Study of Thoracic CT in COVID-19: The STOIC Project.

Background There are conflicting data regarding the diagnostic performance of chest CT for COVID-19 pneumonia. Disease extent at CT has been reported to influence prognosis. Purpose To create a large publicly available data set and assess the diagnostic and prognostic value of CT in COVID-19 pneumonia. Materials and Methods This multicenter, observational, retrospective cohort study involved 20 French university hospitals. Eligible patients presented at the emergency departments of the hospitals involved between March 1 and April 30th, 2020, and underwent both thoracic CT and reverse transcription-polymerase chain reaction (RT-PCR) testing for suspected COVID-19 pneumonia. CT images were read blinded to initial reports, RT-PCR, demographic characteristics, clinical symptoms, and outcome. Readers classified CT scans as either positive or negative for COVID-19 based on criteria published by the French Society of Radiology. Multivariable logistic regression was used to develop a model predicting severe outcome (intubation or death) at 1-month follow-up in patients positive for both RT-PCR and CT, using clinical and radiologic features. Results Among 10 930 patients screened for eligibility, 10 735 (median age, 65 years; interquartile range, 51-77 years; 6147 men) were included and 6448 (60%) had a positive RT-PCR result. With RT-PCR as reference, the sensitivity and specificity of CT were 80.2% (95% CI: 79.3, 81.2) and 79.7% (95% CI: 78.5, 80.9), respectively, with strong agreement between junior and senior radiologists (Gwet AC1 coefficient, 0.79). Of all the variables analyzed, the extent of pneumonia at CT (odds ratio, 3.25; 95% CI: 2.71, 3.89) was the best predictor of severe outcome at 1 month. A score based solely on clinical variables predicted a severe outcome with an area under the curve of 0.64 (95% CI: 0.62, 0.66), improving to 0.69 (95% CI: 0.6, 0.71) when it also included the extent of pneumonia and coronary calcium score at CT. Conclusion Using predefined criteria, CT reading is not influenced by reader's experience and helps predict the outcome at 1 month. ClinicalTrials.gov identifier: NCT04355507 Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Rubin in this issue.

Efficacy of Chest CT for COVID-19 Pneumonia Diagnosis in France.

Background The role and performance of chest CT in the diagnosis of the coronavirus disease 2019 (COVID-19) pandemic remains under active investigation. Purpose To evaluate the French national experience using chest CT for COVID-19, results of chest CT and reverse transcription polymerase chain reaction (RT-PCR) assays were compared together and with the final discharge diagnosis used as the reference standard. Materials and Methods A structured CT scan survey (NCT04339686) was sent to 26 hospital radiology departments in France between March 2, 2020, and April 24, 2020. These dates correspond to the peak of the national COVID-19 epidemic. Radiology departments were selected to reflect the estimated geographic prevalence heterogeneities of the epidemic. All symptomatic patients suspected of having COVID-19 pneumonia who underwent both initial chest CT and at least one RT-PCR test within 48 hours were included. The final discharge diagnosis, based on multiparametric items, was recorded. Data for each center were prospectively collected and gathered each week. Test efficacy was determined by using the Mann-Whitney test, Student t test, χ2 test, and Pearson correlation coefficient. P < .05 indicated a significant difference. Results Twenty-six of 26 hospital radiology departments responded to the survey, with 7500 patients entered; 2652 did not have RT-PCR test results or had unknown or excess delay between the RT-PCR test and CT. After exclusions, 4824 patients (mean age, 64 years ± 19 [standard deviation], 2669 male) were included. With final diagnosis as the reference, 2564 of the 4824 patients had COVID-19 (53%). Sensitivity, specificity, negative predictive value, and positive predictive value of chest CT in the diagnosis of COVID-19 were 2319 of 2564 (90%; 95% CI: 89, 91), 2056 of 2260 (91%; 95% CI: 91, 92), 2056 of 2300 (89%; 95% CI: 87, 90), and 2319 of 2524 (92%; 95% CI: 91, 93), respectively. There was no significant difference for chest CT efficacy among the 26 geographically separate sites, each with varying amounts of disease prevalence. Conclusion Use of chest CT for the initial diagnosis and triage of patients suspected of having coronavirus disease 2019 was successful. © RSNA, 2021 Online supplemental material is available for this article.

AI-driven quantification, staging and outcome prediction of COVID-19 pneumonia.

Coronavirus disease 2019 (COVID-19) emerged in 2019 and disseminated around the world rapidly. Computed tomography (CT) imaging has been proven to be an important tool for screening, disease quantification and staging. The latter is of extreme importance for organizational anticipation (availability of intensive care unit beds, patient management planning) as well as to accelerate drug development through rapid, reproducible and quantified assessment of treatment response. Even if currently there are no specific guidelines for the staging of the patients, CT together with some clinical and biological biomarkers are used. In this study, we collected a multi-center cohort and we investigated the use of medical imaging and artificial intelligence for disease quantification, staging and outcome prediction. Our approach relies on automatic deep learning-based disease quantification using an ensemble of architectures, and a data-driven consensus for the staging and outcome prediction of the patients fusing imaging biomarkers with clinical and biological attributes. Highly promising results on multiple external/independent evaluation cohorts as well as comparisons with expert human readers demonstrate the potentials of our approach.

A nomogram to predict the risk of unfavourable outcome in COVID-19: a retrospective cohort of 279 hospitalized patients in Paris area.

OBJECTIVE: To identify predictive factors of unfavourable outcome among patients hospitalized for COVID-19. METHODS: We conducted a monocentric retrospective cohort study of COVID-19 patients hospitalized in Paris area. An unfavourable outcome was defined as the need for artificial ventilation and/or death. Characteristics at admission were analysed to identify factors predictive of unfavourable outcome using multivariable Cox proportional hazard models. Based on the results, a nomogram to predict 14-day probability of poor outcome was proposed. RESULTS: Between March 15th and April 14th, 2020, 279 COVID-19 patients were hospitalized after a median of 7 days after the first symptoms. Among them, 88 (31.5%) patients had an unfavourable outcome: 48 were admitted to the ICU for artificial ventilation, and 40 patients died without being admitted to ICU. Multivariable analyses retained age, overweight, polypnoea, fever, high C-reactive protein, elevated us troponin-I, and lymphopenia as risk factors of an unfavourable outcome. A nomogram was established with sufficient discriminatory power (C-index 0.75), and proper consistence between the prediction and the observation. CONCLUSION: We identified seven easily available prognostic factors and proposed a simple nomogram for early detection of patients at risk of aggravation, in order to optimize clinical care and initiate specific therapies. KEY MESSAGES Since novel coronavirus disease 2019 pandemic, a minority of patients develops severe respiratory distress syndrome, leading to death despite intensive care. Tools to identify patients at risk in European populations are lacking. In our series, age, respiratory rate, overweight, temperature, C-reactive protein, troponin and lymphocyte counts were risk factors of an unfavourable outcome in hospitalized adult patients. We propose an easy-to-use nomogram to predict unfavourable outcome for hospitalized adult patients to optimize clinical care and initiate specific therapies.

COVID-19 impact assessment on the French radiological centers: a nationwide survey.

PURPOSE: To determine the impact of the COVID-19 on the CT activities in French radiological centers during the epidemic peak. MATERIALS AND METHODS: A cross-sectional prospective CT scan survey was conducted between March 16 and April 12, 2020, in accordance with the local IRB. Seven hundred nine radiology centers were invited to participate in a weekly online survey. Numbers of CT examinations related to COVID-19 including at least chest (CTcovid) and whole chest CT scan activities (CTchest) were recorded each week. A sub-analysis on French departments was performed during the 4 weeks of the study. The impact of the number of RT-PCRs (reverse transcriptase polymerase chain reactions) on the CT workflow was tested using two-sample t test and Pearson's test. RESULTS: Five hundred seventy-seven structures finally registered (78%) with mean response numbers of 336 ± 18.9 (323; 351). Mean CTchest activity per radiologic structure ranged from 75.8 ± 133 (0-1444) on week 12 to 99.3 ± 138.6 (0-1147) on week 13. Mean ratio of CTcovid on CTchest varied from 0.36 to 0.59 on week 12 and week 14 respectively. There was a significant relationship between the number of RT-PCR performed and the number of CTcovid (r = 0.73, p = 3.10-16) but no link with the number of positive RT-PCR results. CONCLUSION: In case of local high density COVID-19, CT workflow is strongly modified and redirected to the management of these specific patients. KEY POINTS: • Over the 4-week survey period, 117,686 chest CT (CTtotal) were performed among the responding centers, including 61,784 (52%) CT performed for COVID-19 (CTcovid). • Across the country, the ratio CTcovid/CTtotal varied from 0.36 to 0.59 and depended significantly on the local epidemic density (p = 0.003). • In clinical practice, in a context of growing epidemic, in France, chest CT was used as a surrogate to RT-PCR for patient triage.