Distinguishing COVID-19 from influenza pneumonia in the early stage through CT imaging and clinical features

  1. Zhiqi Yang
  2. Daiying Lin
  3. Xiaofeng Chen
  4. Jinming Qiu
  5. Shengkai Li
  6. Ruibin Huang
  7. Hongfu Sun
  8. Yuting Liao
  9. Jianning Xiao
  10. Yanyan Tang
  11. Guorui Liu
  12. Renhua Wu
  13. Xiangguang Chen
  14. Zhuozhi Dai
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Abstract

Purpose

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.

Materials and Method

A consecutive cohort of 73 COVID-19 and 48 influenza pneumonia patients were retrospectively recruited from five independent institutions. The courses of both diseases were confirmed to be in the early stages (2.66 ± 2.62 days for COVID-19 and 2.19 ± 2.10 days for influenza pneumonia after onset). The chi-square test, student’s t-test, and Kruskal-Wallis H-test were performed to compare CT imaging and clinical features between the two groups. Spearman or Kendall correlation tests between feature metrics and diagnosis outcomes were also assessed. The diagnostic performance of each feature in differentiating COVID-19 from influenza pneumonia was evaluated with univariate analysis. The corresponding area under the curve (AUC), accuracy, specificity, sensitivity and threshold were reported.

Results

The ground-glass opacification (GGO) was the most common imaging feature in COVID-19, including pure-GGO (75.3%) and mixed-GGO (78.1%), mainly in peripheral distribution. For clinical features, most COVID-19 patients presented normal white blood cell (WBC) count (89.04%) and neutrophil count (84.93%). Twenty imaging features and 6 clinical features were identified to be significantly different between the two diseases. The diagnosis outcomes correlated significantly with the WBC count (r=-0.526, P<0.001) and neutrophil count (r=-0.500, P<0.001). Four CT imaging features had absolute correlations coefficients higher than 0.300 (P<0.001), including crazy-paving pattern, mixed-GGO in peripheral area, pleural effusions, and consolidation.

Conclusions

Among a total of 1537 lesions and 62 imaging and clinical features, 26 features were demonstrated to be significantly different between COVID-19 and influenza pneumonia. The crazy-paving pattern was recognized as the most powerful imaging feature for the differential diagnosis in the early stage, while WBC count yielded the highest diagnostic efficacy in clinical manifestations.

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