Skip to main content

Tomographic findings in patients with COVID-19 according to evolution of the disease



The tomographic findings in COVID-19, its classification, a brief overview of the application of artificial intelligence, and the stages during the course of the disease in patients with moderate COVID-19

Main body

Chest CT allows us to follow the course of COVID-19 in an objective way; each phase has characteristic imaging findings and, consequently, takes the corresponding measures.

A search was made in the PubMed database with the keywords extracted from the DeCs and the combinations of these. Only articles published between December 2019 and June 2020 were included. The search was limited to the English language.


CT serves to monitor the course of the disease since it assesses the severity of lung involvement. The most frequent finding is bilateral ground glass opacities with a subpleural distribution. The progression occurs in two phases: one slow and one fast. At discharge, the patient may have ground glass opacities or areas that will later become fibrosis, leaving sequelae for life.


At the end of 2019, 27 cases of pneumonia of unknown origin were identified in the city of Wuhan, China [1, 2]. The causative agent of this disease was identified from nasopharyngeal swab samples by the Centers for Disease Control and Prevention (CDC), on January 7, 2020; it was called severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2). The disease was named COVID-19 by the World Health Organization [3]. At the moment, the diagnosis is based on clinical findings, the patient’s epidemiological history, findings on chest computed tomography (CT), and reverse transcriptase polymerase chain reaction (RT-PCR), the last being the “gold standard” [4]. Chest CT allows us to monitor the course of the disease in an objective way; each phase has characteristic imaging findings and, consequently, takes the corresponding measures. This review seeks to document the tomographic findings in COVID-19, its classification, a brief overview of the application of artificial intelligence, and the stages during the course of the disease in patients with moderate COVID-19 [5,6,7].

A search was made in the PubMed, Scopus, and Web of Sciences database with the keywords extracted from the DeCs and the combinations of these. Only articles published between December 2019 and June 2020 were included. The search was limited to the English language.

Main text

COVID-19 and radiology

In the current COVID-19 pandemic, radiology plays a fundamental role in patient management. Among the available options we have, X-rays, ultrasound, and CT of the chest; each with its advantages and disadvantages; however, chest CT is the standard in imaging studies of pneumonia [8]. Chest CT is a non-invasive radiological examination that involves the use of X-rays at different angles around the patient’s chest to produce cross-sectional images [9, 10]. As COVID-19 spreads across the world, there is increasing interest in the role of chest CT in screening, diagnosis, and management of patients with suspected or confirmed COVID-19 infection. The American College of Radiology recommends that chest CT should be used with discretion and reserved for hospitalized and symptomatic patients with specific clinical indications for CT. Likewise, it indicates that it should not be used as a first-line diagnostic test [11, 12]. Chest CT is used to assess the severity of the lung affected by COVID-19 [13, 14]. However, in some situations such as a shortage of RT-PCR tests in emergency situations, it can be considered as a diagnostic option. This is because it provides, at initial stages, a higher sensitivity to molecular tests also due to the speed with which the images are obtained, and thus reduce the false negative rate [15]. With this benefit, the appropriate isolation measures can be taken as well as applying the appropriate management [16, 17].

CT: imagenological findings/classification of findings

COVID-19 has a wide spectrum of tomographic characteristics reported in various studies [18, 19]. Chest radiographs have little diagnostic value in the early stages, while CT findings may be present even before the onset of symptoms [18]. Frequent features such as ground glass opacities with or without consolidated anomalies are considered compatible with viral pneumonia [20,21,22]. If these findings are added multifocality, a predominant distribution in subpleural and peribronchovascular regions, they can be considered a COVID-19 pattern [23]. Currently, there is a greater publication of studies on imaging features focused on CT in COVID-19. Among the publications, a consensus study with the participation of the Chinese Society of Radiology stands out, where it is proposed to divide the findings into two large groups: frequent and infrequent (Table 1); the Radiological Society of North America classifies the findings into four categories: typical, indeterminate, atypical, and unrelated findings (Table 2) [24,25,26]; and the Radiological Society of the Netherlands classifies the findings according to the level of suspicion of pulmonary involvement by COVID-19, for this they use a Classification System of Reports and Data in COVID-19 (CO-RADS) (Table 3). This system has values from 0 to 6 [27, 28].

Table 1 Classification according to the Chinese Society of Radiology
Table 2 Classification according to the American Society of Radiology
Table 3 Classification according to the Radiological Society of the Netherlands

Classification according to the Chinese Radiology Consensus


In the study with the largest population to date (940 people), ground glass opacities and mixed attenuation pattern are the most frequent findings [18]. Ground glass opacities present as a slight increase in lung density. With visibility of the vascular structures, they are due to pathological changes such as serous and fibrous exudates as well as vascular congestion of the septa and edema [29,30,31]. Subsequently, these areas grow, cover a larger area, and become denser. To this are added consolidations with an air bronchogram sign. In consolidation, there is an increase in parenchymal density; its extension may be acinar to encompass a whole lung lobe. Consolidations are more prevalent in the elderly. The lung lesions are bilateral; peripheral, with poorly defined edges; asymmetric, with nodular; patched; or confluent morphology, mainly in the caudal regions of the lung and the subpleural dorsal area [32, 33]. The result of the thickening of the interlobular and intralobar septum is the “crazy paving” pattern, all of which is a reflection of interstitial damage [34]. Reverse halo sign, defined as ground glass opacity zone surrounded by peripheral consolidation, and the halo sign, defined by a consolidation zone in which a ground glass opacity zone converges, are two findings closely related to the pneumonia established by COVID-19.


Less frequent findings include pleural effusion, changes in vessel caliber, traction bronchiectasis, and thoracic lymphadenopathy [35]. Kong et al. reported the presence of cavitation caused by the disease in their study [36]. As the studies progress, and new information emerges, not so common findings will appear.

Classification according to the Radiological Society of North America


In the literature, these findings are documented as having high prevalence and specificity for COVID-19 pneumonia. This CT findings including periferal and biateral ground glass opacities accompanied or not by consolidations or “crazy paving” pattern, multifocal ground glass opacities with rounded morphology with or without consolidations or “crazy cobblestone” pattern, and the inverse halo sign along with other findings of organizing pneumonia [37, 38].


Non-specific findings for pneumonia are part of this group. It is defined by the absence of typical findings plus the presence of multifocal, diffuse, perihilar, unilateral, non-peripheral ground glass opacities, lacking rounded morphology with or without associated consolidations. This last component can be replaced by very small ground glass opacities without peripheral distribution or rounded morphology.


These are findings whose presence in documented studies is not common or is not reported. In order to classify them in this group, they must not be part of the 2 previous groups, and to this must be added the presence of segmental consolidations or that affect a single lobe in isolation without ground glass opacities; discrete small nodules (centrilobular or in a “budding tree” pattern); and pulmonary cavitation or mild increase in the thickness of the septum with pleural effusion [39, 40].


The findings correspond to other suggestive pneumonia.

Classification according to the Radiological Society of the Netherlands


It corresponds to findings that cannot be assigned to the other categories because the examination was incomplete or due to the presence of artifacts caused by breathing or coughing at the time of the study.


It implies a very low level of suspicion of pulmonary involvement by COVID-19 and indicates a non-infectious pathology. To this group belong the findings of mild or severe emphysema, nodules around the fissures, lung tumors, or the presence of fibrosis.


It implies a low level of suspicion of pulmonary involvement by COVID-19. The images are typical of other infectious diseases such as bronchitis, infectious bronchiolitis, bronchopneumonia, lobar pneumonia, and lung abscess. The findings of a budding tree pattern, bronchiectasis, centrilobular nodular pattern, lobar or segmental consolidations, and lung cavitation are present.


It implies indeterminate findings of pulmonary involvement by COVID-19, but that can be found in other viral pneumonias and non-infectious pathologies. This includes perihilar ground glass opacities, a homogeneous and extensive ground glass pattern with or without preservation of some secondary pulmonary lobes, or ground glass along with thickening of the interlobular septum with or without pleural effusion in the absence of typical findings.


It implies a high level of suspicion of pulmonary involvement by COVID-19 but whose findings may overlap to some level with other viral pneumonias. The findings are similar to CO-RADS 5 but are not in contact with the visceral pleura or are located strictly in a single lung, have a predominant peribronchiovascular distribution, or overlap with severe pre-existing lung abnormalities.


It implies a very high level of suspicion for lung involvement by COVID-19. These are the typical findings of the disease, ground glass opacities are present, with or without consolidations, in regions close to the surface of the visceral pleura, including fissures, and present a bilateral multifocal distribution. Likewise, there is the presence of a crazy cobblestone pattern, an inverse halo sign, subpleural bands and thickening of the vascular network within the pulmonary anomalies.


Involves confirmation of the disease by positive RT-PCR [27, 28].

Differential diagnosis in CT

Within the differential diagnosis, we have viral pneumonia caused by influenza A virus, influenza B virus, respiratory syncytial virus, adenovirus, cytomegalovirus, coronaviruses other than SARS-CoV-2, as well as pneumonia of bacterial etiology [41]. Pneumonia from another viral etiology caused by SARS-CoV-2 is a challenge, since the findings are very similar. However, pneumonias of other viral origin usually present peribronchial or perivascular interstitial inflammation that is directed towards the inner part of the pulmonary interstitium, subpleural and hilar distribution, high attenuation reticular pattern, accentuated fibrous tracts, pulmonary edema, and atelectasis. On the other hand, pneumonia acquired in the bacterial community is characterized by the presence of a segmental or lobar consolidation in the air space, limited by the pleural surface; it may present with ground glass opacities, centrilobular nodules, thickening of the vascular network, and mucus impaction. If you find an immunosuppressed patient [42].

CT severity index

Quantitative evaluation serves to assess the change or progression of pulmonary injury manifested on chest CT. One instrument is the CT severity index. In this, the degree of involvement of each of the five lung lobes is examined and classified into 5 types: no damage (0%), minimal (1–25%), mild (26–50%), moderate (51–75%), or severe (76-100%). No implication corresponds to a lobe score of 0, a minimal implication corresponds to a lobe score of 1, a slight implication is related to a lobe score of 2, a moderate participation concerns a lobe score of 3, and a severe involvement of a lobe score of 4. CT severity index is achieved by adding the results of the 5 lobes (range of possible scores, 0–20) (Fig. 1) [43,44,45]. Likewise, Li et al. applied the CT severity index in relation to the type of patient COVID-19 in their study: mild, common, and severe-critical. They described that the higher the score, the greater the relationship with the classificatory range of the patients [46]. As days pass from the onset of symptoms, the CT severity index increases, then decreases as improvement is evident [47].

Fig. 1

CT severity index of a 51-year-old male patient, progressive stage. a Right upper lobe, score 1. b Right middle lobe, score 1. c Right lower lobe, score 2. d Upper left lobe, score 2. e Left lower lobe, score 2. f Coronal section. g Air volumetric reconstruction. The CT severity index is 8

Artificial intelligence in the covid-19 diagnosis

The use of artificial intelligence algorithms in the radiological field is increasingly common; it is beneficial since it reduces the radiologist’s workload and can reduce the time to diagnose a specific pathology [48]. Its application in the current pandemic is no exception; work is underway on various projects applied to chest CT in patients with COVID-19 [49]. The prototype “CT Pneumonia Analysis2” by the Siemens company stands out.

The CT Pneumonia Analysis2 is an algorithm used for research purposes that identifies and quantifies abnormal tomographic patterns in the lungs automatically on chest CT scans.

This system initially takes noncontrast chest CT to identify and 3D segment the lobes and lungs before documenting abnormalities.

This study produces two combined measures of severity of lung/lobe involvement, quantifying both the extent of COVID-19 abnormalities and the presence of high opacities. The results are used to analyze the severity as well as to document the progress of the disease [50].

CT changes during the course of disease progression

These manifestations follow a chronological order in the vast majority of cases, with which we can categorize the disease in phases and thus observe the improvement or deterioration of the patient, to solve it [51, 52]. According to Pan et al., we can divide the progression of the disease into 4 radiographic stages, which in turn are related to the patient’s symptoms [7].

Early stage (0–4 days)

It takes place from the onset of symptoms until 4 days later; it is characterized by the presence of an opacity or multiple ground glass opacities distributed subpleurally, generally in the lower lobes, either unilaterally or bilaterally. This is accompanied by vascular accentuation (Fig. 2) [53,54,55].

Fig. 2

Image of a 36-year-old asymptomatic patient with negative RT-PCR. Pulmonary parenchymal window thin section computed tomography shows ground glass opacity in the left lower lobe of the anterobasal segment of peripheral distribution

Progressive stage (5–8 days)

Within 5–8 days, at this stage, infection progresses rapidly and spreads to multiple lobes, ground glass opacities become diffuse, “crazy cobblestone” pattern appears, and consolidations (Fig. 3) [31].

Fig. 3

Image of a 49-year-old patient with a history of HIV in treatment with HAART, 6th day of illness with fever and dyspnea. The pulmonary parenchymal window thin section computed tomography shows the presence of ground glass opacities in the bilateral anterosuperior third (blue arrows) and the “crazy stone” pattern of bilateral posterior location (white arrows). There are no consolidations

Peak stage (10–13 days)

It covers from day 9 to day 13; the lung damage slowly reaches its maximum point, the consolidations are fully developed, the air bronchogram sign is observed, a pattern in “crazy paving,” and a greater diffusion of opacities in frosted glass. There are parenchymal bands and subsegmental atelectasis; in some cases, pleural effusion can be witnessed bilaterally (Fig. 4) [38, 56].

Fig. 4

Sequence of images of a 79-year-old patient with fever, dyspnea, and myalgia of 10 days of evolution. The pulmonary parenchymal window thin section computed tomography shows the following: a Presence of ground glass opacities in the middle third of both hemithorax, of central distribution (blue arrows) and peripheral distribution (white arrows); parenchymal band declining in the upper third of the left lower lobe (gray arrow), a subpleural band (black arrow) and thickening of the axial peribronchovascular interstitium (green arrows) are added. b “Crazy cobblestone” pattern in the lobe of the lingula (black arrow) together with a pleural traction band (blue arrow). c Consolidation in the posterior segment of the right lower lobe associated with a parenchymal band in the same segment (black arrow)

Absorption stage (≥ 14 days)

It occurs after 14 days of symptom onset and corresponds to disease control. Dissipation of the consolidation is observed; in the same way, it can be observed areas of opacities in ground glass that corresponds to regression areas, and there may also be scarring atelectasis, which indicates fibrosis [57]. As for this phase, the pattern in “crazy paving” is no longer present. In some cases, the course of the disease was short and the transition from the early phase to the absorptive phase was witnessed without intermediaries (Fig. 5) [7, 34, 58, 59].

Fig. 5

Images of two patients over 50 years old with disease time of 20 days. a Parenchymal bands with opacity in ground glass are observed in remission. b Observed areas of opacities in ground glass that corresponds to regression areas


In summary, CT is used to monitor the course of the disease since it evaluates the severity of lung involvement. The tomographic characteristic most seen during the stages are bilateral ground glass opacities with a subpleural distribution, which is accompanied by consolidations, a “crazy pavement” pattern, thickening of the vascular network, and air bronchogram, among others. As pneumonia progresses, chest CT provides particular images of COVID-19, with which we can establish, in stages, a sequence of lung injury. The vast majority of patients will show this sequence, especially the first and last stages.

Availability of data and materials

Being a review article, we do not have data base as the primary studies.


CT scan:

Computed tomography


Reverse transcriptase polymerase chain reaction


Severe acute respiratory syndrome coronavirus type 2

COVID-19 :

Official name for the disease caused by SARS-CoV-2


  1. 1.

    World Health Organization declares global emergency_ A review of the 2019 novel coronavirus (COVID-19) | Elsevier Enhanced Reader.2020 [cited 2020 May 26]. Available at:

  2. 2.

    Jin Y, Yang H, Ji W, Wu W, Chen S, Zhang W et al (2020) Virology, epidemiology, pathogenesis, and control of COVID-19. Viruses. 12(4):372

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  3. 3.

    WHO Director-General’s remarks at the media briefing on 2019-nCoV on 11 February 2020. 2020 [cited 2020 May 26]. Available at:

  4. 4.

    Tahamtan A, Ardebili A (2020) Real-time RT-PCR in COVID-19 detection: issues affecting the results. Expert Rev Mol Diagn. 20(5):453–454

    CAS  Article  PubMed  Google Scholar 

  5. 5.

    Clinical management of COVID-19. 2020 [cited 2020 May 31]. Available at:

  6. 6.

    Zu ZY, Jiang MD, Xu PP, Chen W, Ni QQ, Lu GM et al (2020) Coronavirus disease 2019 (COVID-19): a perspective from China. Radiology. 21:200490

    Google Scholar 

  7. 7.

    Pan F, Ye T, Sun P, Gui S, Liang B, Li L et al (2020) Time course of lung changes at chest CT during recovery from coronavirus disease 2019 (COVID-19). Radiology [cited 2020 May 29]; Available at:

  8. 8.

    Yang W, Sirajuddin A, Zhang X, Liu G, Teng Z, Zhao S et al (2020) The role of imaging in 2019 novel coronavirus pneumonia (COVID-19). Eur Radiol 30:4874

    CAS  Article  PubMed  Google Scholar 

  9. 9.

    Lee EYP, Ng MY, Khong PL (2020) COVID-19 pneumonia: what has CT taught us? Lancet Infect Dis 20(4):384–385

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Li M (2020) Chest CT features and their role in COVID-19. Radiol Infect Dis Beijing China [cited 2020 Jun 2]; Available at:

  11. 11.

    Hope MD, Raptis CA, Shah A, Hammer MM, Henry TS (2020) A role for CT in COVID-19? What data really tell us so far. Lancet 395(10231):1189–1190

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  12. 12.

    ACR Recommendations for the use of Chest Radiography and Computed Tomography (CT) for Suspected COVID-19 Infection. 2020 [cited 2020 May 31]. Available at:

  13. 13.

    Yang Z, Shi J, He Z, Lü Y, Xu Q, Ye C et al (2020) Predictors for imaging progression on chest CT from coronavirus disease 2019 (COVID-19) patients. Aging 12(7):6037–6048

    Article  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Wu J, Wu X, Zeng W, Guo D, Fang Z, Chen L et al (2020) Chest CT Findings in patients with coronavirus disease 2019 and its relationship with clinical features. Invest Radiol. 55(5):257–261

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Liu H, Liu F, Li J, Zhang T, Wang D, Lan W. 2020. Clinical and CT imaging features of the COVID-19 pneumonia: Focus on pregnant women and children. J Infect. 80 (5): e7-13.

  16. 16.

    Soyer P (2020) Lessons learned from chest CT in COVID-19. Diagnost Interv Imaging. 101(5):261–262

    CAS  Article  Google Scholar 

  17. 17.

    Lu X, Gong W, Peng Z, Zeng F, Liu F (2020) High resolution CT imaging dynamic follow-up study of novel coronavirus pneumonia. Front Med. 7:168

    Article  Google Scholar 

  18. 18.

    Salehi S, Abedi A, Balakrishnan S, Gholamrezanezhad A (2020) Coronavirus disease 2019 (COVID-19): a systematic review of imaging findings in 919 patients. Am J Roentgenol 14:1–7

    Google Scholar 

  19. 19.

    Wang K, Kang S, Tian R, Zhang X, Zhang X, Wang Y (2020) Imaging manifestations and diagnostic value of chest CT of coronavirus disease 2019 (COVID-19) in the Xiaogan area. Clin Radiol. 75(5):341–347

  20. 20.

    Omar S, Motawea AM, Yasin R (2020) High-resolution CT features of COVID-19 pneumonia in confirmed cases. Egypt J Radiol Nucl Med. 51(1):121

    Article  Google Scholar 

  21. 21.

    Zhao W, Zhong Z, Xie X, Yu Q, Liu J (2020) Relation between chest CT findings and clinical conditions of coronavirus disease (COVID-19) pneumonia: a multicenter study. AJR Am J Roentgenol. 214(5):1072–1077

    Article  PubMed  Google Scholar 

  22. 22.

    Aviram G, Bar-Shai A, Sosna J, Rogowski O, Rosen G, Weinstein I et al (2010) H1N1 influenza: initial chest radiographic findings in helping predict patient outcome. Radiology. 255(1):252–259

    Article  PubMed  Google Scholar 

  23. 23.

    Li Y, Xia L (2020) Coronavirus Disease 2019 (COVID-19): Role of chest CT in diagnosis and management. Am JRoentgenol. 214(6):1280–1286

    Article  Google Scholar 

  24. 24.

    Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J et al (2020) Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus – infected pneumonia in Wuhan, China. JAMA 323(11):1061–1069

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Guan CS, Lv ZB, Yan S, Du YN, Chen H, Wei LG et al (2020) Imaging features of coronavirus disease 2019 (COVID-19): evaluation on thin-section CT. Acad Radiol. 27(5):609–613

    Article  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Simpson S, Kay FU, Abbara S, Bhalla S, Chung JH, Chung M et al (2020) Radiological Society of North America expert consensus statement 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 2(2):e200152

    Article  PubMed Central  Google Scholar 

  27. 27.

    Prokop M, van Everdingen W, van Rees Vellinga T, Quarles van Ufford H, Stöger L, Beenen L, et al. 2020. CO-RADS: a categorical CT assessment scheme for patients suspected of having COVID-19 - definition and evaluation. Radiology. 296 (2): E97-104.

  28. 28.

    Fujioka T, Takahashi M, Mori M, Tsuchiya J, Yamaga E, Horii T et al (2020) Evaluation of the usefulness of CO-RADS for chest CT in patients suspected of having COVID-19. Diagn Basel Switz 10(9):E608

    Google Scholar 

  29. 29.

    Yang Q, Liu Q, Xu H, Lu H, Liu S, Li H (2020) Imaging of coronavirus disease 2019: a Chinese expert consensus statement. Eur J Radiol [cited 2020 Jun 2]; Available at:

  30. 30.

    Vancheri SG, Savietto G, Ballati F, Maggi A, Canino C, Bortolotto C et al (2020) Radiographic findings in 240 patients with COVID-19 pneumonia: time-dependence after the onset of symptoms. Eur Radiol 30:6161

    CAS  Article  PubMed  Google Scholar 

  31. 31.

    Wang Y, Dong C, Hu Y, Li C, Ren Q, Zhang X et al (2020) Temporal changes of CT findings in 90 patients with COVID-19 pneumonia: a longitudinal study. Radiology 296:E55

    Article  PubMed  Google Scholar 

  32. 32.

    Jin YH, Cai L, Cheng ZS, Cheng H, Deng T, Fan YP et al (2020) A rapid advice guideline for the diagnosis and treatment of 2019 novel coronavirus (2019-nCoV) infected pneumonia (standard version). Mil Med Res. 7(1):4

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Bao C, Liu X, Zhang H, Li Y, Liu J (2020) Coronavirus Disease 2019 (COVID-19) CT Findings: A Systematic Review and Meta-anaysis. J Am Coll Radiol 17(6):701–9.

  34. 34.

    Li M, Lei P, Zeng B, Li Z, Yu P, Fan B et al (2020) Coronavirus disease (COVID-19): spectrum of CT findings and temporal progression of the disease. Acad Radiol. 27(5):603–608

    Article  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Li X, Zeng X, Liu B, Yu Y (2020) COVID-19 infection presenting with CT halo sign. Radiol Cardiothorac Imaging. 2(1):e200026

    Article  PubMed Central  Google Scholar 

  36. 36.

    Kong W, Agarwal PP (2020) Chest imaging appearance of COVID-19 infection. Radiol Cardiothorac Imaging. 2(1):e200028

    Article  PubMed Central  Google Scholar 

  37. 37.

    Bhat R, Hamid A, Kunin JR, Saboo SS, Batra K, Baruah D et al (2020) Chest imaging in patients hospitalized with COVID-19 infection - a case series. Curr Probl Diagn Radiol. 49(4):294–301

    Article  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Ding X, Xu J, Zhou J, Long Q (2020) Chest CT findings of COVID-19 pneumonia by duration of symptoms. Eur J Radiol:127 [cited 2020 Jun 2]; Available at:

  39. 39.

    Qu J, Yang R, Song L, Kamel IR (2020) Atypical lung feature on chest CT in a lung adenocarcinoma cancer patient infected with COVID-19. Ann Oncol Off J Eur Soc Med Oncol 31:825

    Article  Google Scholar 

  40. 40.

    Fang Y, Zhang H, Xie J, Lin M, Ying L, Pang P et al (2020) Sensitivity of chest CT for COVID-19: comparison to RT-PCR. Radiology. 19:200432

    Google Scholar 

  41. 41.

    Dai WC, Zhang HW, Yu J, Xu HJ, Chen H, Luo SP et al (2020) CT imaging and differential diagnosis of COVID-19. DogAssoc Radiol JJ Assoc Can Radiol. 71(2):195–200

    Article  Google Scholar 

  42. 42.

    Hani C, Trieu NH, Saab I, Dangeard S, Bennani S, Chassagnon G et al (2020) COVID-19 pneumonia: a review of typical CT findings and differential diagnosis. Diagnost Interv Imaging. 101(5):263–268

    CAS  Article  Google Scholar 

  43. 43.

    Chung M, Bernheim A, Mei X, Zhang N, Huang M, Zeng X et al (2020) CT imaging features of 2019 novel coronavirus (2019-nCoV). Radiology. 295(1):202–207

    Article  PubMed  Google Scholar 

  44. 44.

    Kim MA, Park JS, Lee CW, Choi WI (2019) Pneumonia severity index in viral community acquired pneumonia in adults. PloS One. 14(3):e0210102

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Cheng Z, Qin L, Cao Q, Dai J, Pan A, Yang W et al (2020) Quantitative computed tomography of the coronavirus disease 2019 (COVID-19) pneumonia. Radiol Infect Dis Beijing China [cited 2020 Jun 2]; Available at:

  46. 46.

    Li K, Fang Y, Li W, Pan C, Qin P, Zhong Y et al (2020) CT image visual quantitative evaluation and clinical classification of coronavirus disease (COVID-19). Eur Radiol [cited 2020 Jun 2]; Available at:

  47. 47.

    Hyf W, Hys L, Ah F, St L, Tw C, Csy L et al (2019) Frequency and distribution of chest radiographic findings in COVID-19 positive patients. Radiology [cited 2020 Jun 2]. Available at:

  48. 48.

    Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL (2018) Artificial intelligence in radiology. Nat Rev Cancer. 18(8):500–510

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Mei X, Lee HC, Diao K, Huang M, Lin B, Liu C et al (2020) Artificial intelligence – enabled rapid diagnosis of patients with COVID-19. Nat Med. 19:1–5

    Google Scholar 

  50. 50.

    AI COVID-19 - Siemens Healthineers Global. [cited 2020 Aug 26]. Available at:

  51. 51.

    Shi H, Han X, Jiang N, Cao Y, Alwalid O, Gu J et al (2020) Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study. Lancet Infect Dis. 20(4):425–434

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Lei P, Fan B, Mao J, Wei J, Wang P (2020) The progression of computed tomographic (CT) images in patients with coronavirus disease (COVID-19) pneumonia: running title: the CT progression of COVID-19 pneumonia. J Infect. 80(6):e30–e31

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Pan Y, Guan H, Zhou S, Wang Y, Li Q, Zhu T et al (2020) Initial CT findings and temporal changes in patients with the novel coronavirus pneumonia (2019-nCoV): a study of 63 patients in Wuhan, China. Eur Radiol. 30(6):3306–3309

    CAS  Article  PubMed  Google Scholar 

  54. 54.

    Revel MP, Parkar AP, Prosch H, Silva M, Sverzellati N, Gleeson F et al (2020) COVID-19 patients and the radiology department - advice from the European Society of Radiology (ESR) and the European Society of Thoracic Imaging (ESTI). Eur Radiol. April 30:4903

    CAS  Article  Google Scholar 

  55. 55.

    Sultan OM, Al-Tameemi H, Alghazali DM, Abed M, Ghniem MNA, Hawiji DA et al (2020) Pulmonary CT manifestations of COVID-19: changes within 2 weeks duration from presentation. Egypt J Radiol Nucl Med. 51(1):105

    Article  Google Scholar 

  56. 56.

    Yu M, Liu Y, Xu D, Zhang R, Lan L, Xu H (2020) Prediction of the development of pulmonary fibrosis using serial thin-section CT and clinical features in patients discharged after treatment for COVID-19 pneumonia. Korean JRadiol. 21(6):746–755

    Article  Google Scholar 

  57. 57.

    Guan CS, Wei LG, Xie RM, Lv Z-BDORBDHCMUBC, Yan S, Zhang ZX et al (2020) CT findings of COVID-19 in follow-up: comparison between progression and recovery. Diagnost Interv Radiol Ank Turk 26:301

    Article  Google Scholar 

  58. 58.

    Han X, Cao Y, Jiang N, Chen Y, Alwalid O, Zhang X et al (2020) Novel coronavirus pneumonia (COVID-19) progression course in 17 discharged patients: comparison of clinical and thin-section CT features during recovery. Clin Infect Dis Off Publ Infect Dis Soc Am [cited 2020 Jun 1]; Available at:

  59. 59.

    Shang Y, Xu C, Jiang F, Huang R, Li Y, Zhou Y et al (2020) Clinical characteristics and changes of chest CT features in 307 patients with common COVID-19 pneumonia infected SARS-CoV-2: a multicenter study in Jiangsu, China. Int J Infect Dis IJID Off Publ Int Soc Infect Dis. 96:157–162

    CAS  Google Scholar 

Download references


We thank the Medical School of Universidad Privada Antenor Orrego, Trujillo-Peru.



Author information




AQS, YAD, and ECC contributed to the main idea and the first draft; MCC, OQC, and VML contributed to the review and speciality scope of the manuscript. All authors have read and approved the manuscript.

Corresponding author

Correspondence to Edward Chavez-Cruzado.

Ethics declarations

Ethics approval and consent to participate

For this review article, no ethics committee approval or informed consent was required.

Consent for publication

Not applicable.

Competing interests

The authors declare no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Quispe-Cholan, A., Anticona-De-La-Cruz, Y., Cornejo-Cruz, M. et al. Tomographic findings in patients with COVID-19 according to evolution of the disease. Egypt J Radiol Nucl Med 51, 215 (2020).

Download citation


  • Coronavirus infections
  • X-ray computed tomography
  • Diagnosis
  • Disease progression