Changes of quantitative CT-based airway wall dimensions in patients with COVID-19 during early recovery

Xu, Jiaxuan; Liang, Zhenyu; Jian, Wenhua; Li, Jianyu; Tang, Guoyan; Mo, Xiaoneng; Zhang, Dongying; Zheng, Jinping; Yuan-xin, Qian; Liu, Jinxin; Li, Shiyue

doi:10.21037/jtd-20-2790

Cited by 3 publications

(4 citation statements)

References 38 publications

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“…Upon comparing patients without COVID-19 with patients in the early recovery period, the researchers reported a greater airway wall thickness in the early recovery period. These results indicated that airway wall thickening in COVID-19 patients did not completely return to normal in the early recovery phase (Xu et al, 2021). In our study, the airways were reconstructed, and lumen measurements were performed.…”

Section: Discussionmentioning

confidence: 75%

Morphometric Examination of the Trachea and Bronchi in Follow-up Computed Tomography Scans of COVID-19 Patients

Erkaya,

Kutay-Coskun,

Akyol

et al. 2023

Int. J. Morphol.

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The purpose of this study is to evaluate changes in the trachea and bronchi using 3-dimensional reconstruction images obtained from the initial and follow-up computed tomography (CT) scans of COVID-19 patients. A hundred COVID-19 patients over the age of 18 were included in our study. CT images were transferred to Mimics software, and a 3-dimensional reconstruction of the trachea and bronchi was performed. The initial and follow-up CT images of COVID-19 patients were graded as none (grade 0), mild (grade 1), moderate (grade 2), and severe (grade 3) according to the total lung severity score. The patients were divided into progression and regression groups according to the grade increase/decrease between the initial and follow-up CTs. Moreover, the patients were divided into groups as 0-2 weeks, 2-4 weeks, 4-12 weeks, and over 12 weeks according to the duration between the initial and follow-up CTs. The mean cross-sectional area, circumference, and diameter measurements of the right upper lobar bronchus, intermediate bronchus, middle lobar bronchus, and left lower lobar bronchus decreased in the follow-up CTs of the progression group. This decrease was not found to be statistically significant. In the follow-up CTs of the regression group, the left upper lobar bronchus and left lower lobar bronchus measurements increased but not statistically significant. Upon comparing the onset of the disease and the follow-up period, statistically significant changes did not occur in the trachea, main bronchus, and lobar bronchus of COVID-19 patients.

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Section: Discussionmentioning

confidence: 75%

Morphometric Examination of the Trachea and Bronchi in Follow-up Computed Tomography Scans of COVID-19 Patients

Erkaya,

Kutay-Coskun,

Akyol

et al. 2023

Int. J. Morphol.

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“…In contrast, AWT-Pi10 was not significantly associated with disease severity or other blood biomarkers. One study revealed that patients with COVID-19 have more frequent airway thickening, measured by wall area (%) and AWT-Pi10 compared with those without COVID-19, but there was no significant association between airway thickness and disease severity ( 28 ). The authors suggested that this finding likely results from the fact that the primary structures attacked by SARS-CoV-2 are alveoli, not airways ( 28 ).…”

Section: Discussionmentioning

confidence: 99%

Quantitative Computed Tomography Parameters in Coronavirus Disease 2019 Patients and Prediction of Respiratory Outcomes Using a Decision Tree

Kang

Seo

et al. 2022

Front. Med.

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BackgroundChest computed tomography (CT) scans play an important role in the diagnosis of coronavirus disease 2019 (COVID-19). This study aimed to describe the quantitative CT parameters in COVID-19 patients according to disease severity and build decision trees for predicting respiratory outcomes using the quantitative CT parameters.MethodsPatients hospitalized for COVID-19 were classified based on the level of disease severity: (1) no pneumonia or hypoxia, (2) pneumonia without hypoxia, (3) hypoxia without respiratory failure, and (4) respiratory failure. High attenuation area (HAA) was defined as the quantified percentage of imaged lung volume with attenuation values between −600 and −250 Hounsfield units (HU). Decision tree models were built with clinical variables and initial laboratory values (model 1) and including quantitative CT parameters in addition to them (model 2).ResultsA total of 387 patients were analyzed. The mean age was 57.8 years, and 50.3% were women. HAA increased as the severity of respiratory outcome increased. HAA showed a moderate correlation with lactate dehydrogenases (LDH) and C-reactive protein (CRP). In the decision tree of model 1, the CRP, fibrinogen, LDH, and gene Ct value were chosen as classifiers whereas LDH, HAA, fibrinogen, vaccination status, and neutrophil (%) were chosen in model 2. For predicting respiratory failure, the decision tree built with quantitative CT parameters showed a greater accuracy than the model without CT parameters.ConclusionsThe decision tree could provide higher accuracy for predicting respiratory failure when quantitative CT parameters were considered in addition to clinical characteristics, PCR Ct value, and blood biomarkers.

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“…It has also been reported that the human lung is the most affected organ after SARS-CoV-2 infection [4]. In this context, chest CT has played a key role in the diagnosis and subsequent evaluation of COVID-19 pneumonia [5].…”

Section: Introductionmentioning

confidence: 99%

“…As the COVID-19 pandemic has evolved, there has been a need to recognize and quantify the structural lung changes of this pathology during early convalescence [5]. Most studies and research focus on the damage of the lung parenchyma due to COVID-19 disease and there is not enough information on the impact of the disease on the bronchi and trachea [9], [5]. Furthermore, in some cases evaluation methods using CT can be subjective and have a high dependence on the experience of the evaluator [10].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of CT imaging biomarkers in the trachea and pulmonary vascular tree in patients Post-COVID-19

Guzmán,

Delgado,

Morales

2024

Preprint

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Purpose: To evaluate the performance of discrete compactness (DC) and tortuosity (TR) metrics in the structures of the trachea and pulmonary vascular tree, by processing computed tomography (CT) images to quantify significant differences between populations of control subjects (HC) and patients diagnosed with COVID-19. Methods: A total of 156 subjects were analyzed, 78 of which are HC and 78 patients diagnosed with COVID-19. The structures of the trachea and vascular tree were segmented using image processing, and volume, DC, and TR metrics were calculated. Subsequently, a statistical analysis was carried out to quantify whether there are significant differences between the groups analyzed in this research. Results: The statistical analysis presented significant differences for all the metrics evaluated with a p value < 0.05 between the HC populations and patients diagnosed with COVID-19. In particular, DC and TR measurements were very marked in the structure of the trachea with a value of p < 0.01. Conclusions: All the evaluated metrics presented statistically significant changes. In the case of CD and TR, the statistical variation was more significant in the region of the trachea and airways in patients diagnosed with COVID-19; This suggests that the proposed metrics are sensitive to the morphological changes that occur in these structures and could be considered as potentially useful imaging markers to quantify the damage caused by this pathology.

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Changes of quantitative CT-based airway wall dimensions in patients with COVID-19 during early recovery

Cited by 3 publications

References 38 publications

Morphometric Examination of the Trachea and Bronchi in Follow-up Computed Tomography Scans of COVID-19 Patients

Morphometric Examination of the Trachea and Bronchi in Follow-up Computed Tomography Scans of COVID-19 Patients

Quantitative Computed Tomography Parameters in Coronavirus Disease 2019 Patients and Prediction of Respiratory Outcomes Using a Decision Tree

Evaluation of CT imaging biomarkers in the trachea and pulmonary vascular tree in patients Post-COVID-19

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