CT image visual quantitative evaluation and clinical classification of coronavirus disease (COVID-19)

Abstract

Objectives To explore the relationship between the imaging manifestations and clinical classification of COVID-19. Methods We conducted a retrospective single-center study on patients with COVID-19 from Jan. 18, 2020 to Feb. 7, 2020 in Zhuhai, China. Patients were divided into 3 types based on Chinese guideline: mild (patients with minimal symptoms and negative CT findings), common, and severe-critical (patients with positive CT findings and different extent of clinical manifestations). CT visual quantitative evaluation was based on summing up the acute lung inflammatory lesions involving each lobe, which was scored as 0 (0%), 1 (1-25%), 2 (26-50%), 3 (51-75%), or 4 (76-100%), respectively. The total severity score (TSS) was reached by summing the five lobe scores. The consistency of two observers was evaluated. The TSS was compared with the clinical classification. ROC was used to test the diagnosis ability of TSS for severe-critical type. Results This study included 78 patients, 38 males and 40 females. There were 24 mild (30.8%), 46 common (59.0%), and 8 severecritical (10.2%) cases, respectively. The median TSS of severe-critical-type group was significantly higher than common type (p < 0.001). The ICC value of the two observers was 0.976 (95% CI 0.962-0.985). ROC analysis showed the area under the curve (AUC) of TSS for diagnosing severe-critical type was 0.918. The TSS cutoff of 7.5 had 82.6% sensitivity and 100% specificity. Conclusions The proportion of clinical mild-type patients with COVID-19 was relatively high; CT was not suitable for independent screening tool. The CT visual quantitative analysis has high consistency and can reflect the clinical classification of COVID-19. Key Points • CT visual quantitative evaluation has high consistency (ICC value of 0.976) among the observers. The median TSS of severecritical type group was significantly higher than common type (p < 0.001). • ROC analysis showed the area under the curve (AUC) of TSS for diagnosing severe-critical type was 0.918 (95% CI 0.843-0.994). The TSS cutoff of 7.5 had 82.6% sensitivity and 100% specificity. • The proportion of confirmed COVID-19 patients with normal chest CT was relatively high (30.8%); CT was not a suitable screening modality

Chest CT features of the novel coronavirus disease (COVID-19)

“…Besides, COVID-19 patients may have airway changes such as endobronchial mucus plugging, bronchiectasis, bronchioloectasis, and bronchial wall thickening [9][10][11]13,14, (Table 1). Moreover, the prevalence of bronchial wall thickening in COVID-19 patients with severe-critical clinical features has been shown to be significantly higher than in patients with mild-common clinical features [14,41].…”

“…The reticular pattern is usually seen in COVID-19 patients with pneumonia and reticulation may increase in patients with longer disease course [8,19,31]. The reported prevalence of reticular pattern and linear opacification is very variable in the literature and has been reported between 1% and 81% [13,14,18,19,[23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41]. Moreover, subpleural curvilinear lines and fibrous stripes have been reported in COVID-19 patients (Figures 5a and 5b) [14,, and both may represent the replacement of cellular components by fibrosis [14,19].…”

“…The visual (semiquantitative) evaluation of disease severity on chest CT can reflect the clinical classification (mild, common, severe, or critical disease) and prognosis of patients with COVID-19 pneumonia [11,30,41]. Therefore, the staging of lung involvement has been defined in COVID-19 patients.…”

Chest Computed Tomography for the Diagnosis of Patients with Coronavirus Disease 2019 (COVID-19): A Rapid Review and Meta-Analysis

“…On one hand, when COVID-19 cannot be diagnosed by nucleic acid, CT can be used as an auxiliary diagnostic method; on the other hand, CT can show lesions and also plays an important role in patient follow-up. Since February 2020, several case-control studies (8,9), case series (10,11), and case-report (12,13) of CT diagnosis of COVID-19 have been published. However, there is no systematic review and meta-analysis to find out the performance of chest CT in the diagnosis of COVID-19.…”

Holistic AI-Driven Quantification, Staging and Prognosis of COVID-19 Pneumonia

“…deep-learning to quantify COVID-19 disease extent on CT but none of them used a multi-centric cohort while providing comparisons with segmentations done by radiologists 18,19 . Disease extent is the only parameter that can be visually estimated on chest CT to quantify disease severity 4,5 , but visual quantification is difficult and usually coarse. Several AI-based tools have been recently developed to quantify interstitial lung diseases (ILD) [20][21][22][23] , which share common CT features with COVID-19 pneumonia, especially a predominance of ground glass opacities.…”