Considering the similarity of period of year, clinical symptoms of COVID-19 and influenza pneumonia and the importance of differential diagnosis, previous studies have compared the clinical manifestations, routine blood tests and CT findings between COVID-19 and influenza pneumonia. Shen and Liu et al. found that the clinical manifestations of COVID-19 and influenza pneumonia were very similar, but the monocyte percentage increased and the eosinophil count decreased in COVID-19 patients, and the GGO of COVID-19 on the CT image distributed in the periphery of the lung [
8,
34]. In addition, the progression of chest CT findings is closely related to the prognosis of COVID-19. With the decrease of pure GGO, the increase of consolidation, the expansion of the lesion area, and the appearance of crazy-paving pattern, the COVID-19 patient's prognosis becomes worse [
35,
36]. Diagnosing COVID-19 in the early stage is also beneficial to improve the prognosis. This study systematically analyzed the differences in CT signs and radiomics features COVID-19 and influenza pneumonia within 7 days. Our research found that four signs and seven radiomics features are related to COVID-19 infection. The selected CT signs and radiomics features can be used to construct CT signs model, radiomics models and combined model to distinguish COVID-19 and influenza pneumonia. In this study, the diagnostic performance of the radiomics model was not significantly better than the radiologists’ subjective judgments. However, the combined model which was based on CT signs and radiomics features, can distinguish COVID-19 from influenza pneumonia better than CT signs or radiomics features alone. And the combined model showed excellent and encouraging performance. The CT signs of COVID-19 and influenza pneumonia were compared in this study. We found that peripheral lesion, GGO, intralobular interstitial thickening and halo sign of COVID-19 pneumonia are more common than influenza pneumonia, which is consistent with previous studies [
12,
13,
22,
37]. And the performance of CT signs to identify COVID-19 and influenza pneumonia is acceptable, which is consistent with Bai et al. (Accuracy 60–83%) [
38]. The results of the radiologist's subjective evaluation showed that CT signs are of clinical value in identifying viral pneumonia. Radiomics is the generation of minable high throughput data through conversion of digital medical images (e.g. CT, MRI, PET/CT, and Ultrasound) [
15]. In previous studies, radiomics had outstanding performance in the diagnosis, staging, prognosis, and treatment response prediction of tumors [
15‐
17]. In addition, radiomics can give rise to a deeper understanding of the heterogeneity of pneumonia lesions [
21,
39,
40]. Therefore, radiomics is theoretically a feasible method to distinguish COVID-19 pneumonia from influenza pneumonia. In our study, we selected seven of the most predictive radiological features, and most of them were filtered or transformed first-order or texture features. It might indicate that the distinguishment between such highly imaging overlapped pneumonia may need the emphasized features in the spatial or frequency domains or the relatively higher stability of these higher-order features. In clinical cancer research, radiomics features have been shown to reflect tumor invasiveness, malignancy, and lymph node metastasis potential and other biological characteristics [
41‐
43]. However, we speculate that the cause of CT image heterogeneity between COVID-19 and influenza pneumonia may be different from the tumor. Subsequently, the radiomics prediction model was constructed. The performance of the classifier was 86.5% sensitivity, 78.4% specificity, 83.1% accuracy. In addition, the ROC curve was used for performance evaluation. The AUC was 0.888, indicating a relatively good performance.
In previous study, multiple CNN models were used to distinguish COVID-19 and influenza A pneumonia, And the Noisy-or Bayesian function model can make the accuracy reach 85.0% [
14]. Zeng et al. used radiomics model to distinguish COVID-19 and influenza A pneumonia [
44], and obtained an AUC (0.87) similar to ours. In this study, we selected significant features from more radiomics features (1316 in total), and included influenza A pneumonia and influenza B pneumonia, for which differential diagnosis is more difficult. Besides, in order to further improve the performance of the prediction model, we combined the radiologist's subjective visual assessment and computed radiomics features to construct the prediction model. It was found that the combined model has higher sensitivity, specificity, accuracy and AUC (0.959) than CT signs or radiomics model. The calibration curve and decision curve also showed that the reliability and stability of the combined prediction model were better. Shiri etal. used different radiomic features, feature selection and classifiers of multimodal images to construct prediction models and observed their performance in predicting the mutation status of EGFR and KRAS in non-small cell lung cancer [
45]. The results show that the radiomic features extracted from different image feature sets can not only be used to predict the mutation status of EGFR and KRAS, but also have higher predictive power than conventional images. In addition, other previous studies have also shown that the combination of feature selection method and classification method can improve the predictive or prognostic performance of the model [
46‐
49]. With the expansion of the study population and feature scale, the combination of meaningful biological information, clinical data and imaging omics may further improve prediction or prognostic performance. In addition, it is very important to select features and establish models according to different diseases, and to perform correlation analysis between radiomics features and more physiological and pathological features. Exploring the meaning of radiomics features in physiological or pathological mechanisms can make better use of radiomics features [
45,
50].
This study has some limitations. First, as a retrospective study, there may be selection bias. But the results of our preliminary study are encouraging and will be verified in future larger studies. In addition, because of the small size of other single cases of pneumonia, we did not compare the characteristics of different viral pneumonia. Finally, the response of the lung to the virus is highly related to the host factor. CT data alone cannot completely distinguish the type of viral pneumonia, and more clinical features and laboratory examination data need to be considered. Combined with more clinical data, the predictive model may be better at identifying viral pneumonia.
In conclusion, we determined the chest CT signs and radiomics features that distinguished COVID-19 from influenza pneumonia and developed an effective predictive model. Our research shows that CT signs and radiomics features are effective tools for identifying COVID-19 and influenza pneumonia.