Early diagnosis of COVID-19 is crucial for disease treatment and control. At present, the clinical value of CT for assessing COVID-19 is widely accepted. In our study, RT-PCR was used as the reference standard; the sensitivity, specificity and accuracy of CT were 85.71%, 60.91% and 65.38%, respectively. The corresponding PPV and NPV were 32.43% and 95.12%, respectively. However, our results are quite different from those reported in the literature. Our sensitivity was significantly lower (85.71% vs 97%), while the specificity was higher (60.91% vs 25%) than that reported in the literature. By comparing our results with those in the literature, we found that the main reason was that the majority of our patients had mild pneumonia, and the average age was significantly lower than that of the RT-PCR-positive patients in the literature. In this study, the PPV of CT was 95.12%, which was also consistent with the explanations proffered above. In our study, there were still some patients with positive CT and negative RT-PCR results [19, 20]. We also noticed that the rate of positivity on RT-PCR was not high; this may be related to our sampling time, specimen source, and different stages of pneumonia.
Our results show that almost all RT-PCR-positive patients (12/14) had the characteristic CT features in the early phase, which were described in recent studies [17, 22–24]. Despite the high sensitivity of CT, especially in patients younger than 30 years old (sensitivity is 100%), there was still one patient with no symptoms and no abnormal manifestations on pulmonary CT . Even more surprisingly, a pregnant woman combined with HIV and COVID-19 .
Negative CT results cannot eliminate the possibility of infection with SARS-CoV-2, and CT manifestations suggesting infection with SARS-CoV-2 also do not immediately appear [25, 26]; therefore, these patients may also simply be carriers. In addition, the inflammatory manifestations of COVID-19 on lung CT are very similar to those caused by HIV infection, which may lead to misdiagnosis and improper treatment. This indicates that a comprehensive and detailed history of viral infections can be very useful for the early detection of suspected cases.
As mentioned in previous studies, nearly 30% of patients infected by SARS-CoV-2 are women[27, 28]. Among pregnant women, more than 90% had mild pneumonia, whose clinical histories were very similar. Therefore, whether qualitative CT parameters can accurately reflect the characteristics of pneumonia are become the key point. On the one hand, it is very difficult for radiologists to quantitatively measure lesions due to their different CT manifestations [14, 29]. On the other hand, there may be overlap and subtle differences between pneumonia lesions that are difficult to identify accurately. Clinically, the volume, density and distribution of the manifestations are the most important parameters for clinicians . However, the results suggest our parameters were not significantly different between the groups with positive and negative RT-PCR results. This may be another indication of the diversity and complexity of COVID-19. In addition, the results suggested that pneumonia was more common in the dorsal segment of the lower lobe of the right lung, which was consistent with previous reports. The unexpected results suggested that the original belief that the larger the size of the lesions, the greater the number of lesions, and the wider the lung segment involved, the higher the probability was of positive RT-PCR detection was inaccurate. Other studies have suggested that a higher negative rate of throat swabs compared with sputum samples is a possible cause. In this study, samples were collected two or more times (2–8 times) from each patient to ensure the accuracy of the results as much as possible. These results still need to be confirmed by follow-up antibody testing for COVID-19.
In this study, we found two new valuable parameters, the Hellinger distance and Jaccard coefficient; Ernst Hellinger introduced the Hellinger distance in 1909. On the one hand, when we considered the whole lungs, we found that the Hellinger distance was similar between normal lungs and the lungs of patients with positive RT-PCR results, and the coincidence rate of Jaccard coefficient was relatively higher (72.67%). These results were significantly different from those in patients with negative RT-PCR results. Obviously, the histogram analysis of each lesion indicated that there were no differences in the mean, peak and median CT values between the two groups. These two seemingly contradictory results suggest that the findings are similar whether pneumonia manifests as solid or mixed density lesions. However, the Hellinger distance indicated that the GGOs and fibrosis foci distributed in different positions along the curve were significantly different. This is consistent with the literature regarding the radiological characteristics of SARS-CoV-2 infection, such as GGOs and reticular opacities . These quantitative results strongly suggest that the pulmonary manifestations in patients with positive RT-PCR results are relatively more likely to be associated with GGOs and reticular opacities[19, 32]. However, imaging changes may lag behind changes in patients' clinical symptoms, causing clinicians to miss the optimal timing for the RT-PCR.
The diagnostic value was based on a single figure of merit with a test AUCs of 0.63 for the Jaccard coefficient, while the AUC for Hellinger distance was 0.313. Based on these results, we cannot independently diagnose COVID-19 by using only the Hellinger distance and Jaccard coefficient .
Our study had several limitations. First, there is a limited window of time within which to determine the COVID-19 status of pregnant women before they give birth, and the number of cases that could be included was quite limited. Second, due to the limitations on CT radiation doses, it is difficult to perform follow-up CT reexaminations within a short period. Third, although we performed strict image quality control, there were several patients with small pulmonary nodules, which can affect feature extraction from CT images. Finally, other potential causes of CT imaging errors, such as the CT partial volume effect, could not be estimated.