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Open Access Journal [doi: 10.46683/jmvi.2021.33] Research Article
Journal of Molecular Virology and Immunology
Catching the First Wave in the Pandemic:
A Retrospective Evaluation of Chest CT Images for COVID-19
Pandemide İlk Dalgayı Yakalamak:
COVID-19 için Göğüs BT Görüntülerinin Retrospektif Değerlendirilmesi
Mehmet TAHTABAŞI1 [ID], Ergin KARAMAN1 [ID], Yasin AKIN1 [ID], Osman KONUKOĞLU1 [ID],
Nihat KILIÇASLAN1 [ID], Mehmet GEZER1 [ID], Sadettin ER2 [ID], Selda TEZ3 [ID], Mesut TEZ2 [ID],
Kemal TEKİN4 [ID]
1Department of Radiology, Mehmet Akif Inan Education and Research Hospital, University of Health Sciences, Şanlıurfa, Turkey.
2Department of General Surgery, Ankara City Hospital, Ankara, Turkey.
3Department of Radiology, 19 Mayıs Hospital, Ankara, Turkey.
4Department of Medical Microbiology, Gulhane Training and Research Hospital, University of Health Sciences, Ankara, Turkey.
Article Info: Received: 11.06.2021. Accepted: 20.06.2021.
Correspondence: Mehmet Tahtabaşı; Asst.Prof., Department of Radiology, Mehmet Akif Inan Education and Research
Hospital, University of Health Sciences, Şanlıurfa, Turkey. E-mail: mehmet.tahtabasi@sbu.edu.tr
Abstract
Pandemics generally begin in small areas and subsequently spread widely. In order to detect an
outbreak in its early stage, observation of the data for small regions is important. The aim of this study to
investigate the presence of COVID-19-like pneumonia findings in computed tomography (CT) taken before the
COVID-19 pandemic in Turkey. The chest CTs performed in our hospital between December 1, 2019 and March
10, 2020 (study group) and those performed between December 1, 2018 and March 10, 2019 (control group)
were retrospectively analyzed. A total of 1,432 chest CT images in the study group and 1,443 chest CT images
in the control group were evaluated for COVID-19. The lesion characteristics on CT, length of hospital stay
(LOS), and mortality rate were investigated. Typical lung involvement defined for COVID-19 was present in
1.39% (n=20/1,432) of CTs in the study group and 0.49% (n=7/1,443) in the control group (p=0.011).
Seventy-five percent (n=15/20) of the study group were male, and the mean age of the patients was 51.8
(±17.1) years. All the patients in the study group had at least one of the symptoms of COVID-19, such as
fever, cough, and respiratory distress. Ninety percent (n=18/20) of the patients in the study group had
ground-glass opacities that showed a predominantly peripheral distribution. Five of these had accompanying
consolidation and one had a reverse halo sign. According to clinical records, in-hospital mortality developed in
seven of 20 patients (35%), the LOS was 5.5±6.2 days, and the median time from the symptom onset to
admission was 4 (range: 1-12) days. Our study reveals that the onset of COVID-19 or a similar disease is
more likely to occur earlier than first reported in the country.
Keywords: COVID-19, Early detection, Computed tomography, Epidemiology, Pandemic.
Özet
Pandemiler genellikle küçük alanlarda başlar ve daha sonra geniş bir alana yayılırlar. Bir salgını erken
aşamada tespit etmek için küçük bölgelerdeki verilerin gözlemlenmesi önemlidir. Bu çalışmanın amacı
Türkiye'de COVID-19 pandemisinden önce çekilen bilgisayarlı tomografi (BT) görüntülerinde COVID-19 benzeri
pnömoni bulgularının varlığını araştırmaktır. Hastanemizde 1 Aralık 2019-10 Mart 2020 (çalışma grubu) ve 1
©Copyright JMVI. Licenced by Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0).
Tahtabaşı M, et al. J Mol Virol Immunol 2021; 2(3): 67-74.
68
Aralık 2018-10 Mart 2019 (kontrol grubu) tarihleri arasında yapılan göğüs BT'leri geriye dönük olarak incelendi.
Çalışma grubundaki toplam 1432 göğüs BT görüntüsü ve kontrol grubundaki 1443 göğüs BT görüntüsü COVID-
19 için değerlendirildi. BT'de lezyon özellikleri, hastanede yatış süresi (HYS) ve mortalite oranı araştırıldı.
COVID-19 için tanımlanan tipik akciğer tutulumu, çalışma grubundaki BT'lerin %1.39'unda (n=20/1432) ve
kontrol grubunun %0.49'unda (n=7/1443) mevcuttu (p=0.011). Çalışma grubunun yüzde yetmiş beşi
(n=15/20) erkek olup, hastaların yaş ortalaması 51.8’dir (±17,1). Çalışma grubundaki tüm hastalarda ateş,
öksürük ve solunum sıkıntısı gibi COVID-19 semptomlarından en az biri vardı. Çalışma grubundaki hastaların
yüzde doksanı (n=18/20) ağırlıklı olarak periferik dağılım gösteren buzlu cam opasitelerine sahipti. Bunlardan
beşinde bu bulgulara konsolidasyon eşlik ederken ve birinde ters hale işareti vardı. Klinik kayıtlara göre 20
hastanın yedisinde (%35) hastane içi mortalite gelişti, HYS 5.5±6.2 gün ve semptom başlangıcından
başvuruya kadar geçen medyan süre 4 (aralık: 1-12) gündü. Çalışmamız, COVID-19 veya benzeri bir hastalığın
başlangıcının ülkemizde ilk bildirilenden daha erken ortaya çıkma olasılığının yüksek olduğunu ortaya
koymaktadır.
Anahtar Kelimeler: COVID-19, Erken teşhis, Bilgisayarlı tomografi, Epidemiyoloji, Pandemi.
Introduction
In December 2019, a pneumonia case cluster
of undetermined origin emerged in Wuhan, China,
and this new situation began to be monitored by
the World Health Organization (WHO) [1]. The
cause of the infections that evolved into an
epidemic was confirmed as the 2019 novel
coronavirus (2019-nCov) on January 7, 2020,
when Chinese scientists isolated the virus [2].
Later, on the basis of the similarity of its genomic
structure to the virus causing severe acute
respiratory syndrome (SARS-CoV), this virus was
named SARS-CoV-2 by the international virus
taxonomy committee [3]. The disease caused by
the virus was defined as the coronavirus disease
2019 (COVID-19) [1]. WHO declared the disease
a pandemic and a global public health emergency
on March 11, 2020, considering that it could easily
spread from person to person via respiratory
droplets [4]. On March 10, 2020, a Turkish citizen
who was identified to be infected while traveling
from Turkey to Europe was announced as the first
SARS-CoV-2 case in the country [5,6]. The first
death in Turkey was reported on March 15, 2020,
and the spread of the disease throughout the
country was confirmed on April 1, 2020 [6]. On
April 18, 2020, Turkey was the country with the
highest number of laboratory-confirmed cases in
the Middle East by surpassing Iran, and on April
20, 2020, the total number of cases surpassed
China [7,8]. A precise characterization of the
initial epidemic spread and transmission dynamics
is more than expected in consideration of the need
to improve statistical and epidemiological models.
However, verifying this information can be
difficult. Currently, the reverse transcriptase
polymerase chain reaction (RT-PCR) test, which is
used to detect COVID-19 from nasopharyngeal
swabs, is the gold standard diagnostic test;
however, RT-PCR cannot detect a previous
infection [9]. Serological tests do not provide
information on the precise time of infection, and
the low sensitivity of serological tests and the
rapid disappearance of antibodies against viral
antigens in symptomatic and asymptomatic
patients decrease the value of retrospective
serological examinations [9,10]. However,
whether the thoracic computed tomography (CT)
findings of the pre-pandemic period reveal if the
disease was present before the first confirmed
case in a country is a topic worth investigating.
For COVID-19 cases, most of which present with
pneumonia, chest CT has high clinical sensitivity
in the diagnosis and follow-up of the disease
[11,12]. Chest CT can show characteristic findings
including ground-glass areas with or without signs
of reticulation, called the crazy paving pattern,
consolidative pulmonary opacities in advanced
stages, and the reverse halo sign [1,11]. In
addition, the often peripheral predominant
distribution of lesions has been described as a
typical finding in the literature [13]. Viral
pneumonias such as influenza and organized
pneumonia causing acute lung injury (e.g., due to
drug toxicity or secondary to connective tissue
diseases or idiopathic organizing pneumonia) can
be considered in the differential diagnosis of these
findings [4,14].
Tahtabaşı M, et al. J Mol Virol Immunol 2021; 2(3): 67-74.
69
In this study, the presence of typical
radiological findings of the COVID-19 pneumonia
was retrospectively investigated in the chest CTs
performed between December 1, 2019 and March
10, 2020 in one of the largest referral hospital,
located in the Southeast of Turkey, which is also
in close commercial and touristic relationships
with Iran, a country where the first case of
COVID-19 had been reported earlier than Turkey.
In addition, the CT images from one year earlier
were screened, and the frequency of viral
pneumonia between the two periods was
investigated.
Material and Method
Study population
Local ethics committee approval was received
for this single-center study (date: 21.12.2020,
session: 22). Informed consent was waived given
the retrospective nature and characteristics of the
study. For this study, chest CTs performed in our
hospital between December 1, 2019 and March
10, 2020 (study group) and those performed
between December 1, 2018 and March 10, 2019
(control group) were retrospectively analyzed. CT
images obtained for non-infection indications
(trauma and oncology) were excluded from the
study. A total of 1,432 chest CT images in the
study group and 1,443 in the control group were
evaluated with reference to the consensus guide
of the Radiological Society of North America
(RSNA) experts for COVID-19 [15]. The
demographic characteristics of the patients,
clinical and laboratory findings, characteristics of
the lesions on CT, length of hospital stay, time
from symptom onset to admission (days),
comorbidities (chronic obstructive pulmonary
disease, ischemic heart disease, diabetes, or
hypertension) and mortality were obtained from
the medical records.
CT protocol and evaluation
All chest CT examinations were performed
using two 16 multi-slice CT scanners (Toshiba
Aquilion, Toshiba Corporation, Medical Systems
and Somatom Definition, Siemens Healthineers).
CT images were obtained with the patient in the
supine position at end-inspirium. The scanning
ranged from the bilateral apex to base. The main
scanning parameters were as follow: tube
voltage, 110-130 kV; tube current-time product,
50-350 mAs; pitch, 1.25; matrix, 512 × 512; slice
thickness, 10 mm; and reconstructed slice
thickness, 0.625-1.250 mm. All CT images were
obtained from the picture archiving and
communication system (PACS) and reviewed by
two different radiologists, M.T and E.K. (with eight
and nine years of experience in chest imaging,
respectively). The radiologists were blinded to the
clinical and laboratory data. The chest CT images
were evaluated based on consensus regarding the
presence, distribution, and characteristics of the
lesions. Patients with imaging findings identified
and typical for COVID-19 pneumonia were
recorded. The CT images were analyzed according
to the typical appearance of COVID-19 pneumonia
and commonly reported imaging features. These
findings included peripheral and bilaterally
distributed ground-glass opacities (GGO) with or
without consolidation, visible intralobular lines
(crazy-paving pattern), and the reverse halo sign.
Statistical analyses
All analyses were performed using SPSS
software v. 22.0 (IBM SPSS Statistics Version
22.0. Armonk, NY: IBM Corp.). The variables were
divided into two groups as categorical and
continuous. Categorical variables were expressed
as numbers and percentages and compared with
the χ2 test. Continuous variables were expressed
as mean ± standard deviation. The statistical
significance level was accepted as p < 0.05.
Results
In the study group, 1.39% (n = 20/1432) of
the chest CT examination performed between
December 1, 2019 and March 10, 2020 had typical
lung involvement defined for COVID-19, which
was then classified as non-specific interstitial
pneumonia. The control group was defined based
on 1,443 chest CT scans performed between
December 1, 2018 and March 10, 2019. The CT
findings compatible with COVID-19 were detected
in only seven of the control images (0.49%), and
the frequency of these findings significantly
differed from the study group (p = 0.011). The
demographic data of both groups were similar,
with 75% of the study group (n = 15) consisting
of male and the mean age of the patients being
51.8 ± 17.1 (range: 24-85) years (p = 0.121 and
Tahtabaşı M, et al. J Mol Virol Immunol 2021; 2(3): 67-74.
70
p = 0.162, respectively) (Table 1). All the patients
in the study group had at least one of the
symptoms of COVID-19, such as fever, cough,
and respiratory distress. Sputum culture was
performed in 12 (60%) of 20 patients, and all
were found to be negative. When the chest CTs in
the study group were examined, 90% of the
patients (n = 18/20) had GGOs that infiltrated the
parenchyma of both lungs and showed a
predominantly peripheral distribution. Five of
these had an accompanying consolidation and one
had the reverse halo sign (Figures 1, 2 and 3).
Table 1. Demographic, clinical and laboratory data of the patients in the study and control groups.
Study group
(n = 20)
Control group
(n = 7)
P value
Male gender, n (%)
15 (75)
3 (42.9)
0.121
Age (years)
51.8 ± 17.1
61.3 ± 11.8
0.162
Lymphopenia frequency, n (%)
11 (55)
6 (85.7)
0.148
White blood cell count (103 cells/µL)
9.2 ± 6.8
12.3 ± 5.4
0.255
Lymphocyte count (103 cells/µL)
1.49 ± 0.89
1.55 ± 0.93
1.000
Neutrophil count (103 cells/µL)
7.1 ± 6.1
9.8 ± 4.9
0.149
Platelet count (103 cells/µL)
210.1 ± 79.8
242.9 ± 82.4
0.400
C-reactive protein (mg/L)
133.1 ± 87.1
78.9 ± 60.2
0.255
Mortality rate, n (%)
7 (35)
2 (28.6)
0.571
Figure 1: Chest X-ray and CT images of a 33-year-old male patient taken in January 2020. X-ray image (A)
shows peripherally distributed opacities. CT images in the lung window (B,C,D) shows peripherally distributed
ground glass opacities, and the reverse halo sign (arrowhead) with consolidated periphery and ground glass
opacities in the center.
Tahtabaşı M, et al. J Mol Virol Immunol 2021; 2(3): 67-74.
71
Figure 2: Chest X-ray and CT images of the lung window of a 50-year-old male patient taken in January 2020.
X-ray (A) and CT (B) images show peripherally distributed ground glass opacities.
Figure 3: Chest X-ray and CT images in the lung window of a 39-year-old female patient taken in January
2020. (A) X-ray images show symmetrical opacities (arrows) in the lower zones of the lung. (B) CT images
show ground glass opacities (arrowheads) located in the peripheral and subpleural parts of the lung and patchy
consolidations (asterisk) in the lower lobes.
In addition, 10% (n = 2) of the patients had
CT findings compatible with acute respiratory
distress syndrome (ARDS). Twenty percent of the
patients (n = 4) had comorbidities: chronic
obstructive pulmonary disease (COPD) was
present in three and COPD, ischemic heart
disease, and hypertension in one patient.
According to clinical records, in-hospital mortality
developed in seven (35%) of 20 patients, of whom
three had comorbid diseases. The mean
hospitalization time of the cases in the study
group was 5.5 ± 6.2 (range 1-25) days, and the
median time from symptom onset to admission
was 4 (range: 1-12) days. The laboratory findings
of the patients in the study group revealed high
C-reactive protein (CRP) in all patients and
lymphopenia in 11 patients (55%). Table 1 shows
the clinical and laboratory data of the study and
control groups.
Discussion
In the last week of February 2020, COVID-19
cases began to be reported from European
countries such as Italy, Germany, France and
Spain and quickly spread to all European countries
[16]. In an epidemiological study in Italy [17], it
was reported that COVID-19 cases had emerged
in the country long before the first cases were
Tahtabaşı M, et al. J Mol Virol Immunol 2021; 2(3): 67-74.
72
reported, and these patients had been diagnosed
with other viral pneumonias such as influenza and
adenovirus. Similarly, bilateral GGOs with a
predominantly peripheral distribution were
detected in the CTs of the patients included in our
study, and it was determined that these findings,
which were also found in patients with COVID-19,
had been reported as atypical pneumonia.
Although CT is not a specific method to distinguish
COVID-19 from other viral pneumonias, the
development of mortality in 35% of the cases in
our study and the rapid course of clinical
progression raise strong suspicion that these
patients may be associated with the COVID-19
pandemic. This hypothesis is further supported by
the high rate of viral pneumonia in the CT images
screened in our study group compared to the
previous year (control group) (p = 0.011).
Elevated CRP associated with the severity of the
immune response, other laboratory findings, and
males being more affected also indicate that these
infections may have been associated with COVID-
19 [18]. In this context, according to the data of
our study, it can be predicted that the onset of the
pandemic in Turkey may have been before the
date when the first case was reported. This
information may be an important basis for
planning early and rapid responses to similar
outbreaks that may occur in future. We consider
that an effective early warning system can be
established for similar outbreaks by establishing
an infrastructure to support syndromic
surveillance systems used in the monitoring of
influenza and influenza-like illness (ILI) and
severe acute respiratory disease (SARI) with
newly developed software programs (artificial
intelligence-based) to monitor unexpected
changes in CT data or by integrating recorded CT
data into surveillance programs using other new
technologies [19]. Many studies on this subject
suggest that artificial intelligence-based
diagnostic tools are very accurate in detecting the
presence of SARS-CoV-2 using radiological
imaging data and can be used as decision support
tools [20].
One of the most frequently defined abnormal
findings in the CT scans of patients with COVID-
19 is GGOs. In addition, consolidation, lower lobe
involvement and predominantly peripheral
distribution are among the frequently described
findings [4]. In light of these imaging findings,
some authors suggest that every suspected
patient should undergo chest CT, suggesting that
RT-PCR may provide false negative results at the
first stage of the disease [21]. However, these
imaging findings are not specific to COVID-19
since they can also be seen in other viral
pneumonias, such as influenza, adenovirus, and
non-infectious diseases [22]. In recent studies, in
COVID-19 pneumonia, a mixed pattern of
predominantly bilateral GGOs, followed by GGOs
and consolidation have been observed. Similarly,
this involvement is dominant in non-COVID-19
viral pneumonias. However, compared with other
viral pneumonias, COVID-19 demonstrates higher
prevalence of peripheral distribution and
involvement of the upper and middle lobes [23].
The lesions on CT being distributed peripherally in
the current study can be interpreted in favor of
COVID-19 pneumonia [13]. In addition, other
findings such as ARDS or respiratory failure,
shock, and multi-organ dysfunction may occur in
the most severe form of COVID-19, the type
defined as clinically critical disease [24]. In the
current study, imaging findings compatible with
ARDS were found in two patients.
On the other hand, the use of chest CT as a
primary tool for screening patients suspected to
have COVID-19 poses significant problems
[25]. This approach will result in an increased
number of CT examinations, even in clinically
stable patients, which will not only increase costs
and exposure to the harmful effects of ionizing
radiation but also lead to reduced access to
imaging units since the entire room needs to be
thoroughly sterilized after each suspected case of
COVID-19 [23,25]. Furthermore, CT units can
function as a source of COVID-19 transmission.
Therefore, the American College of Radiology
(ACR) warrants caution in adopting CT as a
standard approach in suspected COVID-19 cases,
especially in the early stages of the disease
considering that it may deter the patient from
viral testing, quarantine, and appropriate
treatment [26]. In this context, unnecessary
imaging should be reduced by establishing a
common decision mechanism and close
communication between radiologists and
Tahtabaşı M, et al. J Mol Virol Immunol 2021; 2(3): 67-74.
73
clinicians to determine CT indications. Although
CT is not always diagnostic due to the similar
radiological findings of COVID-19 and other viral
pneumonias, radiologists' expertise and
experience in relation to minor details may play a
role in the early diagnosis of new viral outbreaks.
A meta-analysis study shows that the incubation
period from the onset of symptoms and the first
clinical visit of COVID-19 is longer in COVID-19
(4.92 days) compared to other acute respiratory
viral infections, such as influenza A (1.43-1.64
days), parainfluenza (2.6 days), respiratory
syncytial virus (4.4 days), and rhinovirus (1.4
days) [27]. In addition, the median incubation
time for SARS was estimated at 4.0 days in 2009
[28]. The longer incubation period of COVID-19 is
seen as one of the main factors that can help
explain the rapid spread of previous coronavirus
infections, namely SARS-CoV and Middle East
respiratory syndrome-coronavirus [18]. In the
current study, the time from the first onset of
symptoms to clinical presentation (median value:
4 days) was found to be higher among the
patients with COVID-19 compared to than that of
non-COVID-19 viral pneumonias. Even this
information is sufficient to warrant clinicians to be
alert to the possibility of an unusually different
viral agent. The most important limitations of this
study are the retrospective design and the small
number of patients. In addition, due to the lack of
a RT-PCR test for patients with viral pneumonia,
we were not able to differentiate between COVID-
19 and other viral pneumonia. Nevertheless, we
consider that the current study will help raise
awareness for the early detection of possible
future pandemics, such as COVID-19.
Conclusion
Our study shows that COVID-19 or a similar
pandemic is more likely to occur earlier than the
first reported case in a country. Given the
complexity of determining the onset, emergence
and acceleration phase of a pandemic, such
situations require a multifaceted and rapidly
adapting public health response, including time to
develop agent-specific vaccines. It is considered
that radiological imaging methods, especially
artificial intelligence-based models, will play an
important role in detecting possible epidemics at
an early stage since it seems difficult for clinicians
who first encounter patients to detect the onset of
the pandemic observationally.
Ethics committee approval: Ethics Committee approval for the study was obtained from the local ethic
committee (date: 21.12.2020, session: 22)
Informed consent: Informed consent was waived given the retrospective nature and characteristics of the
study.
Conflict of interest: The authors have no conflicts of interest to declare.
Financial Disclosure: The authors declared that this study has received no financial support.
References
1. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al.
Clinical features of patients infected with 2019 novel
coronavirus in Wuhan, China. Lancet 2020;
395(10223): 497-506. [Crossref]
2. Wang C, Horby PW, Hayden FG, Gao GF. A novel
coronavirus outbreak of global health concern. Lancet
2020; 395(10223): 470-3. [Crossref]
3. Coronaviridae Study Group of the International
Committee on Taxonomy of Viruses. The species Severe
acute respiratory syndrome-related coronavirus:
classifying 2019-nCoV and naming it SARS-CoV-2. Nat
Microbiol 2020; 5(4): 536-44. [Crossref]
4. Salehi S, Abedi A, Balakrishnan S,
Gholamrezanezhad A. Coronavirus Disease 2019
(COVID-19): A Systematic Review of Imaging Findings
in 919 Patients. AJR Am J Roentgenol 2020; 215(1): 87-
93. [Crossref].
5. Şahin M. Impact of weather on COVID-19 pandemic
in Turkey. Sci Total Environ 2020; 728: 138810.
[Crossref].
6. Oztoprak F, Javed A. Case Fatality Rate estimation of
COVID-19 for European Countries: Turkey’s Current
Scenario Amidst a Global Pandemic; Comparison of
Outbreaks with European Countries. EJMO 2020; 4(2):
149-59. [Crossref].
7. Reuters, London, United Kingdom. Turkey's
coronavirus cases overtake Iran, highest in Middle East.
Available at: https://www.reuters.com/article/us-
health-coronavirus-turkey-idUSKBN2200T4 [Accessed
April 18, 2020].
Tahtabaşı M, et al. J Mol Virol Immunol 2021; 2(3): 67-74.
74
8. Foreign Policy, Washington, USA. Turkey’s
Coronavirus Crisis Grows as Infections Exceed China’s.
Available at:
https://foreignpolicy.com/2020/04/20/turkeys-
coronavirus-crisis-grows-as-infections-exceed-chinas/
[Accessed April 20, 2020].
9. Alpdagtas S, Ilhan E, Uysal E, Sengor M, Ustundag
CB, Gunduz O. Evaluation of current diagnostic methods
for COVID-19. APL Bioeng 2020; 4(4): 041506.
[Crossref].
10. Long QX, Tang XJ, Shi QL, Li Q, Deng HJ, Yuan J, et
al. Clinical and immunological assessment of
asymptomatic SARS-CoV-2 infections. Nat Med 2020;
26(8): 1200-4. [Crossref].
11. Gietema HA, Zelis N, Nobel JM, Lambriks LJG, van
Alphen LB, Oude Lashof AML, et al. CT in relation to RT-
PCR in diagnosing COVID-19 in The Netherlands: A
prospective study. PLoS One 2020; 15(7): e0235844.
[Crossref].
12. Zuo H. Contribution of CT Features in the Diagnosis
of COVID-19. Can Respir J 2020; 2020: 1237418.
[Crossref].
13. Tahtabaşı M. COVID-19 Pneumonia: Experiences
Regarding the Use of Computed Tomography in
Diagnosis and Follow-up. J Mol Virol Immunol 2020;
1(2): 51-3. [Crossref].
14. Bai HX, Hsieh B, Xiong Z, Halsey K, Choi JW, Tran
TML, et al. Performance of Radiologists in Differentiating
COVID-19 from Non-COVID-19 Viral Pneumonia at
Chest CT. Radiology 2020; 296(2): E46-E54.
[Crossref].
15. Simpson S, Kay FU, Abbara S, Bhalla S, Chung JH,
Chung M, et al. Radiological Society of North America
Expert Consensus Document on Reporting Chest CT
Findings Related to COVID-19: Endorsed by the Society
of Thoracic Radiology, the American College of
Radiology, and RSNA. Radiol Cardiothorac Imaging
2020; 2(2): e200152. [Crossref].
16. Bulut C, Kato Y. Epidemiology of COVID-19. Turk J
Med Sci 2020; 50(SI-1): 563-70. [Crossref].
17. Ferraris A, Calandri M, Fonio P. Retrospective
evaluation of CT findings to shape epidemiological
models of COVID-19 outbreak. Eur Radiol 2020.
18. Sümbül HE, Şahiner F. Rapid Spreading of SARS-
CoV-2 Infection and Risk Factors: Epidemiological,
Immunological and Virological Aspects. J Mol Virol
Immunol 2020; 1(2): 36-50. [Crossref].
19. Christaki E. New technologies in predicting,
preventing and controlling emerging infectious
diseases. Virulence 2015; 6(6): 558-65. [Crossref].
20. Syeda HB, Syed M, Sexton KW, Syed S, Begum S,
Syed F, et al. Role of Machine Learning Techniques to
Tackle the COVID-19 Crisis: Systematic Review. JMIR
Med Inform 2021; 9(1): e23811. [Crossref].
21. Ai T, Yang Z, Hou H, Zhan C, Chen C, Lv W, et al.
Correlation of Chest CT and RT-PCR Testing for
Coronavirus Disease 2019 (COVID-19) in China: A
Report of 1014 Cases. Radiology 2020; 296(2): E32-
E40. [Crossref].
22. Amorim VB, Rodrigues RS, Barreto MM, Zanetti G,
Hochhegger B, Marchiori E. Influenza A (H1N1)
pneumonia: HRCT findings. J Bras Pneumol 2013;
39(3): 323-9. [Crossref].
23. Altmayer S, Zanon M, Pacini GS, Watte G, Barros
MC, Mohammed TL, et al. Comparison of the computed
tomography findings in COVID-19 and other viral
pneumonia in immunocompetent adults: a systematic
review and meta-analysis. Eur Radiol 2020; 30(12):
6485-96. [Crossref].
24. Wu Z, McGoogan JM. Characteristics of and
Important Lessons From the Coronavirus Disease 2019
(COVID-19) Outbreak in China: Summary of a Report
of 72 314 Cases From the Chinese Center for Disease
Control and Prevention. JAMA 2020; 323(13): 1239-42.
[Crossref].
25. Kooraki S, Hosseiny M, Myers L, Gholamrezanezhad
A. Coronavirus (COVID-19) Outbreak: What the
Department of Radiology Should Know. J Am Coll Radiol
2020; 17(4): 447-51. [Crossref].
26. American College of Radiology, Virginia, USA. ACR
Recommendations for the use of Chest Radiography and
Computed Tomography (CT) for Suspected COVID-19
Infection. Available at: https://www.acr.org/Advocacy-
and-Economics/ACR-Position-
Statements/Recommendations-for-Chest-Radiography-
and-CT-for-Suspected-COVID19-Infection [Accessed
March 11, 2020].
27. Khalili M, Karamouzian M, Nasiri N, Javadi S,
Mirzazadeh A, Sharifi H. Epidemiological characteristics
of COVID-19: a systematic review and meta-analysis.
Epidemiol Infect 2020; 148: e130. [Crossref].
28. Lessler J, Reich NG, Brookmeyer R, Perl TM, Nelson
KE, Cummings DA. Incubation periods of acute
respiratory viral infections: a systematic review. Lancet
Infect Dis 2009; 9(5): 291-300. [Crossref].
