Temporal lung changes on thin-section CT in patients with COVID-19 pneumonia.
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ABSTRACT:
SUBMITTER: Zhang Z
PROVIDER: S-EPMC7661534 | biostudies-literature | 2020 Nov
REPOSITORIES: biostudies-literature
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Temporal lung changes on thin-section CT in patients with COVID-19 pneumonia.
Zhang Zhiyan Z Tang Runhui R Sun Heyang H Dai Haiyang H Chen Kangyin K Ye Xinmiao X Ye Wei W Li Shengkai S Lan Bowen B Li Li L Ou Chun-Quan CQ
Scientific reports 20201112 1
We examined characteristics of chest CT across different time periods for patients with COVID-19 pneumonia in Huizhou, China. This study included 56 COVID-19 patients with abnormal CT acquired between January 22 and March 3, 2020. The 141 scans of 56 patients were classified into four groups (Groups 1-4) based on dates on which scans were obtained at the 1st, 2nd, 3rd week or longer than three weeks after illness onset. Forty-five patients with follow-up scans were categorized into four groups ( ...[more]
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Project description:Background CT may play a central role in the diagnosis and management of coronavirus disease 2019 (COVID-19) pneumonia. Purpose To perform a longitudinal study to analyze the serial CT findings over time in patients with COVID-19 pneumonia. Materials and Methods During January 16 to February 17, 2020, 90 patients (33 men, 57 women; mean age, 45 years) with COVID-19 pneumonia were prospectively enrolled and followed up until being discharged, death, or the end of the study. A total of 366 CT scans were acquired and reviewed by two groups of radiologists for the patterns and distribution of lung abnormalities, total CT scores, and number of zones involved. Those features were analyzed for temporal change. Results CT scores and number of zones involved progressed rapidly, peaked during illness days 6-11 (median CT score, 5; median number of zones involved, five), and were followed by persistence of high levels. The predominant pattern of abnormalities after symptom onset was ground-glass opacity (35 of 78 scans [45%] to 49 of 79 scans [62%] in different periods). The percentage of mixed pattern peaked on illness days 12-17 (30 of 78 scans [38%]) and became the second most predominant pattern thereafter. Pure ground-glass opacity was the most prevalent subtype of ground-glass opacity after symptom onset (20 of 50 scans [40%] to 20 of 28 scans [71%]). The percentage of ground-glass opacity with irregular linear opacity peaked on illness days 6-11 (14 of 50 scans [28%]) and became the second most prevalent subtype thereafter. The distribution of lesions was predominantly bilateral and subpleural. Sixty-six of the 70 patients discharged (94%) had residual disease on final CT scans (median CT score, 4; median number of zones involved, four), with ground-glass opacity (42 of 70 patients [60%]) and pure ground-glass opacity (31 of 42 patients [74%]) the most common pattern and subtype. Conclusion The extent of lung abnormalities at CT peaked during illness days 6-11. The temporal changes of the diverse CT manifestations followed a specific pattern, which might indicate the progression and recovery of the illness. © RSNA, 2020 Online supplemental material is available for this article.
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Project description:PurposeTo investigate the association of the maximal severity of pneumonia on CT scans obtained within 6-week of diagnosis with the subsequent development of post-COVID-19 lung abnormalities (Co-LA).MethodsCOVID-19 patients diagnosed at our hospital between March 2020 and September 2021 were studied retrospectively. The patients were included if they had (1) at least one chest CT scan available within 6-week of diagnosis; and (2) at least one follow-up chest CT scan available ≥ 6 months after diagnosis, which were evaluated by two independent radiologists. Pneumonia Severity Categories were assigned on CT at diagnosis according to the CT patterns of pneumonia and extent as: 1) no pneumonia (Estimated Extent, 0%); 2) non-extensive pneumonia (GGO and OP, <40%); and 3) extensive pneumonia (extensive OP and DAD, >40%). Co-LA on follow-up CT scans, categorized using a 3-point Co-LA Score (0, No Co-LA; 1, Indeterminate Co-LA; and 2, Co-LA).ResultsOut of 132 patients, 42 patients (32%) developed Co-LA on their follow-up CT scans 6-24 months post diagnosis. The severity of COVID-19 pneumonia was associated with Co-LA: In 47 patients with extensive pneumonia, 33 patients (70%) developed Co-LA, of whom 18 (55%) developed fibrotic Co-LA. In 52 with non-extensive pneumonia, 9 (17%) developed Co-LA: In 33 with no pneumonia, none (0%) developed Co-LA.ConclusionsHigher severity of pneumonia at diagnosis was associated with the increased risk of development of Co-LA after 6-24 months of SARS-CoV-2 infection.
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Project description:The purpose of this study was to describe the temporal evolution of quantitative lung lesion features on chest computed tomography (CT) in patients with common and severe types of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia. Records of patients diagnosed with SARS-CoV-2 pneumonia were reviewed retrospectively from 24 January 2020 to 15 March 2020. Patients were classified into common and severe groups according to the diagnostic criteria of severe pneumonia. The quantitative CT features of lung lesions were automatically calculated using artificial intelligence algorithms, and the percentages of ground-glass opacity volume (PGV), consolidation volume (PCV) and total lesion volume (PTV) were determined in both lungs. PGV, PCV and PTV were analyzed based on the time from the onset of initial symptoms in the common and severe groups. In the common group, PTV increased slowly and peaked at approximately 12 days from the onset of the initial symptoms. In the severe group, PTV peaked at approximately 17 days. The severe pneumonia group exhibited increased PGV, PCV and PTV compared with the common group. These features started to appear in Stage 2 (4-7 days from onset of initial symptoms) and were observed in all subsequent stages (p<0.05). In severe SARS-CoV-2 pneumonia patients, PGV, PCV and PTV began to significantly increase in Stage 2 and decrease in Stage 5 (22-30 days). Compared with common SARS-CoV-2 pneumonia patients, the patients in the severe group exhibited increased PGV, PCV and PTV as well as a later peak time of lesion and recovery time.
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Project description:ObjectiveTo develop a fully automated AI system to quantitatively assess the disease severity and disease progression of COVID-19 using thick-section chest CT images.MethodsIn this retrospective study, an AI system was developed to automatically segment and quantify the COVID-19-infected lung regions on thick-section chest CT images. Five hundred thirty-one CT scans from 204 COVID-19 patients were collected from one appointed COVID-19 hospital. The automatically segmented lung abnormalities were compared with manual segmentation of two experienced radiologists using the Dice coefficient on a randomly selected subset (30 CT scans). Two imaging biomarkers were automatically computed, i.e., the portion of infection (POI) and the average infection HU (iHU), to assess disease severity and disease progression. The assessments were compared with patient status of diagnosis reports and key phrases extracted from radiology reports using the area under the receiver operating characteristic curve (AUC) and Cohen's kappa, respectively.ResultsThe dice coefficient between the segmentation of the AI system and two experienced radiologists for the COVID-19-infected lung abnormalities was 0.74 ± 0.28 and 0.76 ± 0.29, respectively, which were close to the inter-observer agreement (0.79 ± 0.25). The computed two imaging biomarkers can distinguish between the severe and non-severe stages with an AUC of 0.97 (p value < 0.001). Very good agreement (κ = 0.8220) between the AI system and the radiologists was achieved on evaluating the changes in infection volumes.ConclusionsA deep learning-based AI system built on the thick-section CT imaging can accurately quantify the COVID-19-associated lung abnormalities and assess the disease severity and its progressions.Key points• A deep learning-based AI system was able to accurately segment the infected lung regions by COVID-19 using the thick-section CT scans (Dice coefficient ≥ 0.74). • The computed imaging biomarkers were able to distinguish between the non-severe and severe COVID-19 stages (area under the receiver operating characteristic curve 0.97). • The infection volume changes computed by the AI system were able to assess the COVID-19 progression (Cohen's kappa 0.8220).
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Project description:Background The long-term pulmonary sequelae of COVID-19 is not well known. Purpose To characterize patterns and rates of improvement of chest CT abnormalities 1 year after COVID-19 pneumonia. Materials and Methods This was a secondary analysis of a prospective, multicenter observational cohort study conducted from April 29 to August 12, 2020, to assess pulmonary abnormalities at chest CT approximately 2, 3, and 6 months and 1 year after onset of COVID-19 symptoms. Pulmonary findings were graded for each lung lobe using a qualitative CT severity score (CTSS) ranging from 0 (normal) to 25 (all lobes involved). The association of demographic and clinical factors with CT abnormalities after 1 year was assessed with logistic regression. The rate of change of the CTSS at follow-up CT was investigated by using the Friedmann test. Results Of 142 enrolled participants, 91 underwent a 1-year follow-up CT examination and were included in the analysis (mean age, 59 years ± 13 [SD]; 35 women [38%]). In 49 of 91 (54%) participants, CT abnormalities were observed: 31 of 91 (34%) participants showed subtle subpleural reticulation, ground-glass opacities, or both, and 18 of 91 (20%) participants had extensive ground-glass opacities, reticulations, bronchial dilation, microcystic changes, or a combination thereof. At multivariable analysis, age of more than 60 years (odds ratio [OR], 5.8; 95% CI: 1.7, 24; P = .009), critical COVID-19 severity (OR, 29; 95% CI: 4.8, 280; P < .001), and male sex (OR, 8.9; 95% CI: 2.6, 36; P < .001) were associated with persistent CT abnormalities at 1-year follow-up. Reduction of CTSS was observed in participants at subsequent follow-up CT (P < .001); during the study period, 49% (69 of 142) of participants had complete resolution of CT abnormalities. Thirty-one of 49 (63%) participants with CT abnormalities showed no further improvement after 6 months. Conclusion Long-term CT abnormalities were common 1 year after COVID-19 pneumonia. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Leung in this issue.
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Project description:Background & aimsComputed tomography (CT) is widely used to evaluate the severity of COVID-19 infection and track disease progression. We described the changes in chest CT to enable better understanding of the progression of COVID-19 during hospitalization.MethodsConsecutively hospitalized COVID-19 patients admitted from January 11, 2020 to February 16, 2020 and followed until March 26, 2020 at the Third People's Hospital of Shenzhen, China were included. Semi- quantitative analysis was used to assess the shape, distribution, and range of lung lesions. For each image, the lungs were divided into six regions. The total CT score was the sum of individual region scores.Results305 patients underwent a total of 1442 chest CT scans with a mean interval of 5 days (interquartile range (IQR) = 3-6 days). All patients were discharged after an average hospitalization of 25 days (IQR = 20-33 days). From the onset of initial symptoms, the total CT score peaked at an earlier date in the non-severe than the severe cases (13 days versus 15 days). Typical CT image of non-severe cases mainly presented as ground-glass opacities (GGO), whilst GGO mixed with consolidation was more seen in severe cases. In addition, severe versus non-severe cases had higher prevalence of fibrosis and air bronchogram in CT scans (P from <0.001 to 0.05, P = 0.001, respectively). The proportion of patients with fibrosis and air bronchogram appeared to decrease from the fourth (20 days from onset, IQR = 16-24) and the third pulmonary CT scan (15 days from onset, IQR = 12-19), respectively.ConclusionCOVID-19 pneumonia demonstrated progressions in early stage, with the greatest pulmonary damage on CT occurred at approximately 13 days after initial onset of symptoms. Worse bilateral pulmonary infiltrates were found in severe cases, indicating continuous health care for pulmonary rehabilitation and consecutive follow-up to monitor irreversible fibrosis and consolidation are necessary.
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Project description: Not available
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Project description:This SuperSeries is composed of the SubSeries listed below.
2021-03-03 | GSE168019 | GEO
Project description:BackgroundTo develop a machine learning-based CT radiomics model is critical for the accurate diagnosis of the rapid spreading coronavirus disease 2019 (COVID-19).MethodsIn this retrospective study, a total of 326 chest CT exams from 134 patients (63 confirmed COVID-19 patients and 71 non-COVID-19 patients) were collected from January 20 to February 8, 2020. A semi-automatic segmentation procedure was used to delineate the volume of interest (VOI), and radiomic features were extracted. The Support Vector Machine (SVM) model was built on the combination of 4 groups of features, including radiomic features, traditional radiological features, quantifying features, and clinical features. By repeating cross-validation procedure, the performance on the time-independent testing cohort was evaluated by the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.ResultsFor the SVM model built on the combination of 4 groups of features (integrated model), the per-exam AUC was 0.925 (95% CI 0.856 to 0.994) for differentiating COVID-19 on the testing cohort, and the sensitivity and specificity were 0.816 (95% CI 0.651 to 0.917) and 0.923 (95% CI 0.621 to 0.996), respectively. As for the SVM models built on radiomic features, radiological features, quantifying features, and clinical features, individually, the AUC on the testing cohort reached 0.765, 0.818, 0.607, and 0.739, respectively, significantly lower than the integrated model, except for the radiomic model.ConclusionThe machine learning-based CT radiomics models may accurately classify COVID-19, helping clinicians and radiologists to identify COVID-19 positive cases.
| S-EPMC8424152 | biostudies-literature
Project description:Inflammatory burden is associated with COVID-19 severity and outcomes. Residual computed tomography (CT) lung abnormalities have been reported after COVID-19. The aim was to evaluate the association between inflammatory burden during COVID-19 and residual lung CT abnormalities collected on follow-up CT scans performed 2-3 and 6-7 months after COVID-19, in severe COVID-19 pneumonia survivors. C-reactive protein (CRP) curves describing inflammatory burden during the clinical course were built, and CRP peaks, velocities of increase, and integrals were calculated. Other putative determinants were age, sex, mechanical ventilation, lowest PaO2/FiO2 ratio, D-dimer peak, and length of hospital stay (LOS). Of the 259 included patients (median age 65 years; 30.5% females), 202 (78%) and 100 (38.6%) had residual, predominantly non-fibrotic, abnormalities at 2-3 and 6-7 months, respectively. In age- and sex-adjusted models, best CRP predictors for residual abnormalities were CRP peak (odds ratio [OR] for one standard deviation [SD] increase = 1.79; 95% confidence interval [CI] = 1.23-2.62) at 2-3 months and CRP integral (OR for one SD increase = 2.24; 95%CI = 1.53-3.28) at 6-7 months. Hence, inflammation is associated with short- and medium-term lung damage in COVID-19. Other severity measures, including mechanical ventilation and LOS, but not D-dimer, were mediators of the relationship between CRP and residual abnormalities.
| S-EPMC8914439 | biostudies-literature