hemodialysis patient. The possibility of SARS-CoV-2 co-infection should not be overlooked even when other viruses including influenza can explain the clinical symptoms, especially in high-risk patients.


Subject(s)
COVID-19/diagnosis , Influenza, Human/diagnosis , COVID-19/diagnostic imaging , COVID-19/virology , Coinfection/diagnosis , Coinfection/diagnostic imaging , Coinfection/virology , Hospitalization , Humans , Influenza A virus/genetics , Influenza A virus/isolation & purification , Influenza A virus/physiology , Influenza, Human/diagnostic imaging , Influenza, Human/virology , Kidney Failure, Chronic/therapy , Male , Middle Aged , Pandemics , Renal Dialysis , SARS-CoV-2/genetics , SARS-CoV-2/isolation & purification , SARS-CoV-2/physiology , Tomography, X-Ray Computed
16.
Ann Palliat Med ; 10(1): 560-571, 2021 Jan.
Article in English | MEDLINE | ID: covidwho-1063565

ABSTRACT

BACKGROUND: Multicenter retrospective comparison of the first high-resolution computed tomography (HRCT) findings of coronavirus disease 2019 (COVID-19) and other viral pneumonias. METHODS: We retrospectively collected clinical and imaging data from 262 cases of confirmed viral pneumonia in 20 hospitals in Yunnan Province, China, from March 1, 2015 to March 15, 2020. According to the virus responsible for the pneumonia, the pneumonias were divided into non-COVID-19 (141 cases) and COVID-19 (121 cases). The non-COVID-19 pneumonias comprised cytomegalovirus (CMV) (31 cases), influenza A virus (82 cases), and influenza B virus (20 cases). The differences in the basic clinical characteristics, lesion distribution, location and imaging signs among the four viral pneumonias were analyzed and compared. RESULTS: Fever and cough were the most common clinical symptoms of the four viral pneumonias. Compared with the COVID-19 patients, the non-COVID-19 patients had higher proportions of fatigue, sore throat, expectorant and chest tightness (all P<0.000). In addition, in the CMV pneumonia patients, the proportions of acquired immunodeficiency syndrome (AIDS) and leukopenia were high (all PP<0.000). Comparison of the imaging findings of the four viral pneumonias showed that the pulmonary lesions of COVID-19 were more likely to occur in the peripheral and lower lobes of both lungs, whereas those of CMV pneumonia were diffusely distributed. Compared with the non-COVID-19 pneumonias, COVID-19 pneumonia was more likely to present as ground-glass opacity, intralobular interstitial thickening, vascular thickening and halo sign (all PP<0.05). In addition, in the early stage of COVID-19, extensive consolidation, fibrous stripes, subpleural lines, crazy-paving pattern, tree-in-bud, mediastinal lymphadenectasis, pleural thickening and pleural effusion were rare (all PP<0.05). CONCLUSIONS: The HRCT findings of COVID-19 pneumonia and other viral pneumonias overlapped significantly, but many important differential imaging features could still be observed.


Subject(s)
COVID-19/diagnostic imaging , Lung/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Tomography, X-Ray Computed/methods , Adult , Cytomegalovirus Infections/diagnostic imaging , Female , Humans , Influenza A virus , Influenza B virus , Influenza, Human/diagnostic imaging , Lung/virology , Male , Middle Aged , Pneumonia, Viral/virology , Retrospective Studies
17.
Eur J Radiol ; 134: 109442, 2021 Jan.
Article in English | MEDLINE | ID: covidwho-1060223

ABSTRACT

PURPOSE: The vascular enlargement (VE) pattern differs from previously described imaging patterns for pneumonia. This study aimed to investigate the incidence, computed tomography (CT) characteristics, and diagnostic value of the VE pattern in coronavirus disease 2019 (COVID-19). METHOD: The CT data of 106 patients with COVID-19 from January 19 to February 29, 2020, and 52 patients with influenza virus pneumonia (IVP) from January 2018 to February 2020 were retrospectively collected. The incidences of the VE pattern between the two groups were compared. The CT manifestations of COVID-19 were analyzed with a particular focus on the VE pattern's specific CT signs, dynamic changes, and relationships with lesion size and disease severity. RESULTS: Peripheral and multilobar ground-glass opacities (GGOs) or mixed GGOs with various sizes and morphologies were typical features of COVID-19 on initial CT. The VE pattern was more common in COVID-19 (88/106, 83.02 %) than in IVP (10/52, 19.23 %) on initial CT (P < 0.001). Three special VE-pattern-specific CT signs, including central vascular sign, ginkgo leaf sign, and comb sign, were identified. Four types of dynamic changes in the VE pattern were observed on initial and follow-up CT, which were closely associated with the evolution of lesions and the time interval from the onset of symptoms to initial CT scan. The VE pattern in COVID-19 was more commonly seen in larger lesions and patients with severe-critical type (all P < 0.001). CONCLUSIONS: The VE pattern is a valuable CT sign for differentiating COVID-19 from IVP, which correlates with more extensive or serious disease. A good understanding of the CT characteristics of the VE pattern may contribute to the early and accurate diagnosis of COVID-19 and prediction of the evolution of lesions.


Subject(s)
COVID-19/diagnostic imaging , Lung/diagnostic imaging , Pneumonia/diagnostic imaging , Pulmonary Artery/pathology , Pulmonary Veins/diagnostic imaging , Pulmonary Veins/pathology , Tomography, X-Ray Computed/methods , Adolescent , Adult , Aged , Aged, 80 and over , COVID-19/pathology , Child , Diagnosis, Differential , Female , Humans , Influenza, Human/diagnostic imaging , Influenza, Human/pathology , Lung/blood supply , Lung/pathology , Male , Middle Aged , Pneumonia/pathology , Pulmonary Artery/diagnostic imaging , Retrospective Studies , SARS-CoV-2 , Young Adult
18.
Medicine (Baltimore) ; 99(44): e23064, 2020 Oct 30.
Article in English | MEDLINE | ID: covidwho-990918

ABSTRACT

Coronavirus disease 2019 (COVID-19) is the most important global public health issue that we currently face. We aimed to explore the clinical features of patients with COVID-19 and compared them with those of hospitalized community-acquired pneumonia (CAP) patients caused by influenza virus during the same period.From Jan 1, to Mar 4, 2020, patients with COVID-19 or CAP caused by influenza virus who were admitted to the First Affiliated Hospital of Xiamen University were consecutively screened for enrollment.A total of 35 COVID-19 patients and 22 CAP patients caused by influenza virus were included in this study. Most of COVID-19 patients had characteristics of familial clustering (63%), however, in the other group, there was no similar finding. The percentages of patients with a high fever (the highest recorded temperature was ≥39.0°C; 11% vs 45% [COVID-19 vs CAP groups, respectively]), dyspnea (9% vs 59%), leukocytosis (3% vs 32%), elevated C-reactive protein concentrations (>10 mg/L, 48% vs 86%), elevated procalcitonin levels (>0.1 ng/ml, 15% vs 73%), PaO2/FiO2 <200 mm Hg (4% vs 22%), and infiltration on imaging (29% vs 68%) in the COVID-19 group were less than those same indices in the hospitalized CAP patients caused by influenza virus. Ground-glass opacity with reticular pattern (63%) and interlobular septal thickening (71%) in chest CT were commonly observed in the COVID-19 group.COVID-19 and CAP caused by influenza virus appear to share some similarities in clinical manifestaions but they definitely have major distinctions. Influenza infection remains a health problem even during COVID-19 pandemic.


Subject(s)
Coronavirus Infections/epidemiology , Influenza, Human/epidemiology , Pneumonia, Viral/epidemiology , Adult , Aged , Aged, 80 and over , COVID-19 , China/epidemiology , Community-Acquired Infections , Coronavirus Infections/blood , Coronavirus Infections/diagnostic imaging , Coronavirus Infections/drug therapy , Coronavirus Infections/therapy , Cross-Sectional Studies , Female , Humans , Influenza, Human/blood , Influenza, Human/diagnostic imaging , Influenza, Human/therapy , Male , Middle Aged , Pandemics , Pneumonia, Viral/blood , Pneumonia, Viral/diagnostic imaging , Pneumonia, Viral/therapy , Radiography, Thoracic , Retrospective Studies
19.
Rev Med Virol ; 31(3): e2179, 2021 05.
Article in English | MEDLINE | ID: covidwho-842504

ABSTRACT

We compared clinical symptoms, laboratory findings, radiographic signs and outcomes of COVID-19 and influenza to identify unique features. Depending on the heterogeneity test, we used either random or fixed-effect models to analyse the appropriateness of the pooled results. Overall, 540 articles included in this study; 75,164 cases of COVID-19 (157 studies), 113,818 influenza type A (251 studies) and 9266 influenza type B patients (47 studies) were included. Runny nose, dyspnoea, sore throat and rhinorrhoea were less frequent symptoms in COVID-19 cases (14%, 15%, 11.5% and 9.5%, respectively) in comparison to influenza type A (70%, 45.5%, 49% and 44.5%, respectively) and type B (74%, 33%, 38% and 49%, respectively). Most of the patients with COVID-19 had abnormal chest radiology (84%, p < 0.001) in comparison to influenza type A (57%, p < 0.001) and B (33%, p < 0.001). The incubation period in COVID-19 (6.4 days estimated) was longer than influenza type A (3.4 days). Likewise, the duration of hospitalization in COVID-19 patients (14 days) was longer than influenza type A (6.5 days) and influenza type B (6.7 days). Case fatality rate of hospitalized patients in COVID-19 (6.5%, p < 0.001), influenza type A (6%, p < 0.001) and influenza type B was 3%(p < 0.001). The results showed that COVID-19 and influenza had many differences in clinical manifestations and radiographic findings. Due to the lack of effective medication or vaccine for COVID-19, timely detection of this viral infection and distinguishing from influenza are very important.


Subject(s)
COVID-19/physiopathology , Influenza, Human/physiopathology , Respiratory Tract Infections/physiopathology , COVID-19/diagnostic imaging , COVID-19/epidemiology , COVID-19/mortality , Cough/diagnosis , Cough/physiopathology , Dyspnea/diagnosis , Dyspnea/physiopathology , Electronic Health Records , Fever/diagnosis , Fever/physiopathology , Humans , Infectious Disease Incubation Period , Influenza A virus/pathogenicity , Influenza A virus/physiology , Influenza B virus/pathogenicity , Influenza B virus/physiology , Influenza, Human/diagnostic imaging , Influenza, Human/epidemiology , Influenza, Human/mortality , Pharyngitis/diagnosis , Pharyngitis/physiopathology , Respiratory Tract Infections/diagnostic imaging , Respiratory Tract Infections/epidemiology , Respiratory Tract Infections/mortality , Rhinorrhea/diagnosis , Rhinorrhea/physiopathology , SARS-CoV-2/pathogenicity , SARS-CoV-2/physiology , Severity of Illness Index , Survival Analysis , Tomography, X-Ray Computed
20.
AJR Am J Roentgenol ; 216(1): 71-79, 2021 Jan.
Article in English | MEDLINE | ID: covidwho-696116

ABSTRACT

OBJECTIVE. The purpose of this study was to investigate differences in CT manifestations of coronavirus disease (COVID-19) pneumonia and those of influenza virus pneumonia. MATERIALS AND METHODS. We conducted a retrospective study of 52 patients with COVID-19 pneumonia and 45 patients with influenza virus pneumonia. All patients had positive results for the respective viruses from nucleic acid testing and had complete clinical data and CT images. CT findings of pulmonary inflammation, CT score, and length of largest lesion were evaluated in all patients. Mean density, volume, and mass of lesions were further calculated using artificial intelligence software. CT findings and clinical data were evaluated. RESULTS. Between the group of patients with COVID-19 pneumonia and the group of patients with influenza virus pneumonia, the largest lesion close to the pleura (i.e., no pulmonary parenchyma between the lesion and the pleura), mucoid impaction, presence of pleural effusion, and axial distribution showed statistical difference (p < 0.05). The properties of the largest lesion, presence of ground-glass opacity, presence of consolidation, mosaic attenuation, bronchial wall thickening, centrilobular nodules, interlobular septal thickening, crazy paving pattern, air bronchogram, unilateral or bilateral distribution, and longitudinal distribution did not show significant differences (p > 0.05). In addition, no significant difference was seen in CT score, length of the largest lesion, mean density, volume, or mass of the lesions between the two groups (p > 0.05). CONCLUSION. Most lesions in patients with COVID-19 pneumonia were located in the peripheral zone and close to the pleura, whereas influenza virus pneumonia was more prone to show mucoid impaction and pleural effusion. However, differentiating between COVID-19 pneumonia and influenza virus pneumonia in clinical practice remains difficult.


Subject(s)
COVID-19/diagnostic imaging , Influenza, Human/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Pneumonia, Viral/virology , Tomography, X-Ray Computed/methods , Adolescent , Adult , Aged , Artificial Intelligence , COVID-19/virology , Diagnosis, Differential , Female , Humans , Influenza, Human/virology , Male , Middle Aged , Radiographic Image Interpretation, Computer-Assisted , Radiography, Thoracic , Retrospective Studies , SARS-CoV-2
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