Due to the tremendous rise in the number of COVID-19 patients received by our institute and the shortage or sometimes inconsistency of RT-PCR testing, a search for simple and rapid tools to support the clinical and laboratory findings was mandatory.
In this article, the use of chest CT to help diagnose and facilitate clinical decision-making in suspected COVID-19 patients was discussed. Following the Fleischner society recommendation, one CT scanner was preserved for scanning COVID patients following the instruction of the infection control to avoid infection transmission while other scanners were used for regular medical service .
The sensitivity of CT in COVID-19 compared to RT-PCR in the present study was 92.6%. Findings resembled the study done by Wang Y et al.  who concluded a CT sensitivity of 84%. However, this was lower than other studies which concluded CT sensitivity of 97-98% [11, 17, 18]. The possible explanation for this difference is that 7.4% of our positive cases had normal CT which was performed too early (2-3 days after the onset of symptoms). Bernheim A et al.  found 50% of patients had a normal CT at 0-2 days after onset of symptoms.
According to RSNA recommendations, high probability CT criteria were found in 69.7% of positive cases, showed the highest specificity (97.6%) and PPV (98.8%). These results were similar to Fang et al.  who reported 72% of the patients with typical CT findings. Also, 7.4% of the positive cases showed a negative CT pattern which indicated that negative CT should not be used to exclude COVID-19 infection and was consistent with ACR recommendations . The specificity of the CT probability, following the RSNA recommendations, was higher in the high probability group (97.6%) and decreased gradually in the low probability group (67.5%) which denoted an increase in the number of true positive cases among the CT high probability patients. A finding that was similar to Jaegere et al. .
CT patterns of COVID-19 patients, GGO was the predominant pattern in the current study and was found in 85.9% of positive patients with predominant round morphology (55.5%). Bilateral, peripheral, and multilobar distribution of the GGO was predominant. This was similar to Salehi et al.  who studied 919 patients and found GGO in 88% of cases. Ojha V et al.  reviewed 45 studies including 4410 adult patients. They found isolated GGO in 50.2% and combined with consolidation in 44.4%. Other studies found a higher incidence of the GGO in positive cases reaching 98-100% [11, 23]. Bilateral, peripheral, and multilobar distribution of the GGO was found to be the most specific feature for diagnosis of COVID-19 infection and had the highest incidence [10, 23, 24].
The second most common CT manifestation was the crazy-paving pattern, which was found in 45.3%. This was followed by a vascular dilatation sign that was detected in 29.4% of the positive cases. Ojha V et al.  found crazy-paving in 19.5% of the positive cases and described vascular dilatation in 64% of cases. Li et al.  found a crazy-paving pattern in 36%. Bai et al.  described vascular enlargement signs in 59% of their COVID-19 patients.
Consolidation is a sequel of replacement of alveolar air by cellular fibromyxoid exudate which might be considered as a sign of disease progression and severity . In patients with novel COVID-19 infection, the incidence of consolidation reported in the CT studies ranges from 2-64% . In the current study, it was present in 28.8% of positive cases. It was noticed that the incidence of consolidation between ICU patients was high (81.5%). The consolidation pattern was the dominant pattern in 66.7% of the ICU patients. If we can consider the presence of two CT phenotypes for the COVID-19 infection as found in the result, one with predominant GGO while the other with predominant consolidation. The latter one with a predominant consolidation pattern is the more risky phenotype carrying a worse prognosis.
Salehi et al.  and Chung et al.  found consolidation in 31% and 29% of the cases which are similar to the result of the current study. Also, Ojha V et al.  found consolidation in 24.2% of the positive cases and also noticed the increasing incidence of the consolidation between the older age group and patients who had severe pneumonia.
Additionally, Song et al.  found that consolidations were more in patients with delayed CT after the onset of their symptoms and in patients older than 50 years.
Subpleural curvilinear lines occurred in COVID-19 patients likely due to pulmonary edema or early fibrosis . In the present study, 28.6% of positive cases showed curvilinear pulmonary parenchymal lines on their chest CT. This was similar to Ojha et al.  who reported a 25% incidence of subpleural lines. Li et al.  and Wu et al.  found curvilinear lines in 20% of cases.
Pleural effusion was detected in 11.2% of positive cases which was similar to Li et al.  and Song et al.  who found pleural effusion in 8% of their study population. Pleural effusion was found in 42.6% of ICU patients, so it might be considered as a sign of disease progression and the need for ICU admission [22, 23, 30].
Airway changes in the form of bronchiectasis occurred due to extensive inflammatory damage with fibrous tissue formation and subsequent traction bronchiectasis. Some authors have considered it a sign of severity . Bronchiectasis was found in 2.4% of positive cases and this was lower than the results reported by Ojha et al.  and Shi et al.  which were 18% and 11% respectively.
Fibrosis occurred likely due to the healing process. It is not clear whether it was a good or a poor prognostic sign [31, 32]. The present study recorded only 5% of the positive patients with pulmonary parenchymal fibrosis on their chest CT scans. Ojha et al.  found a higher incidence of fibrosis (17.4%).
Lymph nodes, nodules, and cavitation were the least common CT findings between the current study sample population and these were similar to most of the researches .
Concerning the CT severity of COVID-19, the current study showed a significant correlation between the CT severity and the patients’ management decision. The cut-off value of ≥ 14 preferred ICU admission while the cut-off value of ≤ 10 favored home isolation.
Yang R et al.  studied the CT severity score in 102 positive patients. They correlated the score with the clinical severity and found a higher CT severity score in severe COVID-19 infection when compared with the mild cases. They calculated a cut-off value of 19.5 out of 40 with 83.3% sensitivity and 94% specificity.
In the present study, sufficient data were not available on COVID-19 infection in pediatric patients. Only 39 symptomatic pediatric patients were examined in this study and 11 (3.2%) had positive RT-PCR. The low incidence of COVID-19 infection among the pediatric age group in our study was almost similar to most of the available studies about the COVID-19 infection in pediatric population (1.2-5.2%) [34, 35]. Only one patient showed typical and high probability CT findings, while the others presented with negative, low, and intermediate CT probabilities (5, 3, and 2 patients respectively). The chest CT findings of COVID-19 infection among pediatric patients were different from those usually found in adults. These were concluded by others [34, 35].
Owing to the pandemic status and the increasing number of patients, it was not possible to collect full laboratory data about each of the patients which of course was of additive value if compared to the CT scoring. So, we depend on the clinical suspicion as well as we used the decision taken for the patient’s management as a reflection of their clinical condition. Also, during image interpretation any difference in interpretation was resolved by consensus to avoid any inter-observer disagreement as this was not the scope in this study which may make a some bias, so further studies is recommended to study the inter and intra-observer agreement degrees.