The Performance of Chest CT in Evaluating the Clinical Severity of COVID-19 Pneumonia: Identifying Critical Cases Based on CT Characteristics

Abstract

This paper can be cited using the date of access and the unique DOI number which can be found in the footnotes. Objectives: To assess the clinical severity of COVID-19 pneumonia using qualitative and/or quantitative chest CT indicators and identify the CT characteristics of critical cases. Materials and Methods: Fifty-one patients with COVID-19 pneumonia including ordinary cases (group A,n=12), severe cases(group B, n=15) and critical cases (group C,n=24) were retrospectively enrolled. The qualitative and quantitative indicators from chest CT were recorded and compared using Fisher's exact test, one-way ANOVA, Kruskal-Wallis H test and receiver operating characteristic analysis. Results: Depending on the severity of the disease, the number of involved lung segments and lobes, the frequencies of consolidation, crazy-paving pattern and air bronchogram increased in more severe cases. Qualitative indicators including total severity score for the whole lung and total score for crazy-paving and consolidation could distinguish groups B and C from A(69% sensitivity, 83% specificity and 73% accuracy) but were similar between group B and group C. Combined qualitative and quantitative indicators could distinguish these three groups with high sensitivity(B+C vs.A ,90%; C vs. B, 92%),specificity(100%, 87%) and accuracy(92%, 90%). Critical cases had higher total severity score(>10) and higher total score for crazy-paving and consolidation(>4) than ordinary cases, and had higher mean lung density(>-779HU) and full width at half maximum(>128HU) but lower relative volume of normal lung density(≦50%) than ordinary/severe cases. In our critical cases, eight patients with relative volume of normal lung density smaller than 40% received mechanical ventilation for supportive treatment, and two of them had died.

Dynamic Changes of Chest CT Follow-up In Coronavirus Disease-19 (COVID-19) Pneumonia: Relationship To Clinical Typing

“…The study found that the severe and critical group showed more extensive and severer involvement than those in the moderate group, mainly involving the right lung in the initial CT and the whole lung in the initial and follow-up CT. This nding is similar to previous studies [20,23], which also reported the initial CT score of the whole lung in the severe and critical patients was higher than that in the moderate patients. Furthermore, we found that the patients with severe and critical type progressed rapidly with the greatest severity at the second follow-up CT, and then gradually recovered.…”

COVID-19 disease: CT Pneumonia Analysis prototype by using artificial intelligence, predicting the disease severity

“…Previous studies [3,4] reported that the degree of consolidation and crazy-paving pattern was highly suggestive for the disease progression/peak, so we used a total sum extent of crazy-paving and consolidation as an indicator for the disease severity. The severity score for the consolidation and crazy-paving was calculated for each lobe using the same criteria (0-4 scores), and the total score for the lungs is the sum of individual lobes (0-20 scores).…”

“…Coronavirus disease 2019 (COVID-19) was firstly diagnosed in Wuhan, China; was announced by the WHO to be caused by a novel coronavirus (SARS-CoV-2); and causes a respiratory disease pandemic [1][2][3][4].…”

Treatment Intensity Stratification in COVID-19 by Fully Automated Analysis of Pulmonary and Cardiovascular Metrics on Initial Chest CT using Deep Learning

“…Noteworthy, in both studies, CTs had been analyzed manually. Similarly, Lyu et al [7] and Zhang et al [11] showed that the number of lung segments and lobes affected by crazy-paving pattern and consolidation increased with case severity, a fact in line with the increase in LSS and LHOS with increased treatment intensity. The opposite approach was taken by Colombi et al, who quanti ed areas of well-aerated, normal lung to predict adverse outcome in COVID-19 pneumonia [9].…”

“…Chest CT is a primary source of early predictive features in COVID-19, because of its high sensitivity for the detection of the disease and the fact that it's a primary modality for imaging of pneumonia [6], There is growing, consistent evidence that CT features are associated with disease severity in COVID-19 based on (semi)manual assessment of pulmonary parameters on chest CT [7][8][9][10][11]. This study builds on these approaches and expands them in three aspects: First, by introducing a fully automated evaluation, which is especially relevant in times of a pandemic spread with heavy workloads on healthcare providers.…”