Original investigationRegistration-Based Lung Mechanical Analysis of Chronic Obstructive Pulmonary Disease (COPD) Using a Supervised Machine Learning Framework
Section snippets
COPDGene Data
A database of 162 subjects with varying distribution of COPD severity and nonsmoker subjects without COPD were used in this study. All the subjects were approved by the institutional review boards and provided written consent for participation in the study. The distribution of the subjects was: 27 nonsmokers, 30 Global Initiative for Chronic Obstructive Lung Disease (GOLD)0, 29 GOLD1, 29 GOLD2, 28 GOLD3, and 19 GOLD4. These subjects were selected from the Iowa Cohort of the COPDGene study. The
Correlation Results
Pearson's linear correlation coefficient, r, and corresponding P values were calculated for the PFT measurements and the optimal set of CT-based features from each feature set. Spearman's correlation coefficient, ρ, was reported to find the correlation between SGRQ total score and CT-based features. The results are shown in Table 2 and all the correlations are statistically significant, P < .001. The air-trapping measure in the density-based feature set showed a strong negative correlation with
Discussion
The two classification experiments conducted in this study show that the CT-derived estimates of regional lung tissue biomechanics serve as useful quantitative imaging metrics to characterize patients with COPD. The relationship between the CT-derived biomechanical features and clinical diagnostic measures (PFT measurements, SGRQ scores) are shown in Table 2. The Jacobian features, which capture local volume changes, have shown strong correlations with FEV1% predicted (r = 0.80) and FEV1/FVC
Conclusion
This study constructed CT-derived density, texture, and biomechanical feature sets and showed these individual feature sets had statistically significant correlations with measures of airflow obstruction and patient quality of life in subjects with COPD. It also showed that these feature sets could be used individually and in combination to classify subjects with and without COPD and to classify subjects by their severity of COPD using the GOLD staging system for COPD. The biomechanical feature
Acknowledgments
The authors thank the COPDGene research group for providing the data required for this study. This work was supported by Grants HL079406 and HL064368 from the National Institutes of Health. E.A.H. and J.M.R. are founders and shareholders of VIDA Diagnostics, Inc., and J.D.N. is a paid consultant for VIDA Diagnostics Inc.
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2021, Journal of Allergy and Clinical ImmunologyCitation Excerpt :Finally, in this study we demonstrated that lung deformation measurements (Jacobian determinant values), as well as lung deformation spatial heterogenity (Jacobian gradient values), were independently associated with several asthma-related outcomes. The Jacobian determinant is a biomechanical measurement calculated from image coregistration and reflects the local volume change that occurs at a point from expiration to inspiration.56-58 Although lung deformation measurements are less intuitive, Jacobian measurements have previously been found to be of importance in both COPD and asthma.56,57,59-62
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2019, Clinical BiomechanicsCitation Excerpt :Choi et al., 2017) In addition, airway measurements such as total airway length and branch count made using CT have been correlated with pulmonary function tests of lung biomechanics (Karayama et al., 2017; Pu et al., 2012). Using multi-volume CT, Jacobian biomarkers of lung deformation have been evaluated in patients with obstructive lung disease including severe asthmatics (Choi et al., 2013; Jahani et al., 2017) and COPD participants in the COPDgene study (Bhatt et al., 2017; Bodduluri et al., 2013; Bodduluri et al., 2017). Differences and abnormal lung biomechanical properties have been quantified in severe asthmatics including a diminished volume change during breathing, suggestive of gas-trapping (Choi et al., 2013).