scholarly journals Correlation of lung parenchymal MR signal intensity with pulmonary function tests and quantitative computed tomography (CT) evaluation: A pilot study

2007 ◽  
Vol 26 (6) ◽  
pp. 1530-1536 ◽  
Author(s):  
Tae Iwasawa ◽  
Hiroshi Takahashi ◽  
Takashi Ogura ◽  
Akira Asakura ◽  
Toshiyuki Gotoh ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Pilot Study ◽  
Pulmonary Function ◽  
Signal Intensity ◽  
Pulmonary Function Tests ◽  
Quantitative Computed Tomography ◽  
Function Tests ◽  
Ct Evaluation

scholarly journals Predicting Pulmonary Function Testing from Quantified Computed Tomography Using Machine Learning Algorithms in Patients with COPD

Diagnostics ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 33 ◽  
Author(s):  
Joshua Gawlitza ◽  
Timo Sturm ◽  
Kai Spohrer ◽  
Thomas Henzler ◽  
Ibrahim Akin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Lung Function ◽  
Pulmonary Function ◽  
Polynomial Regression ◽  
Pulmonary Function Tests ◽  
Quantitative Computed Tomography ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Function Tests

Introduction: Quantitative computed tomography (qCT) is an emergent technique for diagnostics and research in patients with chronic obstructive pulmonary disease (COPD). qCT parameters demonstrate a correlation with pulmonary function tests and symptoms. However, qCT only provides anatomical, not functional, information. We evaluated five distinct, partial-machine learning-based mathematical models to predict lung function parameters from qCT values in comparison with pulmonary function tests. Methods: 75 patients with diagnosed COPD underwent body plethysmography and a dose-optimized qCT examination on a third-generation, dual-source CT with inspiration and expiration. Delta values (inspiration—expiration) were calculated afterwards. Four parameters were quantified: mean lung density, lung volume low-attenuated volume, and full width at half maximum. Five models were evaluated for best prediction: average prediction, median prediction, k-nearest neighbours (kNN), gradient boosting, and multilayer perceptron. Results: The lowest mean relative error (MRE) was calculated for the kNN model with 16%. Similar low MREs were found for polynomial regression as well as gradient boosting-based prediction. Other models led to higher MREs and thereby worse predictive performance. Beyond the sole MRE, distinct differences in prediction performance, dependent on the initial dataset (expiration, inspiration, delta), were found. Conclusion: Different, partially machine learning-based models allow the prediction of lung function values from static qCT parameters within a reasonable margin of error. Therefore, qCT parameters may contain more information than we currently utilize and can potentially augment standard functional lung testing.

Interstitial lung disease in systemic sclerosis quantification of disease classification and progression with high-resolution computed tomography: An observational study

2021 ◽  
pp. 239719832098537
Author(s):  
Johan Clukers ◽  
Maarten Lanclus ◽  
Dennis Belmans ◽  
Cedric Van Holsbeke ◽  
Wilfried De Backer ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Systemic Sclerosis ◽  
Interstitial Lung Disease ◽  
Pulmonary Function ◽  
Lung Disease ◽  
Pulmonary Function Tests ◽  
Severe Disease ◽  
Quantitative Computed Tomography ◽  
High Resolution Computed Tomography ◽  
Function Tests

Introduction: Systemic sclerosis–associated interstitial lung disease accounts for up to 20% of mortality in these patients and has a highly variable prognosis. Functional respiratory imaging, a quantitative computed tomography imaging technique which allows mapping of regional information, can provide a detailed view of lung structures. It thereby shows potential to better characterize this disease. Purpose: To evaluate the use of functional respiratory imaging quantitative computed tomography in systemic sclerosis–associated interstitial lung disease staging, as well as the relationship between short-term changes in pulmonary function tests and functional respiratory imaging quantitative computed tomography with respect to disease severity. Materials and methods: An observational cohort of 35 patients with systemic sclerosis was retrospectively studied by comparing serial pulmonary function tests and in- and expiratory high-resolution computed tomography over 1.5-year interval. After classification into moderate to severe lung disease and limited lung disease (using a hybrid method integrating quantitative computed tomography and pulmonary function tests), post hoc analysis was performed using mixed-effects models and estimated marginal means in terms of functional respiratory imaging parameters. Results: At follow-up, relative mean forced vital capacity percentage change was not significantly different in the limited (6.37%; N = 13; p = 0.053) and moderate to severe disease (−3.54%; N = 16; p = 0.102) groups, respectively. Specific airway resistance decreased from baseline for both groups. (Least square mean changes −25.11% predicted ( p = 0.006) and −14.02% predicted ( p = 0.001) for limited and moderate to severe diseases.) In contrast to limited disease from baseline, specific airway radius increased in moderate to severe disease by 8.57% predicted ( p = 0.011) with decline of lower lobe volumes of 2.97% predicted ( p = 0.031). Conclusion: Functional respiratory imaging is able to differentiate moderate to severe disease versus limited disease and to detect disease progression in systemic sclerosis.

scholarly journals Multi detector computed tomography evaluation in chronic obstructive pulmonary disease and correlation with severity of disease

2019 ◽  
Vol 7 (8) ◽  
pp. 3116
Author(s):  
Ajay Kumar ◽  
Ankita Rohira ◽  
Ashish Vijay ◽  
Abhay Sharma
Keyword(s):  
Computed Tomography ◽  
Chronic Obstructive Pulmonary Disease ◽  
Pulmonary Function ◽  
Pulmonary Disease ◽  
Pulmonary Function Tests ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Function Tests ◽  
Invaluable Tool ◽  
Multi Detector Computed Tomography

Background: Multi Detector computed tomography (MDCT) may effectively characterize and quantify the extent of emphysema and the air trapping related to the small airway’s disease. Here we highlight the computed-tomography findings of Chronic Obstructive Pulmonary Disease (COPD) and correlation with the Spirometrics values.Methods: The study group included the total of 100 adult patients of either sex with a clinical suspicion of COPD and those who undergone MDCT of thorax. Lung function of the patients with the COPD stages mild to very severe was evaluated by both the MDCT and Spirometrics Pulmonary Function Tests (PFTs). The scanning was done at maximum end inspiration and maximum end expiration.Results: There was a preponderance of male patients with highly significant correlation between values of mean lung density and low attenuation values (p<0.000I). MDCT correlated well with those obtained from spirometric Pulmonary Function Tests in the patients with COPD and that the correlation at expiration was superior to that at inspiration.Conclusions: The study concludes that Multi-detector computed tomography is the invaluable tool in defining and quantifying COPD and the characterization of emphysematous changes.

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