Souhail Bennani

TL;DR: This 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.

Abstract: Background There are conflicting data regarding the diagnostic performance of Chest computed tomography (CT) for COVID-19 pneumonia. Disease extent on CT has been reported to influence prognosis. Purpose To create a large publicly available dataset and assess the diagnostic and prognostic value of CT in COVID-19 pneumonia. Materials and Methods This multicenter observational retrospective cohort study (ClinicalTrials.gov: NCT04355507) involved 20 French university hospitals. Eligible subjects presented at the emergency departments of the hospitals involved between March 1st and April 30th, 2020 and underwent both thoracic CT and RT-PCR 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 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 subjects positive for both RT-PCR and CT, using clinical and radiological features. Results Of 10,930 subjects screened for eligibility, 10,735 (median age 65 years, interquartile range, 51-77 years; 6,147 men) were included and 6,448 (60.0%) had a positive RT-PCR result. With RT-PCR as reference, the sensitivity and specificity and 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's AC1 coefficient: 0.79) Of all the variables analysed, the extent of pneumonia on CT (OR 3.25, 95%CI: 2.71, 3.89) was the best predictor of severe outcome at one month. A score based solely on clinical variables predicted a severe outcome with an AUC 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 on CT. Conclusion Using pre-defined criteria, CT reading is not influenced by reader's experience and helps predict the outcome at one month. Published under a CC BY 4.0 license. See also the editorial by Rubin.

TL;DR: AI-assisted chest radiography interpretation resulted in absolute increases in sensitivity for all radiologists of various levels of expertise and reduced the reading times; specificity increased with AI, except in the diagnosis of pneumothorax.

TL;DR: The cumulative effective dose exceeded 100 mSv in 5 years in 37% of LT recipients, the reason why continuous efforts should be made to optimize chest CT acquisitions accounting for 73% of the radiation dose.