Rola tomografii komputerowej w ilościowej ocenie rozedmy płuc

Choromańska, Agnieszka; Macura, Katarzyna J.

doi:10.12659/pjr.882578

Cited by 20 publications

(16 citation statements)

References 43 publications

(51 reference statements)

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“… 9 QCT in patients with COPD is generally accepted as a robust method, especially for quantifying emphysema. 10 QCT-measured emphysema has been shown to predict mortality 11 and has been linked to lung function decrease. 12 QCT in asthmatic patients has demonstrated tremendous heterogeneity in airway remodeling, showing that change in lumen dimension is an important aspect of proximal airway remodeling 8 and identifying that changes in airway geometry are associated with histologic features of airway remodeling.…”

mentioning

confidence: 99%

Relationship between lung function and quantitative computed tomographic parameters of airway remodeling, air trapping, and emphysema in patients with asthma and chronic obstructive pulmonary disease: A single-center study

Hartley

Barker

Newby

et al. 2016

Journal of Allergy and Clinical Immunology

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BackgroundThere is a paucity of studies comparing asthma and chronic obstructive pulmonary disease (COPD) based on thoracic quantitative computed tomographic (QCT) parameters.ObjectivesWe sought to compare QCT parameters of airway remodeling, air trapping, and emphysema between asthmatic patients and patients with COPD and explore their relationship with airflow limitation.MethodsAsthmatic patients (n = 171), patients with COPD (n = 81), and healthy subjects (n = 49) recruited from a single center underwent QCT and clinical characterization.ResultsProximal airway percentage wall area (%WA) was significantly increased in asthmatic patients (62.5% [SD, 2.2]) and patients with COPD (62.7% [SD, 2.3]) compared with that in healthy control subjects (60.3% [SD, 2.2], P < .001). Air trapping measured based on mean lung density expiratory/inspiratory ratio was significantly increased in patients with COPD (mean, 0.922 [SD, 0.037]) and asthmatic patients (mean, 0.852 [SD, 0.061]) compared with that in healthy subjects (mean, 0.816 [SD, 0.066], P < .001). Emphysema assessed based on lung density measured by using Hounsfield units below which 15% of the voxels lie (Perc15) was a feature of COPD only (patients with COPD: mean, −964 [SD, 19.62] vs asthmatic patients: mean, −937 [SD, 22.7] and healthy subjects: mean, −937 [SD, 17.1], P < .001). Multiple regression analyses showed that the strongest predictor of lung function impairment in asthmatic patients was %WA, whereas in the COPD and asthma subgrouped with postbronchodilator FEV1 percent predicted value of less than 80%, it was air trapping. Factor analysis of QCT parameters in asthmatic patients and patients with COPD combined determined 3 components, with %WA, air trapping, and Perc15 values being the highest loading factors. Cluster analysis identified 3 clusters with mild, moderate, or severe lung function impairment with corresponding decreased lung density (Perc15 values) and increased air trapping.ConclusionsIn asthmatic patients and patients with COPD, lung function impairment is strongly associated with air trapping, with a contribution from proximal airway narrowing in asthmatic patients.

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confidence: 99%

Relationship between lung function and quantitative computed tomographic parameters of airway remodeling, air trapping, and emphysema in patients with asthma and chronic obstructive pulmonary disease: A single-center study

Hartley

Barker

Newby

et al. 2016

Journal of Allergy and Clinical Immunology

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“…It achieved an error smaller than ten on 90% of our data, yielding a mean error of 3.96 overall. Visual interpretation of X-rays has an accuracy of 77%, and low sensitivity when diagnosing mild to moderate emphysema [12]. Our model was able to make predictions with 85.39% accuracy and reached a maximal mean sensitivity of 78.36% when studying emphysema under the condition that a positive emphysema diagnostic is given at a 5% emphysema percentage threshold, significantly improving the X-ray visual diagnostic technique.…”

Section: Discussionmentioning

confidence: 93%

“…However, our results show that even though our model can correctly predict emphysema percentages below 10%, it tends to underestimate measurements above this threshold. Although our model needs to be improved concerning this aspect, it is important to notice that it obtains very accurate scores of small emphysema percentages (mild disease), which are the values that classic image-based diagnosis methods are unable to recognize [12]. When analyzing the activation maps generated by our model, we observed that the main focus of the CNN was the lungs and the diaphragm, and no attention was paid to osseous structures.…”

Section: Discussionmentioning

confidence: 96%

“…However, a consensus has not been attained as different observers have different success rates when assessing the severity of emphysema in X-ray films, and each observer uses different radiological criteria to reach their conclusions [9, 10]. All studies agree that X-ray-based emphysema diagnosis has a high inter-observer variation when relating radiological signs with emphysema severity with sensitivity ranging from 68% to 77% [11, 12]. Typically, emphysema is diagnosed when 2 or more of the radiological signs of emphysema are present, and severity is a personal assessment of the observer.…”

Section: Introductionmentioning

confidence: 99%

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Emphysema quantification on simulated X-rays through deep learning techniques

Campo

Pascau

Estépar

2018

2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)

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Emphysema quantification techniques rely on the use of CT scans, but they are rarely used in the diagnosis and management of patients with COPD; X-ray films are the preferred method to do this. However, this diagnosis method is very controversial, as there are not established guidelines to define the disease, sensitivity is low, and quantification cannot be done. We developed a quantification method based on a CNN, capable of predicting the emphysema percentage of a patient based on an X-ray image. We used real CT scans to simulate X-ray films and to calculate emphysema percentage using the LAA%. The model developed was able to calculate emphysema percentage with an LAA% mean error of 3.96, and it obtained an AUC accuracy of 90.73% for an emphysema definition of ≥10%, with a mean sensitivity of 85.68%, significantly improving X-ray-based emphysema diagnosis.

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“…A clinically established measure of pulmonary capillary function is the diffusing capacity for carbon monoxide (CO) which is closely linked to the degree of lung emphysema as quantified by CT scans [4]. On the other hand, patients with diabetes without known lung disease also may show a decreased CO diffusing capacity [5, 6], and impairment of pulmonary function in type I diabetes patients treated with insulin was linked to the quality of metabolic control [7].…”

Section: Introductionmentioning

confidence: 99%

Transfer factor for carbon monoxide in patients with COPD and diabetes: results from the German COSYCONET cohort

et al. 2017

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BackgroundAn impairment of CO diffusing capacity has been shown in diabetic patients without lung disease. We analyzed how diffusing capacity in patients with COPD is affected by the concurrent diagnosis of diabetes.MethodsData from the initial visit of the German COPD cohort COSYCONET were used for analysis. 2575 patients with complete lung function data were included, among them 358 defined as diabetics with a reported physician diagnosis of diabetes and/or specific medication. Pairwise comparisons between groups and multivariate regression models were used to identify variables predicting the CO transfer factor (TLCO%pred) and the transfer coefficient (KCO%pred).ResultsCOPD patients with diabetes differed from those without diabetes regarding lung function, anthropometric, clinical and laboratory parameters. Moreover, gender was an important covariate. After correction for lung function, gender and body mass index (BMI), TLCO%pred did not significantly differ between patients with and without diabetes. The results for the transfer coefficient KCO were similar, demonstrating an important role of the confounding factors RV%pred, TLC%pred, ITGV%pred, FEV1%pred, FEV1/FVC, age, packyears, creatinine and BMI. There was not even a tendency towards lower values in diabetes.ConclusionThe analysis of data from a COPD cohort showed no significant differences of CO transport parameters between COPD patients with and without diabetes, if BMI, gender and the reduction in lung volumes were taken into account. This result is in contrast to observations in lung-healthy subjects with diabetes and raises the question which factors, among them potential anti-inflammatory effects of anti-diabetes medication are responsible for this finding.

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Rola tomografii komputerowej w ilościowej ocenie rozedmy płuc

Cited by 20 publications

References 43 publications

Relationship between lung function and quantitative computed tomographic parameters of airway remodeling, air trapping, and emphysema in patients with asthma and chronic obstructive pulmonary disease: A single-center study

Relationship between lung function and quantitative computed tomographic parameters of airway remodeling, air trapping, and emphysema in patients with asthma and chronic obstructive pulmonary disease: A single-center study

Emphysema quantification on simulated X-rays through deep learning techniques

Transfer factor for carbon monoxide in patients with COPD and diabetes: results from the German COSYCONET cohort

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