Only 5/60 (8.3%) patients in this study had severe stage disease. This may be related to the government’s measures to actively respond to COVID-19; patients with suspected COVID-19 underwent both RT-PCR testing and chest CT (possibly multiple times as required), and all persons with a history of exposure to suspected COVID-19 patients were isolated for observation. Therefore, it is likely that the COVID-19 cases in the three cities in this study were predominantly imported cases that were then largely transmitted through family aggregation.
Analyses of chest CT scans of COVID-19 patients should be conducted using a combination of anatomical and pathological methods. The predominant distribution of COVID-19 infection is subpleural and of the lower lobes. The conceptual and anatomical differences between the peripheral cortex and the central medulla in the lung region have been proposed by several authors [15, 16]. The peripheral pulmonary cortex consists of 2–3 rows of well-defined secondary pulmonary lobules, forming conical or fan-shaped shapes approximately 3–4cm thick on the outer periphery of the lung or the adjacent cortical pulmonary surfaces of the interlobular fissures. Lobules are the basic unit of the anatomical structure and comprise 3–5 terminal bronchioles and their distal lung tissues. The interlobular septa is multilateral with a size of 1–2.5cm. The normal interlobular septa are not visible on CT [17]. Virus particles are inhaled through the respiratory tract, the main route of invasion, and reach deeper into the lungs, into the lobules of the peripheral pulmonary cortex. The posterior part of the lung has more peripheral pulmonary cortical lobules than the anterior part, which means that bronchioles are more numerous and distributed [18], leading to a greater probability of infection by inhaled virus particles. Furthermore, the anatomical features of the right lower lobe bronchi are relatively straight and steep, increasing the probability of infection beginning in the lobes [19].
Recently, autopsy findings and pathological analyses of COVID-19 cases were reported in China [8–11]. The pathological changes in the lung were caused by inflammatory responses with deep airway and alveolar epithelial injury; direct virus attack and immune damage coexisted. These studies provided a theoretical basis for the current study. Virus particles enter the cell via angiotensin-converting enzyme-2 (ACE2) receptors, which are expressed mainly in type II alveolar epithelia on the endothelial surface [20]. The human immune response is triggered, and the virus is subsequently attacked by clustered immune cells. Thus, the type II alveolar epithelium suffered both direct virus attack and collateral immune damage. Subsequently, alveolar walls are injury edema and thickening, alveolar epithelial hyperplasia, inflammatory cell infiltration (mainly macrophages and monocytes), capillaries expansion hyperemia, and air-filled alveolar space are compressed [9, 19]. These pathological changes resulted in increased density and decreased air content in the area of the lesions. The linear attenuation coefficient of the X-ray is proportional to the material density; therefore these pathological changes are shown as GGO with pulmonary acinus and/or secondary lobule as the basic unit in chest CT. The lesions are mostly conical or fan-shaped (namely the peripheral/subpleural cortex lobular morphology), forming the early imaging findings.
As the course of pneumonia progresses, multiple lobules expand by or undergo fusion; interstitial inflammatory cells infiltration and viral cytopathic effect aggravate [10]; the alveolar wall significantly thickens; pulmonary interstitial edema is observed; and the exudate in the alveolar cavity increases to varying degrees, led to X-ray attenuation of lesions is enhanced compared with the early. This pathological pattern, primarily characterized by alveolar epithelial damage, followed by pulmonary interstitial edema, with subsequent changes in alveolar space with respect to shape and density, resulting in lesions observed on chest CT performed for consolidation, showing reticular or crazy paving pattern. These were common imaging signs observed during the progression of COVID-19, second only to GGO. Crazy paving pattern has a similar pathological basis to reticular pattern, but it is more severe in degree and is also a signal that pneumonia is entering its peak stage [13].
In the severe disease stage, the following features were observed: diffuse alveolar damage with fiber myxoid exudate; hyaline membrane formation in the alveoli; destruction of alveolar walls with focal hemorrhage; hyaline thrombus formation in microvascular vessels; significantly denatured proliferated type II alveolar epithelia; and necrotic shedding and mucous composition in the bronchioles, resulting in airway obstruction, and air-blood barrier damage. The clinical manifestation of these features is acute respiratory distress syndrome [8, 20]. These pathological changes led to further increases in X-ray attenuation, and chest CT scans showed large areas of GGO, reticular pattern or crazy paving pattern and consolidation.
If the patient recovers, the lesions under the pleura are usually the first to clear, forming a subpleural thin curvilinear lucency paralleling the pleural surface that approximate the density of normal lung tissue. This is because the pulmonary cortex contains abundant subpleural lymphatic vessels, and there is a network of subpleural lymphatic vessels that drain lymph from the lung surface [21]. Autopsy studies of COVID-19 patients also reported that the severity of lung lesions varied, and new and old lesions often co-existed [22, 23]. Therefore, GGO can be present in the early, middle and late stages of the disease course [11, 24], which is why it has become the most common imaging sign.
The disease stage and progression of COVID-19 was evaluated using imaging stages and semiquantitative scores, which were significantly positively correlated with one another. The semiquantitative scoring system used in this study was recommended by the radiology branch of the Chongqing Medical Association, is similar to the recently reported scoring method of 5 (maximal CT score, 20 or 25) or 6 (maximal CT score, 24) lung regions on chest CT in patients with COVID-19, and was used to assess the range of infected lung tissue. Their positive correlation with clinical severity has been reported [26, 27, 28]. The comparatively wide range of scores (1–40) used in our study helps to refine the assessment and thus may help clinicians to understand and identify the severity of the patients’ condition. In some cases, RT-PCR testing is limited, and the result of the RT-PCR test can take time (hours or days). In addition, results may be false-negative. Semiquantitative scoring may assist healthcare workers to quickly classify CT-positive cases of suspected COVID-19 [29, 30], and to choose the appropriate treatment to save lives and control the outbreak.
The study has several limitations. First, it is an exploration based on reported autopsy results, combined with anatomy, pathology and radiology analyses, rather than a strict comparison between pathological specimens and imaging, which would require further pathological studies. Second, we were only able to obtain follow-up chest CT images from a small number of patients, which limited our ability for dynamic observation. Third, we did not compare using known semiquantitative scoring methods, but shared our common scoring method.
In summary, the chest CT findings of COVID-19 showed certain characteristics because of the anatomical features of the human body and pathological changes caused by the virus; semiquantitative scoring of affected lung segments may further elucidate diagnosis and assessment of disease severity. This will assist healthcare workers in diagnosing COVID-19 and assessing disease severity, facilitate the selection of appropriate treatment options, which is important for reducing the spread of the virus, saving lives, and controlling the pandemic. Therefore, chest CT is a valuable tool for facilitating the diagnosis of COVID-19.