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Elbehairy AF, Marshall H, Naish JH, Wild JM, Parraga G, Horsley A, Vestbo J. Advances in COPD imaging using CT and MRI: linkage with lung physiology and clinical outcomes. Eur Respir J 2024; 63:2301010. [PMID: 38548292 DOI: 10.1183/13993003.01010-2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 03/16/2024] [Indexed: 05/04/2024]
Abstract
Recent years have witnessed major advances in lung imaging in patients with COPD. These include significant refinements in images obtained by computed tomography (CT) scans together with the introduction of new techniques and software that aim for obtaining the best image whilst using the lowest possible radiation dose. Magnetic resonance imaging (MRI) has also emerged as a useful radiation-free tool in assessing structural and more importantly functional derangements in patients with well-established COPD and smokers without COPD, even before the existence of overt changes in resting physiological lung function tests. Together, CT and MRI now allow objective quantification and assessment of structural changes within the airways, lung parenchyma and pulmonary vessels. Furthermore, CT and MRI can now provide objective assessments of regional lung ventilation and perfusion, and multinuclear MRI provides further insight into gas exchange; this can help in structured decisions regarding treatment plans. These advances in chest imaging techniques have brought new insights into our understanding of disease pathophysiology and characterising different disease phenotypes. The present review discusses, in detail, the advances in lung imaging in patients with COPD and how structural and functional imaging are linked with common resting physiological tests and important clinical outcomes.
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Affiliation(s)
- Amany F Elbehairy
- Department of Chest Diseases, Faculty of Medicine, Alexandria University, Alexandria, Egypt
- Division of Infection, Immunity and Respiratory Medicine, The University of Manchester and Manchester University NHS Foundation Trust, Manchester Academic Health Sciences Centre, Manchester, UK
| | - Helen Marshall
- POLARIS, Imaging, Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Josephine H Naish
- MCMR, Manchester University NHS Foundation Trust, Manchester, UK
- Bioxydyn Limited, Manchester, UK
| | - Jim M Wild
- POLARIS, Imaging, Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
- Insigneo Institute for in silico Medicine, Sheffield, UK
| | - Grace Parraga
- Robarts Research Institute, Western University, London, ON, Canada
- Department of Medical Biophysics, Western University, London, ON, Canada
- Division of Respirology, Western University, London, ON, Canada
| | - Alexander Horsley
- Division of Infection, Immunity and Respiratory Medicine, The University of Manchester and Manchester University NHS Foundation Trust, Manchester Academic Health Sciences Centre, Manchester, UK
| | - Jørgen Vestbo
- Division of Infection, Immunity and Respiratory Medicine, The University of Manchester and Manchester University NHS Foundation Trust, Manchester Academic Health Sciences Centre, Manchester, UK
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Li F, Zhang X, Comellas AP, Hoffman EA, Graham MM, Lin CL. Exploratory Study on COPD Phenotypes and their Progression: Integrating SPECT and qCT Imaging Analysis. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.10.24305577. [PMID: 38645219 PMCID: PMC11030493 DOI: 10.1101/2024.04.10.24305577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Background The objective of this study is to understand chronic obstructive pulmonary disease (COPD) phenotypes and their progressions by quantifying heterogeneities of lung ventilation from the single photon emission computed tomography (SPECT) images and establishing associations with the quantitative computed tomography (qCT) imaging-based clusters and variables. Methods Eight COPD patients completed a longitudinal study of three visits with intervals of about a year. CT scans of these subjects at residual volume, functional residual capacity, and total lung capacity were taken for all visits. The functional and structural qCT-based variables were derived, and the subjects were classified into the qCT-based clusters. In addition, the SPECT variables were derived to quantify the heterogeneity of lung ventilation. The correlations between the key qCT-based variables and SPECT-based variables were examined. Results The SPECT-based coefficient of variation (CVTotal), a measure of ventilation heterogeneity, showed strong correlations (|r| ≥ 0.7) with the qCT-based functional small airway disease percentage (fSAD%Total) and emphysematous tissue percentage (Emph%Total) in the total lung on cross-sectional data. As for the two-year changes, the SPECT-based maximum tracer concentration (TCmax), a measure of hot spots, exhibited strong negative correlations with fSAD%Total, Emph%Total, average airway diameter in the left upper lobe, and airflow distribution in the middle and lower lobes. Conclusion Small airway disease is highly associated with the heterogeneity of ventilation in COPD lungs. TCmax is a more sensitive functional biomarker for COPD progression than CVTotal. Besides fSAD%Total and Emph%Total, segmental airways narrowing and imbalanced ventilation between upper and lower lobes may contribute to the development of hot spots over time.
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Affiliation(s)
- Frank Li
- Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa, USA
- IIHR-Hydroscience & Engineering, University of Iowa, Iowa City, Iowa, USA
| | - Xuan Zhang
- IIHR-Hydroscience & Engineering, University of Iowa, Iowa City, Iowa, USA
- Department of Mechanical Engineering, University of Iowa, Iowa City, Iowa, USA
| | | | - Eric A. Hoffman
- Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa, USA
- Department of Radiology, University of Iowa, Iowa City, Iowa, USA
| | | | - Ching-Long Lin
- Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa, USA
- IIHR-Hydroscience & Engineering, University of Iowa, Iowa City, Iowa, USA
- Department of Mechanical Engineering, University of Iowa, Iowa City, Iowa, USA
- Department of Radiology, University of Iowa, Iowa City, Iowa, USA
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3
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Angelini ED, Yang J, Balte PP, Hoffman EA, Manichaikul AW, Sun Y, Shen W, Austin JHM, Allen NB, Bleecker ER, Bowler R, Cho MH, Cooper CS, Couper D, Dransfield MT, Garcia CK, Han MK, Hansel NN, Hughes E, Jacobs DR, Kasela S, Kaufman JD, Kim JS, Lappalainen T, Lima J, Malinsky D, Martinez FJ, Oelsner EC, Ortega VE, Paine R, Post W, Pottinger TD, Prince MR, Rich SS, Silverman EK, Smith BM, Swift AJ, Watson KE, Woodruff PG, Laine AF, Barr RG. Pulmonary emphysema subtypes defined by unsupervised machine learning on CT scans. Thorax 2023; 78:1067-1079. [PMID: 37268414 PMCID: PMC10592007 DOI: 10.1136/thorax-2022-219158] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 05/03/2023] [Indexed: 06/04/2023]
Abstract
BACKGROUND Treatment and preventative advances for chronic obstructive pulmonary disease (COPD) have been slow due, in part, to limited subphenotypes. We tested if unsupervised machine learning on CT images would discover CT emphysema subtypes with distinct characteristics, prognoses and genetic associations. METHODS New CT emphysema subtypes were identified by unsupervised machine learning on only the texture and location of emphysematous regions on CT scans from 2853 participants in the Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS), a COPD case-control study, followed by data reduction. Subtypes were compared with symptoms and physiology among 2949 participants in the population-based Multi-Ethnic Study of Atherosclerosis (MESA) Lung Study and with prognosis among 6658 MESA participants. Associations with genome-wide single-nucleotide-polymorphisms were examined. RESULTS The algorithm discovered six reproducible (interlearner intraclass correlation coefficient, 0.91-1.00) CT emphysema subtypes. The most common subtype in SPIROMICS, the combined bronchitis-apical subtype, was associated with chronic bronchitis, accelerated lung function decline, hospitalisations, deaths, incident airflow limitation and a gene variant near DRD1, which is implicated in mucin hypersecretion (p=1.1 ×10-8). The second, the diffuse subtype was associated with lower weight, respiratory hospitalisations and deaths, and incident airflow limitation. The third was associated with age only. The fourth and fifth visually resembled combined pulmonary fibrosis emphysema and had distinct symptoms, physiology, prognosis and genetic associations. The sixth visually resembled vanishing lung syndrome. CONCLUSION Large-scale unsupervised machine learning on CT scans defined six reproducible, familiar CT emphysema subtypes that suggest paths to specific diagnosis and personalised therapies in COPD and pre-COPD.
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Affiliation(s)
- Elsa D Angelini
- Department of Biomedical Engineering, Columbia University, New York, New York, USA
- LTCI, Institut Polytechnique de Paris, Telecom Paris, Palaiseau, France
- NIHR Imperial Biomedical Research Centre, ITMAT Data Science Group, Imperial College, London, UK
| | - Jie Yang
- Department of Biomedical Engineering, Columbia University, New York, New York, USA
| | - Pallavi P Balte
- Department of Medicine, Columbia University Irving Medical Center, New York, New York, USA
| | - Eric A Hoffman
- Departments of Radiology, Medicine and Biomedical Engineering, University of Iowa, Iowa City, Iowa, USA
| | - Ani W Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA
| | - Yifei Sun
- Department of Biostatistics, Columbia University Irving Medical Center, New York, New York, USA
| | - Wei Shen
- Department of Pediatrics, Institute of Human Nutrition, Columbia University Irving Medical Center, New York, New York, USA
- Columbia Magnetic Resonance Research Center (CMRRC), Columbia University Irving Medical Center, New York, New York, USA
| | - John H M Austin
- Department of Radiology, Columbia University Irving Medical Center, New York, New York, USA
| | - Norrina B Allen
- Institute for Public Health and Medicine (IPHAM) - Center for Epidemiology and Population Health, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Eugene R Bleecker
- Department of Medicine, University of Arizona Health Sciences, Tucson, Arizona, USA
| | - Russell Bowler
- Department of Medicine, National Jewish Health, Denver, Colorado, USA
| | - Michael H Cho
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | | | - David Couper
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, USA
| | | | - Christine Kim Garcia
- Department of Medicine, Columbia University Irving Medical Center, New York, New York, USA
| | - MeiLan K Han
- Department of Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Nadia N Hansel
- Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Emlyn Hughes
- Department of Physics, Columbia University, New York, New York, USA
| | - David R Jacobs
- Division of Epidemiology and Community Public Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA
| | - Silva Kasela
- Department of Systems Biology, Columbia University Irving Medical Center, New York, New York, USA
- New York Genome Center, New York, New York, USA
| | - Joel Daniel Kaufman
- Departments of Environmental & Occupational Health Sciences, Medicine, and Epidemiology, University of Washington, Seattle, Washington, USA
| | - John Shinn Kim
- Department of Medicine, Columbia University Irving Medical Center, New York, New York, USA
- Department of Medicine, University of Virginia School of Medicine, Charlottesville, Virginia, USA
| | - Tuuli Lappalainen
- Department of Systems Biology, Columbia University Irving Medical Center, New York, New York, USA
| | - Joao Lima
- Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Daniel Malinsky
- Department of Biostatistics, Columbia University Irving Medical Center, New York, New York, USA
| | - Fernando J Martinez
- Department of Medicine, Cornell University Joan and Sanford I Weill Medical College, New York, New York, USA
| | - Elizabeth C Oelsner
- Department of Medicine, Columbia University Irving Medical Center, New York, New York, USA
| | - Victor E Ortega
- Department of Pulmonary Medicine, Mayo Clinic, Phoenix, Arizona, USA
| | - Robert Paine
- Department of Medicine, University of Utah, Salt Lake City, Utah, USA
| | - Wendy Post
- Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Tess D Pottinger
- Department of Medicine, Columbia University Irving Medical Center, New York, New York, USA
| | - Martin R Prince
- Department of Radiology, Cornell University Joan and Sanford I Weill Medical College, New York, New York, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Benjamin M Smith
- Department of Medicine, Columbia University Irving Medical Center, New York, New York, USA
- Department of Medicine, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada
| | - Andrew J Swift
- Department of Medicine, Columbia University Irving Medical Center, New York, New York, USA
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, UK
| | - Karol E Watson
- Department of Medicine, University of California, Los Angeles, California, USA
| | - Prescott G Woodruff
- Department of Medicine, University of California, San Francisco, California, USA
| | - Andrew F Laine
- Department of Biomedical Engineering, Columbia University, New York, New York, USA
- Columbia Magnetic Resonance Research Center (CMRRC), Columbia University Irving Medical Center, New York, New York, USA
- Department of Radiology, Columbia University Irving Medical Center, New York, New York, USA
| | - R Graham Barr
- Department of Medicine, Columbia University Irving Medical Center, New York, New York, USA
- Department of Epidemiology, Columbia University Irving Medical Center, New York, New York, USA
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Joshi I, Devine AJ, Joshi R, Smith NJ, Varisco BM. A titratable murine model of progressive emphysema using tracheal porcine pancreatic elastase. Sci Rep 2023; 13:15259. [PMID: 37709810 PMCID: PMC10502133 DOI: 10.1038/s41598-023-41527-1] [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: 07/03/2023] [Accepted: 08/28/2023] [Indexed: 09/16/2023] Open
Abstract
Progressive emphysema often leads to end-stage lung disease. Most mouse models of emphysema are typically modest (i.e. cigarette smoke exposure), and changes over time are difficult to quantify. The tracheal porcine pancreatic elastase model (PPE) produces severe injury, but the literature is conflicted as to whether emphysema improves, is stable, or progresses over time. We hypothesized a threshold of injury below which repair would occur and above which emphysema would be stable or progress. We treated 8-week-old C57BL6 mixed sex mice with 0, 0.5, 2, or 4 activity units of PPE in 100 µL PBS and performed lung stereology at 21 and 84 days. There were no significant differences in weight gain or mouse health. Despite minimal emphysema at 21-days in the 0.5 units group (2.8 µm increased mean linear intercept, MLI), MLI increased by 4.6 µm between days 21 and 84 (p = 0.0007). In addition to larger MLI at 21 days in 2- and 4-unit groups, MLI increases from day 21 to 84 were 17.2 and 34 µm respectively (p = 0.002 and p = 0.0001). Total lung volume increased, and alveolar surface area decreased with time and injury severity. Contrary to our hypothesis, we found no evidence of alveolar repair over time. Airspace destruction was both progressive and accelerative. Future mechanistic studies in lung immunity, mechano-biology, senescence, and cell-specific changes may lead to novel therapies to slow or halt progressive emphysema in humans.
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Affiliation(s)
- Imani Joshi
- College of Arts and Sciences, Xavier University, Cincinnati, OH, USA
| | - Andrew J Devine
- Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Rashika Joshi
- Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Noah J Smith
- University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Brian M Varisco
- Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
- University of Cincinnati College of Medicine, Cincinnati, OH, USA.
- University of Arkansas for Medical Sciences, 1 Children's Way Slot 663, Little Rock, AR, 72202, USA.
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5
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Chassagnon G, Revel MP. Artificial intelligence in thoracic imaging: the transition from research to practice. Eur Radiol 2023; 33:6318-6319. [PMID: 37186215 DOI: 10.1007/s00330-023-09635-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 03/28/2023] [Accepted: 03/30/2023] [Indexed: 05/17/2023]
Affiliation(s)
- Guillaume Chassagnon
- Radiology Department, Hôpital Cochin, Paris, France.
- Université de Paris, Paris, France.
| | - Marie-Pierre Revel
- Radiology Department, Hôpital Cochin, Paris, France
- Université de Paris, Paris, France
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Kheradmand F, Zhang Y, Corry DB. Contribution of adaptive immunity to human COPD and experimental models of emphysema. Physiol Rev 2023; 103:1059-1093. [PMID: 36201635 PMCID: PMC9886356 DOI: 10.1152/physrev.00036.2021] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 09/15/2022] [Accepted: 09/20/2022] [Indexed: 02/01/2023] Open
Abstract
The pathophysiology of chronic obstructive pulmonary disease (COPD) and the undisputed role of innate immune cells in this condition have dominated the field in the basic research arena for many years. Recently, however, compelling data suggesting that adaptive immune cells may also contribute to the progressive nature of lung destruction associated with COPD in smokers have gained considerable attention. The histopathological changes in the lungs of smokers can be limited to the large or small airways, but alveolar loss leading to emphysema, which occurs in some individuals, remains its most significant and irreversible outcome. Critically, however, the question of why emphysema progresses in a subset of former smokers remained a mystery for many years. The recognition of activated and organized tertiary T- and B-lymphoid aggregates in emphysematous lungs provided the first clue that adaptive immune cells may play a crucial role in COPD pathophysiology. Based on these findings from human translational studies, experimental animal models of emphysema were used to determine the mechanisms through which smoke exposure initiates and orchestrates adaptive autoreactive inflammation in the lungs. These models have revealed that T helper (Th)1 and Th17 subsets promote a positive feedback loop that activates innate immune cells, confirming their role in emphysema pathogenesis. Results from genetic studies and immune-based discoveries have further provided strong evidence for autoimmunity induction in smokers with emphysema. These new findings offer a novel opportunity to explore the mechanisms underlying the inflammatory landscape in the COPD lung and offer insights for development of precision-based treatment to halt lung destruction.
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Affiliation(s)
- Farrah Kheradmand
- Department of Medicine, Baylor College of Medicine, Houston, Texas
- Department of Pathology and Immunology, Baylor College of Medicine, Houston, Texas
- Biology of Inflammation Center, Baylor College of Medicine, Houston, Texas
- Center for Translational Research on Inflammatory Diseases (CTRID), Michael E. DeBakey Department of Veterans Affairs Medical Center, Houston, Texas
| | - Yun Zhang
- Department of Medicine, Baylor College of Medicine, Houston, Texas
- Department of Pathology and Immunology, Baylor College of Medicine, Houston, Texas
| | - David B Corry
- Department of Medicine, Baylor College of Medicine, Houston, Texas
- Department of Pathology and Immunology, Baylor College of Medicine, Houston, Texas
- Biology of Inflammation Center, Baylor College of Medicine, Houston, Texas
- Center for Translational Research on Inflammatory Diseases (CTRID), Michael E. DeBakey Department of Veterans Affairs Medical Center, Houston, Texas
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7
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Haghighi B, Horng H, Noël PB, Cohen EA, Pantalone L, Vachani A, Rendle KA, Wainwright J, Saia C, Shinohara RT, Barbosa EM, Kontos D. Radiomic phenotyping of the lung parenchyma in a lung cancer screening cohort. Sci Rep 2023; 13:2040. [PMID: 36739358 PMCID: PMC9899203 DOI: 10.1038/s41598-023-29058-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 01/30/2023] [Indexed: 02/05/2023] Open
Abstract
High-throughput extraction of radiomic features from low-dose CT scans can characterize the heterogeneity of the lung parenchyma and potentially aid in identifying subpopulations that may have higher risk of lung diseases, such as COPD, and lung cancer due to inflammation or obstruction of the airways. We aim to determine the feasibility of a lung radiomics phenotyping approach in a lung cancer screening cohort, while quantifying the effect of different CT reconstruction algorithms on phenotype robustness. We identified low-dose CT scans (n = 308) acquired with Siemens Healthineers scanners from patients who completed low-dose CT within our lung cancer screening program between 2015 and 2018 and had two different sets of image reconstructions kernel available (i.e., medium (I30f.), sharp (I50f.)) for the same acquisition. Following segmentation of the lung field, a total of 26 radiomic features were extracted from the entire 3D lung-field using a previously validated fully-automated lattice-based software pipeline, adapted for low-dose CT scans. The lattice in-house software was used to extract features including gray-level histogram, co-occurrence, and run-length descriptors. The lattice approach uses non-overlapping windows for traversing along pixels of images and calculates different features. Each feature was averaged for each scan within a range of lattice window sizes (W) of 4, 8 and 20 mm. The extracted imaging features from both datasets were harmonized to correct for differences in image acquisition parameters. Subsequently, unsupervised hierarchical clustering was applied on the extracted features to identify distinct phenotypic patterns of the lung parenchyma, where consensus clustering was used to identify the optimal number of clusters (K = 2). Differences between phenotypes for demographic and clinical covariates including sex, age, BMI, pack-years of smoking, Lung-RADS and cancer diagnosis were assessed for each phenotype cluster, and then compared across clusters for the two different CT reconstruction algorithms using the cluster entanglement metric, where a lower entanglement coefficient corresponds to good cluster alignment. Furthermore, an independent set of low-dose CT scans (n = 88) from patients with available pulmonary function data on lung obstruction were analyzed using the identified optimal clusters to assess associations to lung obstruction and validate the lung phenotyping paradigm. Heatmaps generated by radiomic features identified two distinct lung parenchymal phenotype patterns across different feature extraction window sizes, for both reconstruction algorithms (P < 0.05 with K = 2). Associations of radiomic-based clusters with clinical covariates showed significant differences for BMI and pack-years of smoking (P < 0.05) for both reconstruction kernels. Radiomic phenotype patterns were more similar across the two reconstructed kernels, when smaller window sizes (W = 4 and 8 mm) were used for radiomic feature extraction, as deemed by their entanglement coefficient. Validation of clustering approaches using cluster mapping for the independent sample with lung obstruction also showed two statistically significant phenotypes (P < 0.05) with significant difference for BMI and smoking pack-years. Radiomic analysis can be used to characterize lung parenchymal phenotypes from low-dose CT scans, which appear reproducible for different reconstruction kernels. Further work should seek to evaluate the effect of additional CT acquisition parameters and validate these phenotypes in characterizing lung cancer screening populations, to potentially better stratify disease patterns and cancer risk.
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Affiliation(s)
- Babak Haghighi
- Department of Radiology, Perelman School of Medicine and Hospital of the University of Pennsylvania, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Hannah Horng
- Department of Radiology, Perelman School of Medicine and Hospital of the University of Pennsylvania, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Peter B Noël
- Department of Radiology, Perelman School of Medicine and Hospital of the University of Pennsylvania, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Eric A Cohen
- Department of Radiology, Perelman School of Medicine and Hospital of the University of Pennsylvania, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Lauren Pantalone
- Department of Radiology, Perelman School of Medicine and Hospital of the University of Pennsylvania, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Anil Vachani
- Department of Radiology, Perelman School of Medicine and Hospital of the University of Pennsylvania, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Katharine A Rendle
- Department of Radiology, Perelman School of Medicine and Hospital of the University of Pennsylvania, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Jocelyn Wainwright
- Department of Radiology, Perelman School of Medicine and Hospital of the University of Pennsylvania, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Chelsea Saia
- Department of Radiology, Perelman School of Medicine and Hospital of the University of Pennsylvania, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Russel T Shinohara
- Department of Radiology, Perelman School of Medicine and Hospital of the University of Pennsylvania, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Eduardo Mortani Barbosa
- Department of Radiology, Perelman School of Medicine and Hospital of the University of Pennsylvania, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Despina Kontos
- Department of Radiology, Perelman School of Medicine and Hospital of the University of Pennsylvania, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA.
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8
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Mahdavi MMB, Arabfard M, Rafati M, Ghanei M. A Computer-based Analysis for Identification and Quantification of Small Airway Disease in Lung Computed Tomography Images: A Comprehensive Review for Radiologists. J Thorac Imaging 2023; 38:W1-W18. [PMID: 36206107 DOI: 10.1097/rti.0000000000000683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Computed tomography (CT) imaging is being increasingly used in clinical practice for detailed characterization of lung diseases. Respiratory diseases involve various components of the lung, including the small airways. Evaluation of small airway disease on CT images is challenging as the airways cannot be visualized directly by a CT scanner. Small airway disease can manifest as pulmonary air trapping (AT). Although AT may be sometimes seen as mosaic attenuation on expiratory CT images, it is difficult to identify diffuse AT visually. Computer technology advances over the past decades have provided methods for objective quantification of small airway disease on CT images. Quantitative CT (QCT) methods are being rapidly developed to quantify underlying lung diseases with greater precision than subjective visual assessment of CT images. A growing body of evidence suggests that QCT methods can be practical tools in the clinical setting to identify and quantify abnormal regions of the lung accurately and reproducibly. This review aimed to describe the available methods for the identification and quantification of small airway disease on CT images and to discuss the challenges of implementing QCT metrics in clinical care for patients with small airway disease.
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Affiliation(s)
- Mohammad Mehdi Baradaran Mahdavi
- Chemical Injuries Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran
| | - Masoud Arabfard
- Chemical Injuries Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran
| | - Mehravar Rafati
- Department of Medical Physics and Radiology, Faculty of paramedicine, Kashan University of Medical Sciences, Kashan, Iran
| | - Mostafa Ghanei
- Chemical Injuries Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran
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9
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Li F, Zhang X, Comellas AP, Hoffman EA, Yang T, Lin CL. Contrastive learning and subtyping of post-COVID-19 lung computed tomography images. Front Physiol 2022; 13:999263. [PMID: 36304574 PMCID: PMC9593072 DOI: 10.3389/fphys.2022.999263] [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: 07/20/2022] [Accepted: 09/27/2022] [Indexed: 11/30/2022] Open
Abstract
Patients who recovered from the novel coronavirus disease 2019 (COVID-19) may experience a range of long-term symptoms. Since the lung is the most common site of the infection, pulmonary sequelae may present persistently in COVID-19 survivors. To better understand the symptoms associated with impaired lung function in patients with post-COVID-19, we aimed to build a deep learning model which conducts two tasks: to differentiate post-COVID-19 from healthy subjects and to identify post-COVID-19 subtypes, based on the latent representations of lung computed tomography (CT) scans. CT scans of 140 post-COVID-19 subjects and 105 healthy controls were analyzed. A novel contrastive learning model was developed by introducing a lung volume transform to learn latent features of disease phenotypes from CT scans at inspiration and expiration of the same subjects. The model achieved 90% accuracy for the differentiation of the post-COVID-19 subjects from the healthy controls. Two clusters (C1 and C2) with distinct characteristics were identified among the post-COVID-19 subjects. C1 exhibited increased air-trapping caused by small airways disease (4.10%, p = 0.008) and diffusing capacity for carbon monoxide %predicted (DLCO %predicted, 101.95%, p < 0.001), while C2 had decreased lung volume (4.40L, p < 0.001) and increased ground glass opacity (GGO%, 15.85%, p < 0.001). The contrastive learning model is able to capture the latent features of two post-COVID-19 subtypes characterized by air-trapping due to small airways disease and airway-associated interstitial fibrotic-like patterns, respectively. The discovery of post-COVID-19 subtypes suggests the need for different managements and treatments of long-term sequelae of patients with post-COVID-19.
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Affiliation(s)
- Frank Li
- Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, IA, United States
- IIHR-Hydroscience and Engineering, University of Iowa, Iowa City, IA, United States
| | - Xuan Zhang
- IIHR-Hydroscience and Engineering, University of Iowa, Iowa City, IA, United States
- Department of Mechanical Engineering, University of Iowa, Iowa City, IA, United States
| | | | - Eric A. Hoffman
- Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, IA, United States
- Department of Radiology, University of Iowa, Iowa City, IA, United States
| | - Tianbao Yang
- Department of Computer Science, University of Iowa, Iowa City, IA, United States
| | - Ching-Long Lin
- Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, IA, United States
- IIHR-Hydroscience and Engineering, University of Iowa, Iowa City, IA, United States
- Department of Mechanical Engineering, University of Iowa, Iowa City, IA, United States
- Department of Radiology, University of Iowa, Iowa City, IA, United States
- *Correspondence: Ching-Long Lin,
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10
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Zhang X, Li F, Rajaraman PK, Choi J, Comellas AP, Hoffman EA, Smith BM, Lin CL. A computed tomography imaging-based subject-specific whole-lung deposition model. Eur J Pharm Sci 2022; 177:106272. [PMID: 35908637 PMCID: PMC9477651 DOI: 10.1016/j.ejps.2022.106272] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 07/26/2022] [Accepted: 07/27/2022] [Indexed: 11/27/2022]
Abstract
The respiratory tract is an important route for beneficial drug aerosol or harmful particulate matter to enter the body. To assess the therapeutic response or disease risk, whole-lung deposition models have been developed, but were limited by compartment, symmetry or stochastic approaches. In this work, we proposed an imaging-based subject-specific whole-lung deposition model. The geometries of airways and lobes were segmented from computed tomography (CT) lung images at total lung capacity (TLC), and the regional air-volume changes were calculated by registering CT images at TLC and functional residual capacity (FRC). The geometries were used to create the structure of entire subject-specific conducting airways and acinar units. The air-volume changes were used to estimate the function of subject-specific ventilation distributions among acinar units and regulate flow rates in respiratory airway models. With the airway dimensions rescaled to a desired lung volume and the airflow field simulated by a computational fluid dynamics model, particle deposition fractions were calculated using deposition probability formulae adjusted with an enhancement factor to account for the effects of secondary flow and airway geometry in proximal airways. The proposed model was validated in silico against existing whole-lung deposition models, three-dimensional (3D) computational fluid and particle dynamics (CFPD) for an acinar unit, and 3D CFPD deep lung model comprising conducting and respiratory regions. The model was further validated in vivo against the lobar particle distribution and the coefficient of variation of particle distribution obtained from CT and single-photon emission computed tomography (SPECT) images, showing good agreement. Subject-specific airway structure increased the deposition fraction of 10.0-μm particles and 0.01-μm particles by approximately 10%. An enhancement factor increased the overall deposition fractions, especially for particle sizes between 0.1 and 1.0 μm.
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Affiliation(s)
- Xuan Zhang
- Department of Mechanical Engineering, 2406 Seamans Center for the Engineering Art and Science, University of Iowa, Iowa City, Iowa 52242, USA; IIHR-Hydroscience and Engineering, University of Iowa, Iowa City, Iowa, USA
| | - Frank Li
- IIHR-Hydroscience and Engineering, University of Iowa, Iowa City, Iowa, USA; Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa, USA
| | | | - Jiwoong Choi
- Department of Mechanical Engineering, 2406 Seamans Center for the Engineering Art and Science, University of Iowa, Iowa City, Iowa 52242, USA; Department of Internal Medicine, School of Medicine, University of Kansas, Kansas City, Kansas, USA
| | - Alejandro P Comellas
- Department of Mechanical Engineering, 2406 Seamans Center for the Engineering Art and Science, University of Iowa, Iowa City, Iowa 52242, USA; Department of Internal Medicine, University of Iowa, Iowa City, Iowa, USA
| | - Eric A Hoffman
- Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa, USA; Department of Internal Medicine, School of Medicine, University of Kansas, Kansas City, Kansas, USA; Department of Radiology, University of Iowa, Iowa City, Iowa, USA
| | - Benjamin M Smith
- Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, NY, USA; Department of Medicine, McGill University Health Centre Research Institute, Montreal, Canada
| | - Ching-Long Lin
- Department of Mechanical Engineering, 2406 Seamans Center for the Engineering Art and Science, University of Iowa, Iowa City, Iowa 52242, USA; IIHR-Hydroscience and Engineering, University of Iowa, Iowa City, Iowa, USA; Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa, USA; Department of Radiology, University of Iowa, Iowa City, Iowa, USA.
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11
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Li F, Choi J, Zhang X, Rajaraman PK, Lee CH, Ko H, Chae KJ, Park EK, Comellas AP, Hoffman EA, Lin CL. Characterizing Subjects Exposed to Humidifier Disinfectants Using Computed-Tomography-Based Latent Traits: A Deep Learning Approach. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:11894. [PMID: 36231196 PMCID: PMC9565839 DOI: 10.3390/ijerph191911894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Revised: 09/09/2022] [Accepted: 09/14/2022] [Indexed: 06/16/2023]
Abstract
Around nine million people have been exposed to toxic humidifier disinfectants (HDs) in Korea. HD exposure may lead to HD-associated lung injuries (HDLI). However, many people who have claimed that they experienced HD exposure were not diagnosed with HDLI but still felt discomfort, possibly due to the unknown effects of HD. Therefore, this study examined HD-exposed subjects with normal-appearing lungs, as well as unexposed subjects, in clusters (subgroups) with distinct characteristics, classified by deep-learning-derived computed-tomography (CT)-based tissue pattern latent traits. Among the major clusters, cluster 0 (C0) and cluster 5 (C5) were dominated by HD-exposed and unexposed subjects, respectively. C0 was characterized by features attributable to lung inflammation or fibrosis in contrast with C5. The computational fluid and particle dynamics (CFPD) analysis suggested that the smaller airway sizes observed in the C0 subjects led to greater airway resistance and particle deposition in the airways. Accordingly, women appeared more vulnerable to HD-associated lung abnormalities than men.
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Affiliation(s)
- Frank Li
- Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, IA 52242, USA
- IIHR—Hydroscience & Engineering, University of Iowa, Iowa City, IA 52242, USA
| | - Jiwoong Choi
- Department of Mechanical Engineering, University of Iowa, Iowa City, IA 52242, USA
- Department of Internal Medicine, School of Medicine, University of Kansas, Kansas City, KS 66045, USA
| | - Xuan Zhang
- IIHR—Hydroscience & Engineering, University of Iowa, Iowa City, IA 52242, USA
- Department of Mechanical Engineering, University of Iowa, Iowa City, IA 52242, USA
| | - Prathish K. Rajaraman
- IIHR—Hydroscience & Engineering, University of Iowa, Iowa City, IA 52242, USA
- Department of Mechanical Engineering, University of Iowa, Iowa City, IA 52242, USA
| | - Chang-Hyun Lee
- Department of Radiology, University of Iowa, Iowa City, IA 52242, USA
- Department of Radiology, College of Medicine, Seoul National University, Seoul 100-011, Korea
| | - Hongseok Ko
- Department of Radiology, Kangwon National University Hospital, Chuncheon 200-010, Korea
| | - Kum-Ju Chae
- Department of Radiology, Jeonbuk National University Hospital, Jeonju 560-011, Korea
| | - Eun-Kee Park
- Department of Medical Humanities and Social Medicine, College of Medicine, Kosin University, Busan 600-011, Korea
| | | | - Eric A. Hoffman
- Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, IA 52242, USA
- Department of Radiology, University of Iowa, Iowa City, IA 52242, USA
- Department of Internal Medicine, University of Iowa, Iowa City, IA 52242, USA
| | - Ching-Long Lin
- Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, IA 52242, USA
- IIHR—Hydroscience & Engineering, University of Iowa, Iowa City, IA 52242, USA
- Department of Mechanical Engineering, University of Iowa, Iowa City, IA 52242, USA
- Department of Radiology, University of Iowa, Iowa City, IA 52242, USA
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12
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Kim T, Lim MN, Kim WJ, Ho TT, Lee CH, Chae KJ, Bak SH, Jin GY, Park EK, Choi S. Structural and functional alterations of subjects with cement dust exposure: A longitudinal quantitative computed tomography-based study. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 837:155812. [PMID: 35550893 DOI: 10.1016/j.scitotenv.2022.155812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 04/13/2022] [Accepted: 05/05/2022] [Indexed: 06/15/2023]
Abstract
Cement dust exposure (CDE) can be a risk factor for pulmonary disease, causing changes in segmental airways and parenchymal lungs. This study investigates longitudinal alterations in quantitative computed tomography (CT)-based metrics due to CDE. We obtained CT-based airway structural and lung functional metrics from CDE subjects with baseline CT and follow-up CT scans performed three years later. From the CT, we extracted wall thickness (WT) and bifurcation angle (θ) at total lung capacity (TLC) and functional residual capacity (FRC), respectively. We also computed air volume (Vair), tissue volume (Vtissue), global lung shape, percentage of emphysema (Emph%), and more. Clinical measures were used to associate with CT-based metrics. Three years after their baseline, the pulmonary function tests of CDE subjects were similar or improved, but there were significant alterations in the CT-based structural and functional metrics. The follow-up CT scans showed changes in θ at most of the central airways; increased WT at the subgroup bronchi; smaller Vair at TLC at all except the right upper and lower lobes; smaller Vtissue at all lobes in TLC and FRC except for the upper lobes in FRC; smaller global lung shape; and greater Emph% at the right upper and lower lobes. CT-based structural and functional variables are more sensitive to the early identification of CDE subjects, while most clinical lung function changes were not noticeable. We speculate that the significant long-term changes in CT are uniquely observed in CDE subjects, different from smoking-induced structural changes.
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Affiliation(s)
- Taewoo Kim
- School of Mechanical Engineering, Kyungpook National University, Daegu, Republic of Korea
| | - Myoung-Nam Lim
- Biomedical Research Institute, Kangwon National University Hospital, School of Medicine, Kangwon National University, Chuncheon, Republic of Korea
| | - Woo Jin Kim
- Department of Internal Medicine and Environmental Health Center, Kangwon National University Hospital, School of Medicine, Kangwon National University, Chuncheon, Republic of Korea
| | - Thao Thi Ho
- School of Mechanical Engineering, Kyungpook National University, Daegu, Republic of Korea
| | - Chang Hyun Lee
- Department of Radiology, College of Medicine, Seoul National University, Seoul, Republic of Korea; Department of Radiology, College of Medicine, The University of Iowa, Iowa City, IA, USA
| | - Kum Ju Chae
- Department of Radiology, Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Republic of Korea
| | - So Hyeon Bak
- Department of Radiology, Kangwon National University Hospital, School of Medicine, Kangwon National University, Chuncheon, Republic of Korea
| | - Gong Yong Jin
- Department of Radiology, College of Medicine, The University of Iowa, Iowa City, IA, USA
| | - Eun-Kee Park
- Department of Medical Humanities and Social Medicine, College of Medicine, Kosin University, Busan, Republic of Korea
| | - Sanghun Choi
- School of Mechanical Engineering, Kyungpook National University, Daegu, Republic of Korea.
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13
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Christenson SA, Smith BM, Bafadhel M, Putcha N. Chronic obstructive pulmonary disease. Lancet 2022; 399:2227-2242. [PMID: 35533707 DOI: 10.1016/s0140-6736(22)00470-6] [Citation(s) in RCA: 254] [Impact Index Per Article: 127.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 02/16/2022] [Accepted: 02/25/2022] [Indexed: 12/14/2022]
Abstract
Chronic obstructive pulmonary disease (COPD) is a major cause of morbidity, mortality, and health-care use worldwide. COPD is caused by exposure to inhaled noxious particles, notably tobacco smoke and pollutants. However, the broad range of factors that increase the risk of development and progression of COPD throughout the life course are increasingly being recognised. Innovations in omics and imaging techniques have provided greater insight into disease pathobiology, which might result in advances in COPD prevention, diagnosis, and treatment. Although few novel treatments have been approved for COPD in the past 5 years, advances have been made in targeting existing therapies to specific subpopulations using new biomarker-based strategies. Additionally, COVID-19 has undeniably affected individuals with COPD, who are not only at higher risk for severe disease manifestations than healthy individuals but also negatively affected by interruptions in health-care delivery and social isolation. This Seminar reviews COPD with an emphasis on recent advances in epidemiology, pathophysiology, imaging, diagnosis, and treatment.
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Affiliation(s)
- Stephanie A Christenson
- Division of Pulmonary, Critical Care, Allergy, and Sleep Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Benjamin M Smith
- Department of Medicine, Columbia University Medical Center, New York, NY, USA; Department of Medicine, McGill University Health Centre, Montreal, QC, Canada
| | - Mona Bafadhel
- School of Immunology and Microbial Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK; Department of Respiratory Medicine, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Nirupama Putcha
- Division of Pulmonary and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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14
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Dudurych I, Muiser S, McVeigh N, Kerstjens HAM, van den Berge M, de Bruijne M, Vliegenthart R. Bronchial wall parameters on CT in healthy never-smoking, smoking, COPD, and asthma populations: a systematic review and meta-analysis. Eur Radiol 2022; 32:5308-5318. [PMID: 35192013 PMCID: PMC9279249 DOI: 10.1007/s00330-022-08600-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 12/14/2021] [Accepted: 01/29/2022] [Indexed: 11/25/2022]
Abstract
Objective Research on computed tomography (CT) bronchial parameter measurements shows that there are conflicting results on the values for bronchial parameters in the never-smoking, smoking, asthma, and chronic obstructive pulmonary disease (COPD) populations. This review assesses the current CT methods for obtaining bronchial wall parameters and their comparison between populations. Methods A systematic review of MEDLINE and Embase was conducted following PRISMA guidelines (last search date 25th October 2021). Methodology data was collected and summarised. Values of percentage wall area (WA%), wall thickness (WT), summary airway measure (Pi10), and luminal area (Ai) were pooled and compared between populations. Results A total of 169 articles were included for methodologic review; 66 of these were included for meta-analysis. Most measurements were obtained from multiplanar reconstructions of segmented airways (93 of 169 articles), using various tools and algorithms; third generation airways in the upper and lower lobes were most frequently studied. COPD (12,746) and smoking (15,092) populations were largest across studies and mostly consisted of men (median 64.4%, IQR 61.5 – 66.1%). There were significant differences between populations; the largest WA% was found in COPD (mean SD 62.93 ± 7.41%, n = 6,045), and the asthma population had the largest Pi10 (4.03 ± 0.27 mm, n = 442). Ai normalised to body surface area (Ai/BSA) (12.46 ± 4 mm2, n = 134) was largest in the never-smoking population. Conclusions Studies on CT-derived bronchial parameter measurements are heterogenous in methodology and population, resulting in challenges to compare outcomes between studies. Significant differences between populations exist for several parameters, most notably in the wall area percentage; however, there is a large overlap in their ranges. Key Points • Diverse methodology in measuring airways contributes to overlap in ranges of bronchial parameters among the never-smoking, smoking, COPD, and asthma populations. • The combined number of never-smoking participants in studies is low, limiting insight into this population and the impact of participant characteristics on bronchial parameters. • Wall area percent of the right upper lobe apical segment is the most studied (87 articles) and differentiates all except smoking vs asthma populations. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-022-08600-1.
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Affiliation(s)
- Ivan Dudurych
- Department of Radiology, EB49, University Medical Centre Groningen, University of Groningen, Hanzeplein 1, 9700RB, Groningen, The Netherlands
| | - Susan Muiser
- Department of Pulmonology, University Medical Centre Groningen, University of Groningen, Groningen, The Netherlands
| | - Niall McVeigh
- Department of Cardiothoracic Surgery, St. Vincent's University Hospital, Dublin, Ireland
| | - Huib A M Kerstjens
- Department of Pulmonology, University Medical Centre Groningen, University of Groningen, Groningen, The Netherlands
| | - Maarten van den Berge
- Department of Pulmonology, University Medical Centre Groningen, University of Groningen, Groningen, The Netherlands
| | - Marleen de Bruijne
- Department of Radiology and Nuclear Medicine, Biomedical Imaging Group Rotterdam, Erasmus MC, Rotterdam, The Netherlands
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Rozemarijn Vliegenthart
- Department of Radiology, EB49, University Medical Centre Groningen, University of Groningen, Hanzeplein 1, 9700RB, Groningen, The Netherlands.
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15
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Kim T, Kim WJ, Lee CH, Chae KJ, Bak SH, Kwon SO, Jin GY, Park EK, Choi S. Quantitative computed tomography imaging-based classification of cement dust-exposed subjects with an artificial neural network technique. Comput Biol Med 2021; 141:105162. [PMID: 34973583 DOI: 10.1016/j.compbiomed.2021.105162] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Revised: 12/06/2021] [Accepted: 12/19/2021] [Indexed: 12/12/2022]
Abstract
BACKGROUND AND OBJECTIVE Cement dust exposure is likely to affect the structural and functional alterations in segmental airways and parenchymal lungs. This study develops an artificial neural network (ANN) model for identifying cement dust-exposed (CDE) subjects using quantitative computed tomography-based airway structural and functional features. METHODS We obtained the airway features in five central and five sub-grouped segmental regions and the lung features in five lobar regions and one total lung region from 311 CDE and 298 non-CDE (NCDE) subjects. The five-fold cross-validation method was adopted to train the following classification models:ANN, support vector machine (SVM), logistic regression (LR), and decision tree (DT). For all the classification models, linear discriminant analysis (LDA) and genetic algorithm (GA) were applied for dimensional reduction and hyperparameterization, respectively. The ANN model without LDA was also optimized by the GA method to observe the effect of the dimensional reduction. RESULTS The genetically optimized ANN model without the LDA method was the best in terms of the classification accuracy. The accuracy, sensitivity, and specificity of the GA-ANN model with four layers were greater than those of the other classification models (i.e., ANN, SVM, LR, and DT using LDA and GA methods) in the five-fold cross-validation. The average values of accuracy, sensitivity, and specificity for the five-fold cross-validation were 97.0%, 98.7%, and 98.6%, respectively. CONCLUSIONS We demonstrated herein that a quantitative computed tomography-based ANN model could more effectively detect CDE subjects when compared to their counterpart models. By employing the model, the CDE subjects may be identified early for therapeutic intervention.
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Affiliation(s)
- Taewoo Kim
- School of Mechanical Engineering, Kyungpook National University, Daegu, Republic of Korea
| | - Woo Jin Kim
- Department of Internal Medicine and Environmental Health Center, Kangwon National University Hospital, School of Medicine, Kangwon National University, Chuncheon, Republic of Korea
| | - Chang Hyun Lee
- Department of Radiology, College of Medicine, Seoul National University, Seoul, Republic of Korea; Department of Radiology, College of Medicine, The University of Iowa, Iowa City, IA, USA
| | - Kum Ju Chae
- Department of Radiology, Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Republic of Korea
| | - So Hyeon Bak
- Department of Radiology, Kangwon National University Hospital, School of Medicine, Kangwon National University, Chuncheon, Republic of Korea
| | - Sung Ok Kwon
- Department of Internal Medicine and Environmental Health Center, Kangwon National University Hospital, School of Medicine, Kangwon National University, Chuncheon, Republic of Korea
| | - Gong Yong Jin
- Department of Radiology, Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Republic of Korea
| | - Eun-Kee Park
- Department of Medical Humanities and Social Medicine, College of Medicine, Kosin University, Busan, Republic of Korea
| | - Sanghun Choi
- School of Mechanical Engineering, Kyungpook National University, Daegu, Republic of Korea.
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16
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Tanabe N, Hirai T. Recent advances in airway imaging using micro-computed tomography and computed tomography for chronic obstructive pulmonary disease. Korean J Intern Med 2021; 36:1294-1304. [PMID: 34607419 PMCID: PMC8588974 DOI: 10.3904/kjim.2021.124] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 05/14/2021] [Indexed: 12/13/2022] Open
Abstract
Chronic obstructive pulmonary disease (COPD) is a complex lung disease characterized by a combination of airway disease and emphysema. Emphysema is classified as centrilobular emphysema (CLE), paraseptal emphysema (PSE), or panlobular emphysema (PLE), and airway disease extends from the respiratory, terminal, and preterminal bronchioles to the central segmental airways. Although clinical computed tomography (CT) cannot be used to visualize the small airways, micro-CT has shown that terminal bronchiole disease is more severe in CLE than in PSE and PLE, and micro-CT findings suggest that the loss and luminal narrowing of terminal bronchioles is an early pathological change in CLE. Furthermore, the introduction of ultra-high-resolution CT has enabled direct evaluation of the proximal small (1 to 2-mm diameter) airways, and new CT analytical methods have enabled estimation of small airway disease and prediction of future COPD onset and lung function decline in smokers with and without COPD. This review discusses the literature on micro-CT and the technical advancements in clinical CT analysis for COPD. Hopefully, novel micro-CT findings will improve our understanding of the distinct pathogeneses of the emphysema subtypes to enable exploration of new therapeutic targets, and sophisticated CT imaging methods will be integrated into clinical practice to achieve more personalized management.
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Affiliation(s)
- Naoya Tanabe
- Department of Respiratory Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Toyohiro Hirai
- Department of Respiratory Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
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17
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Zou C, Li F, Choi J, Haghighi B, Choi S, Rajaraman PK, Comellas AP, Newell JD, Lee CH, Barr RG, Bleecker E, Cooper CB, Couper D, Han M, Hansel NN, Kanner RE, Kazerooni EA, Kleerup EC, Martinez FJ, O’Neal W, Paine R, Rennard SI, Smith BM, Woodruff PG, Hoffman EA, Lin CL. Longitudinal Imaging-Based Clusters in Former Smokers of the COPD Cohort Associate with Clinical Characteristics: The SubPopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS). Int J Chron Obstruct Pulmon Dis 2021; 16:1477-1496. [PMID: 34103907 PMCID: PMC8178702 DOI: 10.2147/copd.s301466] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 04/19/2021] [Indexed: 11/23/2022] Open
Abstract
PURPOSE Quantitative computed tomography (qCT) imaging-based cluster analysis identified clinically meaningful COPD former-smoker subgroups (clusters) based on cross-sectional data. We aimed to identify progression clusters for former smokers using longitudinal data. PATIENTS AND METHODS We selected 472 former smokers from SPIROMICS with a baseline visit and a one-year follow-up visit. A total of 150 qCT imaging-based variables, comprising 75 variables at baseline and their corresponding progression rates, were derived from the respective inspiration and expiration scans of the two visits. The COPD progression clusters identified were then associated with subject demography, clinical variables and biomarkers. RESULTS COPD severities at baseline increased with increasing cluster number. Cluster 1 patients were an obese subgroup with rapid progression of functional small airway disease percentage (fSAD%) and emphysema percentage (Emph%). Cluster 2 exhibited a decrease of fSAD% and Emph%, an increase of tissue fraction at total lung capacity and airway narrowing over one year. Cluster 3 showed rapid expansion of Emph% and an attenuation of fSAD%. Cluster 4 demonstrated severe emphysema and fSAD and significant structural alterations at baseline with rapid progression of fSAD% over one year. Subjects with different progression patterns in the same cross-sectional cluster were identified by longitudinal clustering. CONCLUSION qCT imaging-based metrics at two visits for former smokers allow for the derivation of four statistically stable clusters associated with unique progression patterns and clinical characteristics. Use of baseline variables and their progression rates enables identification of longitudinal clusters, resulting in a refinement of cross-sectional clusters.
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Affiliation(s)
- Chunrui Zou
- Department of Mechanical Engineering, University of Iowa, Iowa City, IA, USA
- IIHR-Hydroscience & Engineering, University of Iowa, Iowa City, IA, USA
| | - Frank Li
- IIHR-Hydroscience & Engineering, University of Iowa, Iowa City, IA, USA
- Department of Biomedical Engineering, University of Iowa, Iowa City, IA, USA
| | - Jiwoong Choi
- Department of Mechanical Engineering, University of Iowa, Iowa City, IA, USA
- Department of Internal Medicine, School of Medicine, University of Kansas, Kansas City, KS, USA
| | - Babak Haghighi
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sanghun Choi
- School of Mechanical Engineering, Kyungpook National University, Daegu, Republic of Korea
| | - Prathish K Rajaraman
- Department of Mechanical Engineering, University of Iowa, Iowa City, IA, USA
- IIHR-Hydroscience & Engineering, University of Iowa, Iowa City, IA, USA
| | | | - John D Newell
- Department of Radiology, University of Iowa, Iowa City, IA, USA
| | - Chang Hyun Lee
- Department of Radiology, University of Iowa, Iowa City, IA, USA
- Department of Radiology, College of Medicine, Seoul National University, Seoul, Republic of Korea
| | - R Graham Barr
- Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Eugene Bleecker
- Department of Medicine, The University of Arizona, Tucson, AZ, USA
| | | | - David Couper
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
| | - Meilan Han
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | | | | | - Ella A Kazerooni
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | | | | | - Wanda O’Neal
- School of Medicine, University of North Carolina, Chapel Hill, NC, USA
| | - Robert Paine
- School of Medicine, University of Utah, Salt Lake City, UT, USA
| | - Stephen I Rennard
- Department of Internal Medicine, University of Nebraska College of Medicine, Omaha, NE, USA
| | - Benjamin M Smith
- Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, NY, USA
- Department of Medicine, McGill University Health Centre Research Institute, Montreal, Canada
| | - Prescott G Woodruff
- Department of Medicine, University of California at San Francisco, San Francisco, CA, USA
| | - Eirc A Hoffman
- Department of Biomedical Engineering, University of Iowa, Iowa City, IA, USA
- Department of Internal Medicine, University of Iowa, Iowa City, IA, USA
- Department of Radiology, University of Iowa, Iowa City, IA, USA
| | - Ching-Long Lin
- Department of Mechanical Engineering, University of Iowa, Iowa City, IA, USA
- IIHR-Hydroscience & Engineering, University of Iowa, Iowa City, IA, USA
- Department of Biomedical Engineering, University of Iowa, Iowa City, IA, USA
- Department of Radiology, University of Iowa, Iowa City, IA, USA
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18
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Li F, Choi J, Zou C, Newell JD, Comellas AP, Lee CH, Ko H, Barr RG, Bleecker ER, Cooper CB, Abtin F, Barjaktarevic I, Couper D, Han M, Hansel NN, Kanner RE, Paine R, Kazerooni EA, Martinez FJ, O'Neal W, Rennard SI, Smith BM, Woodruff PG, Hoffman EA, Lin CL. Latent traits of lung tissue patterns in former smokers derived by dual channel deep learning in computed tomography images. Sci Rep 2021; 11:4916. [PMID: 33649381 PMCID: PMC7921389 DOI: 10.1038/s41598-021-84547-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Accepted: 02/15/2021] [Indexed: 11/30/2022] Open
Abstract
Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease and the traditional variables extracted from computed tomography (CT) images may not be sufficient to describe all the topological features of lung tissues in COPD patients. We employed an unsupervised three-dimensional (3D) convolutional autoencoder (CAE)-feature constructor (FC) deep learning network to learn from CT data and derive tissue pattern-clusters jointly. We then applied exploratory factor analysis (EFA) to discover the unobserved latent traits (factors) among pattern-clusters. CT images at total lung capacity (TLC) and residual volume (RV) of 541 former smokers and 59 healthy non-smokers from the cohort of the SubPopulations and Intermediate Outcome Measures in the COPD Study (SPIROMICS) were analyzed. TLC and RV images were registered to calculate the Jacobian (determinant) values for all the voxels in TLC images. 3D Regions of interest (ROIs) with two data channels of CT intensity and Jacobian value were randomly extracted from training images and were fed to the 3D CAE-FC model. 80 pattern-clusters and 7 factors were identified. Factor scores computed for individual subjects were able to predict spirometry-measured pulmonary functions. Two factors which correlated with various emphysema subtypes, parametric response mapping (PRM) metrics, airway variants, and airway tree to lung volume ratio were discriminants of patients across all severity stages. Our findings suggest the potential of developing factor-based surrogate markers for new COPD phenotypes.
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Affiliation(s)
- Frank Li
- Department of Biomedical Engineering, University of Iowa, Iowa City, IA, USA
- IIHR-Hydroscience and Engineering, 2406 Seamans Center for the Engineering Art and Science, University of Iowa, Iowa City, IA, 52242, USA
| | - Jiwoong Choi
- Department of Mechanical Engineering, University of Iowa, Iowa City, IA, USA
- Department of Internal Medicine, School of Medicine, University of Kansas, Kansas City, KS, USA
| | - Chunrui Zou
- IIHR-Hydroscience and Engineering, 2406 Seamans Center for the Engineering Art and Science, University of Iowa, Iowa City, IA, 52242, USA
- Department of Mechanical Engineering, University of Iowa, Iowa City, IA, USA
| | - John D Newell
- Department of Radiology, University of Iowa, Iowa City, IA, USA
| | | | - Chang Hyun Lee
- Department of Radiology, University of Iowa, Iowa City, IA, USA
- Department of Radiology, Seoul National University, Seoul, Republic of Korea
| | - Hongseok Ko
- Department of Radiology, Chungnam National University Sejong Hospital, Sejong, Republic of Korea
| | - R Graham Barr
- Mailman School of Public Health, Columbia University, New York, NY, USA
| | | | | | | | | | - David Couper
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
| | - MeiLan Han
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | | | | | - Robert Paine
- School of Medicine, University of Utah, Salt Lake City, UT, USA
| | - Ella A Kazerooni
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | | | - Wanda O'Neal
- School of Medicine, University of North Carolina, Chapel Hill, NC, USA
| | - Stephen I Rennard
- Department of Internal Medicine, University of Nebraska College of Medicine, Omaha, NE, USA
| | - Benjamin M Smith
- Department of Medicine, Columbia University, New York, NY, USA
- Research Institute, McGill University Health Center, Montreal, Canada
| | | | - Eric A Hoffman
- Department of Biomedical Engineering, University of Iowa, Iowa City, IA, USA
- Department of Radiology, University of Iowa, Iowa City, IA, USA
- Department of Internal Medicine, University of Iowa, Iowa City, IA, USA
| | - Ching-Long Lin
- Department of Biomedical Engineering, University of Iowa, Iowa City, IA, USA.
- IIHR-Hydroscience and Engineering, 2406 Seamans Center for the Engineering Art and Science, University of Iowa, Iowa City, IA, 52242, USA.
- Department of Mechanical Engineering, University of Iowa, Iowa City, IA, USA.
- Department of Radiology, University of Iowa, Iowa City, IA, USA.
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Nagpal P, Guo J, Shin KM, Lim JK, Kim KB, Comellas AP, Kaczka DW, Peterson S, Lee CH, Hoffman EA. Quantitative CT imaging and advanced visualization methods: potential application in novel coronavirus disease 2019 (COVID-19) pneumonia. BJR Open 2021; 3:20200043. [PMID: 33718766 PMCID: PMC7931412 DOI: 10.1259/bjro.20200043] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 12/01/2020] [Accepted: 12/16/2020] [Indexed: 12/13/2022] Open
Abstract
Increasingly, quantitative lung computed tomography (qCT)-derived metrics are providing novel insights into chronic inflammatory lung diseases, including chronic obstructive pulmonary disease, asthma, interstitial lung disease, and more. Metrics related to parenchymal, airway, and vascular anatomy together with various measures associated with lung function including regional parenchymal mechanics, air trapping associated with functional small airways disease, and dual-energy derived measures of perfused blood volume are offering the ability to characterize disease phenotypes associated with the chronic inflammatory pulmonary diseases. With the emergence of COVID-19, together with its widely varying degrees of severity, its rapid progression in some cases, and the potential for lengthy post-COVID-19 morbidity, there is a new role in applying well-established qCT-based metrics. Based on the utility of qCT tools in other lung diseases, previously validated supervised classical machine learning methods, and emerging unsupervised machine learning and deep-learning approaches, we are now able to provide desperately needed insight into the acute and the chronic phases of this inflammatory lung disease. The potential areas in which qCT imaging can be beneficial include improved accuracy of diagnosis, identification of clinically distinct phenotypes, improvement of disease prognosis, stratification of care, and early objective evaluation of intervention response. There is also a potential role for qCT in evaluating an increasing population of post-COVID-19 lung parenchymal changes such as fibrosis. In this work, we discuss the basis of various lung qCT methods, using case-examples to highlight their potential application as a tool for the exploration and characterization of COVID-19, and offer scanning protocols to serve as templates for imaging the lung such that these established qCT analyses have the best chance at yielding the much needed new insights.
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Affiliation(s)
- Prashant Nagpal
- Department of Radiology, University of Iowa, Carver College of Medicine, Iowa City, IA, USA
| | | | | | - Jae-Kwang Lim
- Department of Radiology, School of Medicine, Kyungpook National University, Daegu, South Korea
| | - Ki Beom Kim
- Department of Radiology, Daegu Fatima Hospital, Daegu, South Korea
| | - Alejandro P Comellas
- Department of Internal Medicine, University of Iowa, Carver College of Medicine, Iowa City, IA, USA
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20
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Chae KJ, Choi J, Jin GY, Hoffman EA, Laroia AT, Park M, Lee CH. Relative Regional Air Volume Change Maps at the Acinar Scale Reflect Variable Ventilation in Low Lung Attenuation of COPD patients. Acad Radiol 2020; 27:1540-1548. [PMID: 32024604 DOI: 10.1016/j.acra.2019.12.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2019] [Revised: 12/12/2019] [Accepted: 12/14/2019] [Indexed: 12/25/2022]
Abstract
OBJECTIVES The purpose of this study was to investigate regional air volume changes at the acinar scale of the lung in chronic obstructive pulmonary disease (COPD) patients using an image registration technique. MATERIALS AND METHODS Thirty-four emphysema patients and 24 subjects with normal chest CT and pulmonary function test (PFT) results were included in this retrospective study for which informed consent was waived by the institutional review board. After lung segmentation, a mass-preserving image registration technique was used to compute relative regional air volume changes (RRAVCs) between inspiration and expiration CT scans. After determining the appropriate thresholds of RRAVCs for low ventilation areas (LVAs), they were displayed and analyzed using color maps on the background inspiration CT image, and compared with the low attenuation area (LAA) map. Correlations between quantitative CT parameters and PFTs were assessed using Pearson's correlation test, and parameters were compared between emphysema and normal-CT patients using the Student's t-test. RESULTS LVA percentage with an RRAVC threshold of 0.5 (%LVA0.5) showed the strongest correlations with FEV1/FVC (r = -0.566), FEV1 (r = -0.534), %LAA-950insp (r = 0.712), and %LAA-856exp (r = 0.775). %LVA0.5 was significantly higher (P < 0.001) in COPD patients than normal subjects. Despite the identical appearance of emphysematous lesions on the LAA-950insp map, the RRAVC map depicted a wide range of ventilation differences between these LAA clusters. CONCLUSION RRAVC-based %LVA0.5 correlated well with FEV1/FVC, FEV1, %LAA-950insp and %LAA-856exp. RRAVC holds the potential for providing additional acinar scale functional information for emphysematous LAAs in inspiratory CT images, providing the basis for a novel set for emphysematous phenotypes.
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Nikolaou V, Massaro S, Fakhimi M, Stergioulas L, Price D. COPD phenotypes and machine learning cluster analysis: A systematic review and future research agenda. Respir Med 2020; 171:106093. [PMID: 32745966 DOI: 10.1016/j.rmed.2020.106093] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Revised: 07/19/2020] [Accepted: 07/21/2020] [Indexed: 12/21/2022]
Abstract
Chronic Obstructive Pulmonary Disease (COPD) is a highly heterogeneous condition projected to become the third leading cause of death worldwide by 2030. To better characterize this condition, clinicians have classified patients sharing certain symptomatic characteristics, such as symptom intensity and history of exacerbations, into distinct phenotypes. In recent years, the growing use of machine learning algorithms, and cluster analysis in particular, has promised to advance this classification through the integration of additional patient characteristics, including comorbidities, biomarkers, and genomic information. This combination would allow researchers to more reliably identify new COPD phenotypes, as well as better characterize existing ones, with the aim of improving diagnosis and developing novel treatments. Here, we systematically review the last decade of research progress, which uses cluster analysis to identify COPD phenotypes. Collectively, we provide a systematized account of the extant evidence, describe the strengths and weaknesses of the main methods used, identify gaps in the literature, and suggest recommendations for future research.
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Affiliation(s)
- Vasilis Nikolaou
- Surrey Business School, University of Surrey, Guildford, GU2 7HX, UK.
| | - Sebastiano Massaro
- Surrey Business School, University of Surrey, Guildford, GU2 7HX, UK; The Organizational Neuroscience Laboratory, London, WC1N 3AX, UK
| | - Masoud Fakhimi
- Surrey Business School, University of Surrey, Guildford, GU2 7HX, UK
| | | | - David Price
- Observational and Pragmatic Research Institute, Singapore, Singapore; Centre of Academic Primary Care, Division of Applied Health Sciences, University of Aberdeen, Aberdeen, UK
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22
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Wang S, Yang DM, Rong R, Zhan X, Fujimoto J, Liu H, Minna J, Wistuba II, Xie Y, Xiao G. Artificial Intelligence in Lung Cancer Pathology Image Analysis. Cancers (Basel) 2019; 11:E1673. [PMID: 31661863 PMCID: PMC6895901 DOI: 10.3390/cancers11111673] [Citation(s) in RCA: 97] [Impact Index Per Article: 19.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 10/17/2019] [Accepted: 10/21/2019] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVE Accurate diagnosis and prognosis are essential in lung cancer treatment selection and planning. With the rapid advance of medical imaging technology, whole slide imaging (WSI) in pathology is becoming a routine clinical procedure. An interplay of needs and challenges exists for computer-aided diagnosis based on accurate and efficient analysis of pathology images. Recently, artificial intelligence, especially deep learning, has shown great potential in pathology image analysis tasks such as tumor region identification, prognosis prediction, tumor microenvironment characterization, and metastasis detection. MATERIALS AND METHODS In this review, we aim to provide an overview of current and potential applications for AI methods in pathology image analysis, with an emphasis on lung cancer. RESULTS We outlined the current challenges and opportunities in lung cancer pathology image analysis, discussed the recent deep learning developments that could potentially impact digital pathology in lung cancer, and summarized the existing applications of deep learning algorithms in lung cancer diagnosis and prognosis. DISCUSSION AND CONCLUSION With the advance of technology, digital pathology could have great potential impacts in lung cancer patient care. We point out some promising future directions for lung cancer pathology image analysis, including multi-task learning, transfer learning, and model interpretation.
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Affiliation(s)
- Shidan Wang
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
| | - Donghan M Yang
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
| | - Ruichen Rong
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
| | - Xiaowei Zhan
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
| | - Junya Fujimoto
- Department of Translational Molecular Pathology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
| | - Hongyu Liu
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
| | - John Minna
- Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
- Hamon Center for Therapeutic Oncology Research, UT Southwestern Medical Center, Dallas, TX 75390, USA.
- Departments of Internal Medicine and Pharmacology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
| | - Ignacio Ivan Wistuba
- Department of Translational Molecular Pathology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
| | - Yang Xie
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
- Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
- Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
| | - Guanghua Xiao
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
- Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
- Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
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23
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Qian K, Deng Y, Shen C, Feng YG, Deng B, Tan QY. Combination of electromagnetic navigation bronchoscopy-guided biopsy with a novel staining for peripheral pulmonary lesions. World J Surg Oncol 2019; 17:158. [PMID: 31506081 PMCID: PMC6737641 DOI: 10.1186/s12957-019-1704-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Accepted: 09/05/2019] [Indexed: 11/18/2022] Open
Abstract
Background The diagnosis of peripheral pulmonary lesions (PPLs) is a challenging task for pulmonologists, especially for small PPLs. Conventional localization of these small PPLs, which are > 1 cm away from the visceral pleura in operation, is quite difficult. Currently used methods inevitably damage the visceral pleura and may cause a series of complications, such as pneumothorax and hemothorax. Hence, the present study aimed to find out an intraoperative localization method with no damage to the visceral pleura. Methods We retrospectively reviewed 21 patients with PLLs who underwent electromagnetic navigation bronchoscopy (ENB)-guided biopsy plus a new methylene blue staining with the help of massage (Massage Staining) in our department between August 2017 and December 2018. Results The median age of these 21 patients was 51.3 ± 2.1 years. The diameter of the PPLs was 8.2 ± 2.3 mm. The rate of successful biopsy was 76.2%, and the rate of excellent or satisfactory of Massage Staining was 81.0%, while all lesions of these 21 cases were included in the range of staining, and the median distance from the edge of the stained site to the edge of the lesion was 29 ± 18 mm. The duration of ENB-guided biopsy plus Massage Staining was 26.7 ± 5.3 min, and the intraoperative blood loss was 3.3 ± 1.5 ml. No pneumothorax, hemorrhage, and tracheal injury occurred intraoperatively. Conclusions The ENB-guided biopsy combined with Massage Staining is an innovative one-stop strategy designed to enhance the precision of thoracic surgery. The Massage Staining avoids damage to the visceral pleura, causes the low incidence of complications, but yields precise localization of PPLs.
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Affiliation(s)
- Kai Qian
- Department of Thoracic Surgery, Institute of Surgery Research, Daping Hospital, Army Medical University, Chongqing, China
| | - Yi Deng
- Department of Oncology, Institute of Surgery Research, Daping Hospital, Army Medical University, Chongqing, China
| | - Cheng Shen
- Department of Thoracic Surgery, Institute of Surgery Research, Daping Hospital, Army Medical University, Chongqing, China
| | - Yong-Geng Feng
- Department of Thoracic Surgery, Institute of Surgery Research, Daping Hospital, Army Medical University, Chongqing, China
| | - Bo Deng
- Department of Thoracic Surgery, Institute of Surgery Research, Daping Hospital, Army Medical University, Chongqing, China.
| | - Qun-You Tan
- Department of Thoracic Surgery, Institute of Surgery Research, Daping Hospital, Army Medical University, Chongqing, China.
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