scholarly journals Practical clinical and radiological models to diagnose COVID-19 based on a multicentric teleradiological emergency chest CT cohort

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Paul Schuster ◽  
Amandine Crombé ◽  
Hubert Nivet ◽  
Alice Berger ◽  
Laurent Pourriol ◽  
...  
Keyword(s):  
Clinical Data ◽  
Classification Tree ◽  
Real Life ◽  
Chest Ct ◽  
Stepwise Logistic Regression ◽  
Operating Characteristics ◽  
Rt Pcr ◽  
Radiological Features ◽  
Polymerase Chain ◽  
Cart Model

AbstractOur aim was to develop practical models built with simple clinical and radiological features to help diagnosing Coronavirus disease 2019 [COVID-19] in a real-life emergency cohort. To do so, 513 consecutive adult patients suspected of having COVID-19 from 15 emergency departments from 2020-03-13 to 2020-04-14 were included as long as chest CT-scans and real-time polymerase chain reaction (RT-PCR) results were available (244 [47.6%] with a positive RT-PCR). Immediately after their acquisition, the chest CTs were prospectively interpreted by on-call teleradiologists (OCTRs) and systematically reviewed within one week by another senior teleradiologist. Each OCTR reading was concluded using a 5-point scale: normal, non-infectious, infectious non-COVID-19, indeterminate and highly suspicious of COVID-19. The senior reading reported the lesions’ semiology, distribution, extent and differential diagnoses. After pre-filtering clinical and radiological features through univariate Chi-2, Fisher or Student t-tests (as appropriate), multivariate stepwise logistic regression (Step-LR) and classification tree (CART) models to predict a positive RT-PCR were trained on 412 patients, validated on an independent cohort of 101 patients and compared with the OCTR performances (295 and 71 with available clinical data, respectively) through area under the receiver operating characteristics curves (AUC). Regarding models elaborated on radiological variables alone, best performances were reached with the CART model (i.e., AUC = 0.92 [versus 0.88 for OCTR], sensitivity = 0.77, specificity = 0.94) while step-LR provided the highest AUC with clinical-radiological variables (AUC = 0.93 [versus 0.86 for OCTR], sensitivity = 0.82, specificity = 0.91). Hence, these two simple models, depending on the availability of clinical data, provided high performances to diagnose positive RT-PCR and could be used by any radiologist to support, modulate and communicate their conclusion in case of COVID-19 suspicion. Practically, using clinical and radiological variables (GGO, fever, presence of fibrotic bands, presence of diffuse lesions, predominant peripheral distribution) can accurately predict RT-PCR status.

scholarly journals Practical Clinical and Radiological Models to Diagnose COVID-19 on Chest CT in a French Multicentric Emergency Population

2020 ◽  
Author(s):  
Paul Schuster ◽  
Amandine Crombé ◽  
Hubert Nivet ◽  
Alice Berger ◽  
Laurent Pourriol ◽  
...  
Keyword(s):  
Clinical Data ◽  
Classification Tree ◽  
Stepwise Logistic Regression ◽  
Rt Pcr ◽  
Point Scale ◽  
Radiological Features ◽  
Cart Model ◽  
Multivariate Stepwise Logistic Regression ◽  
Independent Cohort ◽  
Do So

Abstract Our aim was to develop practical models built with simple clinical-radiological features to facilitate COVID-19 diagnosis. To do so, 513 consecutive adult patients suspected of having COVID-19 from 15 emergency departments from 03/13/2020 to 04/14/2020 were included (244 [47.6%] with a positive RT-PCR). Chest CTs were immediately and prospectively analysed by on-call teleradiologists (OCTR) and systematically reviewed within one week by another senior teleradiologist. Each OCTR reading was concluded using a 5-point scale: normal, non-infectious, infectious non-COVID-19, indeterminate and highly suspicious of COVID-19. The senior reading reported the lesions’ semiology, distribution, extent and differential diagnoses. Multivariate stepwise logistic regression (Step-LR) and classification tree (CART) models to predict a positive RT-PCR were trained on 412 patients, validated on an independent cohort of 101 patients and compared with the OCTR performances (295 and 71 with available clinical data, respectively). Regarding models elaborated on radiological variables alone, best performances were reached with the CART model (i.e., AUC=0.92 versus 0.88 for OCTR) while step-LR provided the highest AUC with clinical-radiological variables (0.93 versus 0.86 for OCTR). Hence, these two simple models, depending on the availability of clinical data, could be used by any radiologist to support their conclusion in case of COVID-19 suspicion.

scholarly journals A meta-analysis of accuracy and sensitivity of chest CT and RT-PCR in COVID-19 diagnosis

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Fatemeh Khatami ◽  
Mohammad Saatchi ◽  
Seyed Saeed Tamehri Zadeh ◽  
Zahra Sadat Aghamir ◽  
Alireza Namazi Shabestari ◽  
...  
Keyword(s):  
Polymerase Chain Reaction ◽  
Reverse Transcriptase ◽  
Ct Scan ◽  
Predictive Value ◽  
Meta Analysis ◽  
Chest Ct ◽  
Rt Pcr ◽  
Chain Reaction ◽  
Polymerase Chain ◽  
Chest Ct Scan

AbstractNowadays there is an ongoing acute respiratory outbreak caused by the novel highly contagious coronavirus (COVID-19). The diagnostic protocol is based on quantitative reverse-transcription polymerase chain reaction (RT-PCR) and chests CT scan, with uncertain accuracy. This meta-analysis study determines the diagnostic value of an initial chest CT scan in patients with COVID-19 infection in comparison with RT-PCR. Three main databases; PubMed (MEDLINE), Scopus, and EMBASE were systematically searched for all published literature from January 1st, 2019, to the 21st May 2020 with the keywords "COVID19 virus", "2019 novel coronavirus", "Wuhan coronavirus", "2019-nCoV", "X-Ray Computed Tomography", "Polymerase Chain Reaction", "Reverse Transcriptase PCR", and "PCR Reverse Transcriptase". All relevant case-series, cross-sectional, and cohort studies were selected. Data extraction and analysis were performed using STATA v.14.0SE (College Station, TX, USA) and RevMan 5. Among 1022 articles, 60 studies were eligible for totalizing 5744 patients. The overall sensitivity, specificity, positive predictive value, and negative predictive value of chest CT scan compared to RT-PCR were 87% (95% CI 85–90%), 46% (95% CI 29–63%), 69% (95% CI 56–72%), and 89% (95% CI 82–96%), respectively. It is important to rely on the repeated RT-PCR three times to give 99% accuracy, especially in negative samples. Regarding the overall diagnostic sensitivity of 87% for chest CT, the RT-PCR testing is essential and should be repeated to escape misdiagnosis.

scholarly journals CT findings of 795 COVID-19 positive cases: a multicenter study in Egypt

2020 ◽  
Vol 51 (1) ◽  
Author(s):  
Youssriah Yahia Sabri ◽  
Mohamed Mohsen Tolba Fawzi ◽  
Eman Zaki Nossair ◽  
Safaa Mohamed El-Mandooh ◽  
Amira Aly Hegazy ◽  
...  
Keyword(s):  
Virus Disease ◽  
Chest Ct ◽  
Imaging Features ◽  
Rt Pcr ◽  
Relevant Research ◽  
Non Invasive ◽  
Ct Findings ◽  
Global Pandemic ◽  
Polymerase Chain ◽  
Ct Studies

Abstract Background Corona Virus Disease 2019 (COVID-19) outbreak was officially announced as a global pandemic by the WHO on March 11th 2020. Thorough understanding of CT imaging features of COVID-19 is essential for effective patient management; rationalizing the need for relevant research. The aim of this study was to analyze the chest CT findings of patients with real-time polymerase chain reaction (RT-PCR) proved COVID-19 admitted to four Egyptian hospitals. The recently published RSNA expert consensus statement on reporting COVID-19 chest CT findings was taken into consideration. Results Normal CT “negative for COVID-19” was reported in 26.1% of our RT-PCR proved COVID-19 cases. In descending order of prevalence, imaging findings of the positive CT studies (73.9%) included GGO (69%), consolidation (49.7%), crazy paving (15.4%), and peri-lobular fibrosis (40.6%). These showed a dominantly bilateral (68.2%), peripheral (72.4%), and patchy (64.7%) distribution. Remarkably, thymic hyperplasia was identified in 14.3% of studies. According to the RSNA consensus, CT findings were classified as typical in 68.9%, indeterminate in 3.6%, and atypical in 1.4% of the evaluated CT studies. Conclusion Although COVID-19 cannot be entirely excluded by chest CT, it can be distinguished in more than two-thirds of cases; making CT a widely available, non-invasive, and rapid diagnostic tool.

scholarly journals Chest CT texture-based radiomics analysis in differentiating COVID-19 from other interstitial pneumonia

2021 ◽  
Author(s):  
Damiano Caruso ◽  
Francesco Pucciarelli ◽  
Marta Zerunian ◽  
Balaji Ganeshan ◽  
Domenico De Santis ◽  
...  
Keyword(s):  
Interstitial Pneumonia ◽  
Potential Role ◽  
Texture Features ◽  
Roc Curves ◽  
Chest Ct ◽  
Preliminary Evaluation ◽  
Rt Pcr ◽  
Mean Intensity ◽  
Polymerase Chain

Abstract Purpose To evaluate the potential role of texture-based radiomics analysis in differentiating Coronavirus Disease-19 (COVID-19) pneumonia from pneumonia of other etiology on Chest CT. Materials and methods One hundred and twenty consecutive patients admitted to Emergency Department, from March 8, 2020, to April 25, 2020, with suspicious of COVID-19 that underwent Chest CT, were retrospectively analyzed. All patients presented CT findings indicative for interstitial pneumonia. Sixty patients with positive COVID-19 real-time reverse transcription polymerase chain reaction (RT-PCR) and 60 patients with negative COVID-19 RT-PCR were enrolled. CT texture analysis (CTTA) was manually performed using dedicated software by two radiologists in consensus and textural features on filtered and unfiltered images were extracted as follows: mean intensity, standard deviation (SD), entropy, mean of positive pixels (MPP), skewness, and kurtosis. Nonparametric Mann–Whitney test assessed CTTA ability to differentiate positive from negative COVID-19 patients. Diagnostic criteria were obtained from receiver operating characteristic (ROC) curves. Results Unfiltered CTTA showed lower values of mean intensity, MPP, and kurtosis in COVID-19 positive patients compared to negative patients (p = 0.041, 0.004, and 0.002, respectively). On filtered images, fine and medium texture scales were significant differentiators; fine texture scale being most significant where COVID-19 positive patients had lower SD (p = 0.004) and MPP (p = 0.004) compared to COVID-19 negative patients. A combination of the significant texture features could identify the patients with positive COVID-19 from negative COVID-19 with a sensitivity of 60% and specificity of 80% (p = 0.001). Conclusions Preliminary evaluation suggests potential role of CTTA in distinguishing COVID-19 pneumonia from other interstitial pneumonia on Chest CT.

scholarly journals Chest CT as a Complement to RT-PCR to Confirm and Follow-up COVID-19 Patients

Medicinus ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 31
Author(s):  
Aziza Ghanie Icksan ◽  
Muhammad Hafiz ◽  
Annisa Dian Harlivasari
Keyword(s):  
Chest Ct ◽  
Rt Pcr ◽  
High Resolution Computed Tomography ◽  
X Ray ◽  
First Case ◽  
Chest X Ray ◽  
Polymerase Chain

<p><strong>Background : </strong>The first case of COVID-19 in Indonesia was recorded in March 2020. Limitation of reverse-transcription polymerase chain reaction (RT-PCR) has put chest CT as an essential complementary tool in the diagnosis and follow up treatment for COVID-19. Literatures strongly suggested that High-Resolution Computed Tomography (HRCT) is essential in diagnosing typical symptoms of COVID-19 at the early phase of disease due to its superior sensitivity  (97%) compared to chest x-ray (CXR).</p><p>The two cases presented in this case study showed the crucial role of chest CT with HRCT to establish the working diagnosis and follow up COVID-19 patients as a complement to RT-PCR, currently deemed a gold standard.<strong></strong></p>

scholarly journals A Comparative Study on the Clinical Features of Coronavirus 2019 (COVID-19) Pneumonia With Other Pneumonias

2020 ◽  
Vol 71 (15) ◽  
pp. 756-761 ◽  
Author(s):  
Dahai Zhao ◽  
Feifei Yao ◽  
Lijie Wang ◽  
Ling Zheng ◽  
Yongjun Gao ◽  
...  
Keyword(s):  
Clinical Features ◽  
Clinical Symptoms ◽  
Severe Pneumonia ◽  
Ground Glass Opacity ◽  
Chest Ct ◽  
Rt Pcr ◽  
Bilateral Involvement ◽  
Radiological Features ◽  
History Of ◽  
Novel Coronavirus

Abstract Background A novel coronavirus (COVID-19) has raised world concern since it emerged in Wuhan, China in December 2019. The infection may result in severe pneumonia with clusters of illness onsets. Its impacts on public health make it paramount to clarify the clinical features with other pneumonias. Methods Nineteen COVID-19 and 15 other patients with pneumonia (non-COVID-19) in areas outside of Hubei were involved in this study. Both COVID-19 and non-COVID-19 patients were confirmed to be infected using throat swabs and/or sputa with/without COVID-2019 by real-time RT-PCR. We analyzed the demographic, epidemiological, clinical, and radiological features from those patients, and compared the differences between COVID-19 and non-COVID-19. Results All patients had a history of exposure to confirmed cases of COVID-19 or travel to Hubei before illness. The median (IQR) duration was 8 (6–11) and 5 (4–11) days from exposure to onset in COVID-19 and non-COVID-19 cases, respectively. The clinical symptoms were similar between COVID-19 and non-COVID-19. The most common symptoms were fever and cough. Fifteen (78.95%) COVID-19 but 4 (26.67%) non-COVID-19 patients had bilateral involvement while 17 COVID-19 patients (89.47%) but 1 non-COVID-19 patient (6.67%) had multiple mottling and ground-glass opacity on chest CT images. Compared with non-COVID-19, COVID-19 presents remarkably more abnormal laboratory tests, including AST, ALT, γ-GT, LDH, and α-HBDH. Conclusions The COVID-19 infection has onsets similar to other pneumonias. CT scan may be a reliable test for screening COVID-19 cases. Liver function damage is more frequent in COVID-19 than non-COVID-19 patients. LDH and α-HBDH may be considerable markers for evaluation of COVID-19.

scholarly journals Nowcast Deep Learning Models For Constraining Zero-Day Pathogen Attacks – Application on Chest Radiographs to Covid-19

2020 ◽  
Author(s):  
Michael D. Kuo ◽  
Wan Hang Keith Chiu ◽  
Varut Vardhanabhuti ◽  
Dymtro Poplavskiy ◽  
Philip LH Yu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Sensitivity And Specificity ◽  
Detection System ◽  
Chest Radiographs ◽  
Sufficient Information ◽  
Operating Characteristics ◽  
Rt Pcr ◽  
Chain Reaction ◽  
Prospective Cohorts ◽  
Polymerase Chain

Abstract Outbreaks due to emergent pathogens like Covid-19 are difficult to contain as the time to gather sufficient information to develop a detection system is outpaced by the speed of transmission. Here we develop a general pneumonia (PNA) CXR Deep Learning (DL) model (MAIL1.0) follow by a second-generation DL model (MAIL2.0) for detection of Covid-19 on chest radiographs (CXR). We validate the models on two prospective cohorts of high-risks patients screened for Covid-19 reverse transcriptase-polymerase chain reaction (RT-PCR). MAIL1.0 has an Area Under the Receiver Operating Characteristics (AUC) of 0.93, sensitivity and specificity of 90.5% and 76.7% in detection of visible pneumonia and MAIL2.0 has an AUC of 0.81, sensitivity and specificity of 84.7% and 71.6%, significantly outperforming radiologists, especially amongst asymptomatic and patients presenting with early symptoms. Nowcast DL models may be an effective tool in helping to constrain the outbreak, particularly in resource-stretched healthcare systems.

scholarly journals Imaging of abdominal complications of COVID-19 infection

BJR|Open ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 20200052
Author(s):  
Damiano Caruso ◽  
Marta Zerunian ◽  
Francesco Pucciarelli ◽  
Elena Lucertini ◽  
Benedetta Bracci ◽  
...  
Keyword(s):  
Laboratory Tests ◽  
Gastrointestinal Symptoms ◽  
Rt Pcr ◽  
Chain Reaction ◽  
Abdominal Ct ◽  
Radiological Features ◽  
Abdominal Symptoms ◽  
Polymerase Chain ◽  
Loss Of Appetite ◽  
Symptoms And Signs

Coronavirus disease 2019 (COVID-19) is a respiratory syndrome caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) first described in Wuhan, Hubei Province, China in the last months of 2019 and then declared as a pandemic. Typical symptoms are represented by fever, cough, dyspnea and fatigue, but SARS-CoV-2 infection can also cause gastrointestinal symptoms (vomiting, diarrhoea, abdominal pain, loss of appetite) or be totally asymptomatic. As reported in literature, many patients with COVID-19 pneumonia had a secondary abdominal involvement (bowel, pancreas, gallbladder, spleen, liver, kidneys), confirmed by laboratory tests and also by radiological features. Usually the diagnosis of COVID-19 is suspected and then confirmed by real-time reverse-transcription-polymerase chain reaction (RT-PCR), after the examination of the lung bases of patients, admitted to the emergency department with abdominal symptoms and signs, who underwent abdominal-CT. The aim of this review is to describe the typical and atypical abdominal imaging findings in patients with SARS-CoV-2 infection reported since now in literature.

scholarly journals The exact place of PCR and chest CT in screening, staging, and diagnosis of Covid-19: A meta-analysis

2020 ◽  
Author(s):  
Fatemeh Khatami ◽  
Mohammad Saatchi ◽  
Seyed Saeed Tamehri Zadeh ◽  
Zahra Sadat Aghamir ◽  
Alireza Namazi Shabestari ◽  
...  
Keyword(s):  
Polymerase Chain Reaction ◽  
Reverse Transcriptase ◽  
Ct Scan ◽  
Predictive Value ◽  
Meta Analysis ◽  
Chest Ct ◽  
Rt Pcr ◽  
Chain Reaction ◽  
Polymerase Chain ◽  
Chest Ct Scan

Abstract Introduction: Nowadays there is an ongoing acute respiratory outbreak causing by the novel highly contagious coronavirus (nCoV). There are two diagnostic protocol based on chest CT scan and quantitative reverse-transcription polymerase chain reaction (RT-PCR) which their diagnostic accuracy is under the debate. We designed this meta-analysis study to determine the diagnostic value of initial chest CT scan in patients with nCoV infection in comparison with RT- PCR.Search strategy and statistical analysis: Three main databases the PubMed (MEDLINE), Scopus, and EMBASE was systematically searched for all published literatures from January 1st, 2019, to the 27th march 2020 with key grouping of “COVID19 virus”, “2019 novel coronavirus”, “Wuhan coronavirus”, “2019-nCoV”, “X-Ray Computed Tomography”, “Polymerase Chain Reaction”, “Reverse Transcriptase PCR”, and “PCR Reverse Transcriptase”. All relevant case- series, cross-sectional, and cohort studies were selected. Data extraction was done in Excel 2007 (Microsoft Corporation, Redmond, CA) and their analysis was performed using STATA v.14.0SE (College Station, TX, USA) and RevMan 5.Result: From first recruited 668 articles we end up to the final 47 studies, which comprised a total sample size of 4238 patients. In compare to RT-PCR, the overall sensitivity, specificity, positive predictive value, and negative predictive value of chest CT scan were 86% (95% CI: 83% -90%), 43 % (95% CI: 26% -60%), 67% (95% CI: 57% -78%), and 84% (95% CI: 74% -95%) respectively. However the RT-PCR should be repeated for three times in order to give the 99% accuracy especially in negative samples.Conclusion: According to the acceptable sensitivity of chest CT scan, it can be employed complement to RT-PCR to diagnosis patients who are clinically suspicious for nCoV.

scholarly journals Early chest computed tomography to diagnose COVID-19 from suspected patients: A multicenter retrospective study

2020 ◽  
Author(s):  
Congliang Miao ◽  
Mengdi Jin ◽  
Li Miao ◽  
Xinying Yang ◽  
Peng Huang ◽  
...  
Keyword(s):  
Ground Glass Opacity ◽  
High Specificity ◽  
Diagnostic Value ◽  
Chest Ct ◽  
Imaging Features ◽  
Rt Pcr ◽  
Polymerase Chain ◽  
Low Sensitivity ◽  
Chest Ct Scan ◽  
Ct Features

AbstractObjectiveThe purpose of this study is to distinguish the imaging features of COVID-19 with other chest infectious diseases and evaluate diagnostic value of chest CT for suspected patients.MethodsAdult suspected patients aged>18 years within 14 days who underwent chest CT scan and reverse-transcription polymerase-chain-reaction (RT-PCR) tests were enrolled. The enrolled patients were confirmed and grouped according to results of RT-PCR tests. The data of basic demographics, single chest CT features, and combined chest CT features were analyzed for confirmed and non-confirmed groups.ResultsA total of 130 patients were enrolled with 54 cases positive and 76 cases negative. The typical CT imaging features of positive group were ground glass opacity (GGO), crazy-paving pattern and air bronchogram. The lesions were mostly distributed bilaterally, close to the lower lungs or the pleura. When features combined, GGO with bilateral pulmonary distribution and GGO with pleural distribution were more common, of which were 31 cases (57.4%) and 30 cases (55.6%) respectively. The combinations were almost presented statistically significant (P<0.05) except for the combination of GGO with consolidation. Most combinations presented relatively low sensitivity but extremely high specificity. The average specificity of these combinations is around 90%.ConclusionsThe combinations of GGO could be useful in the identification and differential diagnosis of COVID-19, which alerts clinicians to isolate patients for treatment promptly and repeat RT-PCR tests until incubation ends.

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