Review of the Chest CT Differential Diagnosis of Ground-Glass Opacities in the COVID Era
Coronavirus disease 2019 (COVID-19), a recently emerged lower respiratory tract illness, has quickly become a pandemic. The purpose of this review is to discuss and differentiate typical imaging findings of COVID-19 from those of other diseases, which can appear similar in the first instance. The typical CT findings of COVID-19 are bilateral and peripheral predominant ground-glass opacities. As per the Fleischner Society consensus statement, CT is appropriate in certain scenarios, including for patients who are at risk for and/or develop clinical worsening. The probability that CT findings represent COVID-19, however, depends largely on the pretest probability of infection, which is in turn defined by community prevalence of infection. When the community prevalence of COVID-19 is low, a large gap exists between positive predictive values of chest CT versus those of reverse transcriptase polymerase chain reaction. This implies that with use of chest CT there are a large number of false-positive results. Imaging differentiation is important for management and isolation purposes and for appropriate disposition of patients with false-positive CT findings. Herein the authors discuss differential pathology with close imaging resemblance to typical CT imaging features of COVID-19 and highlight CT features that may help differentiate COVID-19 from other conditions.
© RSNA, 2020
After reading the article and taking the test, the reader will be able to:
■ Identify the multivariate context of appropriate use of imaging in COVID-19 pneumonia
■ Specify the limitations of imaging in the diagnosis of COVID-19 pneumonia
■ Describe the findings and differentiating features of other lung conditions that can be frequently mistaken for COVID-19 pneumonia
Accreditation and Designation Statement
The RSNA is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians. The RSNA designates this journal-based SA-CME activity for a maximum of 1.0 AMA PRA Category 1 Credit™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.
The ACCME requires that the RSNA, as an accredited provider of CME, obtain signed disclosure statements from the authors, editors, and reviewers for this activity. For this journal-based CME activity, author disclosures are listed at the end of this article.
Chest CT findings in coronavirus disease 2019 (COVID-19) pneumonia are variable but can be bilateral, lower lobe, and extend to the pleural surfaces; these features can be helpful in distinguishing COVID-19 pneumonia from other causes of lung abnormality.
■ Typical CT findings of coronavirus disease 2019 (COVID-19) pneumonia have a wide differential diagnosis.
■ The probability that CT findings of any kind represent COVID-19 is highly dependent on the prevalence of severe acute respiratory syndrome coronavirus 2 viral infection in the community.
■ Careful image analysis can aid in differentiating COVID-19 from other conditions with similar imaging features.
An acute lower respiratory tract infection caused by the 2019 novel coronavirus was first reported in China in December 2019 (1,2). The clinical spectrum of disease with coronavirus disease 2019 (COVID-19) infection is variable and ranges from an asymptomatic infection or mild upper respiratory tract illness to severe viral pneumonia with respiratory failure and occasionally death (2). Although the case fatality ratio has been as high as 15%, the incidence of critical illness has been reported to be 7%–26% (3). Patient factors that have been associated with a higher incidence of critical illness and death include male sex, age older than 60 years, obesity, diabetes, hypertension, cardiopulmonary comorbidities, and higher d-dimer and interleukin 6 values (3).
At the time of writing this article, more than 8 million cases and 450 000 deaths worldwide have been reported. The COVID-19 pandemic has resulted in an unprecedented health care crisis with immense strain on health care resources and disruptions in both routine and emergency health care delivery (4). The lack of adequate diagnostic testing has resulted in suboptimal early detection and containment of this infection, which has contributed to rapid and widespread transmission by individuals with mild or no symptoms (5). The primary diagnostic test, reverse transcriptase polymerase chain reaction (RT-PCR) assay for COVID-19, has variable sensitivity ranging from 37% to 71% (5), depending on the rate of viral expression at the time of collection and the site of specimen collection (6). Obstacles to the use of RT-PCR testing include shortage of kits and extended processing period.
Chest CT in COVID-19 pneumonia demonstrates bilateral, peripheral, and basal predominant ground-glass opacities (GGOs) and/or consolidation in nearly 85% of patients with superimposed irregular lines and interfaces; the imaging findings peak 9–13 days after infection (7,8) (Fig 1). Subsequently, a mixed pattern evolves with crazy paving, architectural distortion, and perilobular abnormalities superimposed on GGOs with slow resolution (7) (Fig 1). Importantly, CT scans may be normal in an infected patient, particularly early in the disease (8). Atypical chest CT findings include upper lobe or peribronchovascular distribution of GGOs, cavitation, tree in bud nodules, lymphadenopathy, and pleural thickening (9). Tables 1 and 2 summarize common and uncommon CT findings of COVID-19 (10–21). It is vitally important to remember that the CT imaging appearance is dependent on when CT is performed during the patient’s time course of this disease.
Patients may also develop cardiac involvement with COVID-19. Risk factors that have prognostic implications for developing cardiovascular disease include advancing age, impaired immune system, or elevated levels of angiotensin-converting enzyme 2 (22). Studies have reported the presence of myocardial injury with troponin elevation in 7%–17% of hospitalized patients and 22%–31% of patients admitted to the intensive care unit (8,9,23,24). Cardiac involvement manifests as myocarditis, pericarditis and heart failure, with corresponding imaging findings on chest x-ray, CT and MRI (25–27). The pathophysiology includes virus infiltration, cardiac stress, and inflammation (25,26). The left ventricular ejection fraction may be preserved or reduced. Acute coronary syndromes have been reported with a proposed mechanism of increased thrombotic activity (25).
The Fleischner consensus statement on the role of chest imaging in COVID-19 is written with a multivariate perspective of disease severity, pretest probability, risk factors for and/or evidence of disease progression, and availability of diagnostic testing (5). As per the consensus statement, CT is appropriate in establishing baseline pulmonary status and identifying underlying cardiopulmonary abnormalities in patients with moderate-to-severe disease. CT can help triage resources toward patients at risk for disease progression and may help identify a cause in case of clinical worsening. CT may also help identify an alternate diagnosis (5). CT findings concerning for COVID-19 may be found incidentally in asymptomatic patients in the setting of known community transmission. Asymptomatic carriers of COVID-19 may comprise 17.9%–33.3% of infected cases (28,29). Such patients must be directed toward RT-PCR testing.
In a resource-constrained environment with a high community burden of disease and rapid point-of-care testing either unavailable or showing negative results, CT has been used to rapidly triage patients into non COVID-19, possible COVID-19, or most likely COVID-19 (Table 3). Given the presence of CT abnormalities, the probability that the CT findings represent COVID-19 depends largely on the pretest probability of infection, which is defined by community prevalence of infection and modified by individual factors such as exposure history (5,30). If the disease prevalence is high, even atypical presentations are likely to represent COVID-19. Conversely, if the disease prevalence is low, CT findings that are otherwise quite typical of COVID-19 may be caused by another disease (30). The positive and negative predictive values, which are calculated using disease prevalence in the community, are useful to consider in comparison to sensitivity and specificity, which can only be used when the true COVID-19 status is already known. Therefore, CT diagnosis is not used in isolation without acknowledging the prevalence of disease in the community. As expected, the metrics of diagnostic performance for chest CT (positive predictive value, negative predictive value, sensitivity, and specificity) are strictly valid only for the study population from which they are obtained (30).
Kim et al (31) performed a meta-analysis to assess the diagnostic performance of CT and RT-PCR (31). For chest CT, the positive predictive value ranged from 1.5% to 30.7% and the negative predictive value ranged from 95.4% to 99.8%. For RT-PCR, the positive predictive value ranged from 47.3% to 96.4% and the negative predictive value ranged from 96.8% to 99.9%. They reported a pooled sensitivity of 94% (95% CI: 91, 96; I2 = 95%) for chest CT and 89% (95% CI: 81, 94; I2 = 90%) for RT-PCR. The pooled specificity of chest CT was 35% (95% CI: 26, 50). They found that, given the low specificity of CT, a large gap existed between the positive predictive value of chest CT versus RT-PCR in low-prevalence areas, specifically if the disease prevalence was less than 10% (31). These results imply that the use of chest CT may result in a large number of false-positive results that may lead to further diagnostic testing, greater medical cost, and workload and patient anxiety. Patients with suspected COVID-19 on chest CT scans may be placed in dedicated COVID-19 rule-out units and may experience delay in care or intervention. Thus, attempts at differentiation of chest CT abnormalities are important for management and isolation of patients with high clinical suspicion as well as for appropriate disposition of patients in whom disease prevalence or pretest probability is low.
Given the lack of specificity of chest CT findings for COVID-19, the purpose of this review is to address the range of pulmonary disease processes that can mimic the CT appearance of COVID-19 pneumonia.
Bacterial pneumonia is commonly encountered in clinical practice. Pneumonia is the eighth leading cause of death and the number one cause of death from infectious disease in the United States. Bacterial pneumonias are classified into three main groups: community-acquired pneumonia, aspiration and nosocomial pneumonia, and hospital-acquired pneumonia. Patients typically present with fever, chills, or cough. Chest radiography is the most commonly used imaging tool in pneumonias. CT should be used in unresolved cases or when complications are suspected. The usual pattern of community-acquired pneumonia is that of lobar consolidation. The radiographic patterns of nosocomial pneumonia are very variable, most commonly showing patchy consolidation, and are associated with cavitation and pleural effusion (32) (Fig 2). Patients typically have a high white cell count unless they have neutropenia or are immunocompromised.
In contrast to COVID-19, bacterial pneumonia characteristically produces focal segmental or lobar pulmonary opacities without lower lung predominance. Complications or associated findings such as cavitation, lung abscess, lymphadenopathy, parapneumonic effusions and empyema, when present, are useful imaging differentiating features, as they are not seen in COVID-19 unless the patients are superinfected with bacterial pneumonia (8).
Viruses are the most common causes of respiratory tract infections and are seen more commonly in children, the elderly, and the immunocompromised (33,34). The most common pathogen causing viral pneumonia in both immunocompetent and immunocompromised patients is influenza virus (33). The clinical signs and symptoms of viral pneumonia are often diverse and depend on host immune status (34). The spectrum of CT findings encountered in various pulmonary viral diseases encompasses four main categories: (a) GGO and consolidation; (b) nodules, micronodules, and tree-in-bud opacities; (c) interlobular septal thickening; and (d) bronchial and/or bronchiolar wall thickening (35) (Fig 3). Lymphadenopathy and pleural effusions may also be present (36). Some of the viral pneumonias can manifest as substantial GGO and include cytomegalovirus, adenovirus, herpes simplex virus, varicella zoster, measles, human meta-pneumovirus, and influenza (33,37). Percentage area of lung involvement with GGOs with different viruses has been extensively described. GGOs can be seen in 50%–75% of patients with adenovirus, in more than 75% of patients with cytomegalovirus and herpes simplex virus, and in 10%–25% of patients with human meta-pneumovirus and measles (33).
Of the four patterns of viral disease described earlier, the pattern that can be confused with COVID-19 is GGO as the predominant finding. Therefore, in our discussion we will focus on viruses that demonstrate predominantly this CT finding.
The viral infections most often described to have features that resemble COVID-19 include influenza, cytomegalovirus, and other coronaviruses (38–41). With regard to the differentiation of COVID-19 from influenza, Liu et al (38) found that, although peripheral GGOs and consolidation are seen in both these entities, round opacities and septal thickening are more common in COVID-19. Conversely, nodules, tree-in-bud opacities, and pleural effusion are more common in influenza (38).
Li et al (20) explored differences in CT features of COVID-19 versus other Coronaviridae, severe acute respiratory syndrome (SARS), and Middle East respiratory syndrome (MERS). They report that GGO, consolidation, septal thickening, and air bronchogram sign were similar in COVID-19, SARS, and MERS, whereas reversed halo sign and pulmonary nodules associated with COVID-19 have not been previously described with SARS and MERS. Lung abnormalities in SARS are more commonly reported to be unifocal (40).
Cytomegalovirus, a cause of severe lung infection in immunocompromised patients, such as those with human immunodeficiency virus (HIV) and organ transplant recipients, can result in widespread GGOs. The clinical context and timing since transplant are the best distinguishing features (33).
Human meta-pneumovirus is predominantly seen in stem cell transplant recipients and those with hematologic malignancies. Human para-influenza virus occurs in approximately 21% of patients in the intensive care unit, and bacterial co-infection is a known association (42). Human meta-pneumovirus pneumonia shows multifocal patchy consolidation with GGO on CT scans. Centrilobular nodules and bronchial wall thickening, seen in 25% of human meta-pneumonovirus pneumonia, are useful differentiating features (43).
Overall, according to Bai et al (17), compared to non-COVID-19 viral pneumonia, parenchymal opacities in COVID-19 pneumonia were more likely to be peripheral (80% vs 57%), and have GGO (91% vs 68%), fine reticular opacity (56% vs 22%) and vascular thickening (11% vs 1%). COVID-19 patients were less likely to have central and peripheral distribution (14% vs 35%), air bronchograms (14% vs 23%), pleural thickening (15% vs 33%), pleural effusion (4% vs 39%) and lymphadenopathy (2.7% vs 10.2%) (17).
Pneumocystis jirovecii pneumonia (PJP) is a common opportunistic infection that causes pneumonia in immunocompromised patients and, rarely, in immunocompetent individuals. It typically occurs with CD4 counts less than 200 cells per millimeter (44). The presentation of PJP in a patient with HIV infection is typically subacute, characterized by a slow onset of dry cough and dyspnea. PJP in patients without HIV infection presents as an acute illness associated with severe hypoxia and results in rapid respiratory deterioration and respiratory failure (45). The radiographic findings of PJP are nonspecific, and in as many as one-third of infected patients they may be normal (46).The most common high-resolution CT finding of PJP is diffuse GGO, which is often greater in extent in patients without HIV infection (45). With more advanced disease, crazy-paving pattern, consolidation, nodules, and cysts can also develop. Lung consolidation is more common in patients without HIV infection (47) (Fig 4).
Unlike COVID-19, PJP predominantly affects immunosuppressed patients. Although there may be widespread GGO in PJP, in contrast to COVID-19 pneumonia it is upper lobe predominant. Nodules, cysts, and spontaneous pneumothorax can also develop.
Interstitial Lung Diseases
Nonspecific Interstitial Pneumonia
Nonspecific interstitial pneumonia (NSIP) is a common interstitial lung disease associated with a number of conditions such as connective tissue disorders (ie, systemic sclerosis, Sjögren syndrome, polymyositis, dermatomyositis, and systemic lupus erythematosus). In addition, it can be related to autoimmune diseases such as rheumatoid arthritis, primary biliary cirrhosis, graft-versus-host disease, or drug induced (48,49). NSIP typically manifests in patients aged 40–50 years and has a higher predilection in women. The symptoms are nonspecific and include chronic dyspnea and cough without sputum production. Pulmonary function tests show a restrictive pattern of decreased lung function and reduced gas exchange capacity. High-resolution CT of the chest demonstrates predominantly basilar perivascular GGOs in the earlier stages of the disease, known as cellular NSIP. As the disease progresses, fibrotic changes develop in the form of traction bronchiectasis, volume loss, architectural distortion, and subpleural irregular reticular opacity (Fig 5). A hallmark feature of NSIP on high-resolution chest CT scans is subpleural sparing; however, it is only seen in a few cases (50). Microcystic honeycombing may be seen with NSIP, where there are subpleural cystic spaces measuring less than 4 mm.
In comparison to COVID-19, the symptoms of NSIP are insidious in onset. There is known association with connective tissue disorders or other predisposing condition. Subpleural sparing, when present, is considered specific for NSIP. Stigmata of fibrosis (traction bronchiectasis, architectural distortion, and honeycombing) may be seen. If rapidly developing airspace consolidation or ground-glass abnormality is seen in an acutely ill patient with NSIP, one should consider the possibility of an acute exacerbation (51).
Desquamative Interstitial Pneumonia
Desquamative interstitial pneumonia is a relatively rare interstitial lung disease seen more commonly in men. It can also be related to marijuana smoke inhalation, infections such as HIV, toxins, or occupational exposure (eg, to asbestos) (52–54). Patients are predominantly middle-aged with progressively worsening shortness of breath and chronic cough. The majority of these patients are smokers. High-resolution CT demonstrates predominantly peripheral and lower lobe GGOs (Fig 6). Some cases may demonstrate fine linear or reticular opacities in the peripheral and basal lung zones (55).
The strong association of desquamative interstitial pneumonia with smoking is a useful differentiating feature. Small cystic spaces may develop within the areas of GGO, and these are not usually seen with COVID-19 (56).
Patients with organizing pneumonia present with a relatively short history of breathlessness. In addition, they have nonproductive cough, weight loss, malaise, and fever. Organizing pneumonia may have unilateral or bilateral lung involvement and has myriad pulmonary manifestations. The most frequent features on high-resolution CT scans include bilateral, multifocal, patchy consolidations (present in up to 90% of cases) and ground-glass abnormalities (57) (Fig 7). Less commonly, bronchovascular nodules and bronchial wall thickening can be seen. The reverse halo sign, also called the atoll sign, is considered a hallmark feature; however, it is seen in only 20% of patients (58). A perilobular pattern is seen in more than half of the patients. It appears as polygonal mainly subpleural opacities surrounded by aerated lung (59). The lung manifestations of organizing pneumonia that resemble COVID-19 disease include lower lobe, subpleural, and peribronchovascular predominant GGOs and opacities with reverse halo appearance. The former opacities are migratory in 11%–24% of patients (60).
The presence of predisposing conditions can suggest organizing pneumonia. In contrast to COVID-19, pulmonary opacities are often migratory. Perilobular thickening, if present, is another helpful differentiating feature. Patients typically respond to steroids.
Hypersensitivity pneumonitis.—Hypersensitivity pneumonitis or extrinsic allergic alveolitis is also known as bird fancier disease, farmer lung, and hot tub lung on the basis of the inciting agent. The disease is divided into acute, subacute, and chronic types based on timing since presentation. Each of these stages has its own distinctive appearance on high-resolution CT scans, and patients may present with some degree of overlap between stages (61). In the acute phase, patients typically present with fever, cough, dyspnea of short duration, and myalgia; in the chronic stages, they present with weight loss, fatigue, exertional dyspnea, and cough possibly with clear sputum. There are subtle to more diffuse GGOs in the acute phase, which may mimic pulmonary edema (62). The subacute stage is seen weeks to months after the first exposure to the allergen. There are distinct tiny centrilobular pulmonary nodules, measuring less than 6 mm. There may be accompanying GGOs. In the fibrotic stage, there is bilateral predominantly perihilar fibrosis with mid zone predominance. The distinct feature of hypersensitivity pneumonitis is mosaic attenuation of the lungs. This pattern represents geographic areas of high attenuation interspersed with areas of low attenuation due to air trapping (head cheese sign) (Fig 8). Rarely, lung cysts may be present, related to small airways disease (63).
Although widespread GGO with hypersensitivity pneumonitis can appear similar to COVID-19 pneumonia, other findings, such as poorly defined centrilobular GGO, mosaic attenuation, and air trapping on expiratory images, can help distinguish the two conditions (Fig 8). In late hypersensitivity pneumonitis, mid-to-upper lung zone fibrosis may be present.
Electronic cigarette or vaping product use–associated lung injury (EVALI).—The Centers for Disease Control and Prevention identified an outbreak of a respiratory illness in patients with a history of vaping in 2019. The entity, which is mainly identified in younger patients, was named EVALI given its association with the use of electronic cigarettes (64). EVALI clinically manifests as an acute viral illness, with nearly all patients reporting respiratory symptoms. Seventy-five percent of patients also report gastrointestinal symptoms, whereas 85% reported constitutional symptoms (65). EVALI is a diagnosis of exclusion. Most patients improve on supportive treatment, although a small percentage succumb to the illness (66). At high-resolution CT, patients demonstrate bilateral and symmetric diffuse hazy GGOs with subpleural sparing and without zonal predominance (Fig 9). Upper lung zone–predominant centrilobular nodules may also be present. Later in the disease process there is evidence of organization with architectural distortion and stigmata of fibrosis (64).
Most patients report their last episode of vaping the week before symptom onset. In contrast to COVID-19, the GGOs in EVALI are most pronounced centrally with conspicuous subpleural sparing. Any of the lung zones may be involved. Presence of upper lobe–predominant centrilobular nodules is another useful differentiating feature.
There is a growing list of drugs leading to pulmonary adverse effects, with the recent addition of immunotherapy-related medications to the list. The characteristic feature of drug-induced pneumonitis is the onset of symptoms such as dry cough and breathlessness following the use of a new medication. Drug-induced pneumonitis usually occurs while the patient is taking the drug rather than after withdrawal. There are varied manifestations, including diffuse GGOs, depending on the inciting drug. The on-line website and mobile app Pneumotox is very useful to help sort out the number of drugs that can be associated with lung abnormalities (67). We have discussed the imaging appearance and differentiating features of interstitial lung disease earlier; these are common drug-related findings in the lungs. This section will focus on immune checkpoint inhibitors as pneumonitis related to these drugs bears close resemblance to COVID-19. Drug-related pneumonitis is observed in 3%–6% of patients with non–small cell lung cancer who are receiving immunotherapy (68,69). Four CT patterns have been reported: cryptogenic organizing pneumonia, NSIP, hypersensitivity pneumonitis, and acute interstitial pneumonia (Fig 10).
Diagnosis of drug-induced lung disease is based on the definite temporal relationship between drug intake and development of respiratory symptoms or imaging abnormality. The relationship may be difficult to establish when lung disease develops after drug withdrawal. Drug withdrawal generally results in improvement.
Pulmonary edema is the abnormal accumulation of fluid in the extravascular compartments of the lung and may be classified as increased hydrostatic pressure edema seen in heart failure, permeability edema with diffuse alveolar damage as seen in acute respiratory distress syndrome, permeability edema without diffuse alveolar damage, which can be seen with administration of various drugs and ingestion of toxins, or mixed edema seen in patients with stroke, status epilepticus, and subarachnoid hemorrhage (70). Symptoms include acute breathlessness, cough, wheezing, orthopnea, and paroxysmal nocturnal dyspnea. Findings of interstitial pulmonary edema are GGO and bronchovascular and interlobular septal thickening (71). Alveolar edema manifests as airspace consolidation in addition to the above findings. Pleural effusions are a frequent accompanying finding in cardiogenic pulmonary edema (72) (Fig 11).
History of an acute cardiac event or of progressive symptoms of heart failure suggests this diagnosis over COVID-19. The distribution of GGOs (usually central or gravity-dependent), lymphadenopathy, and pleural effusions in hydrostatic pulmonary edema are useful differentiating features. There may be indicators of cardiac disease at imaging, including coronary artery calcifications, cardiomegaly, and evidence of previous coronary intervention.
Aspiration pneumonia occurs due to an insult from entry of a foreign substance into the respiratory tract. The foreign substance could be solid or liquid. Lung damage is mainly the result of pulmonary infection from aspiration of colonized oropharyngeal secretions. Risk factors include alcohol intoxication, general anesthesia, loss of consciousness, structural abnormalities of the pharynx and esophagus, and neuromuscular disorders (74). Clinical features range from no symptoms to severe distress with respiratory failure. Symptom onset may be acute or subacute. Acute chemical pneumonitis is characterized by a sudden onset of dyspnea, hypoxemia, tachycardia, and diffuse wheezes or crackles (75).
Aspiration can lead to the development of lobar or segmental pneumonia, bronchopneumonia, lung abscess, and empyema. In recumbent patients, the posterior segment of the upper lobes and the superior segment of the lower lobes are most commonly involved. Aspiration is more likely to involve bilateral basal segments, the middle lobe, and lingula when it occurs while the patient is upright (76). A chest radiograph may be negative early in the course of aspiration pneumonia. Komiya et al (77) reported that GGOs, centrilobular nodules, consolidation, and atelectasis are frequently noted CT findings with aspiration. Chronic aspiration results in bronchiectasis and tree-in-bud opacities (74).
There are similarities in the imaging appearance of aspiration and COVID-19 pneumonia due to involvement of peripheral portions of lungs with mixed-attenuation parenchymal opacity. The presence of centrilobular nodules, dependent tree-in-bud nodularity, and, when present, complications such as lung abscess, empyema, or visible aspirated material are helpful differentiating features. In addition, imaging findings of predisposing conditions may be present, for example, dilated esophagus, neuromuscular disorders, and anatomic abnormalities such as tracheo-esophageal fistula and head and neck malignancy (78).
Diffuse Alveolar Hemorrhage
Diffuse alveolar hemorrhage (DAH) occurs due to passage of blood into the alveoli. It can be seen in coagulation disorders, antiphospholipid antibody syndrome, connective tissue diseases, vasculitides, medications, inhaled toxins, pulmonary hemosiderosis, and pulmonary veno-occlusive disorders. Patients with DAH may have hemoptysis and/or anemia at laboratory testing. One-third of patients may not have hemoptysis. Bronchoalveolar lavage is usually required to confirm the diagnosis and rule out other causes for the opacities at imaging. Imaging findings depend on the chronicity of the process. Initially, DAH may manifest as GGOs. After 2–3 days, intralobular and smooth interlobular septal thickening superimpose on areas of GGOs and may sometimes give rise to a crazy-paving pattern (79) (Fig 13). In the chronic stages, the GGOs typically recede and there maybe residual centrilobular nodules.
There is often a history of hemoptysis and a new drop in the hemoglobin level, which favors this diagnosis. DAH is frequently associated with connective tissue disease and renal disease. Bronchoalveolar lavage shows sequentially increasing red blood cell counts. The GGOs in DAH, unlike in COVID-19, do not have a specific pattern; otherwise, imaging findings of DAH are very similar to those of COVID-19 and differentiation may only be possible on the basis of clinical history, comparison with any available prior image, and bronchoalveolar lavage or viral testing.
Pulmonary Alveolar Proteinosis
Pulmonary alveolar proteinosis is characterized by periodic acid–Schiff stain–positive material within the alveoli. It maybe idiopathic in origin, related to hematologic malignancies, and seen in immunosuppressed patients or inhalational lung disease. Typically, patients are asymptomatic or present with minimal dyspnea. Their CT chest findings are out of proportion to their clinical symptoms. Lactate dehydrogenase levels may be elevated. Pulmonary alveolar proteinosis manifests as smooth interlobular septal thickening with GGOs in both lungs, giving the characteristic “crazy-paving” appearance of the lungs (80) (Fig 14). Crazy paving in pulmonary alveolar proteinosis is widespread, with sharply marginated areas of lobular sparing.
Strong association with smoking and patient presentation with nonspecific symptoms and slow development of exercise intolerance guide toward pulmonary alveolar proteinosis. Unlike with COVID-19, there is no zonal predilection. Patients may recall a history of a similar episode in the past and show improvement with bronchoalveolar lavage (Fig 15).
Patients often have asthma and slow onset of fever and respiratory symptoms. The imaging appearance of this condition is classically known as “photographic negative of pulmonary edema” as the opacities are distinctly peripheral (81). The parenchymal abnormality consists of consolidation and GGO with an upper-lung predominance. Crazy paving may be present.
Clinical presentation is indolent, and middle or upper zone predilection and nonsegmental involvement are useful differentiating features.
The typical chest CT imaging features of coronavirus disease 2019 (COVID-19) pneumonia have low specificity due to their overlap with a number of other conditions. This review has focused on highlighting these differences with imaging examples. It is important to note that when the prevalence of COVID-19 is high, even atypical imaging features are more likely to be COVID-19. Although definitive diagnosis cannot be made based on CT imaging features alone, the use of a combination of clinical and imaging findings can substantially improve the accuracy of diagnosis.
- 1. Emerging 2019 Novel Coronavirus (2019-nCoV) Pneumonia. Radiology 2020;295(1):210–217. Link, Google Scholar
- 2. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet 2020;395(10229):1054–1062 [Published correction appears in Lancet 2020;395(10229):1038.]. Crossref, Medline, Google Scholar
- 3. Epidemiology, clinical course, and outcomes of critically ill adults with COVID-19 in New York City: a prospective cohort study. Lancet 2020;395(10239):1763–1770. Crossref, Medline, Google Scholar
- 4. . World Health Organization. https://www.who.int/emergencies/diseases/novel-coronavirus-2019. Accessed June 21, 2020. Google Scholar
- 5. The Role of Chest Imaging in Patient Management during the COVID-19 Pandemic: A Multinational Consensus Statement from the Fleischner Society. Radiology 2020;296(1):172–180. Link, Google Scholar
- 6. Detection of SARS-CoV-2 in Different Types of Clinical Specimens. JAMA 2020;323(18):1843–1844. Medline, Google Scholar
- 7. Temporal Changes of CT Findings in 90 Patients with COVID-19 Pneumonia: A Longitudinal Study. Radiology doi: 10.1148/radiol.2020200843. Published online March 19, 2020. Google Scholar
- 8. . Essentials for Radiologists on COVID-19: An Update—Radiology Scientific Expert Panel. Radiology doi: 10.1148/radiol.2020200527. Published online February 27, 2020. Google Scholar
- 9. CT imaging changes of corona virus disease 2019 (COVID-19): a multi-center study in Southwest China. J Transl Med 2020;18(1):154. Crossref, Medline, Google Scholar
- 10. Chest CT Findings in Patients With Coronavirus Disease 2019 and Its Relationship With Clinical Features. Invest Radiol 2020;55(5):257–261. Crossref, Medline, Google Scholar
- 11. . Relation Between Chest CT Findings and Clinical Conditions of Coronavirus Disease (COVID-19) Pneumonia: A Multicenter Study. AJR Am J Roentgenol 2020;214(5):1072–1077. Crossref, Medline, Google Scholar
- 12. . Coronavirus Disease 2019 (COVID-19): A Systematic Review of Imaging Findings in 919 Patients. AJR Am J Roentgenol 2020;215(1):87–93. Crossref, Medline, Google Scholar
- 13. Time Course of Lung Changes at Chest CT during Recovery from Coronavirus Disease 2019 (COVID-19). Radiology 2020;295(3):715–721. Link, Google Scholar
- 14. . CT Features of Coronavirus Disease 2019 (COVID-19) Pneumonia in 62 Patients in Wuhan, China. AJR Am J Roentgenol 2020;214(6):1287–1294. Crossref, Medline, Google Scholar
- 15. . Early clinical and CT manifestations of coronavirus disease 2019 (COVID-19) pneumonia. AJR Am J Roentgenol doi: 10.2214/AJR.20.22961. Published online March 17, 2020. Google Scholar
- 16. Chest CT Findings in Coronavirus Disease-19 (COVID-19): Relationship to Duration of Infection. Radiology 2020;295(3):200463. Link, Google Scholar
- 17. Performance of radiologists in differentiating COVID-19 from viral pneumonia on chest CT. Radiology doi: 10.1148/radiol.2020200823. Published online March 10, 2020. Google Scholar
- 18. CT imaging and clinical course of asymptomatic cases with COVID-19 pneumonia at admission in Wuhan, China. J Infect 2020;81(1):e33–e39. Crossref, Medline, Google Scholar
- 19. Clinical characteristics of 145 patients with corona virus disease 2019 (COVID-19) in Taizhou, Zhejiang, China. Infection doi: 10.1007/s15010-020-01432-5. Published online April 28, 2020. Google Scholar
- 20. . Coronavirus Disease 2019 (COVID-19): Role of Chest CT in Diagnosis and Management. AJR Am J Roentgenol 2020;214(6):1280–1286. Crossref, Medline, Google Scholar
- 21. CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV). Radiology 2020;295(1):202–207. Link, Google Scholar
- 22. COVID-19 and Cardiovascular Disease. Circulation 2020;141(20):1648–1655. Crossref, Medline, Google Scholar
- 23. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus–infected pneumonia in Wuhan, China. JAMA 2020;323(11):1061–1069. Crossref, Medline, Google Scholar
- 24. . Cardiovascular complications in COVID-19. Am J Emerg Med 2020;38(7):1504–1507. Crossref, Medline, Google Scholar
- 25. . COVID-19 and the Heart. Circ Res 2020;126(10):1443–1455. Crossref, Medline, Google Scholar
- 26. Acute Myocardial Injury at Hospital Admission Is Associated With All-Cause Mortality in COVID-19. J Am Coll Cardiol 2020;76(1):124–125. Crossref, Medline, Google Scholar
- 27. Cardiac involvement in recovered COVID-19 patients identified by magnetic resonance imaging. JACC Cardiovasc Imaging 2020. 10.1016/j.jcmg.2020.05.004. Published online May 12, 2020. Google Scholar
- 28. . Estimating the asymptomatic proportion of coronavirus disease 2019 (COVID-19) cases on board the Diamond Princess cruise ship, Yokohama, Japan, 2020. Euro Surveill 2020;25(10):200180. Crossref, Google Scholar
- 29. Estimation of the asymptomatic ratio of novel coronavirus infections (COVID-19). Int J Infect Dis 2020;94:154–155. Crossref, Medline, Google Scholar
- 30. . Imaging Publications in the COVID-19 Pandemic: Applying New Research Results to Clinical Practice. Radiology doi:10.1148/radiol.2020201724. Published online April 23, 2020. Google Scholar
- 31. . Diagnostic Performance of CT and Reverse Transcriptase-Polymerase Chain Reaction for Coronavirus Disease 2019: A Meta-Analysis. Radiology doi:10.1148/radiol.2020201343. Published online April 17, 2020. Google Scholar
- 32. . Radiology of bacterial pneumonia. Eur J Radiol 2004;51(2):102–113. Crossref, Medline, Google Scholar
- 33. . Radiographic and CT Features of Viral Pneumonia. RadioGraphics 2018;38(3):719–739. Link, Google Scholar
- 34. . Viral pneumonia. Curr Opin Infect Dis 2009;22(2):143–147. Crossref, Medline, Google Scholar
- 35. . Differential diagnosis of chronic diffuse infiltrative lung disease on high-resolution computed tomography. Semin Roentgenol 1991;26(2):132–142. Crossref, Medline, Google Scholar
- 36. . Imaging of pulmonary viral pneumonia. Radiology 2011;260(1):18–39. Link, Google Scholar
- 37. . Isolated diffuse ground-glass opacity in thoracic CT: causes and clinical presentations. AJR Am J Roentgenol 2005;184(2):613–622. Crossref, Medline, Google Scholar
- 38. . COVID-19 pneumonia: CT findings of 122 patients and differentiation from influenza pneumonia. Eur Radiol doi: 10.1007/s00330-020-06928-0. Published online May 12, 2020. Google Scholar
- 39. . A Comparison of Clinical and Chest CT Findings in Patients With Influenza A (H1N1) Virus Infection and Coronavirus Disease (COVID-19). AJR Am J Roentgenol doi: 10.2214/AJR.20.23214. Published online May 26, 2020. Google Scholar
- 40. . Radiology Perspective of Coronavirus Disease 2019 (COVID-19): Lessons From Severe Acute Respiratory Syndrome and Middle East Respiratory Syndrome. AJR Am J Roentgenol 2020;214(5):1078–1082. Crossref, Medline, Google Scholar
- 41. A case of coinfection with SARS-COV-2 and cytomegalovirus in the era of COVID-19. Eur J Case Rep Intern Med 2020;7(5):001652. Medline, Google Scholar
- 42. Viral infection in patients with severe pneumonia requiring intensive care unit admission. Am J Respir Crit Care Med 2012;186(4):325–332. Crossref, Medline, Google Scholar
- 43. CT findings in viral lower respiratory tract infections caused by parainfluenza virus, influenza virus and respiratory syncytial virus. Medicine (Baltimore) 2016;95(26):e4003. Crossref, Medline, Google Scholar
- 44. Accuracy of high-resolution CT in distinguishing between Pneumocystis carinii pneumonia and non-Pneumocystis carinii pneumonia in AIDS patients. Eur Radiol 2003;13(5):1179–1184. Crossref, Medline, Google Scholar
- 45. . Radiological features of Pneumocystis jirovecii Pneumonia in immunocompromised patients with and without AIDS. Lung 2010;188(2):159–163. Crossref, Medline, Google Scholar
- 46. . Pneumocystis carinii pneumonia: spectrum of parenchymal CT findings. Radiology 1990;175(3):711–714. Link, Google Scholar
- 47. Comparison of clinical and radiological features of pneumocystis pneumonia between malignancy cases and acquired immunodeficiency syndrome cases: a multicenter study. Intern Med 2010;49(4):273–281. Crossref, Medline, Google Scholar
- 48. Nonspecific interstitial pneumonia associated with polymyositis and dermatomyositis: serial high-resolution CT findings and functional correlation. Chest 2003;123(4):1096–1103. Crossref, Medline, Google Scholar
- 49. . Current status of idiopathic nonspecific interstitial pneumonia. Semin Respir Crit Care Med 2012;33(5):440–449. Crossref, Medline, Google Scholar
- 50. Chronic hypersensitivity pneumonitis: differentiation from idiopathic pulmonary fibrosis and nonspecific interstitial pneumonia by using thin-section CT. Radiology 2008;246(1):288–297. Link, Google Scholar
- 51. . Nonspecific interstitial pneumonia: radiologic, clinical, and pathologic considerations. RadioGraphics 2009;29(1):73–87. Link, Google Scholar
- 52. . Computed tomography and magnetic resonance of the thorax. J Nucl Med 2007;48(12):2088. Crossref, Google Scholar
- 53. . Desquamative interstitial pneumonia associated with chrysotile asbestos fibres. Br J Ind Med 1991;48(5):332–337. Medline, Google Scholar
- 54. . Desquamative interstitial pneumonia: an analytic review with an emphasis on aetiology. Eur Respir Rev 2013;22(128):117–123. Crossref, Medline, Google Scholar
- 55. . Desquamative interstitial pneumonia: thin-section CT findings in 22 patients. Radiology 1993;187(3):787–790. Link, Google Scholar
- 56. . Smoking-related interstitial lung disease: radiologic-clinical-pathologic correlation. RadioGraphics 2008;28(5):1383–1396; discussion 1396–1398. Link, Google Scholar
- 57. Utility of high-resolution computed tomography and BAL in cryptogenic organizing pneumonia. Respir Med 2010;104(11):1706–1711. Crossref, Medline, Google Scholar
- 58. . “Reversed halo sign”. J Thorac Imaging 2011;26(3):W80. Crossref, Medline, Google Scholar
- 59. . Organizing pneumonia: perilobular pattern at thin-section CT. Radiology 2004;232(3):757–761. Link, Google Scholar
- 60. . From the radiologic pathology archives: organization and fibrosis as a response to lung injury in diffuse alveolar damage, organizing pneumonia, and acute fibrinous and organizing pneumonia. RadioGraphics 2013;33(7):1951–1975. Link, Google Scholar
- 61. . Hypersensitivity pneumonitis. Curr Opin Pulm Med 2004;10(5):401–411. Crossref, Medline, Google Scholar
- 62. . Hypersensitivity pneumonitis: correlation of individual CT patterns with functional abnormalities. Radiology 1996;199(1):123–128. Link, Google Scholar
- 63. . Multidisciplinary Approach to Hypersensitivity Pneumonitis. J Thorac Imaging 2016;31(2):92–103. Crossref, Medline, Google Scholar
- 64. Radiologic, Pathologic, Clinical, and Physiologic Findings of Electronic Cigarette or Vaping Product Use-associated Lung Injury (EVALI): Evolving Knowledge and Remaining Questions. Radiology 2020;294(3):491–505. Link, Google Scholar
- 65. Pulmonary Illness Related to E-Cigarette Use in Illinois and Wisconsin - Final Report. N Engl J Med 2020;382(10):903–916. Crossref, Medline, Google Scholar
- 66. Update: Characteristics of Patients in a National Outbreak of E-cigarette, or Vaping, Product Use-Associated Lung Injuries - United States, October 2019. MMWR Morb Mortal Wkly Rep 2019;68(43):985–989. Crossref, Medline, Google Scholar
- 67. . https://www.pneumotox.com/pattern/index/. Accessed June 21, 2020. Google Scholar
- 68. . Interstitial lung disease induced by drugs and radiation. Respiration 2004;71(4):301–326. Crossref, Medline, Google Scholar
- 69. . Immune Checkpoint Inhibitors in Lung Cancer: Imaging Considerations. AJR Am J Roentgenol 2017;209(3):567–575. Crossref, Medline, Google Scholar
- 70. Clinical and radiologic features of pulmonary edema. RadioGraphics 1999;19(6):1507–1531; discussion 1532–1533. Link, Google Scholar
- 71. . Hydrostatic pulmonary edema: high-resolution CT findings. AJR Am J Roentgenol 1995;165(4):817–820. Crossref, Medline, Google Scholar
- 72. Comparison of chest computed tomography features in the acute phase of cardiogenic pulmonary edema and acute respiratory distress syndrome on arrival at the emergency department. J Thorac Imaging 2013;28(5):322–328. Crossref, Medline, Google Scholar
- 73. Characteristics and Outcomes of 21 Critically Ill Patients With COVID-19 in Washington State. JAMA 2020;323(16):1612–1614. Crossref, Medline, Google Scholar
- 74. . Aspiration diseases: findings, pitfalls, and differential diagnosis. RadioGraphics 2000;20(3):673–685. Link, Google Scholar
- 75. . Aspiration Pneumonia. N Engl J Med 2019;380(7):651–663. Crossref, Medline, Google Scholar
- 76. . MDCT evaluation of foreign bodies and liquid aspiration pneumonia in adults. AJR Am J Roentgenol 2008;190(4):907–915. Crossref, Medline, Google Scholar
- 77. Computed tomography findings of aspiration pneumonia in 53 patients. Geriatr Gerontol Int 2013;13(3):580–585. Crossref, Medline, Google Scholar
- 78. . Patulous upper esophageal sphincter: an unusual endoscopic finding. Turk J Gastroenterol 2018;29(1):116–118. Crossref, Medline, Google Scholar
- 79. . Radiological aspects of diffuse alveolar haemorrhage. Radiol Med (Torino) 2008;113(1):16–28. Crossref, Medline, Google Scholar
- 80. . From the archives of the AFIP: pulmonary alveolar proteinosis. RadioGraphics 2008;28(3):883–899; quiz 915. Link, Google Scholar
- 81. Hypersensitivity Pneumonitis: Radiologic Phenotypes Are Associated With Distinct Survival Time and Pulmonary Function Trajectory. Chest 2019;155(4):699–711. Crossref, Medline, Google Scholar
Article HistoryReceived: May 31 2020
Revision requested: June 8 2020
Revision received: June 21 2020
Accepted: June 24 2020
Published online: July 07 2020
Published in print: Dec 2020