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Makimoto K, Hogg JC, Bourbeau J, Tan WC, Kirby M. Enhancing COPD classification using combined quantitative computed tomography and texture-based radiomics: a CanCOLD cohort study. ERJ Open Res 2024; 10:00968-2023. [PMID: 39040582 PMCID: PMC11261383 DOI: 10.1183/23120541.00968-2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Accepted: 03/13/2024] [Indexed: 07/24/2024] Open
Abstract
Background Recent advances in texture-based computed tomography (CT) radiomics have demonstrated its potential for classifying COPD. Methods Participants from the Canadian Cohort Obstructive Lung Disease (CanCOLD) study were evaluated. A total of 108 features were included: eight quantitative CT (qCT), 95 texture-based radiomic and five demographic features. Machine-learning models included demographics along with texture-based radiomics and/or qCT. Combinations of five feature selection and five classification methods were evaluated; a training dataset was used for feature selection and to train the models, and a testing dataset was used for model evaluation. Models for classifying COPD status and severity were evaluated using the area under the receiver operating characteristic curve (AUC) with DeLong's test for comparison. SHapely Additive exPlanations (SHAP) analysis was used to investigate the features selected. Results A total of 1204 participants were evaluated (n=602 no COPD; n=602 COPD). There were no differences between the groups for sex (p=0.77) or body mass index (p=0.21). For classifying COPD status, the combination of demographics, texture-based radiomics and qCT performed better (AUC=0.87) than the combination of demographics and texture-based radiomics (AUC=0.81, p<0.05) or qCT alone (AUC=0.84, p<0.05). Similarly, for classifying COPD severity, the combination of demographics, texture-based radiomics and qCT performed better (AUC=0.81) than demographics and texture-based radiomics (AUC=0.72, p<0.05) or qCT alone (AUC=0.79, p<0.05). Texture-based radiomics and qCT features were among the top five features selected (15th percentile of the CT density histogram, CT total airway count, pack-years, CT grey-level distance zone matrix zone distance entropy, CT low-attenuation clusters) for classifying COPD status. Conclusion Texture-based radiomics and conventional qCT features in combination improve machine‑learning models for classification of COPD status and severity.
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Affiliation(s)
| | - James C. Hogg
- Center for Heart, Lung Innovation, University of British Columbia, Vancouver, BC, Canada
| | - Jean Bourbeau
- Montreal Chest Institute of the Royal Victoria Hospital, McGill University Health Centre, Montreal, QC, Canada
- Respiratory Epidemiology and Clinical Research Unit, Research Institute of McGill University Health Centre, Montreal, QC, Canada
| | - Wan C. Tan
- Center for Heart, Lung Innovation, University of British Columbia, Vancouver, BC, Canada
| | - Miranda Kirby
- Toronto Metropolitan University, Toronto, ON, Canada
- Center for Heart, Lung Innovation, University of British Columbia, Vancouver, BC, Canada
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Wang JM, Bell AJ, Ram S, Labaki WW, Hoff BA, Murray S, Kazerooni EA, Galban S, Hatt CR, Han MK, Galban CJ. Topologic Parametric Response Mapping Identifies Tissue Subtypes Associated with Emphysema Progression. Acad Radiol 2024; 31:1148-1159. [PMID: 37661554 PMCID: PMC11098545 DOI: 10.1016/j.acra.2023.08.003] [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: 06/09/2023] [Revised: 07/25/2023] [Accepted: 08/03/2023] [Indexed: 09/05/2023]
Abstract
RATIONALE AND OBJECTIVES Small airways disease (SAD) and emphysema are significant components of chronic obstructive pulmonary disease (COPD), a heterogenous disease where predicting progression is difficult. SAD, a principal cause of airflow obstruction in mild COPD, has been identified as a precursor to emphysema. Parametric Response Mapping (PRM) of chest computed tomography (CT) can help distinguish SAD from emphysema. Specifically, topologic PRM can define local patterns of both diseases to characterize how and in whom COPD progresses. We aimed to determine if distribution of CT-based PRM of functional SAD (fSAD) is associated with emphysema progression. MATERIALS AND METHODS We analyzed paired inspiratory-expiratory chest CT scans at baseline and 5-year follow up in 1495 COPDGene subjects using topological analyses of PRM classifications. By spatially aligning temporal scans, we mapped local emphysema at year five to baseline lobar PRM-derived topological readouts. K-means clustering was applied to all observations. Subjects were subtyped based on predominant PRM cluster assignments and assessed using non-parametric statistical tests to determine differences in PRM values, pulmonary function metrics, and clinical measures. RESULTS We identified distinct lobar imaging patterns and classified subjects into three radiologic subtypes: emphysema-dominant (ED), fSAD-dominant (FD), and fSAD-transition (FT: transition from healthy lung to fSAD). Relative to year five emphysema, FT showed rapid local emphysema progression (-57.5% ± 1.1) compared to FD (-49.9% ± 0.5) and ED (-33.1% ± 0.4). FT consisted primarily of at-risk subjects (roughly 60%) with normal spirometry. CONCLUSION The FT subtype of COPD may allow earlier identification of individuals without spirometrically-defined COPD at-risk for developing emphysema.
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Affiliation(s)
- Jennifer M Wang
- Division of Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, Michigan (J.M.W., W.W.L., M.K.H.)
| | - Alexander J Bell
- Department of Radiology, University of Michigan, Ann Arbor, Michigan (A.J.B., S.R., B.A.H., E.A.K., S.G., C.R.H., C.J.G.)
| | - Sundaresh Ram
- Department of Radiology, University of Michigan, Ann Arbor, Michigan (A.J.B., S.R., B.A.H., E.A.K., S.G., C.R.H., C.J.G.); Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan (S.R.)
| | - Wassim W Labaki
- Division of Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, Michigan (J.M.W., W.W.L., M.K.H.)
| | - Benjamin A Hoff
- Department of Radiology, University of Michigan, Ann Arbor, Michigan (A.J.B., S.R., B.A.H., E.A.K., S.G., C.R.H., C.J.G.)
| | - Susan Murray
- School of Public Health, University of Michigan, Ann Arbor, Michigan (S.M.)
| | - Ella A Kazerooni
- Department of Radiology, University of Michigan, Ann Arbor, Michigan (A.J.B., S.R., B.A.H., E.A.K., S.G., C.R.H., C.J.G.)
| | - Stefanie Galban
- Department of Radiology, University of Michigan, Ann Arbor, Michigan (A.J.B., S.R., B.A.H., E.A.K., S.G., C.R.H., C.J.G.)
| | - Charles R Hatt
- Department of Radiology, University of Michigan, Ann Arbor, Michigan (A.J.B., S.R., B.A.H., E.A.K., S.G., C.R.H., C.J.G.); Imbio, LLC, Minneapolis, Minnesota (C.R.H.)
| | - MeiLan K Han
- Division of Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, Michigan (J.M.W., W.W.L., M.K.H.)
| | - Craig J Galban
- Department of Radiology, University of Michigan, Ann Arbor, Michigan (A.J.B., S.R., B.A.H., E.A.K., S.G., C.R.H., C.J.G.).
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Yu K, Sun L, Chen J, Reynolds M, Chaudhary T, Batmanghelich K. DrasCLR: A self-supervised framework of learning disease-related and anatomy-specific representation for 3D lung CT images. Med Image Anal 2024; 92:103062. [PMID: 38086236 PMCID: PMC10872608 DOI: 10.1016/j.media.2023.103062] [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: 02/17/2023] [Revised: 08/24/2023] [Accepted: 12/05/2023] [Indexed: 01/12/2024]
Abstract
Large-scale volumetric medical images with annotation are rare, costly, and time prohibitive to acquire. Self-supervised learning (SSL) offers a promising pre-training and feature extraction solution for many downstream tasks, as it only uses unlabeled data. Recently, SSL methods based on instance discrimination have gained popularity in the medical imaging domain. However, SSL pre-trained encoders may use many clues in the image to discriminate an instance that are not necessarily disease-related. Moreover, pathological patterns are often subtle and heterogeneous, requiring the ability of the desired method to represent anatomy-specific features that are sensitive to abnormal changes in different body parts. In this work, we present a novel SSL framework, named DrasCLR, for 3D lung CT images to overcome these challenges. We propose two domain-specific contrastive learning strategies: one aims to capture subtle disease patterns inside a local anatomical region, and the other aims to represent severe disease patterns that span larger regions. We formulate the encoder using conditional hyper-parameterized network, in which the parameters are dependant on the anatomical location, to extract anatomically sensitive features. Extensive experiments on large-scale datasets of lung CT scans show that our method improves the performance of many downstream prediction and segmentation tasks. The patient-level representation improves the performance of the patient survival prediction task. We show how our method can detect emphysema subtypes via dense prediction. We demonstrate that fine-tuning the pre-trained model can significantly reduce annotation efforts without sacrificing emphysema detection accuracy. Our ablation study highlights the importance of incorporating anatomical context into the SSL framework. Our codes are available at https://github.com/batmanlab/DrasCLR.
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Affiliation(s)
- Ke Yu
- School of Computing and Information, University of Pittsburgh, Pittsburgh, USA.
| | - Li Sun
- Department of Electrical and Computer Engineering, Boston University, Boston, USA
| | - Junxiang Chen
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA
| | - Maxwell Reynolds
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA
| | - Tigmanshu Chaudhary
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA
| | - Kayhan Batmanghelich
- Department of Electrical and Computer Engineering, Boston University, Boston, USA
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Rojas-Quintero J, Ochsner SA, New F, Divakar P, Yang CX, Wu TD, Robinson J, Chandrashekar DS, Banovich NE, Rosas IO, Sauler M, Kheradmand F, Gaggar A, Margaroli C, San Jose Estepar R, McKenna NJ, Polverino F. Spatial Transcriptomics Resolve an Emphysema-Specific Lymphoid Follicle B Cell Signature in Chronic Obstructive Pulmonary Disease. Am J Respir Crit Care Med 2024; 209:48-58. [PMID: 37934672 PMCID: PMC10870877 DOI: 10.1164/rccm.202303-0507le] [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: 03/15/2023] [Accepted: 10/15/2023] [Indexed: 11/09/2023] Open
Abstract
Rationale: Within chronic obstructive pulmonary disease (COPD), emphysema is characterized by a significant yet partially understood B cell immune component. Objectives: To characterize the transcriptomic signatures from lymphoid follicles (LFs) in ever-smokers without COPD and patients with COPD with varying degrees of emphysema. Methods: Lung sections from 40 patients with COPD and ever-smokers were used for LF proteomic and transcriptomic spatial profiling. Formalin- and O.C.T.-fixed lung samples obtained from biopsies or lung explants were assessed for LF presence. Emphysema measurements were obtained from clinical chest computed tomographic scans. High-confidence transcriptional target intersection analyses were conducted to resolve emphysema-induced transcriptional networks. Measurements and Main Results: Overall, 115 LFs from ever-smokers and Global Initiative for Chronic Obstructive Lung Disease (GOLD) 1-2 and GOLD 3-4 patients were analyzed. No LFs were found in never-smokers. Differential gene expression analysis revealed significantly increased expression of LF assembly and B cell marker genes in subjects with severe emphysema. High-confidence transcriptional analysis revealed activation of an abnormal B cell activity signature in LFs (q-value = 2.56E-111). LFs from patients with GOLD 1-2 COPD with emphysema showed significantly increased expression of genes associated with antigen presentation, inflammation, and B cell activation and proliferation. LFs from patients with GOLD 1-2 COPD without emphysema showed an antiinflammatory profile. The extent of centrilobular emphysema was significantly associated with genes involved in B cell maturation and antibody production. Protein-RNA network analysis showed that LFs in emphysema have a unique signature skewed toward chronic B cell activation. Conclusions: An off-targeted B cell activation within LFs is associated with autoimmune-mediated emphysema pathogenesis.
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Affiliation(s)
| | - Scott A. Ochsner
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, Texas
| | - Felicia New
- Spatial Data Analysis Services, Nanostring Biotechnologies, Seattle, Washington
| | - Prajan Divakar
- Spatial Data Analysis Services, Nanostring Biotechnologies, Seattle, Washington
| | - Chen Xi Yang
- Center for Heart Lung Innovation, University of British Columbia, Vancouver, British Columbia, Canada
| | | | - Jerid Robinson
- Field Application Scientists, Nanostring Biotechnologies, Seattle, Washington
| | | | | | | | - Maor Sauler
- Pulmonary and Critical Care Medicine, Yale University, New Haven, Connecticut
| | - Farrah Kheradmand
- Pulmonary Division, Department of Medicine, and
- Michael E. DeBakey Veterans Affairs Medical Center, Houston, Texas
| | - Amit Gaggar
- Pulmonary and Critical Care Medicine, and
- Birmingham Veterans Affairs Medical Center, Birmingham, Alabama; and
| | - Camilla Margaroli
- Pathology – Division of Cellular and Molecular Pathology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Raul San Jose Estepar
- Applied Chest Imaging Laboratory, Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Neil J. McKenna
- Spatial Data Analysis Services, Nanostring Biotechnologies, Seattle, Washington
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Takeshita T, Nambu A, Tago M, Yorita M, Ikezoe M, Nishizawa K, Magome T, Sasaki M. The influence of image reconstruction methods on the diagnosis of pulmonary emphysema with convolutional neural network. Radiol Phys Technol 2023; 16:488-496. [PMID: 37581714 DOI: 10.1007/s12194-023-00736-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 08/07/2023] [Accepted: 08/07/2023] [Indexed: 08/16/2023]
Abstract
This study investigated the influence of iterative reconstruction (IR) methods on computed tomography (CT) images when training convolutional neural network (CNN) models to diagnose pulmonary emphysema. To evaluate the influence of the IR algorithm on CNN, the present study comprised two steps: the comparison of noise reduction by IR algorithms using phantom examinations and the change in performance of CNN with IR algorithms using patient data. We retrospectively analyzed 97 patients. Raw CT data were reconstructed using the filtered back-projection (FBP) and adaptive statistical iterative reconstruction V (ASIR-V) algorithms with blending levels of 30%, 50%, and 70%. The models were trained using reconstructed CT images and were named the FBP, ASIR-V30, ASIR-V50, and ASIR-V70 models. The mean and the standard deviation of the CT values were 11.3 ± 21.2 at FBP, 11.0 ± 17.3 at ASIR-V30, 11.0 ± 14.4 at ASIR-V50, and 11.0 ± 11.8 at ASIR-V70. For all the evaluation metrics, the best values were obtained with the FBP model applied to the ASIR-V70 test images. The worst values were obtained with the ASIR-V70 model applied to the FBP test images. The model trained with FBP images exhibited significantly better performance than the models trained using IR images. The reduction in image noise with the IR algorithm on the test images contributed to improving the accuracy of the classification of emphysema subtypes using CNN.
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Affiliation(s)
- Toshiki Takeshita
- Department of Radiology, Teikyo University Hospital, Mizonokuchi, 5-1-1 Futago, Takatsu-ku, Kawasaki, Kanagawa, 213-8507, Japan.
| | - Atsushi Nambu
- Department of Radiology, Kanto Central Hospital of the Mutual Aid Association of Public School Teachers, 6-25-1 Kamiyoga, Setagaya-ku, Tokyo, 158-8531, Japan
| | - Masao Tago
- Department of Radiology, Teikyo University Hospital, Mizonokuchi, 5-1-1 Futago, Takatsu-ku, Kawasaki, Kanagawa, 213-8507, Japan
| | - Masaki Yorita
- Department of Radiology, Teikyo University Hospital, Mizonokuchi, 5-1-1 Futago, Takatsu-ku, Kawasaki, Kanagawa, 213-8507, Japan
| | - Mariko Ikezoe
- Department of Radiology, Teikyo University Hospital, Mizonokuchi, 5-1-1 Futago, Takatsu-ku, Kawasaki, Kanagawa, 213-8507, Japan
| | - Kentaro Nishizawa
- Department of Radiology, Teikyo University Hospital, Mizonokuchi, 5-1-1 Futago, Takatsu-ku, Kawasaki, Kanagawa, 213-8507, Japan
| | - Taiki Magome
- Department of Radiological Sciences, Faculty of Health Sciences, Komazawa University, 1-23-1 Komazawa, Setagaya-ku, Tokyo, 154-8525, Japan
| | - Masayuki Sasaki
- Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan
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6
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Yang Y, Zeng N, Chen Z, Li W, Guo Y, Wang S, Duan W, Liu Y, Chen R, Kang Y. Multi-Layer Perceptron Classifier with the Proposed Combined Feature Vector of 3D CNN Features and Lung Radiomics Features for COPD Stage Classification. JOURNAL OF HEALTHCARE ENGINEERING 2023; 2023:3715603. [PMID: 37953910 PMCID: PMC10637846 DOI: 10.1155/2023/3715603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 08/02/2022] [Accepted: 04/25/2023] [Indexed: 11/14/2023]
Abstract
Computed tomography (CT) has been regarded as the most effective modality for characterizing and quantifying chronic obstructive pulmonary disease (COPD). Therefore, chest CT images should provide more information for COPD diagnosis, such as COPD stage classification. This paper proposes a features combination strategy by concatenating three-dimension (3D) CNN features and lung radiomics features for COPD stage classification based on the multi-layer perceptron (MLP) classifier. First, 465 sets of chest HRCT images are automatically segmented by a trained ResU-Net, obtaining the lung images with the Hounsfield unit. Second, the 3D CNN features are extracted from the lung region images based on a truncated transfer learning strategy. Then, the lung radiomics features are extracted from the lung region images by PyRadiomics. Third, the MLP classifier with the best classification performance is determined by the 3D CNN features and the lung radiomics features. Finally, the proposed combined feature vector is used to improve the MLP classifier's performance. The results show that compared with CNN models and other ML classifiers, the MLP classifier with the best classification performance is determined. The MLP classifier with the proposed combined feature vector has achieved accuracy, mean precision, mean recall, mean F1-score, and AUC of 0.879, 0.879, 0.879, 0.875, and 0.971, respectively. Compared to the MLP classifier with the 3D CNN features selected by Lasso, our method based on the MLP classifier has improved the classification performance by 5.8% (accuracy), 5.3% (mean precision), 5.8% (mean recall), 5.4% (mean F1-score), and 2.5% (AUC). Compared to the MLP classifier with lung radiomics features selected by Lasso, our method based on the MLP classifier has improved the classification performance by 5.0% (accuracy), 5.1% (mean precision), 5.0% (mean recall), 5.1% (mean F1-score), and 2.1% (AUC). Therefore, it is concluded that our method is effective in improving the classification performance for COPD stage classification.
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Affiliation(s)
- Yingjian Yang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
| | - Nanrong Zeng
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
- School of Applied Technology, Shenzhen University, Shenzhen 518060, China
| | - Ziran Chen
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
| | - Wei Li
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
| | - Yingwei Guo
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
| | - Shicong Wang
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
- School of Applied Technology, Shenzhen University, Shenzhen 518060, China
| | - Wenxin Duan
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
- School of Applied Technology, Shenzhen University, Shenzhen 518060, China
| | - Yang Liu
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
- School of Applied Technology, Shenzhen University, Shenzhen 518060, China
| | - Rongchang Chen
- Shenzhen Institute of Respiratory Diseases, Shenzhen People's Hospital, Shenzhen 518001, China
- The Second Clinical Medical College, Jinan University 518001, Guangzhou, China
- The First Affiliated Hospital, Southern University of Science and Technology 518001, Shenzhen, China
| | - Yan Kang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
- School of Applied Technology, Shenzhen University, Shenzhen 518060, China
- Engineering Research Centre of Medical Imaging and Intelligent Analysis, Ministry of Education, Shenyang 110169, China
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7
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Akturk Y, Ozbal Gunes S, Soyer Guldogan E, Sencan I, Hekimoğlu B. Acute muscle loss and early effects of COVID-19 on skeletal muscle in adult patients: A retrospective cohort study. RADIOLOGIA 2023; 65 Suppl 2:S50-S58. [PMID: 37858353 DOI: 10.1016/j.rxeng.2022.12.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Accepted: 12/23/2022] [Indexed: 10/21/2023]
Abstract
OBJECTIVES It is known that COVID-19 has multisystemic effects. However, its early effects on muscle tissue have not been clearly elucidated. The aim of this study is to investigate early changes in the pectoral muscle in patients with COVID-19 infection. MATERIALS AND METHODS The pectoral muscle areas (PMA) and pectoral muscle index (PMI) of 139 patients diagnosed with COVID-19 were measured from chest CTs taken at the time of the first diagnosis and within 6 months after the diagnosis. The effect of the infection on the muscle area was investigated by evaluating whether there was a change between the two measurements. Lung involvement of the infection in the first CT was scored with the CT severity score (CT-SS). In addition, the effects of patients' clinics, CT-SS, length of hospital stay, and intubation history on changes in the muscle area were investigated. RESULTS When the PMA and PMI values were compared, there was a statistically significant decrease in the values in the control CT group compared to the first diagnosis CT group. The difference was found higher in intubated patients. CT-SS was associated with a decrease in PMI. CONCLUSION COVID-19 is one of the causes of acute sarcopenia. Pectoralis muscle is part of the skeletal muscle, and there may be a decrease in the muscle area in the early period of the disease.
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Affiliation(s)
- Y Akturk
- Servicio de Radiología, Facultad de Medicina, Hospital de Formación e Investigación Diskapi Yildirim Beyazit, Diskapi, Ankara, Turkey.
| | - S Ozbal Gunes
- Servicio de Radiología, Facultad de Medicina, Hospital de Formación e Investigación Diskapi Yildirim Beyazit, Diskapi, Ankara, Turkey
| | - E Soyer Guldogan
- Servicio de Radiología, Facultad de Medicina, Hospital de Formación e Investigación Diskapi Yildirim Beyazit, Diskapi, Ankara, Turkey
| | - I Sencan
- Servicio de Enfermedades Infecciosas y Microbiología Clínica, Facultad de Medicina, Hospital de Formación e Investigación Diskapi Yildirim Beyazit, Diskapi, Ankara, Turkey
| | - B Hekimoğlu
- Servicio de Radiología, Facultad de Medicina, Hospital de Formación e Investigación Diskapi Yildirim Beyazit, Diskapi, Ankara, Turkey
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8
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Raoof S, Shah M, Braman S, Agrawal A, Allaqaband H, Bowler R, Castaldi P, DeMeo D, Fernando S, Hall CS, Han MK, Hogg J, Humphries S, Lee HY, Lee KS, Lynch D, Machnicki S, Mehta A, Mehta S, Mina B, Naidich D, Naidich J, Ohno Y, Regan E, van Beek EJR, Washko G, Make B. Lung Imaging in COPD Part 2: Emerging Concepts. Chest 2023; 164:339-354. [PMID: 36907375 PMCID: PMC10475822 DOI: 10.1016/j.chest.2023.02.049] [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: 10/06/2022] [Revised: 02/23/2023] [Accepted: 02/25/2023] [Indexed: 03/13/2023] Open
Abstract
The diagnosis, prognostication, and differentiation of phenotypes of COPD can be facilitated by CT scan imaging of the chest. CT scan imaging of the chest is a prerequisite for lung volume reduction surgery and lung transplantation. Quantitative analysis can be used to evaluate extent of disease progression. Evolving imaging techniques include micro-CT scan, ultra-high-resolution and photon-counting CT scan imaging, and MRI. Potential advantages of these newer techniques include improved resolution, prediction of reversibility, and obviation of radiation exposure. This article discusses important emerging techniques in imaging patients with COPD. The clinical usefulness of these emerging techniques as they stand today are tabulated for the benefit of the practicing pulmonologist.
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Affiliation(s)
- Suhail Raoof
- Northwell Health, Lenox Hill Hospital, New York, NY.
| | - Manav Shah
- Northwell Health, Lenox Hill Hospital, New York, NY
| | - Sidney Braman
- Icahn School of Medicine at Mount Sinai, New York, NY
| | | | | | | | | | - Dawn DeMeo
- Brigham and Women's Hospital, Boston, MA
| | | | | | | | - James Hogg
- University of British Columbia, Vancouver, BC, Canada
| | | | - Ho Yun Lee
- Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea; Department of Health Sciences and Technology, Sungkyunkwan University, ChangWon, South Korea
| | - Kyung Soo Lee
- Sungkyunkwan University School of Medicine, Samsung ChangWon Hospital, ChangWon, South Korea
| | | | | | | | | | - Bushra Mina
- Northwell Health, Lenox Hill Hospital, New York, NY
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9
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Shima H, Tanabe N, Oguma A, Shimizu K, Kaji S, Terada K, Oguma T, Kubo T, Suzuki M, Makita H, Sato A, Nishimura M, Sato S, Konno S, Hirai T. Subtyping emphysematous COPD by respiratory volume change distributions on CT. Thorax 2023; 78:344-353. [PMID: 35768196 DOI: 10.1136/thoraxjnl-2021-218288] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 05/28/2022] [Indexed: 11/04/2022]
Abstract
BACKGROUND There is considerable heterogeneity among patients with emphysematous chronic obstructive pulmonary disease (COPD). We hypothesised that in addition to emphysema severity, ventilation distribution in emphysematous regions would be associated with clinical-physiological impairments in these patients. OBJECTIVE To evaluate whether the discordance between respiratory volume change distributions (from expiration to inspiration) in emphysematous and non-emphysematous regions affects COPD outcomes using two cohorts. METHODS Emphysema was quantified using a low attenuation volume percentage on inspiratory CT (iLAV%). Local respiratory volume changes were calculated using non-rigidly registered expiratory/inspiratory CT. The Ventilation Discordance Index (VDI) represented the log-transformed Wasserstein distance quantifying discordance between respiratory volume change distributions in emphysematous and non-emphysematous regions. RESULTS Patients with COPD in the first cohort (n=221) were classified into minimal emphysema (iLAV% <10%; n=113) and established emphysema with high VDI and low VDI groups (n=46 and 62, respectively). Forced expiratory volume in 1 s (FEV1) was lower in the low VDI group than in the other groups, with no difference between the high VDI and minimal emphysema groups. Higher iLAV%, more severe airway disease and hyperventilated emphysematous regions in the upper-middle lobes were independently associated with lower VDI. The second cohort analyses (n=93) confirmed these findings and showed greater annual FEV1 decline and higher mortality in the low VDI group than in the high VDI group independent of iLAV% and airway disease on CT. CONCLUSION Lower VDI is associated with severe airflow limitation and higher mortality independent of emphysema severity and airway morphological changes in patients with emphysematous COPD.
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Affiliation(s)
- Hiroshi Shima
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Naoya Tanabe
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Akira Oguma
- Department of Respiratory Medicine, Faculty of Medicine, Hokkaido University, Sapporo, Japan
| | - Kaoruko Shimizu
- Department of Respiratory Medicine, Faculty of Medicine, Hokkaido University, Sapporo, Japan
| | - Shizuo Kaji
- Institute of Mathematics for Industry, Kyushu University, Fukuoka, Japan
| | - Kunihiko Terada
- Terada Clinic, Respiratory Medicine and General Practice, Himeji, Japan
| | - Tsuyoshi Oguma
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Takeshi Kubo
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Masaru Suzuki
- Department of Respiratory Medicine, Faculty of Medicine, Hokkaido University, Sapporo, Japan
| | - Hironi Makita
- Department of Respiratory Medicine, Faculty of Medicine, Hokkaido University, Sapporo, Japan.,Hokkaido Medical Research Institute for Respiratory Diseases, Sapporo, Japan
| | - Atsuyasu Sato
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Masaharu Nishimura
- Department of Respiratory Medicine, Faculty of Medicine, Hokkaido University, Sapporo, Japan.,Hokkaido Medical Research Institute for Respiratory Diseases, Sapporo, Japan
| | - Susumu Sato
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Satoshi Konno
- Department of Respiratory Medicine, Faculty of Medicine, Hokkaido University, Sapporo, Japan
| | - Toyohiro Hirai
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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10
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Akturk Y, Gunes SO, Guldogan ES, Sencan I, Hekimoğlu B. [Acute muscle loss and early effects of COVID-19 on skeletal muscle in adult patients: a retrospective cohort study]. RADIOLOGIA 2023; 65:S0033-8338(23)00026-7. [PMID: 36744157 PMCID: PMC9889253 DOI: 10.1016/j.rx.2022.12.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Accepted: 12/23/2022] [Indexed: 02/04/2023]
Abstract
OBJECTIVES It is known that COVID-19 has multisystemic effects. However, its early effects on muscle tissue have not been clearly elucidated. The aim of this study is to investigate early changes in the pectoral muscle in patients with COVID-19 infection. MATERIALS AND METHODS The pectoral muscle areas (PMA) and pectoral muscle index (PMI) of 139 patients diagnosed with COVID-19 were measured from chest CTs taken at the time of the first diagnosis and within 6 months after the diagnosis. The effect of the infection on the muscle area was investigated by evaluating whether there was a change between the two measurements. Lung involvement of the infection in the first CT was scored with the CT severity score (CT-SS). In addition, the effects of patients' clinics, CT-SS, length of hospital stay, and intubation history on changes in the muscle area were investigated. RESULTS When the PMA and PMI values were compared, there was a statistically significant decrease in the values in the control CT group compared to the first diagnosis CT group. The difference was found higher in intubated patients. CT-SS was associated with a decrease in PMI.COVID-19 is one of the causes of acute sarcopenia. Pectoralis muscle is part of the skeletal muscle, and there may be a decrease in the muscle area in the early period of the disease.
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Affiliation(s)
- Yeliz Akturk
- Facultad de Medicina, Hospital de formación e investigación Diskapi Yildirim Beyazit, Servicio de Radiología, calle Sehit Omerhalisdemir, Diskapi, Ankara, Turquía
| | - Serra Ozbal Gunes
- Facultad de Medicina, Hospital de formación e investigación Diskapi Yildirim Beyazit, Servicio de Radiología, calle Sehit Omerhalisdemir, Diskapi, Ankara, Turquía
| | - Esra Soyer Guldogan
- Facultad de Medicina, Hospital de formación e investigación Diskapi Yildirim Beyazit, Servicio de Radiología, calle Sehit Omerhalisdemir, Diskapi, Ankara, Turquía
| | - Irfan Sencan
- Facultad de Medicina, Hospital de formación e investigación Diskapi Yildirim Beyazit, Servicio de enfermedades infecciosas y microbiología clínica, Sehit Omerhalisdemir Street, Diskapi, Ankara, Turquía
| | - Baki Hekimoğlu
- Facultad de Medicina, Hospital de formación e investigación Diskapi Yildirim Beyazit, Servicio de Radiología, calle Sehit Omerhalisdemir, Diskapi, Ankara, Turquía
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11
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McCague C, Ramlee S, Reinius M, Selby I, Hulse D, Piyatissa P, Bura V, Crispin-Ortuzar M, Sala E, Woitek R. Introduction to radiomics for a clinical audience. Clin Radiol 2023; 78:83-98. [PMID: 36639175 DOI: 10.1016/j.crad.2022.08.149] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 08/31/2022] [Indexed: 01/12/2023]
Abstract
Radiomics is a rapidly developing field of research focused on the extraction of quantitative features from medical images, thus converting these digital images into minable, high-dimensional data, which offer unique biological information that can enhance our understanding of disease processes and provide clinical decision support. To date, most radiomics research has been focused on oncological applications; however, it is increasingly being used in a raft of other diseases. This review gives an overview of radiomics for a clinical audience, including the radiomics pipeline and the common pitfalls associated with each stage. Key studies in oncology are presented with a focus on both those that use radiomics analysis alone and those that integrate its use with other multimodal data streams. Importantly, clinical applications outside oncology are also presented. Finally, we conclude by offering a vision for radiomics research in the future, including how it might impact our practice as radiologists.
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Affiliation(s)
- C McCague
- Department of Radiology, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
| | - S Ramlee
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - M Reinius
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - I Selby
- Department of Radiology, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - D Hulse
- Department of Radiology, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - P Piyatissa
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - V Bura
- Department of Radiology, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK; Department of Radiology and Medical Imaging, County Clinical Emergency Hospital, Cluj-Napoca, Romania
| | - M Crispin-Ortuzar
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; Department of Oncology, University of Cambridge, Cambridge, UK
| | - E Sala
- Department of Radiology, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - R Woitek
- Department of Radiology, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK; Research Centre for Medical Image Analysis and Artificial Intelligence (MIAAI), Department of Medicine, Faculty of Medicine and Dentistry, Danube Private University, Krems, Austria
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12
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Han K, Wang J, Zou Y, Zhang Y, Zhou L, Yin Y. Association between emphysema and other pulmonary computed tomography patterns in COVID-19 pneumonia. J Med Virol 2023; 95:e28293. [PMID: 36358023 PMCID: PMC9828029 DOI: 10.1002/jmv.28293] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 10/22/2022] [Accepted: 11/07/2022] [Indexed: 11/13/2022]
Abstract
To evaluate the chest computed tomography (CT) findings of patients with Corona Virus Disease 2019 (COVID-19) on admission to hospital. And then correlate CT pulmonary infiltrates involvement with the findings of emphysema. We analyzed the different infiltrates of COVID-19 pneumonia using emphysema as the grade of pneumonia. We applied open-source assisted software (3D Slicer) to model the lungs and lesions of 66 patients with COVID-19, which were retrospectively included. we divided the 66 COVID-19 patients into the following two groups: (A) 12 patients with less than 10% emphysema in the low-attenuation area less than -950 Hounsfield units (%LAA-950), (B) 54 patients with greater than or equal to 10% emphysema in %LAA-950. Imaging findings were assessed retrospectively by two authors and then pulmonary infiltrates and emphysema volumes were measured on CT using 3D Slicer software. Differences between pulmonary infiltrates, emphysema, Collapsed, affected of patients with CT findings were assessed by Kruskal-Wallis and Wilcoxon test, respectively. Statistical significance was set at p < 0.05. The left lung (A) affected left lung 20.00/affected right lung 18.50, (B) affected left lung 13.00/affected right lung 11.50 was most frequently involved region in COVID-19. In addition, collapsed left lung, (A) collapsed left lung 4.95/collapsed right lung 4.65, (B) collapsed left lung 3.65/collapsed right lung 3.15 was also more severe than the right one. There were significant differences between the Group A and Group B in terms of the percentage of CT involvement in each lung region (p < 0.05), except for the inflated affected total lung (p = 0.152). The median percentage of collapsed left lung in the Group A was 20.00 (14.00-30.00), right lung was 18.50 (13.00-30.25) and the total was 19.00 (13.00-30.00), while the median percentage of collapsed left lung in the Group B was 13.00 (10.00-14.75), right lung was 11.50 (10.00-15.00) and the total was 12.50 (10.00-15.00). The percentage of affected left lung is an independent predictor of emphysema in COVID-19 patients. We need to focus on the left lung of the patient as it is more affected. The people with lower levels of emphysema may have more collapsed segments. The more collapsed segments may lead to more serious clinical feature.
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Affiliation(s)
- Ke Han
- Department of Cardiothoracic Vascular Surgery, Renmin HospitalHubei University of MedicineShiyanHubeiP. R. China
| | - Jing Wang
- Department of Dermatology, Renmin HospitalHubei University of MedicineShiyanHubeiP. R. China
| | - Yulin Zou
- Department of Dermatology, Renmin HospitalHubei University of MedicineShiyanHubeiP. R. China,Department of Dermatology, Jinzhou Medical University Graduate Training Base, Renmin HospitalHubei University of MedicineShiyanHubeiP. R. China
| | - Yuxin Zhang
- Department of Dermatology, Renmin HospitalHubei University of MedicineShiyanHubeiP. R. China
| | - Lin Zhou
- Department of Medical Imaging Center, Renmin HospitalHubei University of MedicineShiyanHubeiP. R. China
| | - Yiping Yin
- Department of Pulmonary & Critical Care Medicine, Renmin HospitalHubei University of MedicineShiyanHubeiP. R. China
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13
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Yang Y, Chen Z, Li W, Zeng N, Guo Y, Wang S, Duan W, Liu Y, Chen H, Li X, Chen R, Kang Y. Multi-modal data combination strategy based on chest HRCT images and PFT parameters for intelligent dyspnea identification in COPD. Front Med (Lausanne) 2022; 9:980950. [PMID: 36619622 PMCID: PMC9811121 DOI: 10.3389/fmed.2022.980950] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 12/06/2022] [Indexed: 12/24/2022] Open
Abstract
Introduction Because of persistent airflow limitation in chronic obstructive pulmonary disease (COPD), patients with COPD often have complications of dyspnea. However, as a leading symptom of COPD, dyspnea in COPD deserves special consideration regarding treatment in this fragile population for pre-clinical health management in COPD. Methods: Based on the above, this paper proposes a multi-modal data combination strategy by combining the local and global features for dyspnea identification in COPD based on the multi-layer perceptron (MLP) classifier. Methods First, lung region images are automatically segmented from chest HRCT images for extracting the original 1,316 lung radiomics (OLR, 1,316) and 13,824 3D CNN features (O3C, 13,824). Second, the local features, including five selected pulmonary function test (PFT) parameters (SLF, 5), 28 selected lung radiomics (SLR, 28), and 22 selected 3D CNN features (S3C, 22), are respectively selected from the original 11 PFT parameters (OLF, 11), 1,316 OLR, and 13,824 O3C by the least absolute shrinkage and selection operator (Lasso) algorithm. Meantime, the global features, including two fused PFT parameters (FLF, 2), six fused lung radiomics (FLR, 6), and 34 fused 3D CNN features (F3C, 34), are respectively fused by 11 OLF, 1,316 OLR, and 13,824 O3C using the principal component analysis (PCA) algorithm. Finally, we combine all the local and global features (SLF + FLF + SLR + FLR + S3C + F3C, 5+ 2 + 28 + 6 + 22 + 34) for dyspnea identification in COPD based on the MLP classifier. Results Our proposed method comprehensively improves classification performance. The MLP classifier with all the local and global features achieves the best classification performance at 87.7% of accuracy, 87.7% of precision, 87.7% of recall, 87.7% of F1-scorel, and 89.3% of AUC, respectively. Discussion Compared with single-modal data, the proposed strategy effectively improves the classification performance for dyspnea identification in COPD, providing an objective and effective tool for COPD management.
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Affiliation(s)
- Yingjian Yang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China,College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China
| | - Ziran Chen
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China
| | - Wei Li
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China
| | - Nanrong Zeng
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China,School of Applied Technology, Shenzhen University, Shenzhen, China
| | - Yingwei Guo
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China,College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China
| | - Shicong Wang
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China,School of Applied Technology, Shenzhen University, Shenzhen, China
| | - Wenxin Duan
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China,School of Applied Technology, Shenzhen University, Shenzhen, China
| | - Yang Liu
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China,School of Applied Technology, Shenzhen University, Shenzhen, China
| | - Huai Chen
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Xian Li
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Rongchang Chen
- Shenzhen Institute of Respiratory Diseases, Shenzhen People's Hospital, Shenzhen, China,The Second Clinical Medical College, Jinan University, Guangzhou, China,The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China,Rongchang Chen ✉
| | - Yan Kang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China,College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China,School of Applied Technology, Shenzhen University, Shenzhen, China,Engineering Research Centre of Medical Imaging and Intelligent Analysis, Ministry of Education, Shenyang, China,*Correspondence: Yan Kang ✉
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14
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Lung Radiomics Features Selection for COPD Stage Classification Based on Auto-Metric Graph Neural Network. Diagnostics (Basel) 2022; 12:diagnostics12102274. [PMID: 36291964 PMCID: PMC9600898 DOI: 10.3390/diagnostics12102274] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 09/13/2022] [Accepted: 09/18/2022] [Indexed: 11/17/2022] Open
Abstract
Chronic obstructive pulmonary disease (COPD) is a preventable, treatable, progressive chronic disease characterized by persistent airflow limitation. Patients with COPD deserve special consideration regarding treatment in this fragile population for preclinical health management. Therefore, this paper proposes a novel lung radiomics combination vector generated by a generalized linear model (GLM) and Lasso algorithm for COPD stage classification based on an auto-metric graph neural network (AMGNN) with a meta-learning strategy. Firstly, the parenchyma images were segmented from chest high-resolution computed tomography (HRCT) images by ResU-Net. Second, lung radiomics features are extracted from the parenchyma images by PyRadiomics. Third, a novel lung radiomics combination vector (3 + 106) is constructed by the GLM and Lasso algorithm for determining the radiomics risk factors (K = 3) and radiomics node features (d = 106). Last, the COPD stage is classified based on the AMGNN. The results show that compared with the convolutional neural networks and machine learning models, the AMGNN based on constructed novel lung radiomics combination vector performs best, achieving an accuracy of 0.943, precision of 0.946, recall of 0.943, F1-score of 0.943, and ACU of 0.984. Furthermore, it is found that our method is effective for COPD stage classification.
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15
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Yang Y, Li W, Guo Y, Zeng N, Wang S, Chen Z, Liu Y, Chen H, Duan W, Li X, Zhao W, Chen R, Kang Y. Lung radiomics features for characterizing and classifying COPD stage based on feature combination strategy and multi-layer perceptron classifier. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:7826-7855. [PMID: 35801446 DOI: 10.3934/mbe.2022366] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Computed tomography (CT) has been the most effective modality for characterizing and quantifying chronic obstructive pulmonary disease (COPD). Radiomics features extracted from the region of interest in chest CT images have been widely used for lung diseases, but they have not yet been extensively investigated for COPD. Therefore, it is necessary to understand COPD from the lung radiomics features and apply them for COPD diagnostic applications, such as COPD stage classification. Lung radiomics features are used for characterizing and classifying the COPD stage in this paper. First, 19 lung radiomics features are selected from 1316 lung radiomics features per subject by using Lasso. Second, the best performance classifier (multi-layer perceptron classifier, MLP classifier) is determined. Third, two lung radiomics combination features, Radiomics-FIRST and Radiomics-ALL, are constructed based on 19 selected lung radiomics features by using the proposed lung radiomics combination strategy for characterizing the COPD stage. Lastly, the 19 selected lung radiomics features with Radiomics-FIRST/Radiomics-ALL are used to classify the COPD stage based on the best performance classifier. The results show that the classification ability of lung radiomics features based on machine learning (ML) methods is better than that of the chest high-resolution CT (HRCT) images based on classic convolutional neural networks (CNNs). In addition, the classifier performance of the 19 lung radiomics features selected by Lasso is better than that of the 1316 lung radiomics features. The accuracy, precision, recall, F1-score and AUC of the MLP classifier with the 19 selected lung radiomics features and Radiomics-ALL were 0.83, 0.83, 0.83, 0.82 and 0.95, respectively. It is concluded that, for the chest HRCT images, compared to the classic CNN, the ML methods based on lung radiomics features are more suitable and interpretable for COPD classification. In addition, the proposed lung radiomics combination strategy for characterizing the COPD stage effectively improves the classifier performance by 12% overall (accuracy: 3%, precision: 3%, recall: 3%, F1-score: 2% and AUC: 1%).
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Affiliation(s)
- Yingjian Yang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
| | - Wei Li
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
| | - Yingwei Guo
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
| | - Nanrong Zeng
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
| | - Shicong Wang
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
| | - Ziran Chen
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
| | - Yang Liu
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
| | - Huai Chen
- Department of Radiology, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, China
| | - Wenxin Duan
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
| | - Xian Li
- Department of Radiology, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, China
| | - Wei Zhao
- Medical Engineering, Liaoning Provincial Corps Hospital of the Chinese People's Armed Police Force, Shenyang 110141, China
| | - Rongchang Chen
- Shenzhen Institute of Respiratory Diseases, Shenzhen People's Hospital, Shenzhen 518001, China
- The Second Clinical Medical College, Jinan University, Shenzhen 518001, China
- The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen 518001, China
| | - Yan Kang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
- Engineering Research Center of Medical Imaging and Intelligent Analysis, Ministry of Education, Shenyang 110169, China
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16
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Zhang DW, Ye JJ, Sun Y, Ji S, Kang JY, Wei YY, Fei GH. CD19 and POU2AF1 are Potential Immune-Related Biomarkers Involved in the Emphysema of COPD: On Multiple Microarray Analysis. J Inflamm Res 2022; 15:2491-2507. [PMID: 35479834 PMCID: PMC9035466 DOI: 10.2147/jir.s355764] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 04/05/2022] [Indexed: 11/23/2022] Open
Abstract
Purpose Emphysema is the main cause of the progression of chronic obstructive pulmonary disease (COPD). This study aimed to identify the key genes involved in COPD-related emphysema. Patients and Methods GSE76925 was downloaded from Gene Expression Omnibus database. Protein–protein interaction networks of differentially expressed genes (DEGs) between control and COPD groups were constructed to identify hub genes using Cytoscape. Diagnostic performance of hub genes was evaluated using receiver operating characteristic analysis. Correlation analysis was performed to identify the key genes by analyzing the relationship between the hub genes and lung function and computed tomography (CT) indexes of emphysema. COPD patients were then divided into two groups based on the median expression of key genes and DEGs between these two groups were identified. Enrichment analysis of DEGs and correlation analysis between key genes and the infiltration of the immune cells were also analyzed. Finally, the role of key genes was evaluated in a lung tissues dataset (GSE47460) and a blood dataset (GSE76705). Additionally, the expression of key genes was validated by quantitative real-time polymerase chain reaction and immunohistochemistry. Results CD19 and POU2AF1 had diagnostic efficacy for COPD and were significantly correlated with lung function and CT indexes of emphysema. Enrichment and immune analyses revealed that CD19 and POU2AF1 were correlated with the B cells in COPD. These results were consistent in GSE47460. The expression of CD19 and POU2AF1 in blood was the opposite of that in lung tissues, and CD19 and POU2AF1 were both significantly upregulated in COPD lung tissues at both the mRNA and protein levels. Conclusion CD19 and POU2AF1 may serve as key regulators of emphysema and contribute to the progression of COPD by regulating the B-cell immunology. Targeting B cells may be a promising strategy for treating COPD.
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Affiliation(s)
- Da-Wei Zhang
- Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui Province, People’s Republic of China
- Key Laboratory of Respiratory Diseases Research and Medical Transformation of Anhui Province, Hefei, 230022, Anhui Province, People’s Republic of China
| | - Jing-Jing Ye
- Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui Province, People’s Republic of China
- Key Laboratory of Respiratory Diseases Research and Medical Transformation of Anhui Province, Hefei, 230022, Anhui Province, People’s Republic of China
| | - Ying Sun
- Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui Province, People’s Republic of China
- Key Laboratory of Respiratory Diseases Research and Medical Transformation of Anhui Province, Hefei, 230022, Anhui Province, People’s Republic of China
| | - Shuang Ji
- Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui Province, People’s Republic of China
- Key Laboratory of Respiratory Diseases Research and Medical Transformation of Anhui Province, Hefei, 230022, Anhui Province, People’s Republic of China
| | - Jia-Ying Kang
- Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui Province, People’s Republic of China
- Key Laboratory of Respiratory Diseases Research and Medical Transformation of Anhui Province, Hefei, 230022, Anhui Province, People’s Republic of China
| | - Yuan-Yuan Wei
- Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui Province, People’s Republic of China
- Key Laboratory of Respiratory Diseases Research and Medical Transformation of Anhui Province, Hefei, 230022, Anhui Province, People’s Republic of China
| | - Guang-He Fei
- Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui Province, People’s Republic of China
- Key Laboratory of Respiratory Diseases Research and Medical Transformation of Anhui Province, Hefei, 230022, Anhui Province, People’s Republic of China
- Correspondence: Guang-He Fei, Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui Province, People’s Republic of China, Tel +86 551 6292 2013, Fax +86 551 6363 5578, Email
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Kooner HK, McIntosh MJ, Desaigoudar V, Rayment JH, Eddy RL, Driehuys B, Parraga G. Pulmonary functional MRI: Detecting the structure-function pathologies that drive asthma symptoms and quality of life. Respirology 2022; 27:114-133. [PMID: 35008127 PMCID: PMC10025897 DOI: 10.1111/resp.14197] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 11/09/2021] [Accepted: 12/12/2021] [Indexed: 12/21/2022]
Abstract
Pulmonary functional MRI (PfMRI) using inhaled hyperpolarized, radiation-free gases (such as 3 He and 129 Xe) provides a way to directly visualize inhaled gas distribution and ventilation defects (or ventilation heterogeneity) in real time with high spatial (~mm3 ) resolution. Both gases enable quantitative measurement of terminal airway morphology, while 129 Xe uniquely enables imaging the transfer of inhaled gas across the alveolar-capillary tissue barrier to the red blood cells. In patients with asthma, PfMRI abnormalities have been shown to reflect airway smooth muscle dysfunction, airway inflammation and remodelling, luminal occlusions and airway pruning. The method is rapid (8-15 s), cost-effective (~$300/scan) and very well tolerated in patients, even in those who are very young or very ill, because unlike computed tomography (CT), positron emission tomography and single-photon emission CT, there is no ionizing radiation and the examination takes only a few seconds. However, PfMRI is not without limitations, which include the requirement of complex image analysis, specialized equipment and additional training and quality control. We provide an overview of the three main applications of hyperpolarized noble gas MRI in asthma research including: (1) inhaled gas distribution or ventilation imaging, (2) alveolar microstructure and finally (3) gas transfer into the alveolar-capillary tissue space and from the tissue barrier into red blood cells in the pulmonary microvasculature. We highlight the evidence that supports a deeper understanding of the mechanisms of asthma worsening over time and the pathologies responsible for symptoms and disease control. We conclude with a summary of approaches that have the potential for integration into clinical workflows and that may be used to guide personalized treatment planning.
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Affiliation(s)
- Harkiran K Kooner
- Robarts Research Institute, Western University, London, Ontario, Canada
- Department of Medical Biophysics, Western University, London, Ontario, Canada
| | - Marrissa J McIntosh
- Robarts Research Institute, Western University, London, Ontario, Canada
- Department of Medical Biophysics, Western University, London, Ontario, Canada
| | - Vedanth Desaigoudar
- Robarts Research Institute, Western University, London, Ontario, Canada
- Department of Medical Biophysics, Western University, London, Ontario, Canada
| | - Jonathan H Rayment
- Department of Pediatrics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Rachel L Eddy
- Centre of Heart Lung Innovation, Division of Respiratory Medicine, Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Bastiaan Driehuys
- Center for In Vivo Microscopy, Duke University Medical Centre, Durham, North Carolina, USA
| | - Grace Parraga
- Robarts Research Institute, Western University, London, Ontario, Canada
- Department of Medical Biophysics, Western University, London, Ontario, Canada
- Division of Respirology, Department of Medicine, Western University, London, Ontario, Canada
- School of Biomedical Engineering, Western University, London, Ontario, Canada
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18
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Impact of emphysema on the prognosis of Mycobacterium avium complex pulmonary disease. Respir Med 2022; 192:106738. [PMID: 35051876 DOI: 10.1016/j.rmed.2022.106738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 12/10/2021] [Accepted: 01/07/2022] [Indexed: 11/23/2022]
Abstract
BACKGROUND Chronic obstructive pulmonary disease (COPD) is a major comorbid disease of Mycobacterium avium complex pulmonary disease (MAC-PD). Emphysema is one of the main pathological findings in COPD, a risk factor for chronic pulmonary aspergillosis (CPA), and is associated with poor prognosis. We aimed to clarify the effect of emphysema on mortality in MAC-PD. METHODS We retrospectively analyzed 203 patients with MAC-PD at The Jikei Daisan Hospital between January 2014 and December 2018. We investigated the mortality and CPA development rates after MAC-PD diagnosis in patients with or without emphysema. RESULTS Multivariate Cox proportional hazards regression analysis showed the following negative prognostic factors in patients with MAC-PD: emphysema (hazard ratio [HR]: 11.46; 95% confidence interval [CI]: 1.30-100.90; P = 0.028); cavities (HR: 3.12; 95% CI: 1.22-7.94; P = 0.017); and low body mass index (<18.5 kg/m2) (HR: 4.62; 95% CI: 1.63-13.11; P = 0.004). The mortality and occurrence of CPA were higher in MAC-PD patients with than without emphysema (log-rank test, P < 0.0001 and P < 0.0001). CONCLUSION Our study findings showed that emphysema detected by computed tomography was associated with an increased risk of CPA development and mortality in MAC-PD.
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19
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Kinsey CM, Billatos E, Mori V, Tonelli B, Cole BF, Duan F, Marques H, de la Bruere I, Onieva J, San José Estépar R, Cleveland A, Idelkope D, Stevenson C, Bates JHT, Aberle D, Spira A, Washko G, San José Estépar R. A simple assessment of lung nodule location for reduction in unnecessary invasive procedures. J Thorac Dis 2021; 13:4207-4216. [PMID: 34422349 PMCID: PMC8339782 DOI: 10.21037/jtd-20-3093] [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: 11/20/2020] [Accepted: 04/23/2021] [Indexed: 12/05/2022]
Abstract
Background CT screening for lung cancer results in a significant mortality reduction but is complicated by invasive procedures performed for evaluation of the many detected benign nodules. The purpose of this study was to evaluate measures of nodule location within the lung as predictors of malignancy. Methods We analyzed images and data from 3,483 participants in the National Lung Screening Trial (NLST). All nodules (4–20 mm) were characterized by 3D geospatial location using a Cartesian coordinate system and evaluated in logistic regression analysis. Model development and probability cutpoint selection was performed in the NLST testing set. The Geospatial test was then validated in the NLST testing set, and subsequently replicated in a new cohort of 147 participants from The Detection of Early Lung Cancer Among Military Personnel (DECAMP) Consortium. Results The Geospatial Test, consisting of the superior-inferior distance (Z distance), nodule diameter, and radial distance (carina to nodule) performed well in both the NLST validation set (AUC 0.85) and the DECAMP replication cohort (AUC 0.75). A negative Geospatial Test resulted in a less than 2% risk of cancer across all nodule diameters. The Geospatial Test correctly reclassified 19.7% of indeterminate nodules with a diameter over 6mm as benign, while only incorrectly classifying 1% of cancerous nodules as benign. In contrast, the parsimonious Brock Model applied to the same group of nodules correctly reclassified 64.5% of indeterminate nodules as benign but resulted in misclassification of a cancer as benign in 18.2% of the cases. Applying the Geospatial test would result in reducing invasive procedures performed for benign lesions by 11.3% with a low rate of misclassification (1.3%). In contrast, the Brock model applied to the same group of patients results in decreasing invasive procedures for benign lesion by 39.0% but misclassifying 21.1% of cancers as benign. Conclusions Utilizing information about geospatial location within the lung improves risk assessment for indeterminate lung nodules and may reduce unnecessary procedures. Trial Registration NCT00047385, NCT01785342.
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Affiliation(s)
- C Matthew Kinsey
- Division of Pulmonary and Critical Care, University of Vermont Medical Center, Burlington, VT, USA
| | - Ehab Billatos
- Section of Pulmonary and Critical Care Medicine, Department of Medicine, Boston University, Boston, MA, Boston Medical Center, Boston, MA, USA
| | - Vitor Mori
- University of Sao Paolo, Sao Paolo, Brazil
| | | | - Bernard F Cole
- Department of Mathematics and Statistics, University of Vermont, Burlington, VT, USA
| | - Fenghai Duan
- Department of Biostatistics and Center for Statistical Sciences, Brown University School of Public Health, Providence, RI, USA
| | - Helga Marques
- Center for Statistical Sciences, Brown University School of Public Health, Providence, RI, USA
| | | | - Jorge Onieva
- Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA
| | | | | | - Dan Idelkope
- Geisel School of Medicine at Dartmouth College, Hanover, NH, USA
| | | | - Jason H T Bates
- Division of Pulmonary and Critical Care, University of Vermont Medical Center, Burlington, VT, USA
| | - Denise Aberle
- David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Avi Spira
- The Pulmonary Unit, Boston Medical Center, Boston, MA, USA
| | - George Washko
- Division of Pulmonary and Critical Care, Brigham and Women's Hospital, Boston, MA, USA
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20
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Bai S, Zhao L. Imbalance Between Injury and Defense in the COPD Emphysematous Phenotype. Front Med (Lausanne) 2021; 8:653332. [PMID: 34026786 PMCID: PMC8131650 DOI: 10.3389/fmed.2021.653332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 03/10/2021] [Indexed: 11/15/2022] Open
Abstract
The chronic obstructive pulmonary disease (COPD) emphysematous phenotype is characterized by destruction of lung tissue structure. Patients with this phenotype usually present with typical emphysema-like changes on chest computed Tomography CT, experience higher mortality and poorer prognosis, and are insensitive to routine pharmacological COPD therapy. However, the pathogenesis for the COPD emphysematous phenotype remains unclear, resulting in diagnostic and therapeutic challenges. The imbalance between injury and defense mechanisms is essential in the progression of many pulmonary diseases. Thus, in this review, we focus on the pathogenesis of the COPD emphysematous phenotype and discuss the pathophysiological processes involved in disease progression, from the perspective of injury and defense imbalance.
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Affiliation(s)
- Shuang Bai
- Department of Pulmonary and Critical Care Medicine, Shengjing Hospital of China Medical University, Shenyang, China
| | - Li Zhao
- Department of Pulmonary and Critical Care Medicine, Shengjing Hospital of China Medical University, Shenyang, China
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21
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Vestal BE, Carlson NE, Ghosh D. Filtering Spatial Point Patterns Using Kernel Densities. SPATIAL STATISTICS 2021; 41:100487. [PMID: 33409121 PMCID: PMC7781288 DOI: 10.1016/j.spasta.2020.100487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
Understanding spatial inhomogeneity and clustering in point patterns arises in many contexts, ranging from disease outbreak monitoring to analyzing radiologically-based emphysema in biomedical images. This can often involve classifying individual points as being part of a feature/cluster or as being part of a background noise process. Existing methods for this task can struggle when there are differences in the size and/or density of individual clusters. In this work, we propose employing kernel density estimates of the underlying point process intensity function, using an existing data-driven approach to bandwidth selection, to separate feature points from noise. This is achieved by constructing a null distribution, either through asymptotic properties or Monte Carlo simulation, and comparing kernel density estimates to a given quantile of this distribution. We demonstrate that our method, termed Kernel Density and Simulation based Filtering (KDS-Filt), showed superior performance to existing alternative approaches, especially when there is inhomogeneity in cluster sizes and density. We also show the utility of KDS-Filt for identifying clinically relevant information about the spatial distribution of emphysema in lung computed tomography scans. The KDS-Filt methodology is available as part of the sncp R package, which can be downloaded at https://github.com/stop-pre16/sncp.
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Affiliation(s)
- Brian E. Vestal
- Center for Genes, Environment and Health, National Jewish Health, 1400 Jackson St, Denver, CO 80206, USA
- Department of Biostatistics and Informatics, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO
| | - Nichole E. Carlson
- Department of Biostatistics and Informatics, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO
| | - Debashis Ghosh
- Department of Biostatistics and Informatics, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO
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22
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Billatos E, Ash SY, Duan F, Xu K, Romanoff J, Marques H, Moses E, Han MK, Regan EA, Bowler RP, Mason SE, Doyle TJ, San José Estépar R, Rosas IO, Ross JC, Xiao X, Liu H, Liu G, Sukumar G, Wilkerson M, Dalgard C, Stevenson C, Whitney D, Aberle D, Spira A, San José Estépar R, Lenburg ME, Washko GR. Distinguishing Smoking-Related Lung Disease Phenotypes Via Imaging and Molecular Features. Chest 2021; 159:549-563. [PMID: 32946850 PMCID: PMC8039011 DOI: 10.1016/j.chest.2020.08.2115] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2019] [Revised: 08/11/2020] [Accepted: 08/15/2020] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Chronic tobacco smoke exposure results in a broad range of lung pathologies including emphysema, airway disease and parenchymal fibrosis as well as a multitude of extra-pulmonary comorbidities. Prior work using CT imaging has identified several clinically relevant subgroups of smoking related lung disease, but these investigations have generally lacked organ specific molecular correlates. RESEARCH QUESTION Can CT imaging be used to identify clinical phenotypes of smoking related lung disease that have specific bronchial epithelial gene expression patterns to better understand disease pathogenesis? STUDY DESIGN AND METHODS Using K-means clustering, we clustered participants from the COPDGene study (n = 5,273) based on CT imaging characteristics and then evaluated their clinical phenotypes. These clusters were replicated in the Detection of Early Lung Cancer Among Military Personnel (DECAMP) cohort (n = 360), and were further characterized using bronchial epithelial gene expression. RESULTS Three clusters (preserved, interstitial predominant and emphysema predominant) were identified. Compared to the preserved cluster, the interstitial and emphysema clusters had worse lung function, exercise capacity and quality of life. In longitudinal follow-up, individuals from the emphysema group had greater declines in exercise capacity and lung function, more emphysema, more exacerbations, and higher mortality. Similarly, genes involved in inflammatory pathways (tumor necrosis factor-α, interferon-β) are more highly expressed in bronchial epithelial cells from individuals in the emphysema cluster, while genes associated with T-cell related biology are decreased in these samples. Samples from individuals in the interstitial cluster generally had intermediate levels of expression of these genes. INTERPRETATION Using quantitative CT imaging, we identified three groups of individuals in older ever-smokers that replicate in two cohorts. Airway gene expression differences between the three groups suggests increased levels of inflammation in the most severe clinical phenotype, possibly mediated by the tumor necrosis factor-α and interferon-β pathways. CLINICAL TRIAL REGISTRATION COPDGene (NCT00608764), DECAMP-1 (NCT01785342), DECAMP-2 (NCT02504697).
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Affiliation(s)
- Ehab Billatos
- Department of Medicine, Section of Pulmonary and Critical Care Medicine, Boston University, Boston, MA; Department of Medicine, Section of Computational Biomedicine, Boston University, Boston, MA.
| | - Samuel Y Ash
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA; Applied Chest Imaging Laboratory, Brigham and Women's Hospital, Boston, MA
| | - Fenghai Duan
- Department of Biostatistics and Center for Statistical Sciences, Brown University School of Public Health, Providence, RI
| | - Ke Xu
- Department of Medicine, Section of Computational Biomedicine, Boston University, Boston, MA; Bioinformatics Program, Boston University College of Engineering, Boston, MA
| | - Justin Romanoff
- Department of Biostatistics and Center for Statistical Sciences, Brown University School of Public Health, Providence, RI
| | - Helga Marques
- Department of Biostatistics and Center for Statistical Sciences, Brown University School of Public Health, Providence, RI
| | - Elizabeth Moses
- Department of Medicine, Section of Computational Biomedicine, Boston University, Boston, MA
| | - MeiLan K Han
- Department of Internal Medicine, Division of Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, MI
| | - Elizabeth A Regan
- Department of Medicine, Division of Rheumatology, National Jewish Health, Denver, CO
| | - Russell P Bowler
- Department of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, National Jewish Health, Denver, CO
| | - Stefanie E Mason
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA; Applied Chest Imaging Laboratory, Brigham and Women's Hospital, Boston, MA
| | - Tracy J Doyle
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA
| | - Rubén San José Estépar
- Applied Chest Imaging Laboratory, Brigham and Women's Hospital, Boston, MA; Department of Radiology, Brigham and Women's Hospital, Boston, MA
| | - Ivan O Rosas
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA
| | - James C Ross
- Applied Chest Imaging Laboratory, Brigham and Women's Hospital, Boston, MA
| | - Xiaohui Xiao
- Department of Medicine, Section of Computational Biomedicine, Boston University, Boston, MA
| | - Hanqiao Liu
- Department of Medicine, Section of Computational Biomedicine, Boston University, Boston, MA
| | - Gang Liu
- Department of Medicine, Section of Computational Biomedicine, Boston University, Boston, MA
| | - Gauthaman Sukumar
- Department of Anatomy, Physiology & Genetics, The American Genome Center, Collaborative Health Initiative Research Program, Uniformed Services University of the Health Sciences, Bethesda, MD; Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD
| | - Matthew Wilkerson
- Department of Anatomy, Physiology & Genetics, The American Genome Center, Collaborative Health Initiative Research Program, Uniformed Services University of the Health Sciences, Bethesda, MD; Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD
| | - Clifton Dalgard
- Department of Anatomy, Physiology & Genetics, The American Genome Center, Collaborative Health Initiative Research Program, Uniformed Services University of the Health Sciences, Bethesda, MD
| | | | - Duncan Whitney
- Lung Cancer Initiative at Johnson & Johnson, New Brunswick, NJ
| | - Denise Aberle
- Department of Radiology, University of California at Los Angeles, Los Angeles, CA
| | - Avrum Spira
- Department of Medicine, Section of Pulmonary and Critical Care Medicine, Boston University, Boston, MA; Department of Medicine, Section of Computational Biomedicine, Boston University, Boston, MA; Lung Cancer Initiative at Johnson & Johnson, New Brunswick, NJ
| | - Raúl San José Estépar
- Applied Chest Imaging Laboratory, Brigham and Women's Hospital, Boston, MA; Department of Radiology, Brigham and Women's Hospital, Boston, MA
| | - Marc E Lenburg
- Department of Medicine, Section of Computational Biomedicine, Boston University, Boston, MA; Bioinformatics Program, Boston University College of Engineering, Boston, MA
| | - George R Washko
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA; Applied Chest Imaging Laboratory, Brigham and Women's Hospital, Boston, MA
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23
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Tanabe N, Vasilescu DM, Hague CJ, Ikezoe K, Murphy DT, Kirby M, Stevenson CS, Verleden SE, Vanaudenaerde BM, Gayan-Ramirez G, Janssens W, Coxson HO, Paré PD, Hogg JC. Pathological Comparisons of Paraseptal and Centrilobular Emphysema in Chronic Obstructive Pulmonary Disease. Am J Respir Crit Care Med 2020; 202:803-811. [PMID: 32485111 DOI: 10.1164/rccm.201912-2327oc] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Rationale: Although centrilobular emphysema (CLE) and paraseptal emphysema (PSE) are commonly identified on multidetector computed tomography (MDCT), little is known about the pathology associated with PSE compared with that of CLE.Objectives: To assess the pathological differences between PSE and CLE in chronic obstructive pulmonary disease (COPD).Methods: Air-inflated frozen lung specimens (n = 6) obtained from patients with severe COPD treated by lung transplantation were scanned with MDCT. Frozen tissue cores were taken from central (n = 8) and peripheral (n = 8) regions of each lung, scanned with micro-computed tomography (microCT), and processed for histology. The core locations were registered to the MDCT, and a percentage of PSE or CLE was assigned by radiologists to each of the regions. MicroCT scans were used to measure number and structural change of terminal bronchioles. Furthermore, microCT-based volume fractions of CLE and PSE allowed classifying cores into mild emphysema, CLE-dominant, and PSE-dominant.Measurements and Main Results: The percentages of PSE measured on MDCT and microCT were positively associated (P = 0.015). The number of terminal bronchioles per milliliter of lung and cross-sectional lumen area were significantly lower and wall area percentage was significantly higher in CLE-dominant regions compared with mild emphysema and PSE-dominant regions (all P < 0.05), whereas no difference was found between PSE-dominant and mild emphysema samples (all P > 0.5). Immunohistochemistry showed significantly higher infiltration of neutrophils (P = 0.002), but not of macrophages, CD4, CD8, or B cells, in PSE compared with CLE regions.Conclusions: The terminal bronchioles are relatively preserved, whereas neutrophilic inflammation is increased in PSE-dominant regions compared with CLE-dominant regions in patients with COPD.
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Affiliation(s)
- Naoya Tanabe
- Centre for Heart and Lung Innovation, St. Paul's Hospital, and.,Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | | | - Cameron J Hague
- Department of Radiology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Kohei Ikezoe
- Centre for Heart and Lung Innovation, St. Paul's Hospital, and
| | - Darra T Murphy
- Department of Radiology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Miranda Kirby
- Centre for Heart and Lung Innovation, St. Paul's Hospital, and.,Department of Physics, Ryerson University, Toronto, Ontario, Canada
| | - Christopher S Stevenson
- Janssen Disease Interception Accelerator, Janssen Pharmaceutical Companies of Johnson and Johnson, Beerse, Belgium; and
| | - Stijn E Verleden
- Department of Chronic Disease, Metabolism and Aging, Laboratory of Respiratory Diseases, KU Leuven, Leuven, Belgium
| | - Bart M Vanaudenaerde
- Department of Chronic Disease, Metabolism and Aging, Laboratory of Respiratory Diseases, KU Leuven, Leuven, Belgium
| | - Ghislaine Gayan-Ramirez
- Department of Chronic Disease, Metabolism and Aging, Laboratory of Respiratory Diseases, KU Leuven, Leuven, Belgium
| | - Wim Janssens
- Department of Chronic Disease, Metabolism and Aging, Laboratory of Respiratory Diseases, KU Leuven, Leuven, Belgium
| | - Harvey O Coxson
- Centre for Heart and Lung Innovation, St. Paul's Hospital, and
| | - Peter D Paré
- Centre for Heart and Lung Innovation, St. Paul's Hospital, and
| | - James C Hogg
- Centre for Heart and Lung Innovation, St. Paul's Hospital, and
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24
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Takasaka N, Seki Y, Fujisaki I, Uchiyama S, Matsubayashi S, Sato A, Yamanaka Y, Odashima K, Kazuyori T, Seki A, Takeda H, Ishikawa T, Kuwano K. Impact of emphysema on sputum culture conversion in male patients with pulmonary tuberculosis: a retrospective analysis. BMC Pulm Med 2020; 20:287. [PMID: 33160360 PMCID: PMC7648401 DOI: 10.1186/s12890-020-01325-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 10/27/2020] [Indexed: 11/24/2022] Open
Abstract
Background Although cigarette smoking may have a negative impact on the clinical outcome of pulmonary tuberculosis (PTB), few studies have investigated the impact of smoking-associated lung diseases. Emphysema is a major pathological finding of smoking-related lung damage. We aimed to clarify the effect of emphysema on sputum culture conversion rate for Mycobacterium tuberculosis (MTB). Methods We retrospectively studied 79 male patients with PTB confirmed by acid-fast bacillus smear and culture at Jikei University Daisan Hospital between January 2015 and December 2018. We investigated the sputum culture conversion rates for MTB after starting standard anti-TB treatment in patients with or without emphysema. Emphysema was defined as Goddard score ≥ 1 based on low attenuation area < − 950 Hounsfield Unit (HU) using computed tomography (CT). We also evaluated the effect on PTB-related CT findings prior to anti-TB treatment. Results Mycobacterial median time to culture conversion (TCC) in 38 PTB patients with emphysema was 52.0 days [interquartile range (IQR) 29.0–66.0 days], which was significantly delayed compared with that in 41 patients without emphysema (28.0 days, IQR 14.0–42.0 days) (p < 0.001, log-rank test). Multivariate Cox proportional hazards analysis showed that the following were associated with delayed TCC: emphysema [hazard ratio (HR): 2.43; 95% confidence interval (CI): 1.18–4.97; p = 0.015), cavities (HR: 2.15; 95% CI: 1.83–3.89; p = 0.012) and baseline time to TB detection within 2 weeks (HR: 2.95; 95% CI: 1.64–5.31; p < 0.0001). Cavities and consolidation were more often identified by CT in PTB patients with than without emphysema (71.05% vs 43.90%; p = 0.015, and 84.21% vs 60.98%; p = 0.021, respectively). Conclusions This study suggests that emphysema poses an increased risk of delayed TCC in PTB. Emphysema detection by CT might be a useful method for prediction of the duration of PTB treatment required for sputum negative conversion.
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Affiliation(s)
- Naoki Takasaka
- Department of Internal Medicine, Division of Respiratory Diseases, The Jikei University Daisan Hospital, 4-11-1 Izumihoncho Komae-shi, Tokyo, 201-8601, Japan.
| | - Yoshitaka Seki
- Department of Internal Medicine, Division of Respiratory Diseases, The Jikei University Daisan Hospital, 4-11-1 Izumihoncho Komae-shi, Tokyo, 201-8601, Japan
| | - Ikumi Fujisaki
- Department of Internal Medicine, Division of Respiratory Diseases, The Jikei University Daisan Hospital, 4-11-1 Izumihoncho Komae-shi, Tokyo, 201-8601, Japan
| | - Shota Uchiyama
- Department of Internal Medicine, Division of Respiratory Diseases, The Jikei University Daisan Hospital, 4-11-1 Izumihoncho Komae-shi, Tokyo, 201-8601, Japan
| | - Sachi Matsubayashi
- Department of Internal Medicine, Division of Respiratory Diseases, The Jikei University Daisan Hospital, 4-11-1 Izumihoncho Komae-shi, Tokyo, 201-8601, Japan
| | - Akihito Sato
- Department of Internal Medicine, Division of Respiratory Diseases, The Jikei University Daisan Hospital, 4-11-1 Izumihoncho Komae-shi, Tokyo, 201-8601, Japan
| | - Yumie Yamanaka
- Department of Internal Medicine, Division of Respiratory Diseases, The Jikei University Daisan Hospital, 4-11-1 Izumihoncho Komae-shi, Tokyo, 201-8601, Japan
| | - Kyuto Odashima
- Department of Internal Medicine, Division of Respiratory Diseases, The Jikei University Daisan Hospital, 4-11-1 Izumihoncho Komae-shi, Tokyo, 201-8601, Japan
| | - Taisuke Kazuyori
- Department of Internal Medicine, Division of Respiratory Diseases, The Jikei University Daisan Hospital, 4-11-1 Izumihoncho Komae-shi, Tokyo, 201-8601, Japan
| | - Aya Seki
- Department of Internal Medicine, Division of Respiratory Diseases, The Jikei University Daisan Hospital, 4-11-1 Izumihoncho Komae-shi, Tokyo, 201-8601, Japan
| | - Hiroshi Takeda
- Department of Internal Medicine, Division of Respiratory Diseases, The Jikei University Daisan Hospital, 4-11-1 Izumihoncho Komae-shi, Tokyo, 201-8601, Japan
| | - Takeo Ishikawa
- Department of Internal Medicine, Division of Respiratory Diseases, The Jikei University Daisan Hospital, 4-11-1 Izumihoncho Komae-shi, Tokyo, 201-8601, Japan
| | - Kazuyoshi Kuwano
- Department of Internal Medicine, Division of Respiratory Diseases, The Jikei University School of Medicine, Tokyo, Japan
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25
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Diaz AA. Paraseptal Emphysema: From the Periphery of the Lobule to the Center of the Stage. Am J Respir Crit Care Med 2020; 202:783-784. [PMID: 32640164 PMCID: PMC7491391 DOI: 10.1164/rccm.202006-2138ed] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Affiliation(s)
- Alejandro A. Diaz
- Harvard Medical SchoolBrigham and Women’s HospitalBoston, Massachusetts
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Kinsey CM, San José Estépar R, Bates JHT, Cole BF, Washko G, Jantz M, Mehta H. Tumor density is associated with response to endobronchial ultrasound-guided transbronchial needle injection of cisplatin. J Thorac Dis 2020; 12:4825-4832. [PMID: 33145055 PMCID: PMC7578514 DOI: 10.21037/jtd-20-674] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Background Endobronchial ultrasound-guided transbronchial needle injection of cisplatin (EBUS-TBNI cisplatin) is a therapeutic option for patients with recurrent lung cancer. However, the tumor characteristics that influence the distribution of the agent following intratumoral delivery remain largely unknown. Methods We performed a retrospective evaluation of EBUS-TBNI cisplatin cases performed at two centers. Semi-automated tumor segmentation from CT scans was performed while blinded to the outcome of response. Twenty-four algorithmic radiomics features from two categories, Morphology (i.e., shape, volume) and Intensity (i.e., density), were extracted, and feature selection performed via least absolute shrinkage and selection operator (LASSO) regression. Models were constructed from clinicoepidemiologic variables and selected radiomics features and evaluated using the likelihood ratio chi-square assessment and Akaike’s information criterion (AIC). Results Thirty-eight patients with available imaging data were analyzed. Based on RECIST criteria, 27 of 38 treated sites demonstrated complete or partial remission (71%). The top three features identified by LASSO regression were variance, energy, and kurtosis. All three are measures of intensity, a surrogate for tumor density. Two logistic regression models with the outcome of response were created, each with the top 3 categorical features: (I) an Intensity model including variance, energy, and kurtosis, and (II) a Morphology model including surface-to-volume ratio, spherical disproportion, and maximum 3-dimensional (3D) diameter. Only the Intensity model met criteria for significance (P=0.024), and it resulted in a lower AIC and higher pseudo R square value vs. the Morphology model. Conclusions Measures of tumor density are more highly associated with response to EBUS-TBNI cisplatin than measures of morphology.
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Affiliation(s)
- C Matthew Kinsey
- Division of Pulmonary and Critical Care, University of Vermont Medical Center, Burlington, VT, USA
| | | | - Jason H T Bates
- Division of Pulmonary and Critical Care, University of Vermont Medical Center, Burlington, VT, USA
| | - Bernard F Cole
- Department of Mathematics and Statistics, University of Vermont, Burlington, VT, USA
| | - George Washko
- Division of Pulmonary and Critical Care, Brigham and Women's Hospital, Boston MA, USA
| | - Michael Jantz
- Division of Pulmonary and Critical Care, University of Florida, Gainesville, FL, USA
| | - Hiren Mehta
- Division of Pulmonary and Critical Care, University of Florida, Gainesville, FL, USA
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Moll M, Sakornsakolpat P, Shrine N, Hobbs BD, DeMeo DL, John C, Guyatt AL, McGeachie MJ, Gharib SA, Obeidat M, Lahousse L, Wijnant SRA, Brusselle G, Meyers DA, Bleecker ER, Li X, Tal-Singer R, Manichaikul A, Rich SS, Won S, Kim WJ, Do AR, Washko GR, Barr RG, Psaty BM, Bartz TM, Hansel NN, Barnes K, Hokanson JE, Crapo JD, Lynch D, Bakke P, Gulsvik A, Hall IP, Wain L, Weiss ST, Silverman EK, Dudbridge F, Tobin MD, Cho MH. Chronic obstructive pulmonary disease and related phenotypes: polygenic risk scores in population-based and case-control cohorts. THE LANCET. RESPIRATORY MEDICINE 2020; 8:696-708. [PMID: 32649918 PMCID: PMC7429152 DOI: 10.1016/s2213-2600(20)30101-6] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2019] [Revised: 01/24/2020] [Accepted: 02/17/2020] [Indexed: 01/19/2023]
Abstract
BACKGROUND Genetic factors influence chronic obstructive pulmonary disease (COPD) risk, but the individual variants that have been identified have small effects. We hypothesised that a polygenic risk score using additional variants would predict COPD and associated phenotypes. METHODS We constructed a polygenic risk score using a genome-wide association study of lung function (FEV1 and FEV1/forced vital capacity [FVC]) from the UK Biobank and SpiroMeta. We tested this polygenic risk score in nine cohorts of multiple ethnicities for an association with moderate-to-severe COPD (defined as FEV1/FVC <0·7 and FEV1 <80% of predicted). Associations were tested using logistic regression models, adjusting for age, sex, height, smoking pack-years, and principal components of genetic ancestry. We assessed predictive performance of models by area under the curve. In a subset of studies, we also studied quantitative and qualitative CT imaging phenotypes that reflect parenchymal and airway pathology, and patterns of reduced lung growth. FINDINGS The polygenic risk score was associated with COPD in European (odds ratio [OR] per SD 1·81 [95% CI 1·74-1·88] and non-European (1·42 [1·34-1·51]) populations. Compared with the first decile, the tenth decile of the polygenic risk score was associated with COPD, with an OR of 7·99 (6·56-9·72) in European ancestry and 4·83 (3·45-6·77) in non-European ancestry cohorts. The polygenic risk score was superior to previously described genetic risk scores and, when combined with clinical risk factors (ie, age, sex, and smoking pack-years), showed improved prediction for COPD compared with a model comprising clinical risk factors alone (AUC 0·80 [0·79-0·81] vs 0·76 [0·75-0·76]). The polygenic risk score was associated with CT imaging phenotypes, including wall area percent, quantitative and qualitative measures of emphysema, local histogram emphysema patterns, and destructive emphysema subtypes. The polygenic risk score was associated with a reduced lung growth pattern. INTERPRETATION A risk score comprised of genetic variants can identify a small subset of individuals at markedly increased risk for moderate-to-severe COPD, emphysema subtypes associated with cigarette smoking, and patterns of reduced lung growth. FUNDING US National Institutes of Health, Wellcome Trust.
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Affiliation(s)
- Matthew Moll
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA; Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Phuwanat Sakornsakolpat
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA; Department of Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Nick Shrine
- Genetic Epidemiology Group, Department of Health Sciences, University of Leicester, Leicester, UK
| | - Brian D Hobbs
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA; Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Dawn L DeMeo
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA; Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Catherine John
- Genetic Epidemiology Group, Department of Health Sciences, University of Leicester, Leicester, UK
| | - Anna L Guyatt
- Genetic Epidemiology Group, Department of Health Sciences, University of Leicester, Leicester, UK
| | - Michael J McGeachie
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Sina A Gharib
- Computational Medicine Core, Center for Lung Biology, Department of Medicine, University of Washington, Seattle, WA, USA; Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Ma'en Obeidat
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA; University of British Columbia Center for Heart Lung Innovation, St Paul's Hospital, Vancouver, BC, Canada
| | - Lies Lahousse
- Department of Epidemiology, Erasmus Medical Centre, Rotterdam, Netherlands; Department of Bioanalysis, Faculty of Pharmaceutical Sciences, Ghent University, Ghent, Belgium
| | - Sara R A Wijnant
- Department of Epidemiology, Erasmus Medical Centre, Rotterdam, Netherlands; Department of Bioanalysis, Faculty of Pharmaceutical Sciences, Ghent University, Ghent, Belgium; Department of Respiratory Medicine, Ghent University Hospital, Ghent, Belgium
| | - Guy Brusselle
- Department of Epidemiology, Erasmus Medical Centre, Rotterdam, Netherlands; Department of Respiratory Medicine, Erasmus Medical Centre, Rotterdam, Netherlands; Department of Respiratory Medicine, Ghent University Hospital, Ghent, Belgium
| | | | | | - Xingnan Li
- Department of Medicine, University of Arizona, Tucson, AZ, USA
| | - Ruth Tal-Singer
- GlaxoSmithKline Research and Development, Collegeville, PA, USA
| | - Ani Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA; Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA; Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Sungho Won
- Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, South Korea; Interdisciplinary Program of Bioinformatics, College of National Sciences, Seoul National University, Seoul, South Korea; Institute of Health and Environment, Seoul National University, Seoul, South Korea
| | - Woo Jin Kim
- Department of Internal Medicine, Kangwon National University, Chuncheon, South Korea
| | - Ah Ra Do
- Interdisciplinary Program of Bioinformatics, College of National Sciences, Seoul National University, Seoul, South Korea
| | - George R Washko
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - R Graham Barr
- Department of Medicine and Department of Epidemiology, Columbia University Medical Center, New York, NY, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA; Kaiser Permanente Washington Health Research Institute, Seattle, WA
| | - Traci M Bartz
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Nadia N Hansel
- School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Kathleen Barnes
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | | | - James D Crapo
- Division of Pulmonary, Critical Care, and Sleep Medicine, National Jewish Health, Denver, CO, USA
| | - David Lynch
- Department of Radiology, National Jewish Health, Denver, CO, USA
| | - Per Bakke
- Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Amund Gulsvik
- Division of Respiratory Medicine, Queen's Medical Centre, Nottingham, UK
| | - Ian P Hall
- National Institute for Health Research Leicester Respiratory Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Louise Wain
- Genetic Epidemiology Group, Department of Health Sciences, University of Leicester, Leicester, UK; National Institute for Health Research Leicester Respiratory Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Scott T Weiss
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA; Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Frank Dudbridge
- Genetic Epidemiology Group, Department of Health Sciences, University of Leicester, Leicester, UK
| | - Martin D Tobin
- Genetic Epidemiology Group, Department of Health Sciences, University of Leicester, Leicester, UK; National Institute for Health Research Leicester Respiratory Biomedical Research Centre, Glenfield Hospital, Leicester, UK.
| | - Michael H Cho
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA; Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA.
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Chronic Obstructive Pulmonary Disease Quantification Using CT Texture Analysis and Densitometry: Results From the Danish Lung Cancer Screening Trial. AJR Am J Roentgenol 2020; 214:1269-1279. [DOI: 10.2214/ajr.19.22300] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Washko GR, Nardelli P, Ash SY, Rahaghi FN, Vegas Sanchez-Ferrero G, Come CE, Dransfield MT, Kalhan R, Han MK, Bhatt SP, Wells JM, Pistenmaa CL, Diaz AA, Ross JC, Rennard S, Querejeta Roca G, Shah AM, Young K, Kinney GL, Hokanson JE, Agustí A, San José Estépar R. Smaller Left Ventricle Size at Noncontrast CT Is Associated with Lower Mortality in COPDGene Participants. Radiology 2020; 296:208-215. [PMID: 32368963 PMCID: PMC7299752 DOI: 10.1148/radiol.2020191793] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Background Smokers with chronic obstructive pulmonary disease (COPD) have smaller left ventricles (LVs) due to reduced preload. Skeletal muscle wasting is also common in COPD, but less is known about its contribution to LV size. Purpose To explore the relationships between CT metrics of emphysema, venous vascular volume, and sarcopenia with the LV epicardial volume (LVEV) (myocardium and chamber) estimated from chest CT images in participants with COPD and then to determine the clinical relevance of the LVEV in multivariable models, including sex and anthropomorphic metrics. Materials and Methods The COPDGene study (ClinicalTrials.gov identifier: NCT00608764) is an ongoing prospective longitudinal observational investigation that began in 2006. LVEV, distal pulmonary venous blood volume for vessels smaller than 5 mm2 in cross section (BV5), CT emphysema, and pectoralis muscle area were retrospectively extracted from 3318 nongated, unenhanced COPDGene CT scans. Multivariable linear and Cox regression models were used to explore the association between emphysema, venous BV5, pectoralis muscle area, and LVEV as well as the association of LVEV with health status using the St George's Respiratory Questionnaire, 6-minute walk distance, and all-cause mortality. Results The median age of the cohort was 64 years (interquartile range, 57-70 years). Of the 2423 participants, 1806 were men and 617 were African American. The median LVEV between Global Initiative for Chronic Obstructive Lung Disease (GOLD) 1 and GOLD 4 COPD was reduced by 13.9% in women and 17.7% in men (P < .001 for both). In fully adjusted models, higher emphysema percentage (β = -4.2; 95% confidence interval [CI]: -5.0, -3.4; P < .001), venous BV5 (β = 7.0; 95% CI: 5.7, 8.2; P < .001), and pectoralis muscle area (β = 2.7; 95% CI: 1.2, 4.1; P < .001) were independently associated with reduced LVEV. Reductions in LVEV were associated with improved health status (β = 0.3; 95% CI: 0.1, 0.4) and 6-minute walk distance (β = -12.2; 95% CI: -15.2, -9.3). These effects were greater in women than in men. The effect of reduced LVEV on mortality (hazard ratio: 1.07; 95% CI: 1.05, 1.09) did not vary by sex. Conclusion In women more than men with chronic obstructive pulmonary disease, a reduction in the estimated left ventricle epicardial volume correlated with a loss of pulmonary venous vasculature, greater pectoralis muscle sarcopenia, and lower all-cause mortality. © RSNA, 2020 Online supplemental material is available for this article.
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Affiliation(s)
- George R Washko
- From the Division of Pulmonary and Critical Care, Department of Medicine, Applied Chest Imaging Laboratory (G.R.W., S.Y.A., F.N.R., C.E.C., C.L.P., A.A.D.), Department of Radiology, Applied Chest Imaging Laboratory (P.N., G.V.S.F., J.C.R., R.S.J.E.), Department of Anesthesia (G.Q.R.), and Division of Cardiology (A.M.S.), Brigham and Women's Hospital, 1249 Boylston St, Boston, MA 02215; Lung Health Center, University of Alabama at Birmingham, Birmingham, Ala (M.T.D., S.P.B., J.M.W.); Asthma and COPD Program, Division of Pulmonary and Critical Care Medicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (R.K.); Division of Pulmonary and Critical Care Medicine, University of Michigan Medical School, Ann Arbor, Mich (M.K.H.); BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom (S.R.), Department of Internal Medicine, University of Nebraska Medical Center, Omaha, Neb (S.R.); Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colo (K.Y., G.L.K., J.E.H.); and Respiratory Institute, Hospital Clinic, August Pi i Sunyer Biomedical Research Institute, Centro de Investigación Biomédica en Red Enfermedades Respiratorias, University of Barcelona, Barcelona, Spain (A.A.)
| | - Pietro Nardelli
- From the Division of Pulmonary and Critical Care, Department of Medicine, Applied Chest Imaging Laboratory (G.R.W., S.Y.A., F.N.R., C.E.C., C.L.P., A.A.D.), Department of Radiology, Applied Chest Imaging Laboratory (P.N., G.V.S.F., J.C.R., R.S.J.E.), Department of Anesthesia (G.Q.R.), and Division of Cardiology (A.M.S.), Brigham and Women's Hospital, 1249 Boylston St, Boston, MA 02215; Lung Health Center, University of Alabama at Birmingham, Birmingham, Ala (M.T.D., S.P.B., J.M.W.); Asthma and COPD Program, Division of Pulmonary and Critical Care Medicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (R.K.); Division of Pulmonary and Critical Care Medicine, University of Michigan Medical School, Ann Arbor, Mich (M.K.H.); BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom (S.R.), Department of Internal Medicine, University of Nebraska Medical Center, Omaha, Neb (S.R.); Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colo (K.Y., G.L.K., J.E.H.); and Respiratory Institute, Hospital Clinic, August Pi i Sunyer Biomedical Research Institute, Centro de Investigación Biomédica en Red Enfermedades Respiratorias, University of Barcelona, Barcelona, Spain (A.A.)
| | - Samuel Y Ash
- From the Division of Pulmonary and Critical Care, Department of Medicine, Applied Chest Imaging Laboratory (G.R.W., S.Y.A., F.N.R., C.E.C., C.L.P., A.A.D.), Department of Radiology, Applied Chest Imaging Laboratory (P.N., G.V.S.F., J.C.R., R.S.J.E.), Department of Anesthesia (G.Q.R.), and Division of Cardiology (A.M.S.), Brigham and Women's Hospital, 1249 Boylston St, Boston, MA 02215; Lung Health Center, University of Alabama at Birmingham, Birmingham, Ala (M.T.D., S.P.B., J.M.W.); Asthma and COPD Program, Division of Pulmonary and Critical Care Medicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (R.K.); Division of Pulmonary and Critical Care Medicine, University of Michigan Medical School, Ann Arbor, Mich (M.K.H.); BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom (S.R.), Department of Internal Medicine, University of Nebraska Medical Center, Omaha, Neb (S.R.); Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colo (K.Y., G.L.K., J.E.H.); and Respiratory Institute, Hospital Clinic, August Pi i Sunyer Biomedical Research Institute, Centro de Investigación Biomédica en Red Enfermedades Respiratorias, University of Barcelona, Barcelona, Spain (A.A.)
| | - Farbod N Rahaghi
- From the Division of Pulmonary and Critical Care, Department of Medicine, Applied Chest Imaging Laboratory (G.R.W., S.Y.A., F.N.R., C.E.C., C.L.P., A.A.D.), Department of Radiology, Applied Chest Imaging Laboratory (P.N., G.V.S.F., J.C.R., R.S.J.E.), Department of Anesthesia (G.Q.R.), and Division of Cardiology (A.M.S.), Brigham and Women's Hospital, 1249 Boylston St, Boston, MA 02215; Lung Health Center, University of Alabama at Birmingham, Birmingham, Ala (M.T.D., S.P.B., J.M.W.); Asthma and COPD Program, Division of Pulmonary and Critical Care Medicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (R.K.); Division of Pulmonary and Critical Care Medicine, University of Michigan Medical School, Ann Arbor, Mich (M.K.H.); BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom (S.R.), Department of Internal Medicine, University of Nebraska Medical Center, Omaha, Neb (S.R.); Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colo (K.Y., G.L.K., J.E.H.); and Respiratory Institute, Hospital Clinic, August Pi i Sunyer Biomedical Research Institute, Centro de Investigación Biomédica en Red Enfermedades Respiratorias, University of Barcelona, Barcelona, Spain (A.A.)
| | - Gonzalo Vegas Sanchez-Ferrero
- From the Division of Pulmonary and Critical Care, Department of Medicine, Applied Chest Imaging Laboratory (G.R.W., S.Y.A., F.N.R., C.E.C., C.L.P., A.A.D.), Department of Radiology, Applied Chest Imaging Laboratory (P.N., G.V.S.F., J.C.R., R.S.J.E.), Department of Anesthesia (G.Q.R.), and Division of Cardiology (A.M.S.), Brigham and Women's Hospital, 1249 Boylston St, Boston, MA 02215; Lung Health Center, University of Alabama at Birmingham, Birmingham, Ala (M.T.D., S.P.B., J.M.W.); Asthma and COPD Program, Division of Pulmonary and Critical Care Medicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (R.K.); Division of Pulmonary and Critical Care Medicine, University of Michigan Medical School, Ann Arbor, Mich (M.K.H.); BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom (S.R.), Department of Internal Medicine, University of Nebraska Medical Center, Omaha, Neb (S.R.); Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colo (K.Y., G.L.K., J.E.H.); and Respiratory Institute, Hospital Clinic, August Pi i Sunyer Biomedical Research Institute, Centro de Investigación Biomédica en Red Enfermedades Respiratorias, University of Barcelona, Barcelona, Spain (A.A.)
| | - Carolyn E Come
- From the Division of Pulmonary and Critical Care, Department of Medicine, Applied Chest Imaging Laboratory (G.R.W., S.Y.A., F.N.R., C.E.C., C.L.P., A.A.D.), Department of Radiology, Applied Chest Imaging Laboratory (P.N., G.V.S.F., J.C.R., R.S.J.E.), Department of Anesthesia (G.Q.R.), and Division of Cardiology (A.M.S.), Brigham and Women's Hospital, 1249 Boylston St, Boston, MA 02215; Lung Health Center, University of Alabama at Birmingham, Birmingham, Ala (M.T.D., S.P.B., J.M.W.); Asthma and COPD Program, Division of Pulmonary and Critical Care Medicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (R.K.); Division of Pulmonary and Critical Care Medicine, University of Michigan Medical School, Ann Arbor, Mich (M.K.H.); BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom (S.R.), Department of Internal Medicine, University of Nebraska Medical Center, Omaha, Neb (S.R.); Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colo (K.Y., G.L.K., J.E.H.); and Respiratory Institute, Hospital Clinic, August Pi i Sunyer Biomedical Research Institute, Centro de Investigación Biomédica en Red Enfermedades Respiratorias, University of Barcelona, Barcelona, Spain (A.A.)
| | - Mark T Dransfield
- From the Division of Pulmonary and Critical Care, Department of Medicine, Applied Chest Imaging Laboratory (G.R.W., S.Y.A., F.N.R., C.E.C., C.L.P., A.A.D.), Department of Radiology, Applied Chest Imaging Laboratory (P.N., G.V.S.F., J.C.R., R.S.J.E.), Department of Anesthesia (G.Q.R.), and Division of Cardiology (A.M.S.), Brigham and Women's Hospital, 1249 Boylston St, Boston, MA 02215; Lung Health Center, University of Alabama at Birmingham, Birmingham, Ala (M.T.D., S.P.B., J.M.W.); Asthma and COPD Program, Division of Pulmonary and Critical Care Medicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (R.K.); Division of Pulmonary and Critical Care Medicine, University of Michigan Medical School, Ann Arbor, Mich (M.K.H.); BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom (S.R.), Department of Internal Medicine, University of Nebraska Medical Center, Omaha, Neb (S.R.); Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colo (K.Y., G.L.K., J.E.H.); and Respiratory Institute, Hospital Clinic, August Pi i Sunyer Biomedical Research Institute, Centro de Investigación Biomédica en Red Enfermedades Respiratorias, University of Barcelona, Barcelona, Spain (A.A.)
| | - Ravi Kalhan
- From the Division of Pulmonary and Critical Care, Department of Medicine, Applied Chest Imaging Laboratory (G.R.W., S.Y.A., F.N.R., C.E.C., C.L.P., A.A.D.), Department of Radiology, Applied Chest Imaging Laboratory (P.N., G.V.S.F., J.C.R., R.S.J.E.), Department of Anesthesia (G.Q.R.), and Division of Cardiology (A.M.S.), Brigham and Women's Hospital, 1249 Boylston St, Boston, MA 02215; Lung Health Center, University of Alabama at Birmingham, Birmingham, Ala (M.T.D., S.P.B., J.M.W.); Asthma and COPD Program, Division of Pulmonary and Critical Care Medicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (R.K.); Division of Pulmonary and Critical Care Medicine, University of Michigan Medical School, Ann Arbor, Mich (M.K.H.); BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom (S.R.), Department of Internal Medicine, University of Nebraska Medical Center, Omaha, Neb (S.R.); Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colo (K.Y., G.L.K., J.E.H.); and Respiratory Institute, Hospital Clinic, August Pi i Sunyer Biomedical Research Institute, Centro de Investigación Biomédica en Red Enfermedades Respiratorias, University of Barcelona, Barcelona, Spain (A.A.)
| | - MeiLan K Han
- From the Division of Pulmonary and Critical Care, Department of Medicine, Applied Chest Imaging Laboratory (G.R.W., S.Y.A., F.N.R., C.E.C., C.L.P., A.A.D.), Department of Radiology, Applied Chest Imaging Laboratory (P.N., G.V.S.F., J.C.R., R.S.J.E.), Department of Anesthesia (G.Q.R.), and Division of Cardiology (A.M.S.), Brigham and Women's Hospital, 1249 Boylston St, Boston, MA 02215; Lung Health Center, University of Alabama at Birmingham, Birmingham, Ala (M.T.D., S.P.B., J.M.W.); Asthma and COPD Program, Division of Pulmonary and Critical Care Medicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (R.K.); Division of Pulmonary and Critical Care Medicine, University of Michigan Medical School, Ann Arbor, Mich (M.K.H.); BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom (S.R.), Department of Internal Medicine, University of Nebraska Medical Center, Omaha, Neb (S.R.); Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colo (K.Y., G.L.K., J.E.H.); and Respiratory Institute, Hospital Clinic, August Pi i Sunyer Biomedical Research Institute, Centro de Investigación Biomédica en Red Enfermedades Respiratorias, University of Barcelona, Barcelona, Spain (A.A.)
| | - Surya P Bhatt
- From the Division of Pulmonary and Critical Care, Department of Medicine, Applied Chest Imaging Laboratory (G.R.W., S.Y.A., F.N.R., C.E.C., C.L.P., A.A.D.), Department of Radiology, Applied Chest Imaging Laboratory (P.N., G.V.S.F., J.C.R., R.S.J.E.), Department of Anesthesia (G.Q.R.), and Division of Cardiology (A.M.S.), Brigham and Women's Hospital, 1249 Boylston St, Boston, MA 02215; Lung Health Center, University of Alabama at Birmingham, Birmingham, Ala (M.T.D., S.P.B., J.M.W.); Asthma and COPD Program, Division of Pulmonary and Critical Care Medicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (R.K.); Division of Pulmonary and Critical Care Medicine, University of Michigan Medical School, Ann Arbor, Mich (M.K.H.); BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom (S.R.), Department of Internal Medicine, University of Nebraska Medical Center, Omaha, Neb (S.R.); Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colo (K.Y., G.L.K., J.E.H.); and Respiratory Institute, Hospital Clinic, August Pi i Sunyer Biomedical Research Institute, Centro de Investigación Biomédica en Red Enfermedades Respiratorias, University of Barcelona, Barcelona, Spain (A.A.)
| | - J Michael Wells
- From the Division of Pulmonary and Critical Care, Department of Medicine, Applied Chest Imaging Laboratory (G.R.W., S.Y.A., F.N.R., C.E.C., C.L.P., A.A.D.), Department of Radiology, Applied Chest Imaging Laboratory (P.N., G.V.S.F., J.C.R., R.S.J.E.), Department of Anesthesia (G.Q.R.), and Division of Cardiology (A.M.S.), Brigham and Women's Hospital, 1249 Boylston St, Boston, MA 02215; Lung Health Center, University of Alabama at Birmingham, Birmingham, Ala (M.T.D., S.P.B., J.M.W.); Asthma and COPD Program, Division of Pulmonary and Critical Care Medicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (R.K.); Division of Pulmonary and Critical Care Medicine, University of Michigan Medical School, Ann Arbor, Mich (M.K.H.); BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom (S.R.), Department of Internal Medicine, University of Nebraska Medical Center, Omaha, Neb (S.R.); Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colo (K.Y., G.L.K., J.E.H.); and Respiratory Institute, Hospital Clinic, August Pi i Sunyer Biomedical Research Institute, Centro de Investigación Biomédica en Red Enfermedades Respiratorias, University of Barcelona, Barcelona, Spain (A.A.)
| | - Carrie L Pistenmaa
- From the Division of Pulmonary and Critical Care, Department of Medicine, Applied Chest Imaging Laboratory (G.R.W., S.Y.A., F.N.R., C.E.C., C.L.P., A.A.D.), Department of Radiology, Applied Chest Imaging Laboratory (P.N., G.V.S.F., J.C.R., R.S.J.E.), Department of Anesthesia (G.Q.R.), and Division of Cardiology (A.M.S.), Brigham and Women's Hospital, 1249 Boylston St, Boston, MA 02215; Lung Health Center, University of Alabama at Birmingham, Birmingham, Ala (M.T.D., S.P.B., J.M.W.); Asthma and COPD Program, Division of Pulmonary and Critical Care Medicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (R.K.); Division of Pulmonary and Critical Care Medicine, University of Michigan Medical School, Ann Arbor, Mich (M.K.H.); BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom (S.R.), Department of Internal Medicine, University of Nebraska Medical Center, Omaha, Neb (S.R.); Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colo (K.Y., G.L.K., J.E.H.); and Respiratory Institute, Hospital Clinic, August Pi i Sunyer Biomedical Research Institute, Centro de Investigación Biomédica en Red Enfermedades Respiratorias, University of Barcelona, Barcelona, Spain (A.A.)
| | - Alejandro A Diaz
- From the Division of Pulmonary and Critical Care, Department of Medicine, Applied Chest Imaging Laboratory (G.R.W., S.Y.A., F.N.R., C.E.C., C.L.P., A.A.D.), Department of Radiology, Applied Chest Imaging Laboratory (P.N., G.V.S.F., J.C.R., R.S.J.E.), Department of Anesthesia (G.Q.R.), and Division of Cardiology (A.M.S.), Brigham and Women's Hospital, 1249 Boylston St, Boston, MA 02215; Lung Health Center, University of Alabama at Birmingham, Birmingham, Ala (M.T.D., S.P.B., J.M.W.); Asthma and COPD Program, Division of Pulmonary and Critical Care Medicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (R.K.); Division of Pulmonary and Critical Care Medicine, University of Michigan Medical School, Ann Arbor, Mich (M.K.H.); BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom (S.R.), Department of Internal Medicine, University of Nebraska Medical Center, Omaha, Neb (S.R.); Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colo (K.Y., G.L.K., J.E.H.); and Respiratory Institute, Hospital Clinic, August Pi i Sunyer Biomedical Research Institute, Centro de Investigación Biomédica en Red Enfermedades Respiratorias, University of Barcelona, Barcelona, Spain (A.A.)
| | - James C Ross
- From the Division of Pulmonary and Critical Care, Department of Medicine, Applied Chest Imaging Laboratory (G.R.W., S.Y.A., F.N.R., C.E.C., C.L.P., A.A.D.), Department of Radiology, Applied Chest Imaging Laboratory (P.N., G.V.S.F., J.C.R., R.S.J.E.), Department of Anesthesia (G.Q.R.), and Division of Cardiology (A.M.S.), Brigham and Women's Hospital, 1249 Boylston St, Boston, MA 02215; Lung Health Center, University of Alabama at Birmingham, Birmingham, Ala (M.T.D., S.P.B., J.M.W.); Asthma and COPD Program, Division of Pulmonary and Critical Care Medicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (R.K.); Division of Pulmonary and Critical Care Medicine, University of Michigan Medical School, Ann Arbor, Mich (M.K.H.); BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom (S.R.), Department of Internal Medicine, University of Nebraska Medical Center, Omaha, Neb (S.R.); Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colo (K.Y., G.L.K., J.E.H.); and Respiratory Institute, Hospital Clinic, August Pi i Sunyer Biomedical Research Institute, Centro de Investigación Biomédica en Red Enfermedades Respiratorias, University of Barcelona, Barcelona, Spain (A.A.)
| | - Stephen Rennard
- From the Division of Pulmonary and Critical Care, Department of Medicine, Applied Chest Imaging Laboratory (G.R.W., S.Y.A., F.N.R., C.E.C., C.L.P., A.A.D.), Department of Radiology, Applied Chest Imaging Laboratory (P.N., G.V.S.F., J.C.R., R.S.J.E.), Department of Anesthesia (G.Q.R.), and Division of Cardiology (A.M.S.), Brigham and Women's Hospital, 1249 Boylston St, Boston, MA 02215; Lung Health Center, University of Alabama at Birmingham, Birmingham, Ala (M.T.D., S.P.B., J.M.W.); Asthma and COPD Program, Division of Pulmonary and Critical Care Medicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (R.K.); Division of Pulmonary and Critical Care Medicine, University of Michigan Medical School, Ann Arbor, Mich (M.K.H.); BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom (S.R.), Department of Internal Medicine, University of Nebraska Medical Center, Omaha, Neb (S.R.); Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colo (K.Y., G.L.K., J.E.H.); and Respiratory Institute, Hospital Clinic, August Pi i Sunyer Biomedical Research Institute, Centro de Investigación Biomédica en Red Enfermedades Respiratorias, University of Barcelona, Barcelona, Spain (A.A.)
| | - Gabriela Querejeta Roca
- From the Division of Pulmonary and Critical Care, Department of Medicine, Applied Chest Imaging Laboratory (G.R.W., S.Y.A., F.N.R., C.E.C., C.L.P., A.A.D.), Department of Radiology, Applied Chest Imaging Laboratory (P.N., G.V.S.F., J.C.R., R.S.J.E.), Department of Anesthesia (G.Q.R.), and Division of Cardiology (A.M.S.), Brigham and Women's Hospital, 1249 Boylston St, Boston, MA 02215; Lung Health Center, University of Alabama at Birmingham, Birmingham, Ala (M.T.D., S.P.B., J.M.W.); Asthma and COPD Program, Division of Pulmonary and Critical Care Medicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (R.K.); Division of Pulmonary and Critical Care Medicine, University of Michigan Medical School, Ann Arbor, Mich (M.K.H.); BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom (S.R.), Department of Internal Medicine, University of Nebraska Medical Center, Omaha, Neb (S.R.); Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colo (K.Y., G.L.K., J.E.H.); and Respiratory Institute, Hospital Clinic, August Pi i Sunyer Biomedical Research Institute, Centro de Investigación Biomédica en Red Enfermedades Respiratorias, University of Barcelona, Barcelona, Spain (A.A.)
| | - Amil M Shah
- From the Division of Pulmonary and Critical Care, Department of Medicine, Applied Chest Imaging Laboratory (G.R.W., S.Y.A., F.N.R., C.E.C., C.L.P., A.A.D.), Department of Radiology, Applied Chest Imaging Laboratory (P.N., G.V.S.F., J.C.R., R.S.J.E.), Department of Anesthesia (G.Q.R.), and Division of Cardiology (A.M.S.), Brigham and Women's Hospital, 1249 Boylston St, Boston, MA 02215; Lung Health Center, University of Alabama at Birmingham, Birmingham, Ala (M.T.D., S.P.B., J.M.W.); Asthma and COPD Program, Division of Pulmonary and Critical Care Medicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (R.K.); Division of Pulmonary and Critical Care Medicine, University of Michigan Medical School, Ann Arbor, Mich (M.K.H.); BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom (S.R.), Department of Internal Medicine, University of Nebraska Medical Center, Omaha, Neb (S.R.); Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colo (K.Y., G.L.K., J.E.H.); and Respiratory Institute, Hospital Clinic, August Pi i Sunyer Biomedical Research Institute, Centro de Investigación Biomédica en Red Enfermedades Respiratorias, University of Barcelona, Barcelona, Spain (A.A.)
| | - Kendra Young
- From the Division of Pulmonary and Critical Care, Department of Medicine, Applied Chest Imaging Laboratory (G.R.W., S.Y.A., F.N.R., C.E.C., C.L.P., A.A.D.), Department of Radiology, Applied Chest Imaging Laboratory (P.N., G.V.S.F., J.C.R., R.S.J.E.), Department of Anesthesia (G.Q.R.), and Division of Cardiology (A.M.S.), Brigham and Women's Hospital, 1249 Boylston St, Boston, MA 02215; Lung Health Center, University of Alabama at Birmingham, Birmingham, Ala (M.T.D., S.P.B., J.M.W.); Asthma and COPD Program, Division of Pulmonary and Critical Care Medicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (R.K.); Division of Pulmonary and Critical Care Medicine, University of Michigan Medical School, Ann Arbor, Mich (M.K.H.); BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom (S.R.), Department of Internal Medicine, University of Nebraska Medical Center, Omaha, Neb (S.R.); Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colo (K.Y., G.L.K., J.E.H.); and Respiratory Institute, Hospital Clinic, August Pi i Sunyer Biomedical Research Institute, Centro de Investigación Biomédica en Red Enfermedades Respiratorias, University of Barcelona, Barcelona, Spain (A.A.)
| | - Gregory L Kinney
- From the Division of Pulmonary and Critical Care, Department of Medicine, Applied Chest Imaging Laboratory (G.R.W., S.Y.A., F.N.R., C.E.C., C.L.P., A.A.D.), Department of Radiology, Applied Chest Imaging Laboratory (P.N., G.V.S.F., J.C.R., R.S.J.E.), Department of Anesthesia (G.Q.R.), and Division of Cardiology (A.M.S.), Brigham and Women's Hospital, 1249 Boylston St, Boston, MA 02215; Lung Health Center, University of Alabama at Birmingham, Birmingham, Ala (M.T.D., S.P.B., J.M.W.); Asthma and COPD Program, Division of Pulmonary and Critical Care Medicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (R.K.); Division of Pulmonary and Critical Care Medicine, University of Michigan Medical School, Ann Arbor, Mich (M.K.H.); BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom (S.R.), Department of Internal Medicine, University of Nebraska Medical Center, Omaha, Neb (S.R.); Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colo (K.Y., G.L.K., J.E.H.); and Respiratory Institute, Hospital Clinic, August Pi i Sunyer Biomedical Research Institute, Centro de Investigación Biomédica en Red Enfermedades Respiratorias, University of Barcelona, Barcelona, Spain (A.A.)
| | - John E Hokanson
- From the Division of Pulmonary and Critical Care, Department of Medicine, Applied Chest Imaging Laboratory (G.R.W., S.Y.A., F.N.R., C.E.C., C.L.P., A.A.D.), Department of Radiology, Applied Chest Imaging Laboratory (P.N., G.V.S.F., J.C.R., R.S.J.E.), Department of Anesthesia (G.Q.R.), and Division of Cardiology (A.M.S.), Brigham and Women's Hospital, 1249 Boylston St, Boston, MA 02215; Lung Health Center, University of Alabama at Birmingham, Birmingham, Ala (M.T.D., S.P.B., J.M.W.); Asthma and COPD Program, Division of Pulmonary and Critical Care Medicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (R.K.); Division of Pulmonary and Critical Care Medicine, University of Michigan Medical School, Ann Arbor, Mich (M.K.H.); BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom (S.R.), Department of Internal Medicine, University of Nebraska Medical Center, Omaha, Neb (S.R.); Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colo (K.Y., G.L.K., J.E.H.); and Respiratory Institute, Hospital Clinic, August Pi i Sunyer Biomedical Research Institute, Centro de Investigación Biomédica en Red Enfermedades Respiratorias, University of Barcelona, Barcelona, Spain (A.A.)
| | - Alvar Agustí
- From the Division of Pulmonary and Critical Care, Department of Medicine, Applied Chest Imaging Laboratory (G.R.W., S.Y.A., F.N.R., C.E.C., C.L.P., A.A.D.), Department of Radiology, Applied Chest Imaging Laboratory (P.N., G.V.S.F., J.C.R., R.S.J.E.), Department of Anesthesia (G.Q.R.), and Division of Cardiology (A.M.S.), Brigham and Women's Hospital, 1249 Boylston St, Boston, MA 02215; Lung Health Center, University of Alabama at Birmingham, Birmingham, Ala (M.T.D., S.P.B., J.M.W.); Asthma and COPD Program, Division of Pulmonary and Critical Care Medicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (R.K.); Division of Pulmonary and Critical Care Medicine, University of Michigan Medical School, Ann Arbor, Mich (M.K.H.); BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom (S.R.), Department of Internal Medicine, University of Nebraska Medical Center, Omaha, Neb (S.R.); Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colo (K.Y., G.L.K., J.E.H.); and Respiratory Institute, Hospital Clinic, August Pi i Sunyer Biomedical Research Institute, Centro de Investigación Biomédica en Red Enfermedades Respiratorias, University of Barcelona, Barcelona, Spain (A.A.)
| | - Raúl San José Estépar
- From the Division of Pulmonary and Critical Care, Department of Medicine, Applied Chest Imaging Laboratory (G.R.W., S.Y.A., F.N.R., C.E.C., C.L.P., A.A.D.), Department of Radiology, Applied Chest Imaging Laboratory (P.N., G.V.S.F., J.C.R., R.S.J.E.), Department of Anesthesia (G.Q.R.), and Division of Cardiology (A.M.S.), Brigham and Women's Hospital, 1249 Boylston St, Boston, MA 02215; Lung Health Center, University of Alabama at Birmingham, Birmingham, Ala (M.T.D., S.P.B., J.M.W.); Asthma and COPD Program, Division of Pulmonary and Critical Care Medicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (R.K.); Division of Pulmonary and Critical Care Medicine, University of Michigan Medical School, Ann Arbor, Mich (M.K.H.); BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom (S.R.), Department of Internal Medicine, University of Nebraska Medical Center, Omaha, Neb (S.R.); Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colo (K.Y., G.L.K., J.E.H.); and Respiratory Institute, Hospital Clinic, August Pi i Sunyer Biomedical Research Institute, Centro de Investigación Biomédica en Red Enfermedades Respiratorias, University of Barcelona, Barcelona, Spain (A.A.)
| | -
- From the Division of Pulmonary and Critical Care, Department of Medicine, Applied Chest Imaging Laboratory (G.R.W., S.Y.A., F.N.R., C.E.C., C.L.P., A.A.D.), Department of Radiology, Applied Chest Imaging Laboratory (P.N., G.V.S.F., J.C.R., R.S.J.E.), Department of Anesthesia (G.Q.R.), and Division of Cardiology (A.M.S.), Brigham and Women's Hospital, 1249 Boylston St, Boston, MA 02215; Lung Health Center, University of Alabama at Birmingham, Birmingham, Ala (M.T.D., S.P.B., J.M.W.); Asthma and COPD Program, Division of Pulmonary and Critical Care Medicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (R.K.); Division of Pulmonary and Critical Care Medicine, University of Michigan Medical School, Ann Arbor, Mich (M.K.H.); BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom (S.R.), Department of Internal Medicine, University of Nebraska Medical Center, Omaha, Neb (S.R.); Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colo (K.Y., G.L.K., J.E.H.); and Respiratory Institute, Hospital Clinic, August Pi i Sunyer Biomedical Research Institute, Centro de Investigación Biomédica en Red Enfermedades Respiratorias, University of Barcelona, Barcelona, Spain (A.A.)
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Estépar RSJ. Artificial Intelligence in COPD: New Venues to Study a Complex Disease. BARCELONA RESPIRATORY NETWORK REVIEWS 2020; 6:144-160. [PMID: 33521399 PMCID: PMC7842269 DOI: 10.23866/brnrev:2019-0014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Accepted: 09/02/2020] [Indexed: 06/12/2023]
Abstract
Chronic obstructive pulmonary disease (COPD) is a complex and heterogeneous disease that can benefit from novel approaches to understanding its evolution and divergent trajectories. Artificial intelligence (AI) has revolutionized how we can use clinical, imaging, and molecular data to understand and model complex systems. AI has shown impressive results in areas related to automated clinical decision making, radiological interpretation and prognostication. The unique nature of COPD and the accessibility to well-phenotyped populations result in an ideal scenario for AI development. This review provides an introduction to AI and deep learning and presents some recent successes in applying AI in COPD. Finally, we will discuss some of the opportunities, challenges, and limitations for AI applications in the context of COPD.
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Affiliation(s)
- Raúl San José Estépar
- Applied Chest Imaging Laboratory, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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Orting SN, Petersen J, Thomsen LH, Wille MMW, de Bruijne M. Learning to Quantify Emphysema Extent: What Labels Do We Need? IEEE J Biomed Health Inform 2020; 24:1149-1159. [DOI: 10.1109/jbhi.2019.2932145] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Ragland MF, Benway CJ, Lutz SM, Bowler RP, Hecker J, Hokanson JE, Crapo JD, Castaldi PJ, DeMeo DL, Hersh CP, Hobbs BD, Lange C, Beaty TH, Cho MH, Silverman EK. Genetic Advances in Chronic Obstructive Pulmonary Disease. Insights from COPDGene. Am J Respir Crit Care Med 2020; 200:677-690. [PMID: 30908940 DOI: 10.1164/rccm.201808-1455so] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
Chronic obstructive pulmonary disease (COPD) is a common and progressive disease that is influenced by both genetic and environmental factors. For many years, knowledge of the genetic basis of COPD was limited to Mendelian syndromes, such as alpha-1 antitrypsin deficiency and cutis laxa, caused by rare genetic variants. Over the past decade, the proliferation of genome-wide association studies, the accessibility of whole-genome sequencing, and the development of novel methods for analyzing genetic variation data have led to a substantial increase in the understanding of genetic variants that play a role in COPD susceptibility and COPD-related phenotypes. COPDGene (Genetic Epidemiology of COPD), a multicenter, longitudinal study of over 10,000 current and former cigarette smokers, has been pivotal to these breakthroughs in understanding the genetic basis of COPD. To date, over 20 genetic loci have been convincingly associated with COPD affection status, with additional loci demonstrating association with COPD-related phenotypes such as emphysema, chronic bronchitis, and hypoxemia. In this review, we discuss the contributions of the COPDGene study to the discovery of these genetic associations as well as the ongoing genetic investigations of COPD subtypes, protein biomarkers, and post-genome-wide association study analysis.
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Affiliation(s)
- Margaret F Ragland
- Division of Pulmonary Sciences and Critical Care Medicine, School of Medicine, and
| | | | | | | | - Julian Hecker
- Harvard T. H. Chan School of Public Health, Boston, Massachusetts; and
| | - John E Hokanson
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Denver, Aurora, Colorado
| | | | | | - Dawn L DeMeo
- Channing Division of Network Medicine and.,Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Craig P Hersh
- Channing Division of Network Medicine and.,Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Brian D Hobbs
- Channing Division of Network Medicine and.,Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Christoph Lange
- Harvard T. H. Chan School of Public Health, Boston, Massachusetts; and
| | - Terri H Beaty
- Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
| | - Michael H Cho
- Channing Division of Network Medicine and.,Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Edwin K Silverman
- Channing Division of Network Medicine and.,Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, Massachusetts
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Humphries SM, Notary AM, Centeno JP, Strand MJ, Crapo JD, Silverman EK, Lynch DA. Deep Learning Enables Automatic Classification of Emphysema Pattern at CT. Radiology 2020; 294:434-444. [PMID: 31793851 PMCID: PMC6996603 DOI: 10.1148/radiol.2019191022] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Revised: 09/16/2019] [Accepted: 10/10/2019] [Indexed: 12/21/2022]
Abstract
BackgroundPattern of emphysema at chest CT, scored visually by using the Fleischner Society system, is associated with physiologic impairment and mortality risk.PurposeTo determine whether participant-level emphysema pattern could predict impairment and mortality when classified by using a deep learning method.Materials and MethodsThis retrospective analysis of Genetic Epidemiology of COPD (COPDGene) study participants enrolled between 2007 and 2011 included those with baseline CT, visual emphysema scores, and survival data through 2018. Participants were partitioned into nonoverlapping sets of 2407 for algorithm training, 100 for validation and parameter tuning, and 7143 for testing. A deep learning algorithm using convolutional neural network and long short-term memory architectures was trained to classify pattern of emphysema according to Fleischner criteria. Deep learning scores were compared with visual scores and clinical parameters including pulmonary function tests. Cox proportional hazard models were used to evaluate relationships between emphysema scores and survival. The algorithm was also tested by using CT and clinical data in 1962 participants enrolled in the Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points (ECLIPSE) study.ResultsA total of 7143 COPDGene participants (mean age ± standard deviation, 59.8 years ± 8.9; 3734 men and 3409 women) were evaluated. Deep learning emphysema classifications were associated with impaired pulmonary function tests, 6-minute walk distance, and St George's Respiratory Questionnaire at univariate analysis (P < .001 for each). Testing in the ECLIPSE cohort showed similar associations (P < .001). In the COPDGene test cohort, deep learning emphysema classification improved the fit of linear mixed models in the prediction of these clinical parameters compared with visual scoring (P < .001). Compared with participants without emphysema, mortality was greater in participants classified by the deep learning algorithm as having any grade of emphysema (adjusted hazard ratios were 1.5, 1.7, 2.9, 5.3, and 9.7, respectively, for trace, mild, moderate, confluent, and advanced destructive emphysema; P < .05).ConclusionDeep learning automation of the Fleischner grade of emphysema at chest CT is associated with clinical measures of pulmonary insufficiency and the risk of mortality.© RSNA, 2019Online supplemental material is available for this article.
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Affiliation(s)
- Stephen M. Humphries
- From the Department of Radiology (S.M.H., A.M.N., J.P.C., D.A.L.), Division of Biostatistics and Bioinformatics (M.J.S.), and Department of Medicine (J.D.C.), National Jewish Health, 1400 Jackson St, Denver, CO 80206-2761; and Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, Mass (E.K.S.)
| | - Aleena M. Notary
- From the Department of Radiology (S.M.H., A.M.N., J.P.C., D.A.L.), Division of Biostatistics and Bioinformatics (M.J.S.), and Department of Medicine (J.D.C.), National Jewish Health, 1400 Jackson St, Denver, CO 80206-2761; and Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, Mass (E.K.S.)
| | - Juan Pablo Centeno
- From the Department of Radiology (S.M.H., A.M.N., J.P.C., D.A.L.), Division of Biostatistics and Bioinformatics (M.J.S.), and Department of Medicine (J.D.C.), National Jewish Health, 1400 Jackson St, Denver, CO 80206-2761; and Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, Mass (E.K.S.)
| | - Matthew J. Strand
- From the Department of Radiology (S.M.H., A.M.N., J.P.C., D.A.L.), Division of Biostatistics and Bioinformatics (M.J.S.), and Department of Medicine (J.D.C.), National Jewish Health, 1400 Jackson St, Denver, CO 80206-2761; and Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, Mass (E.K.S.)
| | - James D. Crapo
- From the Department of Radiology (S.M.H., A.M.N., J.P.C., D.A.L.), Division of Biostatistics and Bioinformatics (M.J.S.), and Department of Medicine (J.D.C.), National Jewish Health, 1400 Jackson St, Denver, CO 80206-2761; and Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, Mass (E.K.S.)
| | - Edwin K. Silverman
- From the Department of Radiology (S.M.H., A.M.N., J.P.C., D.A.L.), Division of Biostatistics and Bioinformatics (M.J.S.), and Department of Medicine (J.D.C.), National Jewish Health, 1400 Jackson St, Denver, CO 80206-2761; and Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, Mass (E.K.S.)
| | - David A. Lynch
- From the Department of Radiology (S.M.H., A.M.N., J.P.C., D.A.L.), Division of Biostatistics and Bioinformatics (M.J.S.), and Department of Medicine (J.D.C.), National Jewish Health, 1400 Jackson St, Denver, CO 80206-2761; and Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, Mass (E.K.S.)
| | - For the Genetic Epidemiology of COPD (COPDGene) Investigators
- From the Department of Radiology (S.M.H., A.M.N., J.P.C., D.A.L.), Division of Biostatistics and Bioinformatics (M.J.S.), and Department of Medicine (J.D.C.), National Jewish Health, 1400 Jackson St, Denver, CO 80206-2761; and Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, Mass (E.K.S.)
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Refaee T, Wu G, Ibrahim A, Halilaj I, Leijenaar RTH, Rogers W, Gietema HA, Hendriks LEL, Lambin P, Woodruff HC. The Emerging Role of Radiomics in COPD and Lung Cancer. Respiration 2020; 99:99-107. [PMID: 31991420 DOI: 10.1159/000505429] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 12/12/2019] [Indexed: 12/24/2022] Open
Abstract
Medical imaging plays a key role in evaluating and monitoring lung diseases such as chronic obstructive pulmonary disease (COPD) and lung cancer. The application of artificial intelligence in medical imaging has transformed medical images into mineable data, by extracting and correlating quantitative imaging features with patients' outcomes and tumor phenotype - a process termed radiomics. While this process has already been widely researched in lung oncology, the evaluation of COPD in this fashion remains in its infancy. Here we outline the main applications of radiomics in lung cancer and briefly review the workflow from image acquisition to the evaluation of model performance. Finally, we discuss the current assessments of COPD and the potential application of radiomics in COPD.
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Affiliation(s)
- Turkey Refaee
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands, .,Department of Diagnostic Radiology, Faculty of Applied Medical Sciences, Jazan University, Jazan, Saudi Arabia,
| | - Guangyao Wu
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Abdallah Ibrahim
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.,Department of Radiology and Nuclear Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands.,Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, Centre Hospitalier Universitaire de Liège, Liège, Belgium.,Department of Nuclear Medicine and Comprehensive Diagnostic Center Aachen (CDCA), University Hospital RWTH Aachen University, Aachen, Germany
| | - Iva Halilaj
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Ralph T H Leijenaar
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - William Rogers
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.,Department of Thoracic Oncology, IRCCS Foundation National Cancer Institute, Milan, Italy
| | - Hester A Gietema
- Department of Radiology and Nuclear Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Lizza E L Hendriks
- Department of Pulmonary Diseases, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.,Department of Radiology and Nuclear Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Henry C Woodruff
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.,Department of Radiology and Nuclear Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
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35
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Comparative analysis of pathophysiological parameters between emphysematous smokers and emphysematous patients with COPD. Sci Rep 2020; 10:420. [PMID: 31942006 PMCID: PMC6962428 DOI: 10.1038/s41598-019-57354-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Accepted: 12/30/2019] [Indexed: 12/16/2022] Open
Abstract
Emphysematous smokers with normal spirometry form a considerable proportion of the clinical population. However, despite presenting with respiratory symptoms and activity limitation, they cannot be diagnosed with chronic obstructive lung disease (COPD) according to current criteria. Thus, we aimed to determine whether emphysema in smokers has a different pathogenesis from that in patients with COPD. We compared 12 pairs of lung tissue samples from emphysematous patients with normal spirometry and COPD, and determined the degree of emphysema using computed tomography. With a focus on COPD-related pathogenesis, we independently assessed inflammatory response, protease-antiprotease balance, oxidative stress, and apoptosis in both groups. Both groups showed similar pathological changes at a comparable degree of emphysema; the expression of inflammatory factors was comparable, with overexpression of proteases and decreased levels of antiproteases. Moreover, there was no significant difference in the activities of glutathione and superoxide dismutase, and expression of apoptosis-related factors. In conclusion, emphysema in smokers with normal spirometry and in patients with COPD had similar pathogenesis. Forced expiratory volume in 1 second cannot be used as the sole diagnostic criterion in patients with COPD; early intervention is of great importance to such patients.
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Park J, Hobbs BD, Crapo JD, Make BJ, Regan EA, Humphries S, Carey VJ, Lynch DA, Silverman EK. Subtyping COPD by Using Visual and Quantitative CT Imaging Features. Chest 2020; 157:47-60. [PMID: 31283919 PMCID: PMC6965698 DOI: 10.1016/j.chest.2019.06.015] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2019] [Revised: 06/06/2019] [Accepted: 06/10/2019] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Multiple studies have identified COPD subtypes by using visual or quantitative evaluation of CT images. However, there has been no systematic assessment of a combined visual and quantitative CT imaging classification. We integrated visually defined patterns of emphysema with quantitative imaging features and spirometry data to produce a set of 10 nonoverlapping CT imaging subtypes, and we assessed differences between subtypes in demographic features, physiological characteristics, longitudinal disease progression, and mortality. METHODS We evaluated 9,080 current and former smokers in the COPDGene study who had available volumetric inspiratory and expiratory CT images obtained using a standardized imaging protocol. We defined 10 discrete, nonoverlapping CT imaging subtypes: no CT imaging abnormality, paraseptal emphysema (PSE), bronchial disease, small airway disease, mild emphysema, upper lobe predominant centrilobular emphysema (CLE), lower lobe predominant CLE, diffuse CLE, visual without quantitative emphysema, and quantitative without visual emphysema. Baseline and 5-year longitudinal characteristics and mortality were compared across these CT imaging subtypes. RESULTS The overall mortality differed significantly between groups (P < .01) and was highest in the 3 moderate to severe CLE groups. Subjects having quantitative but not visual emphysema and subjects with visual but not quantitative emphysema were unique groups with mild COPD, at risk for progression, and with likely different underlying mechanisms. Subjects with PSE and/or moderate to severe CLE had substantial progression of emphysema over 5 years compared with findings in subjects with no CT imaging abnormality (P < .01). CONCLUSIONS The combination of visual and quantitative CT imaging features reflects different underlying pathological processes in the heterogeneous COPD syndrome and provides a useful approach to reclassify types of COPD. TRIAL REGISTRY ClinicalTrials.gov; No.: NCT00608764; URL: www.clinicaltrials.gov.
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Affiliation(s)
- Jinkyeong Park
- Channing Division of Network Medicine, Boston, MA; Department of Internal Medicine, Dongguk University Ilsan Hospital, Goyang-Si, Gyeonggi-do, South Korea
| | - Brian D Hobbs
- Channing Division of Network Medicine, Boston, MA; Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - James D Crapo
- Department of Medicine, National Jewish Health, Denver, CO
| | - Barry J Make
- Department of Medicine, National Jewish Health, Denver, CO
| | | | | | | | - David A Lynch
- Department of Radiology, National Jewish Health, Denver, CO
| | - Edwin K Silverman
- Channing Division of Network Medicine, Boston, MA; Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA.
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37
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Castaldi PJ, Boueiz A, Yun J, Estepar RSJ, Ross JC, Washko G, Cho MH, Hersh CP, Kinney GL, Young KA, Regan EA, Lynch DA, Criner GJ, Dy JG, Rennard SI, Casaburi R, Make BJ, Crapo J, Silverman EK, Hokanson JE. Machine Learning Characterization of COPD Subtypes: Insights From the COPDGene Study. Chest 2019; 157:1147-1157. [PMID: 31887283 DOI: 10.1016/j.chest.2019.11.039] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Revised: 10/18/2019] [Accepted: 11/29/2019] [Indexed: 12/17/2022] Open
Abstract
COPD is a heterogeneous syndrome. Many COPD subtypes have been proposed, but there is not yet consensus on how many COPD subtypes there are and how they should be defined. The COPD Genetic Epidemiology Study (COPDGene), which has generated 10-year longitudinal chest imaging, spirometry, and molecular data, is a rich resource for relating COPD phenotypes to underlying genetic and molecular mechanisms. In this article, we place COPDGene clustering studies in context with other highly cited COPD clustering studies, and summarize the main COPD subtype findings from COPDGene. First, most manifestations of COPD occur along a continuum, which explains why continuous aspects of COPD or disease axes may be more accurate and reproducible than subtypes identified through clustering methods. Second, continuous COPD-related measures can be used to create subgroups through the use of predictive models to define cut-points, and we review COPDGene research on blood eosinophil count thresholds as a specific example. Third, COPD phenotypes identified or prioritized through machine learning methods have led to novel biological discoveries, including novel emphysema genetic risk variants and systemic inflammatory subtypes of COPD. Fourth, trajectory-based COPD subtyping captures differences in the longitudinal evolution of COPD, addressing a major limitation of clustering analyses that are confounded by disease severity. Ongoing longitudinal characterization of subjects in COPDGene will provide useful insights about the relationship between lung imaging parameters, molecular markers, and COPD progression that will enable the identification of subtypes based on underlying disease processes and distinct patterns of disease progression, with the potential to improve the clinical relevance and reproducibility of COPD subtypes.
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Affiliation(s)
- Peter J Castaldi
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA; General Medicine and Primary Care, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.
| | - Adel Boueiz
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA; Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Jeong Yun
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA; Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Raul San Jose Estepar
- Applied Chest Imaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - James C Ross
- Applied Chest Imaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - George Washko
- Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA; Applied Chest Imaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Michael H Cho
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA; Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Craig P Hersh
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA; Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Gregory L Kinney
- Department of Epidemiology, University of Colorado, Denver, Aurora, CO
| | - Kendra A Young
- Department of Epidemiology, University of Colorado, Denver, Aurora, CO
| | | | - David A Lynch
- Department of Radiology, National Jewish Health, Denver, CO
| | - Gerald J Criner
- Department of Thoracic Medicine and Surgery, Lewis Katz School of Medicine at Temple University, Philadelphia, PA
| | - Jennifer G Dy
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA
| | - Stephen I Rennard
- Pulmonary and Critical Care Medicine, University of Nebraska Medical Center, Omaha, NE
| | - Richard Casaburi
- Rehabilitation Clinical Trials Center, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA
| | | | | | - Edwin K Silverman
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA; Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - John E Hokanson
- Department of Epidemiology, University of Colorado, Denver, Aurora, CO
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Bhatt SP, Washko GR, Hoffman EA, Newell JD, Bodduluri S, Diaz AA, Galban CJ, Silverman EK, San José Estépar R, Lynch DA. Imaging Advances in Chronic Obstructive Pulmonary Disease. Insights from the Genetic Epidemiology of Chronic Obstructive Pulmonary Disease (COPDGene) Study. Am J Respir Crit Care Med 2019; 199:286-301. [PMID: 30304637 DOI: 10.1164/rccm.201807-1351so] [Citation(s) in RCA: 85] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
The Genetic Epidemiology of Chronic Obstructive Pulmonary Disease (COPDGene) study, which began in 2007, is an ongoing multicenter observational cohort study of more than 10,000 current and former smokers. The study is aimed at understanding the etiology, progression, and heterogeneity of chronic obstructive pulmonary disease (COPD). In addition to genetic analysis, the participants have been extensively characterized by clinical questionnaires, spirometry, volumetric inspiratory and expiratory computed tomography, and longitudinal follow-up, including follow-up computed tomography at 5 years after enrollment. The purpose of this state-of-the-art review is to summarize the major advances in our understanding of COPD resulting from the imaging findings in the COPDGene study. Imaging features that are associated with adverse clinical outcomes include early interstitial lung abnormalities, visual presence and pattern of emphysema, the ratio of pulmonary artery to ascending aortic diameter, quantitative evaluation of emphysema, airway wall thickness, and expiratory gas trapping. COPD is characterized by the early involvement of the small conducting airways, and the addition of expiratory scans has enabled measurement of small airway disease. Computational advances have enabled indirect measurement of nonemphysematous gas trapping. These metrics have provided insights into the pathogenesis and prognosis of COPD and have aided early identification of disease. Important quantifiable extrapulmonary findings include coronary artery calcification, cardiac morphology, intrathoracic and extrathoracic fat, and osteoporosis. Current active research includes identification of novel quantitative measures for emphysema and airway disease, evaluation of dose reduction techniques, and use of deep learning for phenotyping COPD.
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Affiliation(s)
- Surya P Bhatt
- 1 UAB Lung Imaging Core and UAB Lung Health Center, Division of Pulmonary, Allergy and Critical Care Medicine, University of Alabama at Birmingham School of Medicine, Birmingham, Alabama
| | | | - Eric A Hoffman
- 3 Department of Radiology, University of Iowa Carver College of Medicine, Iowa City, Iowa
| | - John D Newell
- 3 Department of Radiology, University of Iowa Carver College of Medicine, Iowa City, Iowa
| | - Sandeep Bodduluri
- 1 UAB Lung Imaging Core and UAB Lung Health Center, Division of Pulmonary, Allergy and Critical Care Medicine, University of Alabama at Birmingham School of Medicine, Birmingham, Alabama
| | | | - Craig J Galban
- 4 Department of Radiology and Center for Molecular Imaging, University of Michigan, Ann Arbor, Michigan; and
| | | | - Raúl San José Estépar
- 6 Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - David A Lynch
- 7 Department of Radiology, National Jewish Health, Denver, Colorado
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Abstract
Although chronic obstructive pulmonary disease (COPD) risk is strongly influenced by cigarette smoking, genetic factors are also important determinants of COPD. In addition to Mendelian syndromes such as alpha-1 antitrypsin deficiency, many genomic regions that influence COPD susceptibility have been identified in genome-wide association studies. Similarly, multiple genomic regions associated with COPD-related phenotypes, such as quantitative emphysema measures, have been found. Identifying the functional variants and key genes within these association regions remains a major challenge. However, newly identified COPD susceptibility genes are already providing novel insights into COPD pathogenesis. Network-based approaches that leverage these genetic discoveries have the potential to assist in decoding the complex genetic architecture of COPD.
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Affiliation(s)
- Edwin K Silverman
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA;
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40
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Washko GR, Nardelli P, Ash SY, Vegas Sanchez-Ferrero G, Rahaghi FN, Come CE, Dransfield MT, Kalhan R, Han MK, Bhatt SP, Wells JM, Aaron CP, Diaz AA, Ross JC, Cuttica MJ, Labaki WW, Querejeta Roca G, Shah AM, Young K, Kinney GL, Hokanson JE, Agustí A. Arterial Vascular Pruning, Right Ventricular Size, and Clinical Outcomes in Chronic Obstructive Pulmonary Disease. A Longitudinal Observational Study. Am J Respir Crit Care Med 2019; 200:454-461. [PMID: 30758975 PMCID: PMC6701031 DOI: 10.1164/rccm.201811-2063oc] [Citation(s) in RCA: 63] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Accepted: 02/08/2019] [Indexed: 01/05/2023] Open
Abstract
Rationale: Cor pulmonale (right ventricular [RV] dilation) and cor pulmonale parvus (RV shrinkage) are both described in chronic obstructive pulmonary disease (COPD). The identification of emphysema as a shared risk factor suggests that additional disease characterization is needed to understand these widely divergent cardiac processes.Objectives: To explore the relationship between computed tomography measures of emphysema and distal pulmonary arterial morphology with RV volume, and their association with exercise capacity and mortality in ever-smokers with COPD enrolled in the COPDGene Study.Methods: Epicardial (myocardium and chamber) RV volume (RVEV), distal pulmonary arterial blood vessel volume (arterial BV5: vessels <5 mm2 in cross-section), and objective measures of emphysema were extracted from 3,506 COPDGene computed tomography scans. Multivariable linear and Cox regression models and the log-rank test were used to explore the association between emphysema, arterial BV5, and RVEV with exercise capacity (6-min-walk distance) and all-cause mortality.Measurements and Main Results: The RVEV was approximately 10% smaller in Global Initiative for Chronic Obstructive Lung Disease stage 4 versus stage 1 COPD (P < 0.0001). In multivariable modeling, a 10-ml decrease in arterial BV5 (pruning) was associated with a 1-ml increase in RVEV. For a given amount of emphysema, relative preservation of the arterial BV5 was associated with a smaller RVEV. An increased RVEV was associated with reduced 6-minute-walk distance and in those with arterial pruning an increased mortality.Conclusions: Pulmonary arterial pruning is associated with clinically significant increases in RV volume in smokers with COPD and is related to exercise capacity and mortality in COPD.Clinical trial registered with www.clinicaltrials.gov (NCT00608764).
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Affiliation(s)
| | | | - Samuel Y. Ash
- Division of Pulmonary and Critical Care, Department of Medicine
| | | | | | - Carolyn E. Come
- Division of Pulmonary and Critical Care, Department of Medicine
| | - Mark T. Dransfield
- Lung Health Center, University of Alabama at Birmingham, Birmingham, Alabama
| | - Ravi Kalhan
- Asthma and COPD Program, Division of Pulmonary and Critical Care Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - MeiLan K. Han
- Division of Pulmonary and Critical Care Medicine, University of Michigan Medical School, Ann Arbor, Michigan
| | - Surya P. Bhatt
- Lung Health Center, University of Alabama at Birmingham, Birmingham, Alabama
| | - J. Michael Wells
- Lung Health Center, University of Alabama at Birmingham, Birmingham, Alabama
| | | | | | - James C. Ross
- Applied Chest Imaging Laboratory, Department of Radiology
| | - Michael J. Cuttica
- Asthma and COPD Program, Division of Pulmonary and Critical Care Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Wassim W. Labaki
- Division of Pulmonary and Critical Care Medicine, University of Michigan Medical School, Ann Arbor, Michigan
| | | | - Amil M. Shah
- Division of Cardiovascular, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Kendra Young
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado; and
| | - Gregory L. Kinney
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado; and
| | - John E. Hokanson
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado; and
| | | | - for the COPDGene Investigators
- Division of Pulmonary and Critical Care, Department of Medicine
- Applied Chest Imaging Laboratory, Department of Radiology
- Department of Anesthesia, and
- Division of Cardiovascular, Brigham and Women’s Hospital, Boston, Massachusetts
- Lung Health Center, University of Alabama at Birmingham, Birmingham, Alabama
- Asthma and COPD Program, Division of Pulmonary and Critical Care Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Division of Pulmonary and Critical Care Medicine, University of Michigan Medical School, Ann Arbor, Michigan
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado; and
- Hospital Clinic Barcelona, Barcelona, Spain
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Parker MM, Hao Y, Guo F, Pham B, Chase R, Platig J, Cho MH, Hersh CP, Thannickal VJ, Crapo J, Washko G, Randell SH, Silverman EK, San José Estépar R, Zhou X, Castaldi PJ. Identification of an emphysema-associated genetic variant near TGFB2 with regulatory effects in lung fibroblasts. eLife 2019; 8:e42720. [PMID: 31343404 PMCID: PMC6693893 DOI: 10.7554/elife.42720] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Accepted: 07/25/2019] [Indexed: 02/06/2023] Open
Abstract
Murine studies have linked TGF-β signaling to emphysema, and human genome-wide association studies (GWAS) studies of lung function and COPD have identified associated regions near genes in the TGF-β superfamily. However, the functional regulatory mechanisms at these loci have not been identified. We performed the largest GWAS of emphysema patterns to date, identifying 10 GWAS loci including an association peak spanning a 200 kb region downstream from TGFB2. Integrative analysis of publicly available eQTL, DNaseI, and chromatin conformation data identified a putative functional variant, rs1690789, that may regulate TGFB2 expression in human fibroblasts. Using chromatin conformation capture, we confirmed that the region containing rs1690789 contacts the TGFB2 promoter in fibroblasts, and CRISPR/Cas-9 targeted deletion of a ~ 100 bp region containing rs1690789 resulted in decreased TGFB2 expression in primary human lung fibroblasts. These data provide novel mechanistic evidence linking genetic variation affecting the TGF-β pathway to emphysema in humans.
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Affiliation(s)
- Margaret M Parker
- Channing Division of Network MedicineBrigham and Women’s HospitalBostonUnited States
| | - Yuan Hao
- Channing Division of Network MedicineBrigham and Women’s HospitalBostonUnited States
| | - Feng Guo
- Channing Division of Network MedicineBrigham and Women’s HospitalBostonUnited States
| | - Betty Pham
- Channing Division of Network MedicineBrigham and Women’s HospitalBostonUnited States
| | - Robert Chase
- Channing Division of Network MedicineBrigham and Women’s HospitalBostonUnited States
| | - John Platig
- Channing Division of Network MedicineBrigham and Women’s HospitalBostonUnited States
| | - Michael H Cho
- Channing Division of Network MedicineBrigham and Women’s HospitalBostonUnited States
- Division of Pulmonary and Critical Care MedicineBrigham and Women’s HospitalBostonUnited States
| | - Craig P Hersh
- Channing Division of Network MedicineBrigham and Women’s HospitalBostonUnited States
- Division of Pulmonary and Critical Care MedicineBrigham and Women’s HospitalBostonUnited States
| | - Victor J Thannickal
- Division of Pulmonary, Allergy and Critical Care, Department of MedicineSchool of Medicine, University of Alabama at BirminghamBirminghamUnited States
| | - James Crapo
- Division of Pulmonary, Critical Care and Sleep MedicineNational Jewish HealthDenverUnited States
| | - George Washko
- Division of Pulmonary and Critical Care MedicineBrigham and Women’s HospitalBostonUnited States
| | - Scott H Randell
- Marsico Lung InstituteThe University of North Carolina at Chapel HillChapel HillUnited States
| | - Edwin K Silverman
- Channing Division of Network MedicineBrigham and Women’s HospitalBostonUnited States
- Division of Pulmonary and Critical Care MedicineBrigham and Women’s HospitalBostonUnited States
| | | | - Xiaobo Zhou
- Channing Division of Network MedicineBrigham and Women’s HospitalBostonUnited States
| | - Peter J Castaldi
- Channing Division of Network MedicineBrigham and Women’s HospitalBostonUnited States
- Division of General Internal Medicine and Primary CareBrigham and Women’s HospitalBostonUnited States
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Objectively Measured Chronic Lung Injury on Chest CT. Chest 2019; 156:1149-1159. [PMID: 31233744 DOI: 10.1016/j.chest.2019.05.020] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Revised: 05/20/2019] [Accepted: 05/27/2019] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Tobacco smoke exposure is associated with emphysema and pulmonary fibrosis, both of which are irreversible. We have developed a new objective CT analysis tool that combines densitometry with machine learning to detect high attenuation changes in visually normal appearing lung (NormHA) that may precede these diseases. METHODS We trained the classification tool by placing 34,528 training points in chest CT scans from 297 COPDGene participants. The tool was then used to classify lung tissue in 9,038 participants as normal, emphysema, fibrotic/interstitial, or NormHA. Associations between the quartile of NormHA and plasma-based biomarkers, clinical severity, and mortality were evaluated using Jonckheere-Terpstra, pairwise Wilcoxon rank-sum tests, and multivariable linear and Cox regression. RESULTS A higher percentage of lung occupied by NormHA was associated with higher C-reactive protein and intercellular adhesion molecule 1 (P for trend for both < .001). In analyses adjusted for multiple covariates, including high and low attenuation area, compared with those in the lowest quartile of NormHA, those in the highest quartile had a 6.50 absolute percent lower percent predicted lower FEV1 (P < .001), an 8.48 absolute percent lower percent predicted forced expiratory volume, a 10.78-meter shorter 6-min walk distance (P = .011), and a 56% higher risk of death (P = .003). These findings were present even in those individuals without visually defined interstitial lung abnormalities. CONCLUSIONS A new class of NormHA on CT may represent a unique tissue class associated with adverse outcomes, independent of emphysema and fibrosis.
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Hu WP, Zeng YY, Zuo YH, Zhang J. Identification of novel candidate genes involved in the progression of emphysema by bioinformatic methods. Int J Chron Obstruct Pulmon Dis 2018; 13:3733-3747. [PMID: 30532529 PMCID: PMC6241693 DOI: 10.2147/copd.s183100] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Purpose By reanalyzing the gene expression profile GSE76925 in the Gene Expression Omnibus database using bioinformatic methods, we attempted to identify novel candidate genes promoting the development of emphysema in patients with COPD. Patients and methods According to the Quantitative CT data in GSE76925, patients were divided into mild emphysema group (%LAA-950<20%, n=12) and severe emphysema group (%LAA-950>50%, n=11). Differentially expressed genes (DEGs) were identified using Agilent GeneSpring GX v11.5 (corrected P-value <0.05 and |Fold Change|>1.3). Known driver genes of COPD were acquired by mining literatures and retrieving databases. Direct protein–protein interaction network (PPi) of DEGs and known driver genes was constructed by STRING.org to screen the DEGs directly interacting with driver genes. In addition, we used STRING.org to obtain the first-layer proteins interacting with DEGs’ products and constructed the indirect PPi of these interaction proteins. By merging the indirect PPi with driver genes’ PPi using Cytoscape v3.6.1, we attempted to discover potential pathways promoting emphysema’s development. Results All the patients had COPD with severe airflow limitation (age=62±8, FEV1%=28±12). A total of 57 DEGs (including 12 pseudogenes) and 135 known driving genes were identified. Direct PPi suggested that GPR65, GNB4, P2RY13, NPSR1, BCR, BAG4, and IMPDH2 were potential pathogenic genes. GPR65 could regulate the response of immune cells to the acidic microenvironment, and NPSR1’s expression on eosinophils was associated with asthma’s severity and IgE level. Indirect merging PPi demonstrated that the interacting network of TP53, IL8, CCR2, HSPA1A, ELANE, PIK3CA was associated with the development of emphysema. IL8, ELANE, and PIK3CA were molecules involved in the pathological mechanisms of emphysema, which also in return proved the role of TP53 in emphysema. Conclusion Candidate genes such as GPR65, NPSR1, and TP53 may be involved in the progression of emphysema.
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Affiliation(s)
- Wei-Ping Hu
- Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Shanghai Medical College, Fudan University, Shanghai, China,
| | - Ying-Ying Zeng
- Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Shanghai Medical College, Fudan University, Shanghai, China,
| | - Yi-Hui Zuo
- Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Shanghai Medical College, Fudan University, Shanghai, China,
| | - Jing Zhang
- Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Shanghai Medical College, Fudan University, Shanghai, China,
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Ash SY, Harmouche R, Ross JC, Diaz AA, Rahaghi FN, Vegas Sanchez-Ferrero G, Putman RK, Hunninghake GM, Onieva Onieva J, Martinez FJ, Choi AM, Bowler RP, Lynch DA, Hatabu H, Bhatt SP, Dransfield MT, Wells JM, Rosas IO, San Jose Estepar R, Washko GR. Interstitial Features at Chest CT Enhance the Deleterious Effects of Emphysema in the COPDGene Cohort. Radiology 2018; 288:600-609. [PMID: 29869957 PMCID: PMC6069608 DOI: 10.1148/radiol.2018172688] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2017] [Revised: 02/06/2018] [Accepted: 02/06/2018] [Indexed: 12/28/2022]
Abstract
Purpose To determine if interstitial features at chest CT enhance the effect of emphysema on clinical disease severity in smokers without clinical pulmonary fibrosis. Materials and Methods In this retrospective cohort study, an objective CT analysis tool was used to measure interstitial features (reticular changes, honeycombing, centrilobular nodules, linear scar, nodular changes, subpleural lines, and ground-glass opacities) and emphysema in 8266 participants in a study of chronic obstructive pulmonary disease (COPD) called COPDGene (recruited between October 2006 and January 2011). Additive differences in patients with emphysema with interstitial features and in those without interstitial features were analyzed by using t tests, multivariable linear regression, and Kaplan-Meier analysis. Multivariable linear and Cox regression were used to determine if interstitial features modified the effect of continuously measured emphysema on clinical measures of disease severity and mortality. Results Compared with individuals with emphysema alone, those with emphysema and interstitial features had a higher percentage predicted forced expiratory volume in 1 second (absolute difference, 6.4%; P < .001), a lower percentage predicted diffusing capacity of lung for carbon monoxide (DLCO) (absolute difference, 7.4%; P = .034), a 0.019 higher right ventricular-to-left ventricular (RVLV) volume ratio (P = .029), a 43.2-m shorter 6-minute walk distance (6MWD) (P < .001), a 5.9-point higher St George's Respiratory Questionnaire (SGRQ) score (P < .001), and 82% higher mortality (P < .001). In addition, interstitial features modified the effect of emphysema on percentage predicted DLCO, RVLV volume ratio, 6WMD, SGRQ score, and mortality (P for interaction < .05 for all). Conclusion In smokers, the combined presence of interstitial features and emphysema was associated with worse clinical disease severity and higher mortality than was emphysema alone. In addition, interstitial features enhanced the deleterious effects of emphysema on clinical disease severity and mortality.
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Affiliation(s)
- Samuel Y. Ash
- From the Division of Pulmonary and Critical Care Medicine, Department
of Medicine (S.Y.A., A.A.D., F.N.R., R.K.P., G.M.H., I.O.R., G.R.W.), Laboratory
of Mathematics in Imaging, Department of Radiology (R.H., J.C.R., G.V.S.,
J.O.O., R.S.J.E.), and Department of Radiology (H.H.), Brigham and
Women’s Hospital, 75 Francis St, PBB CA-3, Boston, MA 02115; Department
of Medicine, Weil Cornell Medical College, New York, NY (F.J.M., A.M.C.);
Departments of Medicine (R.P.B.) and Radiology (D.A.L.), National Jewish Health,
Denver, Colo; and Division of Pulmonary, Allergy and Critical Care Medicine,
Department of Medicine, University of Alabama at Birmingham, Birmingham, Ala
(S.P.B., M.T.D., J.M.W.)
| | - Rola Harmouche
- From the Division of Pulmonary and Critical Care Medicine, Department
of Medicine (S.Y.A., A.A.D., F.N.R., R.K.P., G.M.H., I.O.R., G.R.W.), Laboratory
of Mathematics in Imaging, Department of Radiology (R.H., J.C.R., G.V.S.,
J.O.O., R.S.J.E.), and Department of Radiology (H.H.), Brigham and
Women’s Hospital, 75 Francis St, PBB CA-3, Boston, MA 02115; Department
of Medicine, Weil Cornell Medical College, New York, NY (F.J.M., A.M.C.);
Departments of Medicine (R.P.B.) and Radiology (D.A.L.), National Jewish Health,
Denver, Colo; and Division of Pulmonary, Allergy and Critical Care Medicine,
Department of Medicine, University of Alabama at Birmingham, Birmingham, Ala
(S.P.B., M.T.D., J.M.W.)
| | - James C. Ross
- From the Division of Pulmonary and Critical Care Medicine, Department
of Medicine (S.Y.A., A.A.D., F.N.R., R.K.P., G.M.H., I.O.R., G.R.W.), Laboratory
of Mathematics in Imaging, Department of Radiology (R.H., J.C.R., G.V.S.,
J.O.O., R.S.J.E.), and Department of Radiology (H.H.), Brigham and
Women’s Hospital, 75 Francis St, PBB CA-3, Boston, MA 02115; Department
of Medicine, Weil Cornell Medical College, New York, NY (F.J.M., A.M.C.);
Departments of Medicine (R.P.B.) and Radiology (D.A.L.), National Jewish Health,
Denver, Colo; and Division of Pulmonary, Allergy and Critical Care Medicine,
Department of Medicine, University of Alabama at Birmingham, Birmingham, Ala
(S.P.B., M.T.D., J.M.W.)
| | - Alejandro A. Diaz
- From the Division of Pulmonary and Critical Care Medicine, Department
of Medicine (S.Y.A., A.A.D., F.N.R., R.K.P., G.M.H., I.O.R., G.R.W.), Laboratory
of Mathematics in Imaging, Department of Radiology (R.H., J.C.R., G.V.S.,
J.O.O., R.S.J.E.), and Department of Radiology (H.H.), Brigham and
Women’s Hospital, 75 Francis St, PBB CA-3, Boston, MA 02115; Department
of Medicine, Weil Cornell Medical College, New York, NY (F.J.M., A.M.C.);
Departments of Medicine (R.P.B.) and Radiology (D.A.L.), National Jewish Health,
Denver, Colo; and Division of Pulmonary, Allergy and Critical Care Medicine,
Department of Medicine, University of Alabama at Birmingham, Birmingham, Ala
(S.P.B., M.T.D., J.M.W.)
| | - Farbod N. Rahaghi
- From the Division of Pulmonary and Critical Care Medicine, Department
of Medicine (S.Y.A., A.A.D., F.N.R., R.K.P., G.M.H., I.O.R., G.R.W.), Laboratory
of Mathematics in Imaging, Department of Radiology (R.H., J.C.R., G.V.S.,
J.O.O., R.S.J.E.), and Department of Radiology (H.H.), Brigham and
Women’s Hospital, 75 Francis St, PBB CA-3, Boston, MA 02115; Department
of Medicine, Weil Cornell Medical College, New York, NY (F.J.M., A.M.C.);
Departments of Medicine (R.P.B.) and Radiology (D.A.L.), National Jewish Health,
Denver, Colo; and Division of Pulmonary, Allergy and Critical Care Medicine,
Department of Medicine, University of Alabama at Birmingham, Birmingham, Ala
(S.P.B., M.T.D., J.M.W.)
| | - Gonzalo Vegas Sanchez-Ferrero
- From the Division of Pulmonary and Critical Care Medicine, Department
of Medicine (S.Y.A., A.A.D., F.N.R., R.K.P., G.M.H., I.O.R., G.R.W.), Laboratory
of Mathematics in Imaging, Department of Radiology (R.H., J.C.R., G.V.S.,
J.O.O., R.S.J.E.), and Department of Radiology (H.H.), Brigham and
Women’s Hospital, 75 Francis St, PBB CA-3, Boston, MA 02115; Department
of Medicine, Weil Cornell Medical College, New York, NY (F.J.M., A.M.C.);
Departments of Medicine (R.P.B.) and Radiology (D.A.L.), National Jewish Health,
Denver, Colo; and Division of Pulmonary, Allergy and Critical Care Medicine,
Department of Medicine, University of Alabama at Birmingham, Birmingham, Ala
(S.P.B., M.T.D., J.M.W.)
| | - Rachel K. Putman
- From the Division of Pulmonary and Critical Care Medicine, Department
of Medicine (S.Y.A., A.A.D., F.N.R., R.K.P., G.M.H., I.O.R., G.R.W.), Laboratory
of Mathematics in Imaging, Department of Radiology (R.H., J.C.R., G.V.S.,
J.O.O., R.S.J.E.), and Department of Radiology (H.H.), Brigham and
Women’s Hospital, 75 Francis St, PBB CA-3, Boston, MA 02115; Department
of Medicine, Weil Cornell Medical College, New York, NY (F.J.M., A.M.C.);
Departments of Medicine (R.P.B.) and Radiology (D.A.L.), National Jewish Health,
Denver, Colo; and Division of Pulmonary, Allergy and Critical Care Medicine,
Department of Medicine, University of Alabama at Birmingham, Birmingham, Ala
(S.P.B., M.T.D., J.M.W.)
| | - Gary M. Hunninghake
- From the Division of Pulmonary and Critical Care Medicine, Department
of Medicine (S.Y.A., A.A.D., F.N.R., R.K.P., G.M.H., I.O.R., G.R.W.), Laboratory
of Mathematics in Imaging, Department of Radiology (R.H., J.C.R., G.V.S.,
J.O.O., R.S.J.E.), and Department of Radiology (H.H.), Brigham and
Women’s Hospital, 75 Francis St, PBB CA-3, Boston, MA 02115; Department
of Medicine, Weil Cornell Medical College, New York, NY (F.J.M., A.M.C.);
Departments of Medicine (R.P.B.) and Radiology (D.A.L.), National Jewish Health,
Denver, Colo; and Division of Pulmonary, Allergy and Critical Care Medicine,
Department of Medicine, University of Alabama at Birmingham, Birmingham, Ala
(S.P.B., M.T.D., J.M.W.)
| | - Jorge Onieva Onieva
- From the Division of Pulmonary and Critical Care Medicine, Department
of Medicine (S.Y.A., A.A.D., F.N.R., R.K.P., G.M.H., I.O.R., G.R.W.), Laboratory
of Mathematics in Imaging, Department of Radiology (R.H., J.C.R., G.V.S.,
J.O.O., R.S.J.E.), and Department of Radiology (H.H.), Brigham and
Women’s Hospital, 75 Francis St, PBB CA-3, Boston, MA 02115; Department
of Medicine, Weil Cornell Medical College, New York, NY (F.J.M., A.M.C.);
Departments of Medicine (R.P.B.) and Radiology (D.A.L.), National Jewish Health,
Denver, Colo; and Division of Pulmonary, Allergy and Critical Care Medicine,
Department of Medicine, University of Alabama at Birmingham, Birmingham, Ala
(S.P.B., M.T.D., J.M.W.)
| | - Fernando J. Martinez
- From the Division of Pulmonary and Critical Care Medicine, Department
of Medicine (S.Y.A., A.A.D., F.N.R., R.K.P., G.M.H., I.O.R., G.R.W.), Laboratory
of Mathematics in Imaging, Department of Radiology (R.H., J.C.R., G.V.S.,
J.O.O., R.S.J.E.), and Department of Radiology (H.H.), Brigham and
Women’s Hospital, 75 Francis St, PBB CA-3, Boston, MA 02115; Department
of Medicine, Weil Cornell Medical College, New York, NY (F.J.M., A.M.C.);
Departments of Medicine (R.P.B.) and Radiology (D.A.L.), National Jewish Health,
Denver, Colo; and Division of Pulmonary, Allergy and Critical Care Medicine,
Department of Medicine, University of Alabama at Birmingham, Birmingham, Ala
(S.P.B., M.T.D., J.M.W.)
| | - Augustine M. Choi
- From the Division of Pulmonary and Critical Care Medicine, Department
of Medicine (S.Y.A., A.A.D., F.N.R., R.K.P., G.M.H., I.O.R., G.R.W.), Laboratory
of Mathematics in Imaging, Department of Radiology (R.H., J.C.R., G.V.S.,
J.O.O., R.S.J.E.), and Department of Radiology (H.H.), Brigham and
Women’s Hospital, 75 Francis St, PBB CA-3, Boston, MA 02115; Department
of Medicine, Weil Cornell Medical College, New York, NY (F.J.M., A.M.C.);
Departments of Medicine (R.P.B.) and Radiology (D.A.L.), National Jewish Health,
Denver, Colo; and Division of Pulmonary, Allergy and Critical Care Medicine,
Department of Medicine, University of Alabama at Birmingham, Birmingham, Ala
(S.P.B., M.T.D., J.M.W.)
| | - Russell P. Bowler
- From the Division of Pulmonary and Critical Care Medicine, Department
of Medicine (S.Y.A., A.A.D., F.N.R., R.K.P., G.M.H., I.O.R., G.R.W.), Laboratory
of Mathematics in Imaging, Department of Radiology (R.H., J.C.R., G.V.S.,
J.O.O., R.S.J.E.), and Department of Radiology (H.H.), Brigham and
Women’s Hospital, 75 Francis St, PBB CA-3, Boston, MA 02115; Department
of Medicine, Weil Cornell Medical College, New York, NY (F.J.M., A.M.C.);
Departments of Medicine (R.P.B.) and Radiology (D.A.L.), National Jewish Health,
Denver, Colo; and Division of Pulmonary, Allergy and Critical Care Medicine,
Department of Medicine, University of Alabama at Birmingham, Birmingham, Ala
(S.P.B., M.T.D., J.M.W.)
| | - David A. Lynch
- From the Division of Pulmonary and Critical Care Medicine, Department
of Medicine (S.Y.A., A.A.D., F.N.R., R.K.P., G.M.H., I.O.R., G.R.W.), Laboratory
of Mathematics in Imaging, Department of Radiology (R.H., J.C.R., G.V.S.,
J.O.O., R.S.J.E.), and Department of Radiology (H.H.), Brigham and
Women’s Hospital, 75 Francis St, PBB CA-3, Boston, MA 02115; Department
of Medicine, Weil Cornell Medical College, New York, NY (F.J.M., A.M.C.);
Departments of Medicine (R.P.B.) and Radiology (D.A.L.), National Jewish Health,
Denver, Colo; and Division of Pulmonary, Allergy and Critical Care Medicine,
Department of Medicine, University of Alabama at Birmingham, Birmingham, Ala
(S.P.B., M.T.D., J.M.W.)
| | - Hiroto Hatabu
- From the Division of Pulmonary and Critical Care Medicine, Department
of Medicine (S.Y.A., A.A.D., F.N.R., R.K.P., G.M.H., I.O.R., G.R.W.), Laboratory
of Mathematics in Imaging, Department of Radiology (R.H., J.C.R., G.V.S.,
J.O.O., R.S.J.E.), and Department of Radiology (H.H.), Brigham and
Women’s Hospital, 75 Francis St, PBB CA-3, Boston, MA 02115; Department
of Medicine, Weil Cornell Medical College, New York, NY (F.J.M., A.M.C.);
Departments of Medicine (R.P.B.) and Radiology (D.A.L.), National Jewish Health,
Denver, Colo; and Division of Pulmonary, Allergy and Critical Care Medicine,
Department of Medicine, University of Alabama at Birmingham, Birmingham, Ala
(S.P.B., M.T.D., J.M.W.)
| | - Surya P. Bhatt
- From the Division of Pulmonary and Critical Care Medicine, Department
of Medicine (S.Y.A., A.A.D., F.N.R., R.K.P., G.M.H., I.O.R., G.R.W.), Laboratory
of Mathematics in Imaging, Department of Radiology (R.H., J.C.R., G.V.S.,
J.O.O., R.S.J.E.), and Department of Radiology (H.H.), Brigham and
Women’s Hospital, 75 Francis St, PBB CA-3, Boston, MA 02115; Department
of Medicine, Weil Cornell Medical College, New York, NY (F.J.M., A.M.C.);
Departments of Medicine (R.P.B.) and Radiology (D.A.L.), National Jewish Health,
Denver, Colo; and Division of Pulmonary, Allergy and Critical Care Medicine,
Department of Medicine, University of Alabama at Birmingham, Birmingham, Ala
(S.P.B., M.T.D., J.M.W.)
| | - Mark T. Dransfield
- From the Division of Pulmonary and Critical Care Medicine, Department
of Medicine (S.Y.A., A.A.D., F.N.R., R.K.P., G.M.H., I.O.R., G.R.W.), Laboratory
of Mathematics in Imaging, Department of Radiology (R.H., J.C.R., G.V.S.,
J.O.O., R.S.J.E.), and Department of Radiology (H.H.), Brigham and
Women’s Hospital, 75 Francis St, PBB CA-3, Boston, MA 02115; Department
of Medicine, Weil Cornell Medical College, New York, NY (F.J.M., A.M.C.);
Departments of Medicine (R.P.B.) and Radiology (D.A.L.), National Jewish Health,
Denver, Colo; and Division of Pulmonary, Allergy and Critical Care Medicine,
Department of Medicine, University of Alabama at Birmingham, Birmingham, Ala
(S.P.B., M.T.D., J.M.W.)
| | - J. Michael Wells
- From the Division of Pulmonary and Critical Care Medicine, Department
of Medicine (S.Y.A., A.A.D., F.N.R., R.K.P., G.M.H., I.O.R., G.R.W.), Laboratory
of Mathematics in Imaging, Department of Radiology (R.H., J.C.R., G.V.S.,
J.O.O., R.S.J.E.), and Department of Radiology (H.H.), Brigham and
Women’s Hospital, 75 Francis St, PBB CA-3, Boston, MA 02115; Department
of Medicine, Weil Cornell Medical College, New York, NY (F.J.M., A.M.C.);
Departments of Medicine (R.P.B.) and Radiology (D.A.L.), National Jewish Health,
Denver, Colo; and Division of Pulmonary, Allergy and Critical Care Medicine,
Department of Medicine, University of Alabama at Birmingham, Birmingham, Ala
(S.P.B., M.T.D., J.M.W.)
| | - Ivan O. Rosas
- From the Division of Pulmonary and Critical Care Medicine, Department
of Medicine (S.Y.A., A.A.D., F.N.R., R.K.P., G.M.H., I.O.R., G.R.W.), Laboratory
of Mathematics in Imaging, Department of Radiology (R.H., J.C.R., G.V.S.,
J.O.O., R.S.J.E.), and Department of Radiology (H.H.), Brigham and
Women’s Hospital, 75 Francis St, PBB CA-3, Boston, MA 02115; Department
of Medicine, Weil Cornell Medical College, New York, NY (F.J.M., A.M.C.);
Departments of Medicine (R.P.B.) and Radiology (D.A.L.), National Jewish Health,
Denver, Colo; and Division of Pulmonary, Allergy and Critical Care Medicine,
Department of Medicine, University of Alabama at Birmingham, Birmingham, Ala
(S.P.B., M.T.D., J.M.W.)
| | - Raul San Jose Estepar
- From the Division of Pulmonary and Critical Care Medicine, Department
of Medicine (S.Y.A., A.A.D., F.N.R., R.K.P., G.M.H., I.O.R., G.R.W.), Laboratory
of Mathematics in Imaging, Department of Radiology (R.H., J.C.R., G.V.S.,
J.O.O., R.S.J.E.), and Department of Radiology (H.H.), Brigham and
Women’s Hospital, 75 Francis St, PBB CA-3, Boston, MA 02115; Department
of Medicine, Weil Cornell Medical College, New York, NY (F.J.M., A.M.C.);
Departments of Medicine (R.P.B.) and Radiology (D.A.L.), National Jewish Health,
Denver, Colo; and Division of Pulmonary, Allergy and Critical Care Medicine,
Department of Medicine, University of Alabama at Birmingham, Birmingham, Ala
(S.P.B., M.T.D., J.M.W.)
| | - George R. Washko
- From the Division of Pulmonary and Critical Care Medicine, Department
of Medicine (S.Y.A., A.A.D., F.N.R., R.K.P., G.M.H., I.O.R., G.R.W.), Laboratory
of Mathematics in Imaging, Department of Radiology (R.H., J.C.R., G.V.S.,
J.O.O., R.S.J.E.), and Department of Radiology (H.H.), Brigham and
Women’s Hospital, 75 Francis St, PBB CA-3, Boston, MA 02115; Department
of Medicine, Weil Cornell Medical College, New York, NY (F.J.M., A.M.C.);
Departments of Medicine (R.P.B.) and Radiology (D.A.L.), National Jewish Health,
Denver, Colo; and Division of Pulmonary, Allergy and Critical Care Medicine,
Department of Medicine, University of Alabama at Birmingham, Birmingham, Ala
(S.P.B., M.T.D., J.M.W.)
| | - for the COPDGene Investigators
- From the Division of Pulmonary and Critical Care Medicine, Department
of Medicine (S.Y.A., A.A.D., F.N.R., R.K.P., G.M.H., I.O.R., G.R.W.), Laboratory
of Mathematics in Imaging, Department of Radiology (R.H., J.C.R., G.V.S.,
J.O.O., R.S.J.E.), and Department of Radiology (H.H.), Brigham and
Women’s Hospital, 75 Francis St, PBB CA-3, Boston, MA 02115; Department
of Medicine, Weil Cornell Medical College, New York, NY (F.J.M., A.M.C.);
Departments of Medicine (R.P.B.) and Radiology (D.A.L.), National Jewish Health,
Denver, Colo; and Division of Pulmonary, Allergy and Critical Care Medicine,
Department of Medicine, University of Alabama at Birmingham, Birmingham, Ala
(S.P.B., M.T.D., J.M.W.)
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Lynch DA, Moore CM, Wilson C, Nevrekar D, Jennermann T, Humphries SM, Austin JHM, Grenier PA, Kauczor HU, Han MK, Regan EA, Make BJ, Bowler RP, Beaty TH, Curran-Everett D, Hokanson JE, Curtis JL, Silverman EK, Crapo JD. CT-based Visual Classification of Emphysema: Association with Mortality in the COPDGene Study. Radiology 2018; 288:859-866. [PMID: 29762095 PMCID: PMC6122195 DOI: 10.1148/radiol.2018172294] [Citation(s) in RCA: 115] [Impact Index Per Article: 19.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
Purpose To determine whether visually assessed patterns of emphysema at CT might provide a simple assessment of mortality risk among cigarette smokers. Materials and Methods Of the first 4000 cigarette smokers consecutively enrolled between 2007 and 2011 in this COPDGene study, 3171 had data available for both visual emphysema CT scores and survival. Each CT scan was retrospectively visually scored by two analysts using the Fleischner Society classification system. Severity of emphysema was also evaluated quantitatively by using percentage lung volume occupied by low-attenuation areas (voxels with attenuation of −950 HU or less) (LAA-950). Median duration of follow-up was 7.4 years. Regression analysis for the relationship between imaging patterns and survival was based on the Cox proportional hazards model, with adjustment for age, race, sex, height, weight, pack-years of cigarette smoking, current smoking status, educational level, LAA-950, and (in a second model) forced expiratory volume in 1 second (FEV1). Results Observer agreement in visual scoring was good (weighted κ values, 0.71–0.80). There were 519 deaths in the study cohort. Compared with subjects who did not have visible emphysema, mortality was greater in those with any grade of emphysema beyond trace (adjusted hazard ratios, 1.7, 2.5, 5.0, and 4.1, respectively, for mild centrilobular emphysema, moderate centrilobular emphysema, confluent emphysema, and advanced destructive emphysema, P < .001). This increased mortality generally persisted after adjusting for LAA-950. Conclusion The visual presence and severity of emphysema is associated with significantly increased mortality risk, independent of the quantitative severity of emphysema. Online supplemental material is available for this article.
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Affiliation(s)
- David A Lynch
- From the Department of Radiology (D.A.L., D.N., T.J., S.M.H.), Division of Biostatistics (C.M.M., C.W., D.C.E.), and Department of Medicine (E.A.R., B.J.M., R.P.B., J.D.C.), National Jewish Health, 1400 Jackson St, Denver, CO 80206; Department of Radiology, Columbia University Medical Center, New York, NY (J.H.M.A.); Department of Diagnostic Radiology, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Sorbonne Universités, Paris, France (P.A.G.); Department of Diagnostic and Interventional Radiology, University of Heidelberg, Translational Lung Research Center Heidelberg, Heidelberg, Germany (H.U.K.); Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, Mich (M.K.H., J.L.C.); Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Md (T.H.B.); Department of Epidemiology, Colorado School of Public Health, University of Colorado Denver, Anschutz Medical Campus, Aurora, Colo (J.E.H.); Medical Service, VA Ann Arbor Healthcare System, Ann Arbor, Mich (J.L.C.); and Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Mass (E.K.S.)
| | - Camille M Moore
- From the Department of Radiology (D.A.L., D.N., T.J., S.M.H.), Division of Biostatistics (C.M.M., C.W., D.C.E.), and Department of Medicine (E.A.R., B.J.M., R.P.B., J.D.C.), National Jewish Health, 1400 Jackson St, Denver, CO 80206; Department of Radiology, Columbia University Medical Center, New York, NY (J.H.M.A.); Department of Diagnostic Radiology, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Sorbonne Universités, Paris, France (P.A.G.); Department of Diagnostic and Interventional Radiology, University of Heidelberg, Translational Lung Research Center Heidelberg, Heidelberg, Germany (H.U.K.); Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, Mich (M.K.H., J.L.C.); Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Md (T.H.B.); Department of Epidemiology, Colorado School of Public Health, University of Colorado Denver, Anschutz Medical Campus, Aurora, Colo (J.E.H.); Medical Service, VA Ann Arbor Healthcare System, Ann Arbor, Mich (J.L.C.); and Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Mass (E.K.S.)
| | - Carla Wilson
- From the Department of Radiology (D.A.L., D.N., T.J., S.M.H.), Division of Biostatistics (C.M.M., C.W., D.C.E.), and Department of Medicine (E.A.R., B.J.M., R.P.B., J.D.C.), National Jewish Health, 1400 Jackson St, Denver, CO 80206; Department of Radiology, Columbia University Medical Center, New York, NY (J.H.M.A.); Department of Diagnostic Radiology, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Sorbonne Universités, Paris, France (P.A.G.); Department of Diagnostic and Interventional Radiology, University of Heidelberg, Translational Lung Research Center Heidelberg, Heidelberg, Germany (H.U.K.); Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, Mich (M.K.H., J.L.C.); Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Md (T.H.B.); Department of Epidemiology, Colorado School of Public Health, University of Colorado Denver, Anschutz Medical Campus, Aurora, Colo (J.E.H.); Medical Service, VA Ann Arbor Healthcare System, Ann Arbor, Mich (J.L.C.); and Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Mass (E.K.S.)
| | - Dipti Nevrekar
- From the Department of Radiology (D.A.L., D.N., T.J., S.M.H.), Division of Biostatistics (C.M.M., C.W., D.C.E.), and Department of Medicine (E.A.R., B.J.M., R.P.B., J.D.C.), National Jewish Health, 1400 Jackson St, Denver, CO 80206; Department of Radiology, Columbia University Medical Center, New York, NY (J.H.M.A.); Department of Diagnostic Radiology, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Sorbonne Universités, Paris, France (P.A.G.); Department of Diagnostic and Interventional Radiology, University of Heidelberg, Translational Lung Research Center Heidelberg, Heidelberg, Germany (H.U.K.); Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, Mich (M.K.H., J.L.C.); Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Md (T.H.B.); Department of Epidemiology, Colorado School of Public Health, University of Colorado Denver, Anschutz Medical Campus, Aurora, Colo (J.E.H.); Medical Service, VA Ann Arbor Healthcare System, Ann Arbor, Mich (J.L.C.); and Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Mass (E.K.S.)
| | - Theodore Jennermann
- From the Department of Radiology (D.A.L., D.N., T.J., S.M.H.), Division of Biostatistics (C.M.M., C.W., D.C.E.), and Department of Medicine (E.A.R., B.J.M., R.P.B., J.D.C.), National Jewish Health, 1400 Jackson St, Denver, CO 80206; Department of Radiology, Columbia University Medical Center, New York, NY (J.H.M.A.); Department of Diagnostic Radiology, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Sorbonne Universités, Paris, France (P.A.G.); Department of Diagnostic and Interventional Radiology, University of Heidelberg, Translational Lung Research Center Heidelberg, Heidelberg, Germany (H.U.K.); Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, Mich (M.K.H., J.L.C.); Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Md (T.H.B.); Department of Epidemiology, Colorado School of Public Health, University of Colorado Denver, Anschutz Medical Campus, Aurora, Colo (J.E.H.); Medical Service, VA Ann Arbor Healthcare System, Ann Arbor, Mich (J.L.C.); and Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Mass (E.K.S.)
| | - Stephen M Humphries
- From the Department of Radiology (D.A.L., D.N., T.J., S.M.H.), Division of Biostatistics (C.M.M., C.W., D.C.E.), and Department of Medicine (E.A.R., B.J.M., R.P.B., J.D.C.), National Jewish Health, 1400 Jackson St, Denver, CO 80206; Department of Radiology, Columbia University Medical Center, New York, NY (J.H.M.A.); Department of Diagnostic Radiology, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Sorbonne Universités, Paris, France (P.A.G.); Department of Diagnostic and Interventional Radiology, University of Heidelberg, Translational Lung Research Center Heidelberg, Heidelberg, Germany (H.U.K.); Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, Mich (M.K.H., J.L.C.); Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Md (T.H.B.); Department of Epidemiology, Colorado School of Public Health, University of Colorado Denver, Anschutz Medical Campus, Aurora, Colo (J.E.H.); Medical Service, VA Ann Arbor Healthcare System, Ann Arbor, Mich (J.L.C.); and Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Mass (E.K.S.)
| | - John H M Austin
- From the Department of Radiology (D.A.L., D.N., T.J., S.M.H.), Division of Biostatistics (C.M.M., C.W., D.C.E.), and Department of Medicine (E.A.R., B.J.M., R.P.B., J.D.C.), National Jewish Health, 1400 Jackson St, Denver, CO 80206; Department of Radiology, Columbia University Medical Center, New York, NY (J.H.M.A.); Department of Diagnostic Radiology, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Sorbonne Universités, Paris, France (P.A.G.); Department of Diagnostic and Interventional Radiology, University of Heidelberg, Translational Lung Research Center Heidelberg, Heidelberg, Germany (H.U.K.); Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, Mich (M.K.H., J.L.C.); Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Md (T.H.B.); Department of Epidemiology, Colorado School of Public Health, University of Colorado Denver, Anschutz Medical Campus, Aurora, Colo (J.E.H.); Medical Service, VA Ann Arbor Healthcare System, Ann Arbor, Mich (J.L.C.); and Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Mass (E.K.S.)
| | - Philippe A Grenier
- From the Department of Radiology (D.A.L., D.N., T.J., S.M.H.), Division of Biostatistics (C.M.M., C.W., D.C.E.), and Department of Medicine (E.A.R., B.J.M., R.P.B., J.D.C.), National Jewish Health, 1400 Jackson St, Denver, CO 80206; Department of Radiology, Columbia University Medical Center, New York, NY (J.H.M.A.); Department of Diagnostic Radiology, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Sorbonne Universités, Paris, France (P.A.G.); Department of Diagnostic and Interventional Radiology, University of Heidelberg, Translational Lung Research Center Heidelberg, Heidelberg, Germany (H.U.K.); Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, Mich (M.K.H., J.L.C.); Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Md (T.H.B.); Department of Epidemiology, Colorado School of Public Health, University of Colorado Denver, Anschutz Medical Campus, Aurora, Colo (J.E.H.); Medical Service, VA Ann Arbor Healthcare System, Ann Arbor, Mich (J.L.C.); and Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Mass (E.K.S.)
| | - Hans-Ulrich Kauczor
- From the Department of Radiology (D.A.L., D.N., T.J., S.M.H.), Division of Biostatistics (C.M.M., C.W., D.C.E.), and Department of Medicine (E.A.R., B.J.M., R.P.B., J.D.C.), National Jewish Health, 1400 Jackson St, Denver, CO 80206; Department of Radiology, Columbia University Medical Center, New York, NY (J.H.M.A.); Department of Diagnostic Radiology, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Sorbonne Universités, Paris, France (P.A.G.); Department of Diagnostic and Interventional Radiology, University of Heidelberg, Translational Lung Research Center Heidelberg, Heidelberg, Germany (H.U.K.); Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, Mich (M.K.H., J.L.C.); Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Md (T.H.B.); Department of Epidemiology, Colorado School of Public Health, University of Colorado Denver, Anschutz Medical Campus, Aurora, Colo (J.E.H.); Medical Service, VA Ann Arbor Healthcare System, Ann Arbor, Mich (J.L.C.); and Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Mass (E.K.S.)
| | - MeiLan K Han
- From the Department of Radiology (D.A.L., D.N., T.J., S.M.H.), Division of Biostatistics (C.M.M., C.W., D.C.E.), and Department of Medicine (E.A.R., B.J.M., R.P.B., J.D.C.), National Jewish Health, 1400 Jackson St, Denver, CO 80206; Department of Radiology, Columbia University Medical Center, New York, NY (J.H.M.A.); Department of Diagnostic Radiology, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Sorbonne Universités, Paris, France (P.A.G.); Department of Diagnostic and Interventional Radiology, University of Heidelberg, Translational Lung Research Center Heidelberg, Heidelberg, Germany (H.U.K.); Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, Mich (M.K.H., J.L.C.); Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Md (T.H.B.); Department of Epidemiology, Colorado School of Public Health, University of Colorado Denver, Anschutz Medical Campus, Aurora, Colo (J.E.H.); Medical Service, VA Ann Arbor Healthcare System, Ann Arbor, Mich (J.L.C.); and Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Mass (E.K.S.)
| | - Elizabeth A Regan
- From the Department of Radiology (D.A.L., D.N., T.J., S.M.H.), Division of Biostatistics (C.M.M., C.W., D.C.E.), and Department of Medicine (E.A.R., B.J.M., R.P.B., J.D.C.), National Jewish Health, 1400 Jackson St, Denver, CO 80206; Department of Radiology, Columbia University Medical Center, New York, NY (J.H.M.A.); Department of Diagnostic Radiology, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Sorbonne Universités, Paris, France (P.A.G.); Department of Diagnostic and Interventional Radiology, University of Heidelberg, Translational Lung Research Center Heidelberg, Heidelberg, Germany (H.U.K.); Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, Mich (M.K.H., J.L.C.); Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Md (T.H.B.); Department of Epidemiology, Colorado School of Public Health, University of Colorado Denver, Anschutz Medical Campus, Aurora, Colo (J.E.H.); Medical Service, VA Ann Arbor Healthcare System, Ann Arbor, Mich (J.L.C.); and Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Mass (E.K.S.)
| | - Barry J Make
- From the Department of Radiology (D.A.L., D.N., T.J., S.M.H.), Division of Biostatistics (C.M.M., C.W., D.C.E.), and Department of Medicine (E.A.R., B.J.M., R.P.B., J.D.C.), National Jewish Health, 1400 Jackson St, Denver, CO 80206; Department of Radiology, Columbia University Medical Center, New York, NY (J.H.M.A.); Department of Diagnostic Radiology, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Sorbonne Universités, Paris, France (P.A.G.); Department of Diagnostic and Interventional Radiology, University of Heidelberg, Translational Lung Research Center Heidelberg, Heidelberg, Germany (H.U.K.); Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, Mich (M.K.H., J.L.C.); Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Md (T.H.B.); Department of Epidemiology, Colorado School of Public Health, University of Colorado Denver, Anschutz Medical Campus, Aurora, Colo (J.E.H.); Medical Service, VA Ann Arbor Healthcare System, Ann Arbor, Mich (J.L.C.); and Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Mass (E.K.S.)
| | - Russell P Bowler
- From the Department of Radiology (D.A.L., D.N., T.J., S.M.H.), Division of Biostatistics (C.M.M., C.W., D.C.E.), and Department of Medicine (E.A.R., B.J.M., R.P.B., J.D.C.), National Jewish Health, 1400 Jackson St, Denver, CO 80206; Department of Radiology, Columbia University Medical Center, New York, NY (J.H.M.A.); Department of Diagnostic Radiology, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Sorbonne Universités, Paris, France (P.A.G.); Department of Diagnostic and Interventional Radiology, University of Heidelberg, Translational Lung Research Center Heidelberg, Heidelberg, Germany (H.U.K.); Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, Mich (M.K.H., J.L.C.); Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Md (T.H.B.); Department of Epidemiology, Colorado School of Public Health, University of Colorado Denver, Anschutz Medical Campus, Aurora, Colo (J.E.H.); Medical Service, VA Ann Arbor Healthcare System, Ann Arbor, Mich (J.L.C.); and Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Mass (E.K.S.)
| | - Terri H Beaty
- From the Department of Radiology (D.A.L., D.N., T.J., S.M.H.), Division of Biostatistics (C.M.M., C.W., D.C.E.), and Department of Medicine (E.A.R., B.J.M., R.P.B., J.D.C.), National Jewish Health, 1400 Jackson St, Denver, CO 80206; Department of Radiology, Columbia University Medical Center, New York, NY (J.H.M.A.); Department of Diagnostic Radiology, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Sorbonne Universités, Paris, France (P.A.G.); Department of Diagnostic and Interventional Radiology, University of Heidelberg, Translational Lung Research Center Heidelberg, Heidelberg, Germany (H.U.K.); Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, Mich (M.K.H., J.L.C.); Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Md (T.H.B.); Department of Epidemiology, Colorado School of Public Health, University of Colorado Denver, Anschutz Medical Campus, Aurora, Colo (J.E.H.); Medical Service, VA Ann Arbor Healthcare System, Ann Arbor, Mich (J.L.C.); and Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Mass (E.K.S.)
| | - Douglas Curran-Everett
- From the Department of Radiology (D.A.L., D.N., T.J., S.M.H.), Division of Biostatistics (C.M.M., C.W., D.C.E.), and Department of Medicine (E.A.R., B.J.M., R.P.B., J.D.C.), National Jewish Health, 1400 Jackson St, Denver, CO 80206; Department of Radiology, Columbia University Medical Center, New York, NY (J.H.M.A.); Department of Diagnostic Radiology, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Sorbonne Universités, Paris, France (P.A.G.); Department of Diagnostic and Interventional Radiology, University of Heidelberg, Translational Lung Research Center Heidelberg, Heidelberg, Germany (H.U.K.); Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, Mich (M.K.H., J.L.C.); Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Md (T.H.B.); Department of Epidemiology, Colorado School of Public Health, University of Colorado Denver, Anschutz Medical Campus, Aurora, Colo (J.E.H.); Medical Service, VA Ann Arbor Healthcare System, Ann Arbor, Mich (J.L.C.); and Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Mass (E.K.S.)
| | - John E Hokanson
- From the Department of Radiology (D.A.L., D.N., T.J., S.M.H.), Division of Biostatistics (C.M.M., C.W., D.C.E.), and Department of Medicine (E.A.R., B.J.M., R.P.B., J.D.C.), National Jewish Health, 1400 Jackson St, Denver, CO 80206; Department of Radiology, Columbia University Medical Center, New York, NY (J.H.M.A.); Department of Diagnostic Radiology, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Sorbonne Universités, Paris, France (P.A.G.); Department of Diagnostic and Interventional Radiology, University of Heidelberg, Translational Lung Research Center Heidelberg, Heidelberg, Germany (H.U.K.); Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, Mich (M.K.H., J.L.C.); Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Md (T.H.B.); Department of Epidemiology, Colorado School of Public Health, University of Colorado Denver, Anschutz Medical Campus, Aurora, Colo (J.E.H.); Medical Service, VA Ann Arbor Healthcare System, Ann Arbor, Mich (J.L.C.); and Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Mass (E.K.S.)
| | - Jeffrey L Curtis
- From the Department of Radiology (D.A.L., D.N., T.J., S.M.H.), Division of Biostatistics (C.M.M., C.W., D.C.E.), and Department of Medicine (E.A.R., B.J.M., R.P.B., J.D.C.), National Jewish Health, 1400 Jackson St, Denver, CO 80206; Department of Radiology, Columbia University Medical Center, New York, NY (J.H.M.A.); Department of Diagnostic Radiology, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Sorbonne Universités, Paris, France (P.A.G.); Department of Diagnostic and Interventional Radiology, University of Heidelberg, Translational Lung Research Center Heidelberg, Heidelberg, Germany (H.U.K.); Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, Mich (M.K.H., J.L.C.); Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Md (T.H.B.); Department of Epidemiology, Colorado School of Public Health, University of Colorado Denver, Anschutz Medical Campus, Aurora, Colo (J.E.H.); Medical Service, VA Ann Arbor Healthcare System, Ann Arbor, Mich (J.L.C.); and Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Mass (E.K.S.)
| | - Edwin K Silverman
- From the Department of Radiology (D.A.L., D.N., T.J., S.M.H.), Division of Biostatistics (C.M.M., C.W., D.C.E.), and Department of Medicine (E.A.R., B.J.M., R.P.B., J.D.C.), National Jewish Health, 1400 Jackson St, Denver, CO 80206; Department of Radiology, Columbia University Medical Center, New York, NY (J.H.M.A.); Department of Diagnostic Radiology, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Sorbonne Universités, Paris, France (P.A.G.); Department of Diagnostic and Interventional Radiology, University of Heidelberg, Translational Lung Research Center Heidelberg, Heidelberg, Germany (H.U.K.); Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, Mich (M.K.H., J.L.C.); Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Md (T.H.B.); Department of Epidemiology, Colorado School of Public Health, University of Colorado Denver, Anschutz Medical Campus, Aurora, Colo (J.E.H.); Medical Service, VA Ann Arbor Healthcare System, Ann Arbor, Mich (J.L.C.); and Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Mass (E.K.S.)
| | - James D Crapo
- From the Department of Radiology (D.A.L., D.N., T.J., S.M.H.), Division of Biostatistics (C.M.M., C.W., D.C.E.), and Department of Medicine (E.A.R., B.J.M., R.P.B., J.D.C.), National Jewish Health, 1400 Jackson St, Denver, CO 80206; Department of Radiology, Columbia University Medical Center, New York, NY (J.H.M.A.); Department of Diagnostic Radiology, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Sorbonne Universités, Paris, France (P.A.G.); Department of Diagnostic and Interventional Radiology, University of Heidelberg, Translational Lung Research Center Heidelberg, Heidelberg, Germany (H.U.K.); Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, Mich (M.K.H., J.L.C.); Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Md (T.H.B.); Department of Epidemiology, Colorado School of Public Health, University of Colorado Denver, Anschutz Medical Campus, Aurora, Colo (J.E.H.); Medical Service, VA Ann Arbor Healthcare System, Ann Arbor, Mich (J.L.C.); and Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Mass (E.K.S.)
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- From the Department of Radiology (D.A.L., D.N., T.J., S.M.H.), Division of Biostatistics (C.M.M., C.W., D.C.E.), and Department of Medicine (E.A.R., B.J.M., R.P.B., J.D.C.), National Jewish Health, 1400 Jackson St, Denver, CO 80206; Department of Radiology, Columbia University Medical Center, New York, NY (J.H.M.A.); Department of Diagnostic Radiology, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Sorbonne Universités, Paris, France (P.A.G.); Department of Diagnostic and Interventional Radiology, University of Heidelberg, Translational Lung Research Center Heidelberg, Heidelberg, Germany (H.U.K.); Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, Mich (M.K.H., J.L.C.); Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Md (T.H.B.); Department of Epidemiology, Colorado School of Public Health, University of Colorado Denver, Anschutz Medical Campus, Aurora, Colo (J.E.H.); Medical Service, VA Ann Arbor Healthcare System, Ann Arbor, Mich (J.L.C.); and Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Mass (E.K.S.)
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Rice MB, Li W, Dorans KS, Wilker EH, Ljungman P, Gold DR, Schwartz J, Koutrakis P, Kloog I, Araki T, Hatabu H, San Jose Estepar R, O'Connor GT, Mittleman MA, Washko GR. Exposure to Traffic Emissions and Fine Particulate Matter and Computed Tomography Measures of the Lung and Airways. Epidemiology 2018; 29:333-341. [PMID: 29384790 PMCID: PMC6095201 DOI: 10.1097/ede.0000000000000809] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
BACKGROUND Exposure to ambient air pollution has been associated with lower lung function in adults, but few studies have investigated associations with radiographic lung and airway measures. METHODS We ascertained lung volume, mass, density, visual emphysema, airway size, and airway wall area by computed tomography (CT) among 2,545 nonsmoking Framingham CT substudy participants. We examined associations of home distance to major road and PM2.5 (2008 average from a spatiotemporal model using satellite data) with these outcomes using linear and logistic regression models adjusted for age, sex, height, weight, census tract median household value and population density, education, pack-years of smoking, household tobacco exposure, cohort, and date. We tested for differential susceptibility by sex, smoking status (former vs. never), and cohort. RESULTS The mean participant age was 60.1 years (standard deviation 11.9 years). Median PM2.5 level was 9.7 µg/m (interquartile range, 1.6). Living <100 m from a major road was associated with a 108 ml (95% CI = 8, 207) higher lung volume compared with ≥400 m away. There was also a log-linear association between proximity to road and higher lung volume. There were no convincing associations of proximity to major road or PM2.5 with the other pulmonary CT measures. In subgroup analyses, road proximity was associated with lower lung density among men and higher odds of emphysema among former smokers. CONCLUSIONS Living near a major road was associated with higher average lung volume, but otherwise, we found no association between ambient pollution and radiographic measures of emphysema or airway disease.
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MacNee W. Computed tomography-derived pathological phenotypes in COPD. Eur Respir J 2018; 48:10-3. [PMID: 27365503 DOI: 10.1183/13993003.00958-2016] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2016] [Accepted: 05/16/2016] [Indexed: 11/05/2022]
Affiliation(s)
- William MacNee
- University of Edinburgh/MRC Centre for Inflammation Research, Queen's Medical Research Institute, Edinburgh, UK
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48
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Bermejo-Peláez D, San José Estépar R, Ledesma-Carbayo MJ. EMPHYSEMA CLASSIFICATION USING A MULTI-VIEW CONVOLUTIONAL NETWORK. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2018; 2018:519-522. [PMID: 32454948 PMCID: PMC7243961 DOI: 10.1109/isbi.2018.8363629] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
In this article we propose and validate a fully automatic tool for emphysema classification in Computed Tomography (CT) images. We hypothesize that a relatively simple Convolutional Neural Network (CNN) architecture can learn even better discriminative features from the input data compared with more complex and deeper architectures. The proposed architecture is comprised of only 4 convolutional and 3 pooling layers, where the input corresponds to a 2.5D multiview representation of the pulmonary segment tissue to classify, corresponding to axial, sagittal and coronal views. The proposed architecture is compared to similar 2D CNN and 3D CNN, and to more complex architectures which involve a larger number of parameters (up to six times larger). This method has been evaluated in 1553 tissue samples, and achieves an overall sensitivity of 81.78 % and a specificity of 97.34%, and results show that the proposed method outperforms deeper state-of-the-art architectures particularly designed for lung pattern classification. The method shows satisfactory results in full-lung classification.
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Affiliation(s)
- David Bermejo-Peláez
- Biomedical Image Technologies, Universidad Politécnica de Madrid & CIBER-BBN, Madrid, Spain
| | | | - M J Ledesma-Carbayo
- Biomedical Image Technologies, Universidad Politécnica de Madrid & CIBER-BBN, Madrid, Spain
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49
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Podolanczuk AJ, Oelsner EC, Barr RG, Bernstein EJ, Hoffman EA, Easthausen IJ, Stukovsky KH, RoyChoudhury A, Michos ED, Raghu G, Kawut SM, Lederer DJ. High-Attenuation Areas on Chest Computed Tomography and Clinical Respiratory Outcomes in Community-Dwelling Adults. Am J Respir Crit Care Med 2017; 196:1434-1442. [PMID: 28613921 PMCID: PMC5736977 DOI: 10.1164/rccm.201703-0555oc] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2017] [Accepted: 06/13/2017] [Indexed: 11/16/2022] Open
Abstract
RATIONALE Areas of increased lung attenuation visualized by computed tomography are associated with all-cause mortality in the general population. It is uncertain whether this association is attributable to interstitial lung disease (ILD). OBJECTIVES To determine whether high-attenuation areas are associated with the risk of ILD hospitalization and mortality in the general population. METHODS We performed a cohort study of 6,808 adults aged 45-84 years sampled from six communities in the United States. High-attenuation areas were defined as the percentage of imaged lung volume with attenuation values between -600 and -250 Hounsfield units. An adjudication panel determined ILD hospitalization and death. MEASUREMENTS AND MAIN RESULTS After adjudication, 52 participants had a diagnosis of ILD during 75,232 person-years (median, 12.2 yr) of follow-up. There were 48 hospitalizations attributable to ILD (crude rate, 6.4 per 10,000 person-years). Twenty participants died as a result of ILD (crude rate, 2.7 per 10,000 person-years). High-attenuation areas were associated with an increased rate of ILD hospitalization (adjusted hazard ratio, 2.6 per 1-SD increment in high-attenuation areas; 95% confidence interval, 1.9-3.5; P < 0.001), a finding that was stronger among men, African Americans, and Hispanics. High-attenuation areas were also associated with an increased rate of ILD-specific death (adjusted hazard ratio, 2.3; 95% confidence interval, 1.7-3.0; P < 0.001). Our findings were consistent among both smokers and nonsmokers. CONCLUSIONS Areas of increased lung attenuation are a novel risk factor for ILD hospitalization and mortality. Measurement of high-attenuation areas by screening and diagnostic computed tomography may be warranted in at-risk adults.
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Affiliation(s)
| | | | | | | | - Eric A. Hoffman
- Department of Radiology
- Department of Internal Medicine, and
- Department of Biomedical Engineering, University of Iowa Carver College of Medicine, Iowa City, Iowa
| | | | | | - Arindam RoyChoudhury
- Department of Biostatistics, Columbia University Medical Center, New York, New York
| | - Erin D. Michos
- Division of Cardiology, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland; and
| | - Ganesh Raghu
- Department of Medicine, University of Washington, Seattle, Washington
| | - Steven M. Kawut
- Department of Medicine and
- Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
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50
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Disease Severity Dependence of the Longitudinal Association Between CT Lung Density and Lung Function in Smokers. Chest 2017; 153:638-645. [PMID: 29066389 DOI: 10.1016/j.chest.2017.10.012] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2017] [Revised: 08/04/2017] [Accepted: 10/02/2017] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND In smokers, the lung parenchyma is characterized by inflammation and emphysema, processes that can result in local gain and loss of lung tissue. CT measures of lung density might reflect lung tissue changes; however, longitudinal data regarding the effects of CT lung tissue on FEV1 in smokers with and without COPD are scarce. METHODS The 15th percentile of CT lung density was obtained from the scans of 3,390 smokers who completed baseline and 5-year follow-up of the Genetic Epidemiology of COPD (COPDGene) study visits. The longitudinal relationship between total lung capacity-adjusted lung density (TLC-PD15) and FEV1 was assessed by using multivariable mixed models. Separate models were performed in smokers at risk, smokers with preserved ratio and impaired spirometry (PRISm), and smokers with COPD according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) staging system. RESULTS The direction of the relationship between lung density and lung function was GOLD stage dependent. In smokers with PRISm, a 1-g/L decrease in TLC-PD15 was associated with an increase of 2.8 mL FEV1 (P = .02). In contrast, among smokers with GOLD III to IV COPD, a 1-g/L decrease in TLC-PD15 was associated with a decrease of 4.1 mL FEV1 (P = .002). CONCLUSIONS A decline in TLC-PD15 was associated with an increase or decrease in FEV1 depending on disease severity. The associations are GOLD stage specific, and their presence might influence the interpretation of future studies that use CT lung density as an intermediate study end point for a decline in lung function. TRIAL REGISTRY ClinicalTrials.gov; No.: NCT00608764; URL: www.clinicaltrials.gov.
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