Observer agreement and clinical significance of chest CT reporting in patients suspected of COVID-19.

OBJECTIVES: To assess interobserver agreement and clinical significance of chest CT reporting in patients suspected of COVID-19. METHODS: From 16 to 24 March 2020, 241 consecutive patients addressed to hospital for COVID-19 suspicion had both chest CT and SARS-CoV-2 RT-PCR. Eight observers (2 thoracic and 2 general senior radiologists, 2 junior radiologists, and 2 emergency physicians) retrospectively categorized each CT into one out of 4 categories (evocative, compatible for COVID-19 pneumonia, not evocative, and normal). Observer agreement for categorization between all readers and pairs of readers with similar experience was evaluated with the Kappa coefficient. The results of a consensus categorization were correlated to RT-PCR. RESULTS: Observer agreement across the 4 categories was good between all readers (κ value 0.61 95% CI 0.60-0.63) and moderate to good between pairs of readers (0.54-0.75). It was very good (κ 0.81 95% CI 0.79-0.83), fair (κ 0.32 95% CI 0.29-0.34), moderate (κ 0.56 95% CI 0.54-0.58), and moderate (0.58 95% CI 0.56-0.61) for the categories evocative, compatible, not evocative, and normal, respectively. RT-PCR was positive in 97%, 50%, 31%, and 11% of cases in the respective categories. Observer agreement was lower (p < 0.001) and RT-PCR positive cases less frequently categorized evocative in the presence of an underlying pulmonary disease (p < 0.001). CONCLUSION: Interobserver agreement for chest CT reporting using categorization of findings is good in patients suspected of COVID-19. Among patients considered for hospitalization in an epidemic context, CT categorized evocative is highly predictive of COVID-19, whereas the predictive value of CT decreases between the categories compatible and not evocative. KEY POINTS: • In patients suspected of COVID-19, interobserver agreement for chest CT reporting into categories is good, and very good to categorize CT "evocative." • Chest CT can participate in estimating the likelihood of COVID-19 in patients presenting to hospital during the outbreak, CT categorized "evocative" being highly predictive of the disease whereas almost a third of patients with CT "not evocative" had a positive RT-PCR in our study. • Observer agreement is lower and CTs of positive RT-PCR cases less frequently "evocative" in presence of an underlying pulmonary disease.

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.