CT findings and dynamic imaging changes of COVID-19 in 2908 patients: a systematic review and meta-analysis

Abstract

Quick screening patients with COVID-19 is the most important way of controlling transmission by isolation and medical treatment. Chest computed tomography (CT) has been widely used during the initial screening process, including pneumonia diagnosis, severity assessment, and differential diagnosis of COVID-19. The course of COVID-19 changes rapidly. Serial CT imaging could observe the distribution, density, and range of lesions dynamically, monitor the changes, and then guide towards appropriate treatment. The aim of the review was to explore the chest CT findings and dynamic CT changes of COVID-19 using systematic evaluation methods, instructing the clinical imaging diagnosis. A systematic literature search was performed. The quality of included literature was evaluated with a quality assessment tool, followed by data extraction and meta-analysis. Homogeneity and publishing bias were analyzed. A total of 109 articles were included, involving 2908 adults with COVID-19. The lesions often occurred in bilateral lungs (74%) and were multifocal (77%) with subpleural distribution (81%). Lesions often showed ground-glass opacity (GGO) (68%), followed by GGO with consolidation (48%). The thickening of small vessels (70%) and thickening of intralobular septum (53%) were also common. The dynamic changes of chest CT manifestations showed that lesions were absorbed and improved gradually after reaching the peak (80%), had progressive deterioration (55%), were absorbed and improved gradually (46%), fluctuated (22%), or remained stable (26%). The review showed the common and key CT features and the dynamic imaging change patterns of COVID-19, helping with timely management during COVID-19 pandemic.

Keywords

2019 novel coronavirus disease X-ray computed tomography dynamic change retrospective study meta-analysis

Introduction

In late December 2019, an unexplained pneumonia occurred, which was eventually confirmed to be the novel coronavirus disease 19 (COVID-19) (1,2). The World Health Organization (WHO) officially announced that COVID-19 created a global pandemic on 11 March 2020. By 11 May 2020, over 4 million patients worldwide had been diagnosed with COVID-19 infection and there had been about 280,000 deaths. The quick screening of COVID-19 is the most important way to control the transmission – by isolation and medical treatment. The main clinical symptoms of COVID-19 usually include fever, dry cough, and fatigue, and a small number of patients are accompanied by symptoms such as nasal congestion, rhinorrhea, sore throat, myalgia, and diarrhea (3). The current gold standard of diagnosis is the reverse transcription polymerase chain reaction (RT-PCR) detection of viral nucleotides in samples obtained by oropharyngeal swabs, nasopharyngeal swabs, bronchoalveolar lavage, or tracheal aspiration (4). However, it has been reported that the sensitivity of RT-PCR in the detection of COVID-19 is as low as 60%–71% (5 –7). In contrast, computed tomography (CT) of the chest plays a great role in the detection of pneumonia, although some confirmed cases may not appear as pneumonia on CT images, such as the mild category of COVID-19. The presence of viral pneumonia is one of the most important diagnostic criteria for suspected cases. It has been reported that CT has high accuracy in reference to the RT-PCR. Ai et al. found the diagnostic sensitivity of chest CT for COVID-19 was 97% (580/601, 95% confidence interval [CI] = 95–98) (5). Besides, CT also plays an important role in evaluating the severity of pneumonia and the changes of imaging manifestation. However, we should keep in mind that the imaging features of COVID-19 pneumonia are non-specific, sometimes overlapping with other viral pneumonia. Therefore, recognizing the CT features and imaging changes of COVID-19 and grasping the key points for differentiating COVID-19 from other viral pneumonia is very important when screening suspected cases and controlling transmission. Most available literature is scattered and limited to single-center studies with a small sample size, while studies on the multicentric, large-sample size and varied countries and regions are rare. Therefore, the aim of the present article was to summarize the CT findings and dynamic changes of COVID-19 through a systematic review in order to improve the diagnostic accuracy based on CT images.

Material and Methods

The study followed the recommendations established by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) reporting checklist (8).

Literature search

A systematic literature search of PubMed, Embase (Elsevier), China Knowledge Network (CNKI), SionMed (CBM), Wanfang Database, Weipu Series Database, Cochrane Library, and other online databases was performed using the keywords “COVID-19,” “2019 nCoV,” “SARS-CoV-2,” “2019 novel Coronavirus,” “nCoV,” and other synonyms of coronaviruses retrieved from PubMed, such as “Deltacoronavirus,” “Deltacoronaviruses,” “Munia coronavirus HKU13,” “Coronavirus HKU15,” and “Rabbit Coronavirus.” All articles published between 1 December 2019 to 11 May 2020 were searched. Because many studies were published in Chinese and published earlier than any other language, all the literature published in Chinese and English was included in the analysis. One of the reviewers experienced in database searches designed the search strategy, which was subsequently revised by other reviewers.

Literature selection

Endnote software was used to manage all the literature. The inclusion criteria for the final analysis were as follows: (i) the patients with confirmed COVID-19 by RT-PCR tests and the corresponding chest CT findings; (ii) chest CT image changes were recorded continuously from the initial CT examination after clinical onset; (iii) published in Chinese or English; and (iv) original articles. Exclusion criteria included the following: (i) repeated publication of documents; (ii failed to extract CT imaging features; (iii) patients without RT-PCR or with negative RT-PCR; (iv) patients with pneumonia caused by other viruses, not SARS-CoV-2; (v) pregnant patients; and (vi) article type of review, meta-analysis, consensus, guidelines, editorial, and correspondence.

Quality evaluation of included literature

This article belonged to a case series study with a low level of evidence. However, case series might be the only available research evidence for this emerging infectious disease. At present, there are a large number of case series examining the role of chest CT in the evaluation of COVID-19. A large multicenter study has been formed through the literature selection. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS), a 14-item quality assessment tool, was used to evaluate the quality of included literature. Two reviewers pre-evaluated the studies, then the expert team made the final decision on the quality assessment.

Data extraction and analysis

One reviewer performed the data extraction, and the other reviewer checked the accuracy of the extraction. Consensus was reached by discussion. Publication date, case numbers, sex, and age were collected, a total of 19 CT patterns and features including subpleural distribution, ground-glass opacity (GGO), consolidation, GGO with consolidation, nodules, halo sign, reversed halo sign, patchy, other shapes, thickening of intralobular septum, fibrosis, thickening of small vessel, air bronchograms, crazy-paving pattern, cavity, thickening of pleural, subpleural translucent, pleural effusion, mediastinal lymphadenopathy and negative CT finding, were extracted. Besides, the CT manifestations were divided into negative CT, early, progressive, and severe stages (9,10). The early stage was defined as the GGO in the bilateral lower lobes with predominate subpleural distribution. The progressive stage was defined as multiple GGO combined with consolidation, subpleural and along bronchial vascular bundle distribution. Some accompanied by a small amount of pleural effusion. The severe stage was defined as diffuse lesions in both lungs and extensive density increase, which was also called “white lung”. In order to quantify the dynamic imaging changes, 0,1,3,5 was assigned for the negative CT, early, progressive and severe stage, respectively. During the follow up CT imaging, if the lesion increased or decresed not yet for the criteria of next stage, the assigned value would add or subtract 1.

Four patterns of radiographical progression in MERS and SARS patients were classified (11). On this basis, five patterns of the dynamic changes of chest CT manifestation were defined: pattern 1 = the lesions were gradually absorbed and improved; pattern 2 = the initial lesions deteriorated to peak level followed by improvement; pattern 3 = fluctuation of lesions; pattern 4 = lesions remained stable; and pattern 5 = progressive deterioration of lesions. The fluctuation of lesions showed that the initial lesion was absorbed and improved but new lesions appeared. Stable lesions showed no significant change during follow-up. In order to observe the dynamic change of CT findings over time, we used the restricted cubic spline to draw a parabola.

Stata15.0 software was used for the meta-analysis. The chi-square test was used to analyze the heterogeneity of each CT sign, and I² was used to evaluate the heterogeneity of the included literature. When P > 0.1 and I² < 50%, indicating no statistical heterogeneity in each CT sign, a fixed effect model was used; otherwise, a random effect model was selected. The publication bias was evaluated by funnel graph, Begg’s test, and Egger’s test. Data conversion was performed for those studies lacking certain data. The non-linear relationship in R software was used to analyze the timing of CT findings. The time to the severity peak of pneumonia was observed.

Results

Overview of the included literature

A total of 2035 articles were obtained through the preliminary screening of the database. Figure 1 showed the process of literature selection. Finally, 108 articles were selected for meta-analysis, 66 in Chinese and 42 in English. The methodologic quality of the literature was evaluated as high quality by QUADAS (Table 1) (12 –119). A total of 2908 adults with COVID-19 were included: 2630 patients with initial CT scanning only for the analysis of imaging features, 345 patients with a series of CT scanning for the evaluation of dynamic imaging changes, and 67 patients for the evaluation of both initial imaging features and dynamic imaging changes. The features of initial chest CT examination, stages of the initial CT manifestation, and patterns of dynamic changes of CT imaging are shown in Tables 2 –4, respectively.

Fig. 1.

Study selection and characteristics.

Table 1.

Quality evaluation form of QUADAS.*

Author	1	2	3	4	5	6	7	8	9	10	11	12	13	14
Mingqiang Zhang (12)	×	×	√	√	√	√	√	√	√	×	?	√	√	×
Lu Huang (13)	×	×	√	√	√	√	√	√	√	×	?	√	×	×
Chengcheng Yu (14)	×	√	√	√	√	√	√	√	√	×	?	√	√	×
Xueyan Liu (15)	×	√	√	√	√	√	√	√	√	√	?	√	×	×
Jing Wu (16)	×	×	√	√	√	√	√	√	√	?	?	√	×	×
Xiaoming Gong (17)	×	×	√	√	√	√	√	√	√	√	?	?	√	×
Jia Cao (18)	×	×	√	√	√	√	√	√	√	√	?	?	×	×
Kai Wang (19)	×	×	√	√	√	√	√	√	√	√	?	√	√	×
Guanghai Ji (20)	×	×	√	√	√	√	√	√	√	×	?	√	×	×
Xiaohu Li (21)	×	×	√	√	√	√	√	√	√	√	?	√	√	×
Xingnan Liao (22)	√	×	√	√	√	√	√	√	√	√	?	√	×	×
Binbin Shi (23)	×	×	√	√	√	√	√	√	√	?	?	√	×	×
Weibin Yang (24)	×	×	√	√	√	√	√	√	√	√	?	√	√	×
Tian Chen (25)	√	×	√	√	√	√	√	√	√	×	?	√	√	√
Chong Meng (26)	×	×	√	√	√	√	√	√	√	?	?	√	×	×
Yueying Pan (27)	?	×	√	√	√	√	√	√	√	?	?	√	×	×
Tao Yang (28)	√	×	√	√	√	√	√	√	√	×	×	√	×	×
Zhaowei Zhu (29)	√	×	√	√	√	√	√	√	√	?	?	√	×	×
Haifeng Liu (30)	×	×	√	√	√	√	√	√	√	?	?	√	×	×
Nanchuan Jiang (31)	×	×	√	√	√	√	√	√	√	?	?	√	×	×
Yonghao Du (32)	×	×	√	√	√	√	√	√	√	?	?	√	×	×
Lu Gao (33)	×	×	√	√	√	√	√	√	√	?	?	√	×	×
Jie Huang (34)	×	×	√	√	√	√	√	√	√	?	?	√	×	×
Ming Li (35)	×	×	√	√	√	√	√	√	√	√	√	√	×	×
Sha Li (36)	×	×	√	√	√	√	√	√	√	?	?	√	√	×
Jing Shen (37)	×	×	√	√	√	√	√	√	√	?	?	√	×	×
Yan Zhang (38)	×	×	√	√	√	√	√	√	√	√	√	√	×	×
Rongrong Liu (39)	×	√	√	√	√	√	√	√	√	?	?	√	×	×
Michael Chung (40)	×	×	√	√	√	√	√	√	√	×	?	√	×	×
Yueying Pan (41)	×	×	√	√	√	√	√	√	√	?	?	√	×	×
Kaiyue Diao (42)	×	×	√	√	√	√	√	√	√	√	?	√	×	×
Shuchang Zhou (43)	×	×	√	√	√	√	√	√	√	?	?	√	×	×
Xi Xu (44)	×	×	√	√	√	√	√	√	√	?	?	√	×	×
Rui Han (45)	×	√	√	√	√	√	√	√	√	?	?	√	×	×
Yan Li (46)	×	√	√	√	√	√	√	√	√	√	?	√	×	×
Heshui Shi (47)	×	×	√	√	√	√	√	√	√	√	?	√	×	×
Wei Zhao (48)	×	√	√	√	√	√	√	√	√	√	?	√	×	×
Adam Bernheim (49)	×	√	√	√	√	√	√	√	√	?	?	√	×	×
Xiaohu Li (50)	×	√	√	√	√	√	√	√	√	×	?	√	×	×
Jing Zhou (51)	×	×	√	√	√	√	√	√	√	×	?	√	×	×
Xiaohu Li (52)	×	×	√	√	√	√	√	√	√	×	?	√	×	×
Yicheng Fang (53)	×	×	√	√	√	√	√	√	√	×	?	√	×	×
Ya-ni Duan (54)	×	×	√	√	√	√	√	√	√	×	?	√	×	×
Yan Wu (55)	×	?	√	√	√	√	√	√	√	×	?	√	×	×
Lin Chen (56)	×	×	√	√	√	√	√	√	√	×	?	√	×	×
Jiangping Wei (98)	×	×	√	√	√	√	√	√	√	×	?	√	×	×
Hehui Shi (57)	×	×	√	√	√	√	√	√	√	×	?	√	×	×
Daoxue Xiong (58)	×	×	√	√	√	√	√	√	√	×	?	√	×	×
Rong Lv (59)	×	√	√	√	√	√	√	√	√	×	?	√	×	×
Ruoxu Li (60)	×	√	√	√	√	√	√	√	√	×	?	√	×	×
Li Dong (61)	×	√	√	√	√	√	√	√	√	×	?	√	×	×
KaiLi Wang (62)	×	×	√	√	√	√	√	√	√	√	?	√	×	×
Ruixin Yan (63)	×	√	√	√	√	√	√	√	√	×	?	√	×	×
Lanjin Lai (64)	×	?	√	√	√	√	√	√	√	×	?	√	×	×
Xianlong Zhao (65)	×	×	√	√	√	√	√	√	√	×	?	√	×	×
Chunning Ma (66)	×	×	√	√	√	√	√	√	√	×	?	√	×	×
Xianghu Yan (67)	×	√	√	√	√	√	√	√	√	×	?	√	×	×
Baoxue Li (68)	×	√	√	√	√	√	√	√	√	×	?	√	×	×
Ying Xiang (69)	×	×	√	√	√	√	√	√	√	√	?	√	×	×
Zhoukun Ling (70)	×	√	√	√	√	√	√	√	√	×	?	√	×	×
Yi Ding (71)	×	√	√	√	√	√	√	√	√	×	?	√	×	×
Hanxiong Guan (72)	×	×	√	√	√	√	√	√	√	×	?	√	×	×
Zhiyong Chen (73)	×	√	√	√	√	√	√	√	√	×	?	√	×	×
Xiaoxue Ye (74)	×	√	√	√	√	√	√	√	√	×	?	√	×	×
Jincheng Wang (75)	×	√	√	√	√	√	√	√	√	×	?	√	×	×
Hongmei Yu (76)	×	√	√	√	√	√	√	√	√	×	?	√	×	×
Jingxian Xiong (77)	×	√	√	√	√	√	√	√	√	×	?	√	×	×
Jun Mou (78)	×	√	√	√	√	√	√	√	√	×	?	√	×	×
Yanfang Wang (79)	×	√	√	√	√	√	√	√	√	×	?	√	×	×
Shaoping Hu (80)	×	×	√	√	√	√	√	√	√	×	?	√	×	×
Chenxi Li (81)	×	√	√	√	√	√	√	√	√	×	×	√	×	×
Xianlong Zhao (82)	×	×	√	√	√	√	√	√	√	×	?	√	√	×
Shaoping Cheng (83)	×	√	√	√	√	√	√	√	√	×	?	√	×	×
Kai Feng (84)	×	√	√	√	√	√	√	√	√	×	?	√	×	×
Weiguo Wang (85)	×	√	√	√	√	√	√	√	√	×	?	√	×	×
Huayong Zhang (86)	×	×	√	√	√	√	√	√	√	×	?	√	×	×
Zuxu Wang (87)	×	×	√	√	√	√	√	√	√	×	?	√	×	×
Yizi Lin (88)	×	×	√	√	√	√	√	√	√	×	?	√	×	×
Deyang Huang (89)	×	√	√	√	√	√	√	√	√	×	?	√	√	×
Yun Zhou (90)	×	√	√	√	√	√	√	√	√	×	?	√	×	×
Lianfang Yuan (91)	×	×	√	√	√	√	√	√	√	×	?	√	×	×
Yuzhu Li (92)	×	×	√	√	√	√	√	√	√	√	?	√	×	×
Lihong Wang (93)	×	?	√	√	√	√	√	√	√	×	?	√	×	×
Rui Xu (94)	×	×	√	√	√	√	√	√	√	×	?	√	×	×
Mingzhi Li (95)	×	×	√	√	√	√	√	√	√	×	?	√	×	×
X.Fang (96)	×	√	√	√	√	√	√	√	√	×	?	√	×	×
Shuchang Zhou (97)	×	√	√	√	√	√	√	√	√	×	?	√	×	×
Liang Ting (99)	×	√	√	√	√	√	√	√	√	×	?	√	×	×
Jun Chen (100)	×	√	√	√	√	√	√	√	√	×	?	√	×	×
Peng An (101)	×	×	√	√	√	√	√	√	√	×	?	√	√	×
Junqiang Lei (102)	×	×	√	√	√	√	√	√	√	×	?	√	×	×
Jing Gao (103)	×	×	√	√	√	√	√	√	√	×	?	√	×	×
José Millán-Oñate (104)	×	×	√	√	√	√	√	√	√	×	?	√	×	×
Xiaoming Li (105)	×	√	√	√	√	√	√	√	√	×	?	√	×	×
Xi Xu (106)	×	×	√	√	√	√	√	√	√	×	?	√	×	×
Ruihong Sun (107)	×	?	√	√	√	√	√	√	√	×	?	√	×	×
Binkun Jie (108)	×	√	√	√	√	√	√	√	√	×	?	√	×	×
Fengxia Shi (109)	×	×	√	√	√	√	√	√	√	×	?	√	×	×
Xiaoping Yin (110)	×	×	√	√	√	√	√	√	√	×	?	√	√	×
Lijuan Mao (111)	×	×	√	√	√	√	√	√	√	×	?	√	×	×
Jw Tang (112)	×	×	√	√	√	√	√	√	√	×	?	√	×	√
Kefu Liu (113)	×	?	√	√	√	√	√	√	√	×	?	√	×	×
Zheng Zhong (114)	×	×	√	√	√	√	√	√	√	×	?	√	×	√
Fengxiang Song (115)	×	×	√	√	√	√	√	√	√	×	?	√	×	√
Xiaoyu Han (116)	×	×	√	√	√	√	√	√	√	×	?	√	√	×
Xiaofei Hu (117)	×	×	√	√	√	√	√	√	√	×	?	√	×	×
Luther B Adair 2nd (118)	×	?	√	√	√	√	√	√	√	×	?	√	×	×
Heshui Shi (119)	×	×	√	√	√	√	√	√	√	×	?	√	×	×

√, Yes; ×, No; ?, Uncertain.

*The 14 items related to the evaluation: 1. Does the case spectrum include various cases and confounded disease cases? 2. Does the selection of the research object accurately and clearly define the definition of inclusion and exclusion criteria? 3. Can the gold standard accurately distinguish between diseased and disease-free states? 4. Is the interval between the gold standard and the test to be evaluated short enough to avoid changes in the disease? 5. Have all samples or randomly selected samples received the gold standard test? 6. Do all cases receive the same gold standard test regardless of the results of the test to be evaluated? 7. Is the gold standard test independent of the test to be evaluated (the test to be evaluated is not included in the gold standard)? 8. Is the operation of the test to be evaluated described clearly and repeatable? 9. Is the operation of the gold standard test described clearly enough and repeatable? 10. Is the interpretation of the results of the test to be evaluated carried out without knowing the results of the gold standard test? 11. Are the results of the gold standard test interpreted without knowing the test results to be evaluated? 12. Is the clinical data available when interpreting the test results consistent with the clinical data available in practical applications? 13. Have you reported difficult to interpret/intermediate test results? 14. Are there any explanations for cases withdrawn from the study?

QUADAS, Quality Assessment of Diagnostic Accuracy Studies.

Table 2.

Characteristics of the included studies of COVID-19, 2020.

Author	Date (MM/DD)	n	Gender		Median age (years)	Age range (years)	Imaging performance*	CT negative
Author	Date (MM/DD)	n	Male	Female	Median age (years)	Age range (years)	Imaging performance*	CT negative
Mingqiang Zhang (12)	02/15	8	5	4	36	15–49	2, 4	2
Lu Huang (13)	02/11	103	60	43	57	20–28	1, 2, 3, 4, 8, 18, 19	–
Chengcheng Yu (14)	02/19	91	39	52	50	33–62	1, 2, 3, 8, 10, 12, 13, 16, 18, 19	24
Xueyan Liu (15)	02/27	17	11	6	55	34–70	1, 2, 3, 10, 13, 17, 18, 19	–
Jing Wu (16)	03/03	130	78	52	43	25–80	1, 2, 4, 6, 7, 10, 12, 17, 18	–
Xiaoming Gong (17)	02/18	33	13	20	51	23–79	2, 3, 4, 6, 9, 10, 11, 13, 14, 16, 18, 19	–
Jia Cao (18)	02/28	36	20	16	72.45	61–82	2, 4, 10, 13, 14, 18, 19	–
Kai Wang (19)	02/11	30	15	15	48.9	27–78	1, 2, 3, 4, 8, 9	2
Guanghai Ji (20)	02/13	45	27	18	45.4	21–67	1, 2, 3, 4, 5, 6, 9, 10, 11, 12, 13, 18, 19	–
Xiaohu Li (21)	02/24	12	8	4	37	21–71	1, 2, 4, 8, 9, 10, 11, 13	–
Xingnan Liao (22)	02/26	42	29	13	51.6	22–69	2, 3, 5, 11, 13, 14, 18	–
Binbin Shi (23)	03/04	23	10	13	50.2	22–72	1, 2, 3, 10, 12, 13, 14, 18, 19	–
Weibin Yang (24)	03/06	8	6	2	33.5	30–40	1, 2, 3, 6, 8, 9	1
Tian Chen (25)	03/10	76	40	36	62	28–86	1, 2, 3, 4, 10, 12, 13, 16, 17, 18	–
Chong Meng (26)	03/17	20	13	7	51	27–73	1, 2, 4, 12, 13	1
Yueying Pan (27)	03/17	938	522	416	67.4	60–96	2, 3, 5	–
Tao Yang (28)	02/09	10	5	5	–	24–65	1, 2, 5, 18	–
Zhaowei Zhu (29)	03/04	12	8	4	52	32–73	1, 2, 9, 10	–
Haifeng Liu (30)	02/13	106	64	42	57	22–82	1, 2, 11, 12, 13, 14, 16, 18, 19	–
Nanchuan Jiang (31)	02/15	17	4	13	39.8	25–51	1, 2, 5, 13, 14	–
Yonghao Du (32)	02/13	7	4	3	40	24–55	1, 2, 6, 8, 10, 12, 13	0
Lu Gao (33)	02/13	10	6	4	41.8	22–70	1, 2, 3, 4, 12, 13, 18	1
Jie Huang (34)	02/29	7	3	4	56	49–64	2, 4, 11, 12, 18	–
Ming Li (35)	03/15	57	30	27	47	18–82	1, 2, 4, 7, 8, 9, 12, 13, 18	–
Sha Li (36)	03/15	21	12	9	45.05	21–75	2, 3, 5, 6, 7, 10, 11, 12	–
Jing Shen (37)	02/20	10	6	4	60	33–62	2, 4	0
Yan Zhang (38)	03/05	24	10	14	54	21– 76	2, 3, 4, 6, 7, 8, 11, 14, 16	–
Rongrong Liu (39)	02/22	33	20	13	50	20–70	1, 2, 3, 9, 10, 16	3
Michael Chung (40)	02/04	21	13	8	51	29–77	1, 2, 9, 14	3
Yueying Pan (41)	02/13	63	33	30	44.9	29–60	2, 4, 5, 8, 11	–
Kaiyue Diao (42)	02/09	6	3	3	47.5	19–59	1, 2, 3, 11	–
Shuchang Zhou (43)	02/19	62	39	23	52.8	30–70	1, 2, 11, 12, 13, 16, 17, 18	–
Xi Xu (44)	02/28	90	39	51	50	18–86	1, 2, 4, 10, 13, 14, 16, 18, 19	–
Rui Han (45)	02/15	108	38	70	45	21–90	1, 2, 4, 5, 6, 13, 14	–
Yan Li (46)	03/04	51	28	23	58	26–83	2, 3, 4, 5, 6, 7, 10, 11, 12, 13, 14, 18	–
Heshui Shi (47)	02/24	81	42	39	49.5	–	1, 2, 5, 10, 13, 14, 16, 18, 19	–
Wei Zhao (48)	03/03	101	56	45	44.44	17–75	1, 2, 3, 4, 5, 10, 12, 17, 18, 19	8
Adam Bernheim (49)	02/20	121	61	60	45	18–80	1, 2, 3, 4, 7, 9, 14, 18	27

*1 = subpleural distribution; 2 = GGO; 3 = consolidation; 4 = GGO with consolidation; 5 = nodules; 6 = halo sign; 7 = “Reversed halo” sign; 8 = patchy GGO; 9 = other shapes; 10 = thickening of intralobular septum; 11 = fibrosis; 12 = thickening of small vessel; 13 = air bronchograms; 14 = crazy-paving pattern; 15 = cavity; 16 = pleural; 17 = subpleural translucent; 18 = pleural effusion; 19 = mediastinal lymphadenopathy.

CT, computed tomography; GGO, ground-glass opacity; N, number of cases.

Table 3.

Examination time and CT manifestations of patients with COVID-19.

Author	Date	Sample size (n)	From onset to first examination (days)	Time interval of repeat chest CT examination (days)	CT examination (times)	Negative results with all the series CT examination	Negative results with first CT examination	CT appearance typing
Author	Date	Sample size (n)	From onset to first examination (days)	Time interval of repeat chest CT examination (days)	CT examination (times)	Negative results with all the series CT examination	Negative results with first CT examination	Early CT findings	Progressive Stage	Severe stage
Xueyan Liu (15)	3/15	16	3–19	2–7	3	–	–	8	5	3
Xiaohu Li (50)	5/10	60	1–2	8	3	3	2	19	36	–
Jing Zhou (51)	2/8	1	6	6	3	–	–	1	–	–
Tao Ai (5)	1/1	3	4–6	4–7	3–5	–	1	2	–	–
Xiaohu Li (52)	1/1	1	4	4	2	–	–	1	–	–
Yicheng Fang (53)	1/1	2	1–2	2–3	2–4	–	–	1	1	–
Ya-ni Duan (54)	4/1	1	7	6	3	–	–	1	–	–
Yan Wu (55)	2/1	1	2	5	3	–	–	1	–	–
Lin Chen (56)	2/20	1	0	5	5	–	–	1	–	–
Nanchuan Jiang (31)	4/10	17	0	3–6	2	–	–	17	–	–
Jiangping Wei (98)	4/1	1	3	4	3	–	–	1	–	–
Hehui Shi (57)	1/1	1	7	7	4	–	–	1	–	–
Daoxue Xiong (58)	2/25	2	1–3	5–6	2	–	–	2	–	–
Guanghai Ji (20)	2/24	7	1–4	4–7	2–3	–	–	5	2	–
Rong lv (59)	3/10	1	2	3	2	–	–	1	–	–
Ruoxu Li (60)	4/20	2	1–2	5–10	3	–	–	2	–	–
Li Dong (61)	4/1	3	3–6	5–6	2–3	–	–	2	–	1
Likai Wang (62)	2/29	1	3	4	4	–	–	1	–	–
Jing Shen (37)	2/20	4	1–9	5–9	2	–	–	4	–	–
Ruixin Yan (63)	2/25	2	4–13	3–5	2	–	–	2	–	–
Lanjin Lai (64)	4/21	1	1	8	2	–	–	1	–	–
Xianlong Zhao (65)	2/27	2	3–5	3–5	2–3	–	–	2	–	–
Chunning Ma (66)	4/26	4	2–3	3–4	3–5	2	–	2	–	–
Xianghu Yan (67)	4/20	4	6–14	7–10	2–4	–	–	4	–	–
Baoxue Li (68)	4/20	4	5–15	3–4	3–5	–	–	2	1	1
Ying Xiang (69)	3/20	6	3–8	2–7	4	–	–	6	–	–
Zhoukun Ling (70)	3/20	35	0		3	35	–	–	–	–
Yi Ding (71)	3/17	3	3–5	4–6	2–4	–	–	3	–	–
Hanxiong Guan (72)	2/3	3	2–7	4–7	3–5	–	–	3	–	–
Chong Meng (26)	3/17	1	0	6	2	–	–	1	–	–
Zhiyong Chen (73)	3/17	3	2–3	3–5	3–4	–	–	3	–	–
Xiaoxue Ye (74)	3/31	3	3–4	3–5	3	–	–	3	–	–
Kai Wang (19)	2/19	9	7	4–5	3	–	–	5	3	1
Jincheng Wang (75)	2/25	7	1–7	2–10	2–5	–	1	4	2	–
Hongmei Yu (76)	3/17	3	4–6	3–5	4–5	–	–	3	–	–
Jingxian Xiong (77)	4/3	2	3	5	3	–	–	2	–	–
Jun Mou (78)	3/21	2	7–10	3–5	3–4	–	–	1	1	–
Yanfang Wang (79)	2/24	4	0	3–16	2–4	–	–	4	–	–
Shaoping Hu (80)	2/22	1	3	9	4	–	–	1	–	–
Chenxi Li (81)	3/15	3	2	6	3	–	–	3	–	–
Xianlong Zhao (82)	4/2	2	2	4	2	–	–	2	–	–
Shaoping Cheng (83)	4/15	3	6–12	8–11	2–4	–	–	2	1	–
Kai Feng (84)	4/2	4	0	3–5	2	–	3	1	–	–
Weibin Yang (24)	3/31	8	0	3–5	2	1	–	6	1	–
Weiguo Wang (85)	2/25	2	3–7	5–7	2–3	–	2	–	–	–
Huayong Zhang (86)	5/6	2	1	6	2	1	1	–	–	–
Zuxu Wang (87)	2/25	1	5	4	3	–	1	–	–	–
Yizi Lin (88)	3/31	1	1	4	5	–	–	1	–	–
Deyang Huang (89)	4/14	31	8	3–5	3	–	–	31	–	–
Yun Zhou (90)	3/4	8	1–2	4–10	2	–	–	8	–	–
Lianfang Yuan (91)	4/21	1	4	7	4	–	1	–	–	–
Yuzhu Li (92)	5/6	1	2	7	3	–	–	1	–	–
Lihong Wang (93)	2/19	1	1	3	4	–	–	1	–	–
Rui Xu (94)	3/12	6	3–7	2–3	2	3	1	2	–	–
Mingzhi Li (95)	5/20	4	3–5	3–9	2	–	–	4	–	–
X Fang (96)	4/1	1	3	3	3	–	–	1	–	–
Shuchang Zhou (97)	5/4	3	3–6	4–5	3–5	–	–	3	–	–
Liang Ting (99)	4/15	2	1–2	3	4–5	–	–	2	–	–
Jun Chen (100)	1/1	1	3	3	3	–	–	1	–	–
Peng An (101)	4/12	1	7	6	2	–	–	1	–	–
Junqiang Lei (102)	1/31	1	5	3	2	–	–	1	–	–
Jing Gao (103)	4/22	2	3–7	4–6	2–5	–	–	2	–	–
José Millán-Oñate (104)	4/24	1	3	4	2	–	–	1	–	–
Xiaoming Li (105)	4/6	4	1–10	2–4	2	–	1	2	–	1
Xi Xu (44)	2/28	1	2	5	2	–	–	1	–	–
Xi Xu (106)	2/14	1	8	4	2	–	–	1	–	–
Ruihong Sun (107)	3/20	1	3	9	5	–	–	1	–	–
Binkun Jie (108)	4/20	3	3–6	3	4	–	–	3	–	–
Fengxia Shi (109)	3/13	1	1	7	5	–	–	1	–	–
Lin Chen (56)	2/22	1	0	2	4	–	–	1	–	–
Xiaoping Yin (110)	1/1	1	0	2	3	1	–	–	–	–
Lijuan Mao (111)	5/7	1	2	23	3	–	–	1	–	–
Rongrong Liu (39)	4/7	3	2	4–5	2	–	–	2	1	–
Zhu Y (112)	4/8	1	1	4	4	–	–	1	–	–
Kefu Liu (113)	4/7	2	6–9	4–21	2–3	–	–	–	2	–
Zheng Zhong (114)	3/28	1	0	4	2	1	–	–	–	–
Fengxiang Song (115)	2/6	4	4	2–5	2	–	–	3	1	–
Xiaoyu Han (116)	3/12	1	8	11	2	–	–		1	–
Xiaofei Hu (117)	3/12	2	5–6	2	2	–	–	2	–	–
Luther B Adair II (118)	4/20	1	7	2	2	–	–	–	1	–
Lu Gao (33)	5/5	1	3	4	2	–	1	–	–	–
Heshui Shi (119)	2/6	1	3	5	4	–	–	1	–	–

CT, computed tomography.

Table 4.

Classification of dynamic changes of chest CT manifestation.

Author	Date	Sample size (n)	Gender		Age (years)	Classification of imaging performance progress*
			Gender			1	2	3	4	5
			M	F		1	2	3	4	5
Xueyan Liu (15)	3/15	16	10	6	55 (34–75)	8	5	–	–	3
Xiaohu Li (50)	5/10	60	40	20	15–57 (39 ± 11）	21	36	–	–	–
Jing Zhou (51)	2/8	1		1	48	–	1	–	–	–
Tao Ai (5)	1/1	3	2	1	34 (29–46)	2	1	–	–	–
Xiaohu Li (52)	1/1	1		1	27	–	–	–	–	1
Yicheng Fang (53)	1/1	2		2	38.5	2	–	–	–	–
Ya-ni Duan (54)	4/1	1	1		46	1	–	–	–	–
Yan Wu (55)	2/1	1		1	54	–	1	–	–	–
Lin Chen (56)	2/20	1		1	61	–	1	–	–	–
Nanchuan Jiang (31)	4/10	17	13	4	25–51 (39.8 ± 7.5）	6	–	–	–	11
Jiangping Wei (98)	4/1	1		1	40	–	1	–	–	–
Hehui Shi (57)	1/1	1	1		42	1	–	–	–	–
Daoxue Xiong (58)	2/25	2	2		3	2	–	–	–	–
Guanghai Ji (20)	2/24	7	2	5	48 (22–67)	2	2	–	–	3
Rong lv (59)	3/10	1	1		59	–	–	–	–	1
Ruoxu Li (60)	4/20	2	2		51.5	1	1	–	–	–
Li Dong (61)	4/1	3	3		43 (9–51)	1	1	–	1	–
Likai Wang (62)	2/29	1	1		48	1	–	–	–	–
Jing Shen (37)	2/20	4	3	1	48.5 (37–61)	1	–	–	–	3
Ruixin Yan (63)	2/25	2	2		29	–	–	–	–	2
Lanjin Lai (64)	4/21	1	1		46	–	–	–	–	1
Xianlong Zhao (65)	2/27	2	2		23	1	–	–	–	1
Chunning Ma (66)	4/26	4	3	1	33.5 (9–66)	–	–	1	–	1
Xianghu Yan (67)	4/20	4	1	3	49 (32–63)	4	–	–	–	–
Baoxue Li (68)	4/20	4	1	3	60 (43–68)	–	2	–	–	2
Ying Xiang (69)	3/20	6	4	2	17–56 (41.83 ± 13.61)	–	6	–	–	–
Zhoukun Ling (70)	3/20	35	14	21	3–62 (32 ± 2)	–	–	–	–	–
Yi Ding (71)	3/17	3	2	1	31	1	1	–	–	1
Hanxiong Guan (72)	2/3	3	2	1	33 (30–63)	–	1	–	–	2
Chong Meng (26)	3/17	1	1		39	–	1	–	–	–
Zhiyong Chen (73)	3/17	3		3	42.5	–	3	–	–	–
Xiaoxue Ye (74)	3/31	3	3		47 (30–54)	–	3	–	–	–
Kai Wang (19)	2/19	9	4	5	54 (40–6254)	2	–	–	–	7
Jincheng Wang (75)	2/25	7	3	4	51 (24–70)	1	4	1	–	1
Hongmei Yu (76)	3/17	3	1	2	52 (31–69)	–	2	–	–	1
Jingxian Xiong (77)	4/3	2	1	1	3	2	–	–	–	–
Jun Mou (78)	3/21	2	1	1	39	2	–	–	–	–
Yanfang Wang (79)	2/24	4		4	46 (22–64)	2	–	–	1	1
Shaoping Hu (80)	2/22	1		1	42	–	1	–	–	–
Chenxi Li (81)	3/15	3	2	1	55 (37–65)	3	–	–	–	–
Xianlong Zhao (82)	4/2	2	2		76.5 (75–78)	–	–	–	–	2
Shaoping Cheng (83)	4/15	3	1	2	50 (30–80)	1	2	–	–	–
Kai Feng (84)	4/2	4		4	5 (4–10)	1	–	–	–	3
Weibin Yang (24)	3/31	8	6	2	30.0–40.0 (33.5 ± 3.3)	7	–	–	–	–
Weiguo Wang (85)	2/25	2	2		47.5	–	1	–	–	1
Huayong Zhang (86)	5/6	2	2		13	1	–	–	–	–
Zuxu Wang (87)	2/25	1		1	39	–	1	–	–	–
Yizi Lin (88)	3/31	1	1		60	–	1	–	–	–
Deyang Huang (89)	4/14	31	17	14	23–73 (48.3 ± 13.4)	–	31	–	–	–
Yun Zhou (90)	3/4	8	3	5	1 (1–3)	4	–	2	2	–
Lianfang Yuan (91)	4/21	1	1		35	–	1	–	–	–
Yuzhu Li (92)	5/6	1	1		31	–	1	–	–	–
Lihong Wang (93)	2/19	1		1	27	–	1	–	–	–
Rui Xu (94)	3/12	6	3	3	39 (10–43)	2	–	–	–	1
Mingzhi Li (95)	5/20	4	2	2	37.5 (35–60)	2	–	–	–	2
X.Fang (96)	4/1	1	1		47	–	–	1	–	–
Shuchang Zhou (97)	5/4	3	2	1	46 (41–46)	–	3	–	–	–
Liang Ting (99)	4/15	2	1	1	39	–	1	–	–	1
Jun Chen (100)	1/1	1		1	75	–	1	–	–	–
Peng An (101)	4/12	1		1	50	1	–	–	–	–
Junqiang Lei (102)	1/31	1		1	33	–	–	–	–	1
Jing Gao (103)	4/22	2	2		26.5	1	1	–	–	–
José Millán-Oñate (104)	4/24	1	1		34	1	–	–	–	–
Xiaoming Li (105)	4/6	4	2	2	50.5 (38–64)	2	–	–	–	2
Xi Xu (44)	2/28	1	1		53	–	–	–	–	1
Xi Xu (106)	2/14	1		1	49	–	–	–	–	1
Ruihong Sun (107)	3/20	1	1		38	–	–	–	–	1
Binkun Jie (108)	4/20	3	3		47 (35–79)	1	–	–	–	2
Fengxia Shi (109)	3/13	1	1		57	–	1	–	–	–
Lin Chen (56)	2/22	1	1		61	–	–	–	–	1
Xiaoping Yin (110)	1/1	1	1		9	–	–	–	–	–
Lijuan Mao (111)	5/7	1	1		1	1	–	–	–	–
Rongrong Liu (39)	4/7	3	2	1	36 (32–59)	1	–	–	–	2
Zhu Y (112)	4/8	1		1	27	–	1	–	–	–
Kefu Liu (113)	4/7	2	2		36.5	–	2	–	–	–
Zheng Zhong (114)	3/28	1	1		8	–	–	–	–	–
Fengxiang Song (115)	2/6	4	4		63.5 (35–75)	1	–	–	–	3
Xiaoyu Han (116)	3/12	1	1		23	1	–	–	–	–
Xiaofei Hu (117)	3/12	2	1	1	42.5	–	–	–	–	2
Luther B Adair 2nd (118)	4/20	1	1		60	–	–	–	–	1
Lu Gao (33)	5/5	1	1		44	–	–	–	–	1
Heshui Shi (119)	2/6	1	1		57	–	1	–	–	–

Values are given as mean ± SD or median (range) unless otherwise specified.

*Pattern 1 = lesions gradually absorbed and improved; pattern 2 = initial lesions deteriorated to peak level followed by improvement; pattern 3 = fluctuation of lesions; pattern 4 = lesion remained stable; pattern 5 = progressive deterioration of lesions.

CT, computed tomography.

Results of the chest CT findings and meta-analysis

Feature of initial chest CT imaging

Of 2630 patients, 72 (2.74%) presented without any abnormal findings on CT imaging. The distribution and amount of pneumonia was classified as unilateral, bilateral, unifocal, multifocal, and diffuse in the retrieved literature (Table 5). Bilateral distribution (74%, 95% CI = 66–82) and multifocal lesions (77%, 95% CI = 66–86) were more common than the unilateral or unifocal lesions. Supplementary Table 1 showed the detailed CT findings that were reported in the included literature. Table 6 showed the meta-analysis results of typical CT manifestations. Subpleural distribution (81%, 95% CI = 71–90), thickening of small vessels (70%, 95% CI = 62–78) (Fig. 2), GGO (68%, 95% CI =57–78) and patchy (60%, 95% CI = 38–81) were the most common CT features. The thickening of intralobular septum (53%, 95% CI = 42–64) and GGO with consolidation (48%, 95% CI = 37–51) (Fig. 3) were also common, while pleural effusion, reversed halo sign, mediastinal lymphadenopathy, and cavity were rare in this review.

Table 5.

Distribution and number of lesions by different classification methods, 2020.

Author	Date (MM/DD)	N	Unilateral	Bilateral	Unifocal	Multifocal	Diffuse
Mingqiang Zhang* (12)	2/15	8	2	5	–	–	–
Lu Huang (13)	2/11	103	17	41	15	–	–
Chengcheng Yu (14)	2/19	91	–	–	8	57	2
Xueyan Liu (15)	2/27	17	–	–	–	–	–
Jing Wu (16)	3/3	130	14	116	9	113	8
Xiaoming Gong (17)	2/18	33	8	25	2	31	–
Jia Cao (18)	2/28	36	11	25	–	–	–
Kai Wang (19)	2/11	30	3	25	–	–	–
Guanghai Ji (20)	2/13	45	12	–	–	–	–
Xiaohu Li (21)	2/24	12	2	10	–	–	–
Xingnan Liao (22)	2/26	42	11	31	–	–	–
Binbin Shi (23)	3/4	23	–	–	–	–	–
Weibin Yang (24)	3/6	8	2	5	2	5	1
Tian Chen (25)	3/10	76	11	–	–	–	–
Chong Meng (26)	3/17	20	2	17	–	–	–
Yueying Pan (27)	3/17	938	–	–	–	–	–
Tao Yang (28)	2/9	10	1	9	–	–	–
Zhaowei Zhu (29)	3/4	12	–	–	–	–	–
Haifeng Liu (30)	2/13	106	–	–	–	–	–
Nanchuan Jiang (31)	2/15	17	–	–	–	–	–
Yonghao Du (32)	2/13	7	–	–	–	–	–
Lu Gao (33)	2/13	10	2	7	–	–	–
Jie Huang (34)	2/29	7	1	6	–	–	–
Ming Li (35)	3/15	57	10	47	8	49	–
Sha Li (36)	3/15	21	–	–	–	–	–
Jing Shen (37)	2/20	10	2	7	–	–	–
Yan Zhang (38)	3/5	24	6	14	–	–	–
Rongrong Liu (39)	2/22	33	1	29	–	–	–
Michael Chung (40)	2/4	21	5	16	–	–	–
Yueying Pan (41)	2/13	63	–	–	–	–	–
Kaiyue Diao (42)	2/9	6	–	–	–	–	–
Shuchang Zhou (43)	2/19	62	–	–	10	52	–
Xi Xu (44)	2/28	90	–	53	7	62	–
Rui Han (45)	2/15	108	–	–	–	–	–
Yan Li (46)	3/4	51	–	–	–	–	–
Heshui Shi^† (47)	2/24	81	–	–	–	–	–
Wei Zhao (48)	3/3	101	10	83	6	55	32
Adam Bernheim (49)	2/20	121	–	73	–	–	–

*A total of nine cases, of which one is a male child.

^†The age range was not mentioned in the text; therefore, we classified them all as adults.

Table 6.

Meta-analysis results of typical CT manifestations of adult COVID-19, 2020.

Variable	n	Sample size	Occurrence (n)	Rate (%)	95% CI	χ²	I²*	Begg’s test	Egger’s test	t²^†	P
Lesion distribution
Unilateral	21	816	133	16	12–19	29.67	32.60	0.628	0.157	0.01	0.08
Bilateral	21	906	644	74	66–82	107.28	81.36	0.628	0.454	0.1	<0.01
Unifocal	9	675	67	9	7–12	11.50	30.41	0.466	0.209	0.01	0.18
Multifocal	8	572	424	77	66–86	55.15	87.31	0.386	0.758	0.1	<0.01
Diffuse	4	330	43	11	0–29	42.39	92.92	1.000	0.975	0.18	<0.01
Image features
Subpleural distribution	27	1397	1080	81	71–90	489.30	94.69	0.269	0.782	0.35	<0.01
GGO	38	2630	1892	68	57–78	1020.11	96.37	0.204	0.015	0.43	<0.01
Consolidation	27	2154	424	18	12–24	217.71	88.06	0.234	0.708	0.11	<0.01
GGO with consolidation	21	1011	487	48	37–51	91.06	78.04	0.123	0.099	0.08	<0.01
Nodules	14	1636	158	10	5–15	85.86	84,86	0.380	0.267	7	<0.01
Halo sign	9	427	146	30	13–50	116.37	93.13	0.917	0.858	0.31	<0.01
“Reversed halo” sign	6	404	18	4	2–6	8.43	40.71	0.452	0.155	0.01	0.13
Patchy	10	503	329	60	38–81	197.42	95.44	0.474	0.375	0.44	<0.01
Other shapes	10	372	207	54	39–68	61.59	85.39	0.367	0.615	0.17	<0.01
Thickening of intralobular septum	15	722	403	53	42–64	111.71	87.47	0.552	0.395	0.15	<0.01
Fibrosis	15	722	104	14	5–26	184.09	92.40	0.125	0.198	0.26	<0.01
Thickening of small vessel	16	915	639	70	62–78	99.66	84.95	0.528	0.504	0.1	<0.01
Air bronchograms	20	984	405	39	27–52	276.69	93.13	0.846	0.943	0.28	<0.01
Crazy-paving pattern	12	717	150	20	11–31	118.09	90.68	0.451	0.851	0.17	<0.01
Cavity	7	505	0	0	0–0	0.71	<0.01	1.000	–	<0.01	0.99
Pleural thickening	10	704	237	32	14–54	303.26	97.03	0.530	0.408	0.47	<0.01
Subpleural translucent	4	256	90	37	24–50	12.33	75.67	0.308	0.592	0.05	0.01
Pleural effusion	29	1491	66	3	1–4	48.10	41.79	0.133	0.692	0.01	0.01
Mediastinal lymphadenopathy	20	1134	44	2	0–4	89.01	78.66	0.018	0.946	0.07	<0.01

Fixed effect model was used for unilateral, cavity and “reversed halo” sign; the random effect model was used for others.

*Index for the degree of heterogeneity.

^†Tau-squared measure of heterogeneity.

CI, confidence interval; CT, computed tomography.

Fig. 2.

Initial computed tomography scan of COVID-19 in a 34-year-old woman showed a thickening of small vessel with ground-glass opacity. Clinical category, moderate. She had an epidemiological contact, complaint of fever for two days (37.8 °C), no cough. White blood cell count = 4.7*10⁹/L, lymphocyte count = 15.6%.

Fig. 3.

Initial computed tomography scan of COVID-19 in a 24-year-old woman showed patchy GGO and consolidation in the lingual segment of left upper lobe. Clinical category, moderate. She had a history of close contact with a confirmed case, complaints of fever for 10 days (37.6 °C) and sore throat. White blood cell count = 4.1*10⁹/L, lymphocyte count = 33.3%.

Dynamic changes of chest CT manifestation

According to the first CT manifestations, there were more patients at the early stage (93%, 95% CI = 84–99) than at the progressive stage (45%, 95% CI = 35–56) and severe stage (17%, 95% CI = 5–34) (Table 7). Pattern 2 (80%, 95% CI = 63–93) was more common than pattern 1 (46%, 95% CI = 37–54), pattern 3 (22%, 95% CI = 2–48), pattern 4 (26%, 95% CI = 4–54), and pattern 5 (55%, 95% CI = 44–66) in this review (Table 7). The time of the deterioration of pneumonia to the peak (10–15 days) is shown in Fig. 4.

Table 7.

Meta-analysis results of dynamic change of chest CT manifestations.

Variable	N	Sample size	Occurrences (n)	Rate (%)	95% CI	χ²	I²*	t^2†	P	Egger’s test	Begg’s test
CT examination with negative results
Negative results with the CT examination series	8	117	47	57	2–100	144.77	95.16	1.74	<0.01	0.545	0.322
Negative results with first CT examination	11	91	15	42	10–77	37.3	73.19	0.48	<0.01	0.000	0.010
Initial CT findings
Early stage	71	297	217	93	84–99	133.42	47.54	0.2	<0.01	0.000	0.035
Progressive stage	15	129	59	45	35–56	17.84	21.51	0.04	0.21	0.526	0.009
Severe stage	5	36	7	17	5–34	1.02	<0.01	<0.01	0.91	0.197	0.142
CT dynamic changes
The lesions were gradually absorbed and improved (pattern 1)	38	210	95	46	37–54	47.2	21.62	0.05	0.12	0.000	0.001
Initial lesions deteriorated to peak level followed by improvement (pattern 2)	36	183	124	80	63–93	69.31	49.5	0.2	<0.01	0.200	0.027
Fluctuation of lesions (pattern 3)	4	20	5	22	2–48	2.71	<0.01	＜0.01	0.44	0.090	0.308
Lesion remained stable (pattern 4)	3	15	4	26	4–54	0.1	<0.01	＜0.01	0.95	0.467	0.296
Progressive deterioration of lesions (pattern 5)	35	131	70	55	44–66	37.21	8.63	0.02	0.32	0.000	0.000

Random effect model was used if whole CT examination showed negative results, initial CT examination was negative, early stage, and lesions gradually being absorbed and improved after reaching the peak; the fixed effect model was used for others.

*Index for the degree of heterogeneity.

^†Tau-squared measure of heterogeneity.

CI, confidence interval; CT, computed tomography.

Fig. 4.

Time course of lesion changes on chest CT. X = days; Y = quantify the dynamic CT imaging changes (0, 1, and 3 were assigned for the negative CT, early stage, and progressive stage, respectively; 2 was an approximate value if the lesion had not yet increased or decreased to the next stage, the assigned value would add or subtract 1). CT, computed tomography.

Forest plots showing the relationship between several common CT features and COVID-19 are shown in Figs. 5 and 6. The relationship between several dynamic changes of chest CT manifestations and COVID-19 are shown in Supplementary Figs. 1–3. No significant heterogeneity was found in unifocal lesions (I² = 30.41, P = 0.18), reversed halo sign (I² = 40.71, P = 0.13), cavity (I² < 0.01, P = 0.99), progressive stage (I² =21.51, P = 0.21), severe stage (I² < 0.01, P = 0.91), pattern 1 (I² = 21.62, P = 0.12), pattern 3 (I² < 0.01, P = 0.44), pattern 4 (I² < 0.01, P = 0.95), and pattern 5 (I² = 8.63, P = 0.32). Therefore, a fixed-effect model was used for them. A random-effect model was used for the remaining CT features.

Fig. 5.

The forest plots of (a) bilateral, (b) multifocal, (c) subpleural distribution, and (d) ground-glass opacity.

Fig. 6.

Forest plots showing (a) GGO mixed with consolidation, (b) patchy GGO, (c) thickening of intralobular septum, and (d) thickening of small vessel. GGO, ground-glass opacity.

Publication bias

Publication bias was evaluated on each CT feature corresponding to each study. In general, the funnel plots of all the distribution patterns, lesion amount, and CT features were basically symmetrical. The inconsistent results of GGO by Begg’s test (P = 0.204 > 0.05) and Egger’s test (P = 0.015 < 0.05), mediastinal lymphadenopathy by Begg’s test (P = 0.018 < 0.05) and Egger’s test (P = 0.946 > 0.05). The inconsistent results for patients at the early stage in the initial CT examination by Begg’s test (P = 0.305 > 0.05) and Egger’s test (P = 0.000 < 0.05), patients at the progressive stage by Begg’s test (P = 0.009 < 0.05) and Egger’s test (P = 0.526 > 0.05), and the lesions gradually being absorbed and improved after reaching the peak by Begg’s test (P = 0.027 < 0.05) and Egger’s test (P = 0.200 > 0.05) showed mild publication bias, but not significant bias. The remaining features had no publication bias, indicating a good stability in the meta-analysis. The 109 articles were basically located within 95% CI and distributed symmetrically, with inverted funnel-shaped distribution, suggesting no significant publication bias. An example of the funnel chart of unilateral distribution is shown in Fig. 7. In addition, inclusion, numerous biases, inclusion bias, information bias, and verification bias might occur in the reviewed studies, as the QUADAS tool showed that many articles did not clearly define their population. We tried our best to control these biases through the selection of the research participants.

Fig. 7.

The funnel chart of unilateral distribution.

Discussion

The current global outbreak of COVID-19 poses a serious threat to public health. The quick screening of COVID-19 and taking isolation measures and medical treatment are the most important ways to reduce transmission, depending on local prevention policy. The gold standard of COVID-19 diagnosis is RT-PCR; however, the limitation of RT-PCR is the relatively high false-negative rate. Chest CT has played an important role in patient management during the COVID-19 pandemic. Therefore, comprehensive analysis of CT findings of COVID-19 is imperative. We found that the most common chest CT features and patterns of dynamic CT manifestations by meta-analysis predict the progress of COVID-19 and evaluate the clinical treatment effect and prognosis.

This meta-analysis showed that most patients presented with the involvement of bilateral lungs (74%) and multifocal lesions (77%), which was slightly lower than in a previous study (bilateral lungs = 89%, multiple lesions = 87%) (16). These differences may be related to the sample size and course of disease. Subpleural distribution is one of the key CT features of COVID-19: 81% of patients had subpleural distribution in this meta-analysis, a little higher than in a previous study (78%) (16). It has been reported that the peripheral and lower lobes are more predominant (120), similar to the subpleural pattern.

GGO was the most common density, with an occurrence of 68%; the occurrence of GGO with consolidation and consolidation alone was 48% and 18%, respectively. It has been reported that the occurrence of GGO was 68.5% (121). There was no significant difference in the present study, while 96% was reported by other studies (27), which may be related to the sample size and different disease stage. The first pathological findings of COVID-19 by postmortem biopsy showed the bilateral diffuse alveolar damage with cellular fibrous exudate, which is the basis of GGO and consolidation (122). Due to pathological findings, patchy is the most common shape of GGO. The occurrence of patchy GGO in the meta-analysis was 60% in 10 studies. Patchy GGO can be seen in many diseases, such as allergic pneumonia, non-specific interstitial pneumonia, organizing pneumonia, etc. The differential diagnosis should consider the epidemiological history and laboratory test.

The thickening of small vessels was one of the most common signs in this review, with an occurrence of 70%. Vessel enlargement was described in the vicinity of the area with GGO, which may be caused by the type II alveolar epithelial cells being attacked by the virus, causing serious damage and severe vascular reactions and thrombo-inflammatory processes (123). Subsegment vascular enlargement (> 3 mm diameter) in area of lung opacity was observed in 89% of patients with confirmed COVID-19 pneumonia (124). This sign should be taken into account to distinguish COVID-19 from viral pneumonia. In a previous study (30), air bronchogram and thickening of the bronchial vascular bundle were seen (85.1%), which was consistent with the results of our study. The thickening of interlobular septum (53%) and crazy paving sign (20%) were also observed in this meta-analysis, which was related to the diffuse alveolar damage. The crazy-paving sign is caused by GGO superimposed on the thickening of interlobular or intralobular septa. Subpleural translucence (37%), pleural thickening (32%), and halo sign (30%) could also be observed in patients with COVID-19. Halo signs (50%) have been reported as a typical feature in pediatric patients with COVID-19 (125), while this meta-analysis only included the adults (30%), indicating children have a higher incidence of halo sign.

This meta-analysis showed that there were more patients at the early stage (93%) than at the progressive stage (45%) and severe stage (17%), which was consistent with the study by Huang et al. (126). Clinical symptoms and first CT findings were related to age, resistance, complications, and contact history of patients. The guidelines for Imaging Diagnosis in COVID-19 (the second edition in 2020) from China (10) showed that mild COVID-19 pneumonia was the main clinical manifestation in children and adolescents due to better immunity and less basic diseases. Older people showed higher susceptibility and worse prognosis to the pneumonia according to the study by Liu et al. (127).

The occurrence of initial lesions deteriorating to peak level and followed by an improvement (80%) was higher than the occurrence of lesions being gradually absorbed and improved (46%), fluctuation of lesions (22%), lesion remaining stable (26%), or deterioration of progressive lesions (55%). Liu et al. (128) found the initial lesions deteriorating to peak level followed by an improvement was the common pattern, which was consistent with our results. Liu et al. (129) found the gradual improvement of lesions was most common in patients with severe COVID-19, which may be related to the course of the virus infection and clinical intervention. We also found most patients showed the peak severity of pneumonia approximately 10–15 days after the initial onset of symptoms. Pan et al. (130) found improved lesions on chest CT began at approximately 14 days after the onset of initial symptoms, which was consistent with our findings. In the study by Shen et al. (131), the lesion was detected automatically and the parameters were computed for quantification. They found that the severity of COVID-19 increased from the day of initial symptoms, reached its peak at around day 8, and then decreased. This may be related to the interval between CT scanning at the onset of clinical symptoms to the follow-up chest CT, which needs more further research.

The present review has some limitations. First, some studies were excluded because the whole text could not be retrieved due to the limited priority of the search. Therefore, the results should be interpreted cautiously. Second, although our study included articles published in Chinese and English, the number of English articles should increase in the future and update the results, considering the global COVID-19 epidemic. Third, there were obvious subjective factors and lack of objective quantitative standards in the quantitative score of CT images. Artificial intelligence would be used in the further research.

In conclusion, the typical CT features of adult COVID-19 on initial CT imaging helped to diagnose the disease by combining epidemic history and clinical symptoms. The comprehensive dynamic CT changes could predict the progression of a chest CT manifestation during the COVID-19 pandemic. Mastering the CT features and dynamic changes was valuable for the early diagnosis, early isolation, early treatment, and evaluation of the disease.

Supplemental Material

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Supplemental material, sj-pdf-1-acr-10.1177_0284185121992655 for CT findings and dynamic imaging changes of COVID-19 in 2908 patients: a systematic review and meta-analysis by Xiuxiu Zhou, Yu Pu, Di Zhang, Yi Xia, Yu Guan, Shiyuan Liu and Li Fan in Acta Radiologica

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Supplemental material, sj-pdf-2-acr-10.1177_0284185121992655 for CT findings and dynamic imaging changes of COVID-19 in 2908 patients: a systematic review and meta-analysis by Xiuxiu Zhou, Yu Pu, Di Zhang, Yi Xia, Yu Guan, Shiyuan Liu and Li Fan in Acta Radiologica

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Supplemental material, sj-pdf-3-acr-10.1177_0284185121992655 for CT findings and dynamic imaging changes of COVID-19 in 2908 patients: a systematic review and meta-analysis by Xiuxiu Zhou, Yu Pu, Di Zhang, Yi Xia, Yu Guan, Shiyuan Liu and Li Fan in Acta Radiologica

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Supplemental material, sj-pdf-4-acr-10.1177_0284185121992655 for CT findings and dynamic imaging changes of COVID-19 in 2908 patients: a systematic review and meta-analysis by Xiuxiu Zhou, Yu Pu, Di Zhang, Yi Xia, Yu Guan, Shiyuan Liu and Li Fan in Acta Radiologica

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Natural Science Foundation of China (grant numbers 81871321 and 81930049) and National Key R&D Program of China (grant numbers 2016YFE0103000 and 2017YFC1308703).

ORCID iD

Li Fan

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