Distinguishing COVID-19 From Influenza Pneumonia in the Early Stage Through CT Imaging and Clinical Features

Abstract

Background: Both coronavirus disease 2019 (COVID-19) and influenza pneumonia are highly contagious and present with similar symptoms. We aimed to identify differences in CT imaging and clinical features between COVID-19 and influenza pneumonia in the early stage and to identify the most valuable features in the differential diagnosis.

Methods: Seventy-three patients with COVID-19 confirmed by real-time reverse transcription-polymerase chain reaction (RT-PCR) and 48 patients with influenza pneumonia confirmed by direct/indirect immunofluorescence antibody staining or RT-PCR were retrospectively reviewed. Clinical data including course of disease, age, sex, body temperature, clinical symptoms, total white blood cell (WBC) count, lymphocyte count, lymphocyte ratio, neutrophil count, neutrophil ratio, and C-reactive protein, as well as 22 qualitative and 25 numerical imaging features from non-contrast-enhanced chest CT images were obtained and compared between the COVID-19 and influenza pneumonia groups. Correlation tests between feature metrics and diagnosis outcomes were assessed. The diagnostic performance of each feature in differentiating COVID-19 from influenza pneumonia was also evaluated.

Results: Seventy-three COVID-19 patients including 41 male and 32 female with mean age of 41.9 ± 14.1 and 48 influenza pneumonia patients including 30 male and 18 female with mean age of 40.4 ± 27.3 were reviewed. Temperature, WBC count, crazy paving pattern, pure GGO in peripheral area, pure GGO, lesion sizes (1-3 cm), emphysema, and pleural traction were significantly independent associated with COVID-19. The AUC of clinical-based model on the combination of temperature and WBC count is 0.880 (95% CI: 0.819-0.940). The AUC of radiological-based model on the combination of crazy paving pattern, pure GGO in peripheral area, pure GGO, lesion sizes (1-3 cm), emphysema, and pleural traction is 0.957 (95% CI: 0.924-0.989). The AUC of combined model based on the combination of clinical and radiological is 0.991 (95% CI: 0.980-0.999).

Conclusion: COVID-19 can be distinguished from influenza pneumonia based on CT imaging and clinical features, with the highest AUC of 0.991, of which crazy-paving pattern and WBC count play most important role in the differential diagnosis.

Keywords: COVID-19; CT features; clinical features; differential diagnosis; influenza pneumonia.

Figure 1

Flowchart showing influenza pneumonia patient selection along with the inclusion and exclusion criteria.

Figure 2

Axial non-contrast-enhanced CT image from a 26-year-old female patient with COVID-19. Pure ground-glass opacities were observed in the peripheral area in the left lower lobe. The maximum diameter of the lesion was 4.5 cm. The left lower lobe score was 1 because the lung parenchyma was less than 25%.

Figure 3

Box plot graphs revealing statistically significant differences in both the white blood cell (WBC) counts (A) and the neutrophil count (B) between COVID-19 and influenza pneumonia patients. Most patients with both diseases had normal WBC counts and neutrophil counts; however, the overall values in influenza pneumonia were higher than those in COVID-19 (p < 0.001).

Figure 4

Typical CT imaging features in both COVID-19 patients (A,B) and influenza pneumonia patients (C,D). A 65-year-old man with COVID-19 (A) shows the crazy-paving pattern sign in the posterior segment of the right upper lobe along with bilateral peripheral multifocal ground-glass opacities (GGOs). A 46-year-old man with a COVID-19 (B) shows multifocal mixed GGOs in the lower lobe of both lungs, mainly in the peripheral. A 44-year-old female with influenza pneumonia shows lower lobe atelectasis in the posterior basal segment of both lungs, along with bilateral pleural effusions. A 60-year-old man with influenza pneumonia shows local consolidations in the posterior segment and lateral basal segment of both lower lobes.