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Virdee S, Tan WC, Hogg JC, Bourbeau J, Hague CJ, Kirby M. CT Chest Imaging Using Normalized Join-Count: Predicting Emphysema Progression in the CanCOLD Study. Radiology 2024; 312:e233265. [PMID: 39012250 DOI: 10.1148/radiol.233265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/17/2024]
Abstract
Background Pre-existing emphysema is recognized as an indicator of future worsening in patients with chronic obstructive pulmonary disease (COPD) when observed through CT imaging. However, it remains uncertain whether additional factors, such as the spatial compactness of CT emphysema, might also serve as predictors of disease progression. Purpose To evaluate the relationship between the compactness of CT emphysema voxels and emphysema progression. Materials and Methods This secondary analysis uses data from the prospective Canadian Cohort Obstructive Lung Disease (CanCOLD) study, examining CT images obtained in participants with and without COPD at baseline and a 3-year follow-up time point (November 2009 to November 2018). Measurements of forced expiratory volume in first second of expiration (FEV1) and diffusing capacity of lung for carbon monoxide (DLco) were collected. The normalized join-count (NJC) measurement from baseline CT images and lung density (LD) changes were analyzed. Emphysema progression was defined as an annualized LD change of less than half an SD below the mean of the participants without COPD with no smoking history. Multivariable linear and logistic regression models were used to assess the association between baseline CT NJC measurements and the annualized change in LD, FEV1, DLco, and emphysema progression versus nonprogression. Results A total of 524 participants (mean age, 66 years ± 10 [SD]; 293 male) (FEV1 percent predicted, 88% ± 19; FEV1/FVC, 67% ± 9; DLco percent predicted, 105% ± 25) were analyzed, 187 (36%) of whom had COPD. CT NJC was associated with the annualized change in LD (P < .001), FEV1 (P = .02), and DLco (P = .01). Additionally, CT NJC predicted emphysema progression versus nonprogression (odds ratio, 2.24; 95% CI: 1.37, 3.50; P < .001). Conclusion The spatial distribution, or "compactness," of CT emphysema voxels predicted emphysema progression in individuals with and without COPD. ClinicalTrials.gov Identifier: NCT00920348 © RSNA, 2024 Supplemental material is available for this article.
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Affiliation(s)
- Sukhraj Virdee
- From the Department of Physics, Toronto Metropolitan University, 350 Victoria St, Kerr Hall South Bldg, Rm KHS-344, Toronto, ON, Canada M5B 2K3 (S.V., M.K.); Center for Heart Lung Innovation, St Paul's Hospital, Vancouver, Canada (W.C.T., J.C.H., C.J.H., M.K.); and McGill University Health Centre, McGill University, Montreal, Canada (J.B.)
| | - Wan C Tan
- From the Department of Physics, Toronto Metropolitan University, 350 Victoria St, Kerr Hall South Bldg, Rm KHS-344, Toronto, ON, Canada M5B 2K3 (S.V., M.K.); Center for Heart Lung Innovation, St Paul's Hospital, Vancouver, Canada (W.C.T., J.C.H., C.J.H., M.K.); and McGill University Health Centre, McGill University, Montreal, Canada (J.B.)
| | - James C Hogg
- From the Department of Physics, Toronto Metropolitan University, 350 Victoria St, Kerr Hall South Bldg, Rm KHS-344, Toronto, ON, Canada M5B 2K3 (S.V., M.K.); Center for Heart Lung Innovation, St Paul's Hospital, Vancouver, Canada (W.C.T., J.C.H., C.J.H., M.K.); and McGill University Health Centre, McGill University, Montreal, Canada (J.B.)
| | - Jean Bourbeau
- From the Department of Physics, Toronto Metropolitan University, 350 Victoria St, Kerr Hall South Bldg, Rm KHS-344, Toronto, ON, Canada M5B 2K3 (S.V., M.K.); Center for Heart Lung Innovation, St Paul's Hospital, Vancouver, Canada (W.C.T., J.C.H., C.J.H., M.K.); and McGill University Health Centre, McGill University, Montreal, Canada (J.B.)
| | - Cameron J Hague
- From the Department of Physics, Toronto Metropolitan University, 350 Victoria St, Kerr Hall South Bldg, Rm KHS-344, Toronto, ON, Canada M5B 2K3 (S.V., M.K.); Center for Heart Lung Innovation, St Paul's Hospital, Vancouver, Canada (W.C.T., J.C.H., C.J.H., M.K.); and McGill University Health Centre, McGill University, Montreal, Canada (J.B.)
| | - Miranda Kirby
- From the Department of Physics, Toronto Metropolitan University, 350 Victoria St, Kerr Hall South Bldg, Rm KHS-344, Toronto, ON, Canada M5B 2K3 (S.V., M.K.); Center for Heart Lung Innovation, St Paul's Hospital, Vancouver, Canada (W.C.T., J.C.H., C.J.H., M.K.); and McGill University Health Centre, McGill University, Montreal, Canada (J.B.)
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Gottman DC, Smith BJ. A scale-free model of acute and ventilator-induced lung injury: a network theory approach inspired by seismology. FRONTIERS IN NETWORK PHYSIOLOGY 2024; 4:1392701. [PMID: 38757066 PMCID: PMC11097687 DOI: 10.3389/fnetp.2024.1392701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Accepted: 04/16/2024] [Indexed: 05/18/2024]
Abstract
Introduction Acute respiratory distress syndrome (ARDS) presents a significant clinical challenge, with ventilator-induced lung injury (VILI) being a critical complication arising from life-saving mechanical ventilation. Understanding the spatial and temporal dynamics of VILI can inform therapeutic strategies to mitigate lung damage and improve outcomes. Methods Histological sections from initially healthy mice and pulmonary lavage-injured mice subjected to a second hit of VILI were segmented with Ilastik to define regions of lung injury. A scale-free network approach was applied to assess the correlation between injury regions, with regions of injury represented as 'nodes' in the network and 'edges' quantifying the degree of correlation between nodes. A simulated time series analysis was conducted to emulate the temporal sequence of injury events. Results Automated segmentation identified different lung regions in good agreement with manual scoring, achieving a sensitivity of 78% and a specificity of 85% across 'injury' pixels. Overall accuracy across 'injury', 'air', and 'other' pixels was 81%. The size of injured regions followed a power-law distribution, suggesting a 'rich-get-richer' phenomenon in the distribution of lung injury. Network analysis revealed a scale-free distribution of injury correlations, highlighting hubs of injury that could serve as focal points for therapeutic intervention. Simulated time series analysis further supported the concept of secondary injury events following an initial insult, with patterns resembling those observed in seismological studies of aftershocks. Conclusion The size distribution of injured regions underscores the spatially heterogeneous nature of acute and ventilator-induced lung injury. The application of network theory demonstrates the emergence of injury 'hubs' that are consistent with a 'rich-get-richer' dynamic. Simulated time series analysis demonstrates that the progression of injury events in the lung could follow spatiotemporal patterns similar to the progression of aftershocks in seismology, providing new insights into the mechanisms of injury distribution and propagation. Both phenomena suggest a potential for interventions targeting these injury 'hubs' to reduce the impact of VILI in ARDS management.
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Affiliation(s)
- Drew C. Gottman
- University of Colorado School of Medicine, University of Colorado Denver, Aurora, CO, United States
| | - Bradford J. Smith
- Department of Bioengineering, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, United States
- Section of Pulmonary and Sleep Medicine, Department of Pediatrics, School of Medicine, University of Colorado Denver, Aurora, CO, United States
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3
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Vestal BE, Ghosh D, Estépar RSJ, Kechris K, Fingerlin T, Carlson NE. Quantifying the spatial clustering characteristics of radiographic emphysema explains variability in pulmonary function. Sci Rep 2023; 13:13862. [PMID: 37620507 PMCID: PMC10449810 DOI: 10.1038/s41598-023-40950-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 08/18/2023] [Indexed: 08/26/2023] Open
Abstract
Quantitative assessment of emphysema in CT scans has mostly focused on calculating the percentage of lung tissue that is deemed abnormal based on a density thresholding strategy. However, this overall measure of disease burden discards virtually all the spatial information encoded in the scan that is implicitly utilized in a visual assessment. This simplification is likely grouping heterogenous disease patterns and is potentially obscuring clinical phenotypes and variable disease outcomes. To overcome this, several methods that attempt to quantify heterogeneity in emphysema distribution have been proposed. Here, we compare three of those: one based on estimating a power law for the size distribution of contiguous emphysema clusters, a second that looks at the number of emphysema-to-emphysema voxel adjacencies, and a third that applies a parametric spatial point process model to the emphysema voxel locations. This was done using data from 587 individuals from Phase 1 of COPDGene that had an inspiratory CT scan and plasma protein abundance measurements. The associations between these imaging metrics and visual assessment with clinical measures (FEV[Formula: see text], FEV[Formula: see text]-FVC ratio, etc.) and plasma protein biomarker levels were evaluated using a variety of regression models. Our results showed that a selection of spatial measures had the ability to discern heterogeneous patterns among CTs that had similar emphysema burdens. The most informative quantitative measure, average cluster size from the point process model, showed much stronger associations with nearly every clinical outcome examined than existing CT-derived emphysema metrics and visual assessment. Moreover, approximately 75% more plasma biomarkers were found to be associated with an emphysema heterogeneity phenotype when accounting for spatial clustering measures than when they were excluded.
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Affiliation(s)
- Brian E Vestal
- Center for Genes, Environment and Health, National Jewish Health, Denver, CO, USA.
| | - Debashis Ghosh
- Department of Biostatistics and Informatics, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, USA
| | - Raúl San José Estépar
- Applied Chest Imaging Laboratory (ACIL), Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Katerina Kechris
- Department of Biostatistics and Informatics, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, USA
| | - Tasha Fingerlin
- Center for Genes, Environment and Health, National Jewish Health, Denver, CO, USA
| | - Nichole E Carlson
- Department of Biostatistics and Informatics, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, USA
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Tran HM, Chen TT, Lu YH, Tsai FJ, Chen KY, Ho SC, Wu CD, Wu SM, Lee YL, Chung KF, Kuo HP, Lee KY, Chuang HC. Climate-mediated air pollution associated with COPD severity. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 843:156969. [PMID: 35760178 DOI: 10.1016/j.scitotenv.2022.156969] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 06/08/2022] [Accepted: 06/21/2022] [Indexed: 06/15/2023]
Abstract
Air pollution has been reported to be associated with chronic obstructive pulmonary disease (COPD). Our study aim was to examine the mediating effects of air pollution on climate-associated health outcomes of COPD patients. A cross-sectional study of 117 COPD patients was conducted in a hospital in Taiwan. We measured the lung function, 6-min walking distance, oxygen desaturation, white blood cell count, and percent emphysema (low attenuation area, LAA) and linked these to 0-1-, 0-3-, and 0-5-year lags of individual-level exposure to relative humidity (RH), temperature, and air pollution. Linear regression models were conducted to examine associations of temperature, RH, and air pollution with severity of health outcomes. A mediation analysis was conducted to examine the mediating effects of air pollution on the associations of RH and temperature with health outcomes. We observed that a 1 % increase in the RH was associated with increases in forced expiratory volume in 1 s (FEV1), eosinophils, and lymphocytes, and a decrease in the total-lobe LAA. A 1 °C increase in temperature was associated with decreases in oxygen desaturation, and right-, left-, and upper-lobe LAA values. Also, a 1 μg/m3 increase in PM2.5 was associated with a decrease in the FEV1 and an increase in oxygen desaturation. A 1 μg/m3 increases in PM10 and PM2.5 was associated with increases in the total-, right-, left, upper-, and lower-lobe (PM2.5 only) LAA. A one part per billion increase in NO2 was associated with a decrease in the FEV1 and an increase in the upper-lobe LAA. Next, we found that NO2 fully mediated the association between RH and FEV1. We found PM2.5 fully mediated associations of temperature with oxygen saturation and total-, right-, left-, and upper-lobe LAA. In conclusion, climate-mediated air pollution increased the risk of decreasing FEV1 and oxygen saturation and increasing emphysema severity among COPD patients. Climate change-related air pollution is an important public health issue, especially with regards to respiratory disease.
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Affiliation(s)
- Huan Minh Tran
- Ph.D. Program in Global Health and Health Security, College of Public Health, Taipei Medical University, Taipei, Taiwan; Faculty of Public Health, Da Nang University of Medical Technology and Pharmacy, Da Nang, Viet Nam.
| | - Tzu-Tao Chen
- Division of Pulmonary Medicine, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan; Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.
| | - Yueh-Hsun Lu
- Department of Radiology, Shuang-Ho Hospital, Taipei Medical University, New Taipei City, Taiwan; Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan; Department of Radiology, School of Medicine, National Yang-Ming University, Taipei, Taiwan.
| | - Feng-Jen Tsai
- Ph.D. Program in Global Health and Health Security, College of Public Health, Taipei Medical University, Taipei, Taiwan.
| | - Kuan-Yuan Chen
- Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan; Graduate Institute of Clinical Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
| | - Shu-Chuan Ho
- School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan.
| | - Chih-Da Wu
- Department of Geomatics, National Cheng Kung University, Tainan, Taiwan; National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli, Taiwan.
| | - Sheng-Ming Wu
- Division of Pulmonary Medicine, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan; Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.
| | - Yueh-Lun Lee
- Department of Microbiology and Immunology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
| | - Kian Fan Chung
- National Heart and Lung Institute, Imperial College London, London, UK.
| | - Han-Pin Kuo
- Division of Pulmonary Medicine, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
| | - Kang-Yun Lee
- Division of Pulmonary Medicine, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan; Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.
| | - Hsiao-Chi Chuang
- Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan; School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan; Cell Physiology and Molecular Image Research Center, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.
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5
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Mattson CL, Okamura K, Hume PS, Smith BJ. Spatiotemporal distribution of cellular injury and leukocytes during the progression of ventilator-induced lung injury. Am J Physiol Lung Cell Mol Physiol 2022; 323:L281-L296. [PMID: 35700201 PMCID: PMC9423727 DOI: 10.1152/ajplung.00207.2021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 05/26/2022] [Accepted: 06/12/2022] [Indexed: 11/22/2022] Open
Abstract
Supportive mechanical ventilation is a necessary lifesaving treatment for acute respiratory distress syndrome (ARDS). This intervention often leads to injury exacerbation by ventilator-induced lung injury (VILI). Patterns of injury in ARDS and VILI are recognized to be heterogeneous; however, quantification of these injury distributions remains incomplete. Developing a more detailed understanding of injury heterogeneity, particularly how it varies in space and time, can help elucidate the mechanisms of VILI pathogenesis. Ultimately, this knowledge can be used to develop protective ventilation strategies that slow disease progression. To expand existing knowledge of VILI heterogeneity, we document the spatial evolution of cellular injury distribution and leukocyte infiltration, on the micro- and macroscales, during protective and injurious mechanical ventilation. We ventilated naïve mice using either high inspiratory pressure and zero positive end-expiratory pressure ventilation or low tidal volume with positive end-expiratory pressure. Distributions of cellular injury, identified with propidium iodide staining, were microscopically analyzed at three levels of injury severity. Cellular injury initiated in diffuse, quasi-random patterns, and progressed through expansion of high-density regions of injured cells termed "injury clusters." The density profile of the expanding injury regions suggests that stress shielding occurs, protecting the already injured regions from further damage. Spatial distribution of leukocytes did not correlate with that of cellular injury or ventilation-induced changes in lung function. These results suggest that protective ventilation protocols should protect the interface between healthy and injured regions to stymie injury propagation.
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Affiliation(s)
- Courtney L Mattson
- Department of Bioengineering, University of Colorado Denver Anschutz Medical Campus, Aurora, Colorado
| | - Kayo Okamura
- Department of Bioengineering, University of Colorado Denver Anschutz Medical Campus, Aurora, Colorado
| | - Patrick S Hume
- Department of Pulmonary, Critical Care, and Sleep Medicine, National Jewish Health, Denver, Colorado
- Department of Pediatrics, Pulmonary and Sleep Medicine, School of Medicine, University of Colorado, Aurora, Colorado
| | - Bradford J Smith
- Department of Bioengineering, University of Colorado Denver Anschutz Medical Campus, Aurora, Colorado
- Department of Pediatrics, Pulmonary and Sleep Medicine, School of Medicine, University of Colorado, Aurora, Colorado
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6
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Moslemi A, Kontogianni K, Brock J, Wood S, Herth F, Kirby M. Differentiating COPD and Asthma using Quantitative CT Imaging and Machine Learning. Eur Respir J 2022; 60:13993003.03078-2021. [PMID: 35210316 DOI: 10.1183/13993003.03078-2021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 02/04/2022] [Indexed: 11/05/2022]
Abstract
There are similarities and differences between chronic obstructive pulmonary disease (COPD) and asthma patients in terms of computed tomography (CT) disease-related features. Our objective was to determine the optimal subset of CT imaging features for differentiating COPD and asthma using machine learning.COPD and asthma patients were recruited from Heidelberg University Hospital. CT was acquired and 93 features were extracted (VIDA Diagnostics): percentage of low-attenuating-areas below -950HU (LAA950), LAA950 hole count, estimated airway-wall-thickness for a 10 mm internal perimeter airway (Pi10), total-airway-count (TAC), as well as inner/outer perimeter/areas and wall thickness for each of five segmental airways, and the average of those five airways. Hybrid feature selection was used to select the optimum number of features, and support vector machine was used to classify COPD and asthma.Ninety-five participants were included (n=48 COPD; n=47 asthma); there were no differences between COPD and asthma for age (p=0.25) or FEV1 (p=0.31). In a model including all CT features, the accuracy and F1-score was 80% and 81%, respectively. The top features were: LAA950, LAA950 hole count, average outer and inner airway perimeter, outer and inner airway area RB1, and TAC. In the model with only airway features, the accuracy and F1-score were 66% and 68%, respectively. The top features were: inner area RB1, wall thickness RB1, outer area LB1, TAC LB10, average outer/inner perimeter, Pi10, and TAC.In conclusions, COPD and asthma can be differentiated using machine learning with moderate-high accuracy by a subset of only 7 CT features.
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Affiliation(s)
- Amir Moslemi
- Department of Physics, Ryerson University, Toronto, ON, Canada.,Co-first authors
| | - Konstantina Kontogianni
- Department of Pneumology and Critical Care Medicine, Thoraxklinik and Translational Lung Research Center (TLRCH), University of Heidelberg, Germany.,Co-first authors
| | - Judith Brock
- Department of Pneumology and Critical Care Medicine, Thoraxklinik and Translational Lung Research Center (TLRCH), University of Heidelberg, Germany
| | | | - Felix Herth
- Department of Pneumology and Critical Care Medicine, Thoraxklinik and Translational Lung Research Center (TLRCH), University of Heidelberg, Germany .,Co-senior authors
| | - Miranda Kirby
- Department of Physics, Ryerson University, Toronto, ON, Canada.,Co-senior authors
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7
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Yuan Z, Herrmann J, Murthy S, Peters K, Gerard SE, Nia HT, Lutchen KR, Suki B. A Personalized Spring Network Representation of Emphysematous Lungs From CT Images. FRONTIERS IN NETWORK PHYSIOLOGY 2022; 2:828157. [PMID: 36926064 PMCID: PMC10013051 DOI: 10.3389/fnetp.2022.828157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 02/21/2022] [Indexed: 11/13/2022]
Abstract
Emphysema is a progressive disease characterized by irreversible tissue destruction and airspace enlargement, which manifest as low attenuation area (LAA) on CT images. Previous studies have shown that inflammation, protease imbalance, extracellular matrix remodeling and mechanical forces collectively influence the progression of emphysema. Elastic spring network models incorporating force-based mechanical failure have been applied to investigate the pathogenesis and progression of emphysema. However, these models were general without considering the patient-specific information on lung structure available in CT images. The aim of this work was to develop a novel approach that provides an optimal spring network representation of emphysematous lungs based on the apparent density in CT images, allowing the construction of personalized networks. The proposed method takes into account the size and curvature of LAA clusters on the CT images that correspond to a pre-stressed condition of the lung as opposed to a naïve method that excludes the effects of pre-stress. The main findings of this study are that networks constructed by the new method 1) better preserve LAA cluster sizes and their distribution than the naïve method; and 2) predict different course of emphysema progression compared to the naïve method. We conclude that our new method has the potential to predict patient-specific emphysema progression which needs verification using clinical data.
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Affiliation(s)
- Ziwen Yuan
- Department of Biomedical Engineering, Boston University, Boston, MA, United States
| | - Jacob Herrmann
- Department of Biomedical Engineering, Boston University, Boston, MA, United States
| | - Samhita Murthy
- Department of Biomedical Engineering, Boston University, Boston, MA, United States
| | - Kevin Peters
- Department of Biomedical Engineering, Boston University, Boston, MA, United States
| | - Sarah E Gerard
- Department of Radiology, University of Iowa, Iowa City, IA, United States
| | - Hadi T Nia
- Department of Biomedical Engineering, Boston University, Boston, MA, United States
| | - Kenneth R Lutchen
- Department of Biomedical Engineering, Boston University, Boston, MA, United States
| | - Béla Suki
- Department of Biomedical Engineering, Boston University, Boston, MA, United States
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8
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Tanabe N, Kaji S, Sato S, Yokoyama T, Oguma T, Tanizawa K, Handa T, Sakajo T, Hirai T. A homological approach to a mathematical definition of pulmonary fibrosis and emphysema on computed tomography. J Appl Physiol (1985) 2021; 131:601-612. [PMID: 34138650 DOI: 10.1152/japplphysiol.00150.2021] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
Three-dimensional imaging is essential to evaluate local abnormalities and understand structure-function relationships in an organ. However, quantifiable and interpretable methods to localize abnormalities remain unestablished. Visual assessments are prone to bias, machine learning methods depend on training images, and the underlying decision principle is usually difficult to interpret. Here, we developed a homological approach to mathematically define emphysema and fibrosis in the lungs on computed tomography (CT). With the use of persistent homology, the density of homological features, including connected components, tunnels, and voids, was extracted from the volumetric CT scans of lung diseases. A pair of CT values at which each homological feature appeared (birth) and disappeared (death) was computed by sweeping the threshold levels from higher to lower CT values. Consequently, fibrosis and emphysema were defined as voxels with dense voids having a longer lifetime (birth-death difference) and voxels with dense connected components having a lower birth, respectively. In an independent dataset including subjects with idiopathic pulmonary fibrosis (IPF), chronic obstructive pulmonary disease (COPD), and combined pulmonary fibrosis and emphysema (CPFE), the proposed definition enabled accurate segmentation with comparable quality to deep learning in terms of Dice coefficients. Persistent homology-defined fibrosis was closely associated with physiological abnormalities such as impaired diffusion capacity and long-term mortality in subjects with IPF and CPFE, and persistent homology-defined emphysema was associated with impaired diffusion capacity in subjects with COPD. The present persistent homology-based evaluation of structural abnormalities could help explore the clinical and physiological impacts of structural changes and morphological mechanisms of disease progression.NEW & NOTEWORTHY This study proposes a homological approach to mathematically define a three-dimensional texture feature of emphysema and fibrosis on chest computed tomography using persistent homology. The proposed definition enabled accurate segmentation with comparable quality to deep learning while offering higher interpretability than deep learning-based methods.
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Affiliation(s)
- Naoya Tanabe
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Shizuo Kaji
- Institute of Mathematics for Industry, Kyushu University, Fukuoka, Japan
| | - Susumu Sato
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Tomoo Yokoyama
- Department of Mathematics, Kyoto University of Education, Kyoto, Japan
| | - Tsuyoshi Oguma
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Kiminobu Tanizawa
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Tomohiro Handa
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.,Department of Advanced Medicine for Respiratory Failure, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Takashi Sakajo
- Department of Mathematics, Kyoto University, Kyoto, Japan
| | - Toyohiro Hirai
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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9
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Chen Q, Chen L, Liu S, Chen L, Li M, Chen Z, You J, Zhang B, Zhang S. Three-Dimensional CT for Quantification of Longitudinal Lung and Pneumonia Variations in COVID-19 Patients. Front Med (Lausanne) 2021; 8:643917. [PMID: 33842505 PMCID: PMC8027238 DOI: 10.3389/fmed.2021.643917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Accepted: 02/04/2021] [Indexed: 11/21/2022] Open
Abstract
Objectives: Visual chest CT is subjective with interobserver variability. We aimed to quantify the dynamic changes of lung and pneumonia on three-dimensional CT (3D-CT) images in coronavirus disease 2019 (COVID-19) patients during hospitalization. Methods: A total of 110 laboratory-confirmed COVID-19 patients who underwent chest CT from January 3 to February 29, 2020 were retrospectively reviewed. Pneumonia lesions were classified as four stages: early, progressive, peak, and absorption stages on chest CT. A computer-aided diagnostic (CAD) system calculated the total lung volume (TLV), the percentage of low attenuation areas (LAA%), the volume of pneumonia, the volume of ground-glass opacities (GGO), the volume of consolidation plus the GGO/consolidation ratio. The CT score was visually assessed by radiologists. Comparisons of lung and pneumonia parameters among the four stages were performed by one-way ANOVA with post-hoc tests. The relationship between the CT score and the volume of pneumonia, and between LAA% and the volume of pneumonia in four stages was assessed by Spearman's rank correlation analysis. Results: A total of 534 chest CT scans were performed with a median interval of 4 days. TLV, LAA%, and the GGO/consolidation ratio were significantly decreased, while the volume of pneumonia, GGO, and consolidation were significantly increased in the progressive and peak stages (for all, P < 0.05). The CT score was significantly correlated with the pneumonia volume in the four stages (r = 0.731, 0.761, 0.715, and 0.669, respectively, P < 0.001). Conclusion: 3D-CT could be used as a useful quantification method in monitoring the dynamic changes of COVID-19 pneumonia.
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Affiliation(s)
- Qiuying Chen
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Lv Chen
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Shuyi Liu
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Luyan Chen
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Minmin Li
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Zhuozhi Chen
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Jingjing You
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Bin Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Shuixing Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, China
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10
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Tanabe N, Sato S, Suki B, Hirai T. Fractal Analysis of Lung Structure in Chronic Obstructive Pulmonary Disease. Front Physiol 2021; 11:603197. [PMID: 33408642 PMCID: PMC7779609 DOI: 10.3389/fphys.2020.603197] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2020] [Accepted: 11/25/2020] [Indexed: 12/12/2022] Open
Abstract
Chest CT is often used for localizing and quantitating pathologies associated with chronic obstructive pulmonary disease (COPD). While simple measurements of areas and volumes of emphysema and airway structure are common, these methods do not capture the structural complexity of the COPD lung. Since the concept of fractals has been successfully applied to evaluate complexity of the lung, this review is aimed at describing the fractal properties of airway disease, emphysema, and vascular abnormalities in COPD. An object forms a fractal if it exhibits the property of self-similarity at different length scales of evaluations. This fractal property is governed by power-law functions characterized by the fractal dimension (FD). Power-laws can also manifest in other statistical descriptors of structure such as the size distribution of emphysema clusters characterized by the power-law exponent D. Although D is not the same as FD of emphysematous clusters, it is a useful index to characterize the spatial pattern of disease progression and predict clinical outcomes in patients with COPD. The FD of the airway tree shape and the D of the size distribution of airway branches have been proposed indexes of structural assessment and clinical predictions. Simulations are also useful to understand the mechanism of disease progression. Therefore, the power-law and fractal analysis of the parenchyma and airways, especially when combined with computer simulations, could lead to a better understanding of the structural alterations during the progression of COPD and help identify subjects at a high risk of severe COPD.
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Affiliation(s)
- Naoya Tanabe
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Susumu Sato
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Béla Suki
- Department of Biomedical Engineering, Boston University, Boston, MA, United States
| | - Toyohiro Hirai
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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11
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Basharat F, Belli M, Kirby M, Tanguay J. Theoretical feasibility of dual‐energy radiography for structural and functional imaging of chronic obstructive pulmonary disease. Med Phys 2020; 47:6191-6206. [DOI: 10.1002/mp.14530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Revised: 09/12/2020] [Accepted: 09/25/2020] [Indexed: 11/09/2022] Open
Affiliation(s)
| | - Michael Belli
- Department of Physics Ryerson University Toronto ON Canada
| | - Miranda Kirby
- Department of Physics Ryerson University Toronto ON Canada
| | - Jesse Tanguay
- Department of Physics Ryerson University Toronto ON Canada
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12
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Toumpanakis D, Mizi E, Vassilakopoulou V, Dettoraki M, Chatzianastasiou A, Perlikos F, Giatra G, Moscholaki M, Theocharis S, Vassilakopoulos T. Spontaneous Breathing Through Increased Airway Resistance Augments Elastase-Induced Pulmonary Emphysema. Int J Chron Obstruct Pulmon Dis 2020; 15:1679-1688. [PMID: 32764913 PMCID: PMC7367735 DOI: 10.2147/copd.s256750] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Accepted: 07/02/2020] [Indexed: 11/23/2022] Open
Abstract
Introduction Resistive breathing (RB), the pathophysiologic hallmark of chronic obstructive pulmonary disease (COPD), especially during exacerbations, is associated with significant inflammation and mechanical stress on the lung. Mechanical forces are implicated in the progression of emphysema that is a major pathologic feature of COPD. We hypothesized that resistive breathing exacerbates emphysema. Methods C57BL/6 mice were exposed to 0.75 units of pancreatic porcine elastase intratracheally to develop emphysema. Resistive breathing was applied by suturing a nylon band around the trachea to reduce surface area to half for the last 24 or 72 hours of a 21-day time period after elastase treatment in total. Following RB (24 or 72 hours), lung mechanics were measured and bronchoalveolar lavage (BAL) was performed. Emphysema was quantified by the mean linear intercept (Lm) and the destructive index (DI) in lung tissue sections. Results Following 21 days of intratracheal elastase exposure, Lm and DI increased in lung tissue sections [Lm (μm), control 39.09±0.76, elastase 62.05±2.19, p=0.003 and DI, ctr 30.95±2.75, elastase 73.12±1.75, p<0.001]. RB for 72 hours further increased Lm by 64% and DI by 19%, compared to elastase alone (p<0.001 and p=0.02, respectively). RB induced BAL neutrophilia in elastase-treated mice. Static compliance (Cst) increased in elastase-treated mice [Cst (mL/cmH2O), control 0.067±0.001, elastase 0.109±0.006, p<0.001], but superimposed RB decreased Cst, compared to elastase alone [Cst (mL/cmH2O), elastase+RB24h 0.090±0.004, p=0.006 to elastase, elastase+RB72h 0.090±0.005, p=0.006 to elastase]. Conclusion Resistive breathing augments pulmonary inflammation and emphysema in an elastase-induced emphysema mouse model.
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Affiliation(s)
- Dimitrios Toumpanakis
- "Marianthi Simou" Applied Biomedical Research and Training Center, Medical School, University of Athens, Evangelismos Hospital, Athens, Greece.,3rd Department of Critical Care Medicine, Evgenideio Hospital, Medical School, University of Athens, Athens, Greece
| | - Eleftheria Mizi
- "Marianthi Simou" Applied Biomedical Research and Training Center, Medical School, University of Athens, Evangelismos Hospital, Athens, Greece
| | - Vyronia Vassilakopoulou
- "Marianthi Simou" Applied Biomedical Research and Training Center, Medical School, University of Athens, Evangelismos Hospital, Athens, Greece
| | - Maria Dettoraki
- "Marianthi Simou" Applied Biomedical Research and Training Center, Medical School, University of Athens, Evangelismos Hospital, Athens, Greece
| | - Athanasia Chatzianastasiou
- "Marianthi Simou" Applied Biomedical Research and Training Center, Medical School, University of Athens, Evangelismos Hospital, Athens, Greece
| | - Fotis Perlikos
- "Marianthi Simou" Applied Biomedical Research and Training Center, Medical School, University of Athens, Evangelismos Hospital, Athens, Greece
| | - Georgia Giatra
- 3rd Department of Critical Care Medicine, Evgenideio Hospital, Medical School, University of Athens, Athens, Greece
| | - Marina Moscholaki
- "Marianthi Simou" Applied Biomedical Research and Training Center, Medical School, University of Athens, Evangelismos Hospital, Athens, Greece
| | | | - Theodoros Vassilakopoulos
- "Marianthi Simou" Applied Biomedical Research and Training Center, Medical School, University of Athens, Evangelismos Hospital, Athens, Greece.,3rd Department of Critical Care Medicine, Evgenideio Hospital, Medical School, University of Athens, Athens, Greece
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13
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Feldhaus FW, Theilig DC, Hubner RH, Kuhnigk JM, Neumann K, Doellinger F. Quantitative CT analysis in patients with pulmonary emphysema: is lung function influenced by concomitant unspecific pulmonary fibrosis? Int J Chron Obstruct Pulmon Dis 2019; 14:1583-1593. [PMID: 31409984 PMCID: PMC6646798 DOI: 10.2147/copd.s204007] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2019] [Accepted: 05/16/2019] [Indexed: 11/30/2022] Open
Abstract
Purpose Quantitative analysis of CT scans has proven to be a reproducible technique, which might help to understand the pathophysiology of chronic obstructive pulmonary disease (COPD) and combined pulmonary fibrosis and emphysema. The aim of this retrospective study was to find out if the lung function of patients with COPD with Global Initiative for Chronic Obstructive Lung Disease (GOLD) stages III or IV and pulmonary emphysema is measurably influenced by high attenuation areas as a correlate of concomitant unspecific fibrotic changes of lung parenchyma. Patients and methods Eighty-eight patients with COPD GOLD stage III or IV underwent CT and pulmonary function tests. Quantitative CT analysis was performed to determine low attenuation volume (LAV) and high attenuation volume (HAV), which are considered to be equivalents of fibrotic (HAV) and emphysematous (LAV) changes of lung parenchyma. Both parameters were determined for the whole lung, as well as peripheral and central lung areas only. Multivariate regression analysis was used to correlate HAV with different parameters of lung function. Results Unlike LAV, HAV did not show significant correlation with parameters of lung function. Even in patients with a relatively high HAV of more than 10%, in contrast to HAV (p=0.786) only LAV showed a significantly negative correlation with forced expiratory volume in 1 second (r=-0.309, R2=0.096, p=0.003). A severe decrease of DLCO% was associated with both larger HAV (p=0.045) and larger LAV (p=0.001). Residual volume and FVC were not influenced by LAV or HAV. Conclusion In patients with COPD GOLD stage III-IV, emphysematous changes of lung parenchyma seem to have such a strong influence on lung function, which is a possible effect of concomitant unspecific fibrosis is overwhelmed.
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Affiliation(s)
- Felix W Feldhaus
- Charité Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Department of Radiology, Berlin, Germany
| | - Dorothea Cornelia Theilig
- Charité Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Department of Radiology, Berlin, Germany
| | - Ralf-Harto Hubner
- Department of Internal Medicine/Infectious and Respiratory Disease, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Jan-Martin Kuhnigk
- Institute for Medical Image Computing, Fraunhofer MEVIS, Bremen, Germany
| | - Konrad Neumann
- Institute of Biometrics and Clinical Epidemiology, Charité Universitätsmedizin Berlin, Berlin, Gemany
| | - Felix Doellinger
- Charité Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Department of Radiology, Berlin, Germany
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