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Stoleriu MG, Pienn M, Joerres RA, Alter P, Fero T, Urschler M, Kovacs G, Olschewski H, Kauczor HU, Wielpütz M, Jobst B, Welte T, Behr J, Trudzinski FC, Bals R, Watz H, Vogelmeier CF, Biederer J, Kahnert K. Expiratory Venous Volume and Arterial Tortuosity are Associated with Disease Severity and Mortality Risk in Patients with COPD: Results from COSYCONET. Int J Chron Obstruct Pulmon Dis 2024; 19:1515-1529. [PMID: 38974817 PMCID: PMC11227296 DOI: 10.2147/copd.s458905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2024] [Accepted: 06/10/2024] [Indexed: 07/09/2024] Open
Abstract
Purpose The aim of this study was to evaluate the association between computed tomography (CT) quantitative pulmonary vessel morphology and lung function, disease severity, and mortality risk in patients with chronic obstructive pulmonary disease (COPD). Patients and Methods Participants of the prospective nationwide COSYCONET cohort study with paired inspiratory-expiratory CT were included. Fully automatic software, developed in-house, segmented arterial and venous pulmonary vessels and quantified volume and tortuosity on inspiratory and expiratory scans. The association between vessel volume normalised to lung volume and tortuosity versus lung function (forced expiratory volume in 1 sec [FEV1]), air trapping (residual volume to total lung capacity ratio [RV/TLC]), transfer factor for carbon monoxide (TLCO), disease severity in terms of Global Initiative for Chronic Obstructive Lung Disease (GOLD) group D, and mortality were analysed by linear, logistic or Cox proportional hazard regression. Results Complete data were available from 138 patients (39% female, mean age 65 years). FEV1, RV/TLC and TLCO, all as % predicted, were significantly (p < 0.05 each) associated with expiratory vessel characteristics, predominantly venous volume and arterial tortuosity. Associations with inspiratory vessel characteristics were absent or negligible. The patterns were similar for relationships between GOLD D and mortality with vessel characteristics. Expiratory venous volume was an independent predictor of mortality, in addition to FEV1. Conclusion By using automated software in patients with COPD, clinically relevant information on pulmonary vasculature can be extracted from expiratory CT scans (although not inspiratory scans); in particular, expiratory pulmonary venous volume predicted mortality. Trial Registration NCT01245933.
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Affiliation(s)
- Mircea Gabriel Stoleriu
- Division for Thoracic Surgery Munich, Ludwig-Maximilians-University of Munich (LMU) and Asklepios Medical Center; Munich-Gauting, Gauting, 82131, Germany
- Institute for Lung Health and Immunity and Comprehensive Pneumology Center with the CPC-M bioArchive; Helmholtz Center Munich; Member of the German Lung Research Center (DZL), Munich, 81377, Germany
| | - Michael Pienn
- Ludwig Boltzmann Institute for Lung Vascular Research, Graz, Austria
- Division of Pulmonology, Department of Internal Medicine, Medical University of Graz, Graz, Austria
| | - Rudolf A Joerres
- Institute and Outpatient Clinic for Occupational, Social and Environmental Medicine, Hospital of Ludwig-Maximilians-University Munich (LMU), Munich, 80336, Germany
| | - Peter Alter
- Department of Medicine, Pulmonary and Critical Care Medicine, University of Marburg (UMR), Member of the German Center for Lung Research (DZL), Marburg, 35033, Germany
| | - Tamas Fero
- Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany
| | - Martin Urschler
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria
| | - Gabor Kovacs
- Ludwig Boltzmann Institute for Lung Vascular Research, Graz, Austria
- University Clinic for Internal Medicine, Medical University of Graz, Division of Pulmonology, Graz, Austria
| | - Horst Olschewski
- Ludwig Boltzmann Institute for Lung Vascular Research, Graz, Austria
- University Clinic for Internal Medicine, Medical University of Graz, Division of Pulmonology, Graz, Austria
| | - Hans-Ulrich Kauczor
- Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany
- Translational Lung Research Center Heidelberg, Member of the German Center for Lung Research DZL, Heidelberg, Germany
| | - Mark Wielpütz
- Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany
- Translational Lung Research Center Heidelberg, Member of the German Center for Lung Research DZL, Heidelberg, Germany
| | - Bertram Jobst
- Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany
- Translational Lung Research Center Heidelberg, Member of the German Center for Lung Research DZL, Heidelberg, Germany
| | - Tobias Welte
- Department of Respiratory Medicine and Infectious Disease, Member of the German Center of Lung Research, Hannover School of Medicine, Hannover, Germany
| | - Jürgen Behr
- Institute for Lung Health and Immunity and Comprehensive Pneumology Center with the CPC-M bioArchive; Helmholtz Center Munich; Member of the German Lung Research Center (DZL), Munich, 81377, Germany
- Department of Medicine V, LMU University Hospital, LMU Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Franziska C Trudzinski
- Translational Lung Research Center Heidelberg, Member of the German Center for Lung Research DZL, Heidelberg, Germany
- Department of Pneumology and Critical Care Medicine, Thoraxklinik, University of Heidelberg, Heidelberg, Germany
| | - Robert Bals
- Department of Internal Medicine V-Pulmonology, Allergology and Respiratory Critical Care Medicine, Saarland University, Homburg, 66421, Germany
- Helmholtz Institute for Pharmaceutical Research, Saarbrücken, 66123, Germany
| | - Henrik Watz
- Pulmonary Research Institute at LungenClinic Grosshansdorf, Airway Research Centre North, German Centre for Lung Research, Großhansdorf, Germany
| | - Claus F Vogelmeier
- Department of Medicine, Pulmonary and Critical Care Medicine, University of Marburg (UMR), Member of the German Center for Lung Research (DZL), Marburg, 35033, Germany
| | - Jürgen Biederer
- Translational Lung Research Center Heidelberg, Member of the German Center for Lung Research DZL, Heidelberg, Germany
- Faculty of Medicine, Christian-Albrechts-Universität Zu Kiel, Kiel, Germany
- University of Latvia, Faculty of Medicine, Riga, LV-1586, Latvia
| | - Kathrin Kahnert
- Institute for Lung Health and Immunity and Comprehensive Pneumology Center with the CPC-M bioArchive; Helmholtz Center Munich; Member of the German Lung Research Center (DZL), Munich, 81377, Germany
- Department of Medicine V, LMU University Hospital, LMU Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
- MediCenterGermering, Germering, Germany
| | - On behalf of the COSYCONET Study Group
- Division for Thoracic Surgery Munich, Ludwig-Maximilians-University of Munich (LMU) and Asklepios Medical Center; Munich-Gauting, Gauting, 82131, Germany
- Institute for Lung Health and Immunity and Comprehensive Pneumology Center with the CPC-M bioArchive; Helmholtz Center Munich; Member of the German Lung Research Center (DZL), Munich, 81377, Germany
- Ludwig Boltzmann Institute for Lung Vascular Research, Graz, Austria
- Division of Pulmonology, Department of Internal Medicine, Medical University of Graz, Graz, Austria
- Institute and Outpatient Clinic for Occupational, Social and Environmental Medicine, Hospital of Ludwig-Maximilians-University Munich (LMU), Munich, 80336, Germany
- Department of Medicine, Pulmonary and Critical Care Medicine, University of Marburg (UMR), Member of the German Center for Lung Research (DZL), Marburg, 35033, Germany
- Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria
- University Clinic for Internal Medicine, Medical University of Graz, Division of Pulmonology, Graz, Austria
- Translational Lung Research Center Heidelberg, Member of the German Center for Lung Research DZL, Heidelberg, Germany
- Department of Respiratory Medicine and Infectious Disease, Member of the German Center of Lung Research, Hannover School of Medicine, Hannover, Germany
- Department of Medicine V, LMU University Hospital, LMU Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
- Department of Pneumology and Critical Care Medicine, Thoraxklinik, University of Heidelberg, Heidelberg, Germany
- Department of Internal Medicine V-Pulmonology, Allergology and Respiratory Critical Care Medicine, Saarland University, Homburg, 66421, Germany
- Helmholtz Institute for Pharmaceutical Research, Saarbrücken, 66123, Germany
- Pulmonary Research Institute at LungenClinic Grosshansdorf, Airway Research Centre North, German Centre for Lung Research, Großhansdorf, Germany
- Faculty of Medicine, Christian-Albrechts-Universität Zu Kiel, Kiel, Germany
- University of Latvia, Faculty of Medicine, Riga, LV-1586, Latvia
- MediCenterGermering, Germering, Germany
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Salyapongse AM, Kanne JP, Nagpal P, Laucis NC, Markhardt BK, Yin Z, Slavic S, Lubner MG, Szczykutowicz TP. Spatial Resolution Fidelity Comparison Between Energy Integrating and Deep Silicon Photon Counting CT: Implications for Pulmonary Imaging. J Thorac Imaging 2024:00005382-990000000-00137. [PMID: 38712920 DOI: 10.1097/rti.0000000000000788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
PURPOSE We investigated spatial resolution loss away from isocenter for a prototype deep silicon photon-counting detector (PCD) CT scanner and compare with a clinical energy-integrating detector (EID) CT scanner. MATERIALS AND METHODS We performed three scans on a wire phantom at four positions (isocenter, 6.7, 11.8, and 17.1 cm off isocenter). The acquisition modes were 120 kV EID CT, 120 kV high-definition (HD) EID CT, and 120 kV PCD CT. HD mode used double the projection view angles per rotation as the "regular" EID scan mode. The diameter of the wire was calculated by taking the full width of half max (FWHM) of a profile drawn over the radial and azimuthal directions of the wire. Change in wire diameter appearance was assessed by calculating the ratio of the radial and azimuthal diameter relative to isocenter. t tests were used to make pairwise comparisons of the wire diameter ratio with each acquisition and mean ratios' difference from unity. RESULTS Deep silicon PCD CT had statistically smaller (P<0.05) changes in diameter ratio for both radial and azimuthal directions compared with both regular and HD EID modes and was not statistically different from unity (P<0.05). Maximum increases in FWMH relative to isocenter were 36%, 12%, and 1% for regular EID, HD EID, and deep silicon PCD, respectively. CONCLUSION Deep silicon PCD CT exhibits less change in spatial resolution in both the radial and azimuthal directions compared with EID CT.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Timothy P Szczykutowicz
- Departments of Radiology, Medical Physics, and Biomedical Engineering, University of Wisconsin Madison, Madison, WI
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Brath MSG, Alsted SD, Sahakyan M, Mark EB, Frøkjær JB, Rasmussen HH, Østergaard LR, Christensen RB, Weinreich UM. Association between the Static and Dynamic Lung Function and CT-Derived Thoracic Skeletal Muscle Measurements-A Retrospective Analysis of a 12-Month Observational Follow-Up Pilot Study. Adv Respir Med 2024; 92:123-144. [PMID: 38525774 PMCID: PMC10961694 DOI: 10.3390/arm92020015] [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: 12/23/2023] [Revised: 02/26/2024] [Accepted: 02/28/2024] [Indexed: 03/26/2024]
Abstract
BACKGROUND Patients with chronic obstructive pulmonary disease (COPD) with low skeletal muscle mass and severe airway obstruction have higher mortality risks. However, the relationship between dynamic/static lung function (LF) and thoracic skeletal muscle measurements (SMM) remains unclear. This study explored patient characteristics (weight, BMI, exacerbations, dynamic/static LF, sex differences in LF and SMM, and the link between LF and SMM changes. METHODS A retrospective analysis of a 12-month prospective follow-up study patients with stable COPD undergoing standardized treatment, covering mild to severe stages, was conducted. The baseline and follow-up assessments included computed tomography and body plethysmography. RESULTS This study included 35 patients (17 females and 18 males). This study revealed that females had more stable LF but tended to have greater declines in SMM areas and indices than males (-5.4% vs. -1.9%, respectively), despite the fact that females were younger and had higher LF and less exacerbation than males. A multivariate linear regression showed a negative association between the inspiratory capacity/total lung capacity ratio (IC/TLC) and muscle fat area. CONCLUSIONS The findings suggest distinct LF and BC progression patterns between male and female patients with COPD. A low IC/TLC ratio may predict increased muscle fat. Further studies are necessary to understand these relationships better.
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Affiliation(s)
- Mia Solholt Godthaab Brath
- Respiratory Research Aalborg (Reaal), Aalborg University Hospital, 9000 Aalborg, Denmark;
- Department of Clinical Medicine, Aalborg University, 9220 Aalborg, Denmark; (E.B.M.); (J.B.F.); (H.H.R.)
- Department of Respiratory Diseases, Aalborg University Hospital, 9000 Aalborg, Denmark
| | - Sisse Dyrman Alsted
- Department of General Medicine, North Region Hospital–Hjørring, 9800 Hjørring, Denmark;
| | - Marina Sahakyan
- Department of Radiology, Aalborg University Hospital, 9000 Aalborg, Denmark; (M.S.); (R.B.C.)
| | - Esben Bolvig Mark
- Department of Clinical Medicine, Aalborg University, 9220 Aalborg, Denmark; (E.B.M.); (J.B.F.); (H.H.R.)
- Mech-Sense, Department of Gastroenterology and Hepatology, Aalborg University Hospital, 9000 Aalborg, Denmark
| | - Jens Brøndum Frøkjær
- Department of Clinical Medicine, Aalborg University, 9220 Aalborg, Denmark; (E.B.M.); (J.B.F.); (H.H.R.)
- Department of Radiology, Aalborg University Hospital, 9000 Aalborg, Denmark; (M.S.); (R.B.C.)
| | - Henrik Højgaard Rasmussen
- Department of Clinical Medicine, Aalborg University, 9220 Aalborg, Denmark; (E.B.M.); (J.B.F.); (H.H.R.)
- Department of Gastroenterology and Hepatology, Aalborg University Hospital, 9000 Aalborg, Denmark
- Danish Nutrition Science Center, Aalborg University Hospital, 9000 Aalborg, Denmark
- Center for Nutrition and Intestinal Failure, Aalborg University Hospital, 9000 Aalborg, Denmark
- Department of Dietetic and Nutritional Research, Copenhagen University Hospitals, Herlev and Gentofte Hospitals, 2730 Herlev, Denmark
| | - Lasse Riis Østergaard
- Medical Informatics Group, Department of Health Science and Technology, Aalborg University, 9220 Aalborg, Denmark;
| | | | - Ulla Møller Weinreich
- Respiratory Research Aalborg (Reaal), Aalborg University Hospital, 9000 Aalborg, Denmark;
- Department of Clinical Medicine, Aalborg University, 9220 Aalborg, Denmark; (E.B.M.); (J.B.F.); (H.H.R.)
- Department of Respiratory Diseases, Aalborg University Hospital, 9000 Aalborg, Denmark
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Liu Y, Lu C, Chen W, Liu Z, Wu S, Ye H, Lv Y, Peng Z, Wang P, Li G, Tan B, Wu G. Clinical evaluation of pulmonary quantitative computed tomography parameters for diagnosing eosinophilic chronic obstructive pulmonary disease: Characteristics and diagnostic performance. Health Sci Rep 2024; 7:e1734. [PMID: 38500635 PMCID: PMC10944982 DOI: 10.1002/hsr2.1734] [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: 07/05/2023] [Revised: 10/27/2023] [Accepted: 11/05/2023] [Indexed: 03/20/2024] Open
Abstract
Aims To investigate the characteristics and diagnostic performance of quantitative computed tomography (QCT) parameters in eosinophilic chronic obstructive pulmonary disease (COPD) patients. Methods High-resolution CT scans of COPD patients were retrospectively analyzed, and various emphysematous parenchyma measurements, including lung volume (LC), lung mean density (LMD), lung standard deviation (LSD), full-width half maximum (FWHM), and lung relative voxel number (LRVN) were performed. The QCT parameters were compared between eosinophilic and noneosinophilic COPD patients, using a definition of eosinophilic COPD as blood eosinophil values ≥ 300 cells·µL-1 on at least three times. Receiver operating characteristic curves and area under the curve (ROC-AUC) and python were used to evaluate discriminative efficacy of QCT. Results Noneosinophilic COPD patients had a significantly lower TLMD (-846.3 ± 47.9 Hounsfield Unit [HU]) and TFWHM(162.5 ± 30.6 HU) compared to eosinophilic COPD patients (-817.8 ± 54.4, 177.3 ± 33.1 HU, respectively) (p = 0.018, 0.03, respectively). Moreover, the total LC (TLC) and TLSD were significantly lower in eosinophilic COPD group (3234.4 ± 1145.8, 183.8 ± 33.9 HU, respectively) than the noneosinophilic COPD group (5600.2 ± 1248.4, 203.5 ± 20.4 HU, respectively) (p = 0.009, 0.002, respectively). The ROC-AUC values for TLC, TLMD, TLSD, and TFWHM were 0.91 (95% confidence interval [CI], 0.828-0.936), 0.66 (95% CI, 0.546-0.761), 0.64 (95% CI, 0.524-0.742), and 0.63 (95% CI, 0.511-0.731), respectively. When the TLC value was 4110 mL, the sensitivity was 90.7% (95% CI, 79.7-96.9), specificity was 77.8% (95% CI, 57.7-91.4) and accuracy was 86.4%. Notably, TLC demonstrated the highest discriminative efficiency with an F1 Score of 0.79, diagnostic Odds Ratio of 34.3 and Matthews Correlation Coefficient of 0.69, surpassing TLMD (0.55, 3.66, 0.25), TLSD (0.56, 3.95, 0.26), and TFWHM (0.56, 4.16, 0.33). Conclusion Eosinophilic COPD patients exhibit lower levels of emphysema and a more uniform density distribution throughout the lungs compared to noneosinophilic COPD patients. Furthermore, TLC demonstrated the highest diagnostic efficiency and may serve as a valuable diagnostic marker for distinguishing between the two groups.
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Affiliation(s)
- Yumeng Liu
- Department of RadiologyShenzhen University General Hospital, Shenzhen University Clinical Medical AcademyShenzhenChina
| | - Chao Lu
- Department of RadiologyShenzhen University General Hospital, Shenzhen University Clinical Medical AcademyShenzhenChina
| | - Wenfang Chen
- Department of Respiratory MedicineShenzhen University General Hospital, Shenzhen University Clinical Medical AcademyShenzhenChina
| | - Zhenyu Liu
- Department of GastroenterologyShenzhen University General Hospital, Shenzhen University Clinical Medical AcademyShenzhenChina
| | - Songxiong Wu
- Department of RadiologyShenzhen University General Hospital, Shenzhen University Clinical Medical AcademyShenzhenChina
| | - Hai Ye
- Department of RadiologyShenzhen University General Hospital, Shenzhen University Clinical Medical AcademyShenzhenChina
| | - Yungang Lv
- Department of RadiologyShenzhen University General Hospital, Shenzhen University Clinical Medical AcademyShenzhenChina
| | - Zhengkun Peng
- Department of RadiologyShenzhen University General Hospital, Shenzhen University Clinical Medical AcademyShenzhenChina
| | - Panying Wang
- Department of RadiologyShenzhen University General Hospital, Shenzhen University Clinical Medical AcademyShenzhenChina
| | - Guangyao Li
- Department of RadiologyShenzhen University General Hospital, Shenzhen University Clinical Medical AcademyShenzhenChina
| | - Biwen Tan
- Department of RadiologyShenzhen University General Hospital, Shenzhen University Clinical Medical AcademyShenzhenChina
| | - Guangyao Wu
- Department of RadiologyShenzhen University General Hospital, Shenzhen University Clinical Medical AcademyShenzhenChina
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Feng X, Gao P, Li Y, Hui H, Jiang J, Xie F, Tian J. First magnetic particle imaging to assess pulmonary vascular leakage in vivo in the acutely injured and fibrotic lung. Bioeng Transl Med 2024; 9:e10626. [PMID: 38435827 PMCID: PMC10905553 DOI: 10.1002/btm2.10626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 10/17/2023] [Accepted: 11/14/2023] [Indexed: 03/05/2024] Open
Abstract
Increased pulmonary vascular permeability is a characteristic feature of lung injury. However, there are no established methods that allow the three-dimensional visualization and quantification of pulmonary vascular permeability in vivo. Evans blue extravasation test and total protein test of bronchoalveolar lavage fluid (BALF) are permeability assays commonly used in research settings. However, they lack the ability to identify the spatial and temporal heterogeneity of endothelial barrier disruption, which is typical in lung injuries. Magnetic resonance (MR) and near-infrared (NIR) imaging have been proposed to image pulmonary permeability, but suffer from limited sensitivity and penetration depth, respectively. In this study, we report the first use of magnetic particle imaging (MPI) to assess pulmonary vascular leakage noninvasively in vivo in mice. A dextran-coated superparamagnetic iron oxide (SPIO), synomag®, was employed as the imaging tracer, and pulmonary SPIO extravasation was imaged and quantified to evaluate the vascular leakage. Animal models of acute lung injury and pulmonary fibrosis (PF) were used to validate the proposed method. MPI sensitively detected the SPIO extravasation in both acutely injured and fibrotic lungs in vivo, which was confirmed by ex vivo imaging and Prussian blue staining. Moreover, 3D MPI illustrated the spatial heterogeneity of vascular leakage, which correlated well with CT findings. Based on the in vivo 3D MPI images, we defined the SPIO extravasation index (SEI) to quantify the vascular leakage. A significant increase in SEI was observed in the injured lungs, in consistent with the results obtained via ex vivo permeability assays. Overall, our results demonstrate that 3D quantitative MPI serves as a useful tool to examine pulmonary vascular integrity in vivo, which shows promise for future clinical translation.
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Affiliation(s)
- Xin Feng
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular ImagingInstitute of Automation, Chinese Academy of SciencesBeijingChina
- School of Artificial Intelligence, University of Chinese Academy of SciencesBeijingChina
| | - Pengli Gao
- School of Biological Science and Medicine Engineering & School of Engineering Medicine, Beihang UniversityBeijingChina
- Key Laboratory of Big Data‐Based Precision Medicine (Beihang University)Ministry of Industry and Information TechnologyBeijingChina
- School of Engineering Medicine, Beihang UniversityBeijingChina
| | - Yabin Li
- College of Pulmonary and Critical Care Medicine, Chinese PLA General HospitalBeijingChina
| | - Hui Hui
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular ImagingInstitute of Automation, Chinese Academy of SciencesBeijingChina
- School of Artificial Intelligence, University of Chinese Academy of SciencesBeijingChina
| | - Jingying Jiang
- Key Laboratory of Big Data‐Based Precision Medicine (Beihang University)Ministry of Industry and Information TechnologyBeijingChina
- School of Engineering Medicine, Beihang UniversityBeijingChina
| | - Fei Xie
- College of Pulmonary and Critical Care Medicine, Chinese PLA General HospitalBeijingChina
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular ImagingInstitute of Automation, Chinese Academy of SciencesBeijingChina
- Key Laboratory of Big Data‐Based Precision Medicine (Beihang University)Ministry of Industry and Information TechnologyBeijingChina
- School of Engineering Medicine, Beihang UniversityBeijingChina
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Lim WH, Jeong S, Park CM. Cigarette smoking and disproportionate changes of thoracic skeletal muscles in low-dose chest computed tomography. Sci Rep 2023; 13:20110. [PMID: 37978301 PMCID: PMC10656498 DOI: 10.1038/s41598-023-46360-0] [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: 05/22/2023] [Accepted: 10/31/2023] [Indexed: 11/19/2023] Open
Abstract
Association between smoking intensity and the quantity and quality of thoracic skeletal muscles (TSMs) remains unexplored. Skeletal muscle index (SMI; skeletal muscle area/height2) and percentage of normal attenuation muscle area (NAMA%) were measured to represent the quantity and quality of the skeletal muscles, respectively, and quantification was performed in pectoralis muscle at aortic arch (AA-PM), TSM at carina (C-TSM), erector spinae muscle at T12 (T12-ESM), and skeletal muscle at L1 (L1-SM). Among the 258 men (median age, 62 years [IQR: 58-69]), 183 were current smokers (median smoking intensity, 40 pack-years [IQR: 30-46]). SMI and NAMA% of AA-PM significantly decreased with pack-year (β = - 0.028 and - 0.076; P < 0.001 and P = 0.021, respectively). Smoking intensity was inversely associated with NAMA% of C-TSM (β = - 0.063; P = 0.001), whereas smoking intensity showed a borderline association with SMI of C-TSM (β = - 0.023; P = 0.057). Smoking intensity was associated with the change in NAMA% of L1-SM (β = - 0.040; P = 0.027), but was not associated with SMI of L1-SM (P > 0.05). Neither NAMA% nor SMI of T12-ESM was affected by smoking intensity (P > 0.05). In conclusion, smoking intensity was associated with the change of TSMs. Its association varied according to the location of TSMs, with the most associated parts being the upper (AA-PM) and middle TSMs (C-TSM).
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Affiliation(s)
- Woo Hyeon Lim
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Suhyun Jeong
- Department of Radiology, Namwon Medical Center, 365 Chungjeong-no, Namwon, Jeollabuk-do, 55726, Republic of Korea
| | - Chang Min Park
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
- Department of Radiology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea.
- Institute of Medical and Biological Engineering, Medical Research Center, Seoul National University, Seoul, Republic of Korea.
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Wu Y, Du R, Feng J, Qi S, Pang H, Xia S, Qian W. Deep CNN for COPD identification by Multi-View snapshot integration of 3D airway tree and lung field. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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8
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Zac J, Zac S, Pérez-Padilla R, Remigio-Luna A, Guzmán-Boulloud N, Gochicoa-Rangel L, Guzmán-Valderrábano C, Thirión-Romero I. Lung volumes measurement using novel pressure derived method in participants with obstructive, restrictive and healthy lungs. Physiol Meas 2022; 43. [PMID: 36537615 DOI: 10.1088/1361-6579/aca7ac] [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: 06/06/2022] [Accepted: 11/30/2022] [Indexed: 12/05/2022]
Abstract
Background.Lung volumes can be measured by body plethysmography (BP), by inert gas dilution during a single-breath or multiple breaths and by radiographic methods based on chest roentgenogram or CT scanning. Our objective was to analyze the concordance between several methods including a new pressure-derived method (PDM) in a variety of pulmonary conditions.Methods. We recruited four groups of adult volunteers at the chronic obstructive pulmonary disease and tobacco clinic of a respiratory referral hospital: patients with lung bullae, with obstructive lung diseases, with restrictive lung diseases and healthy controls; all subjects underwent lung volume measurements according to ATS/ERS standards in random order with each method and then CT scanning. Differences among groups were estimated by Kruskal-Wallis tests. Concordance correlation coefficients (CCC) and Bland-Altman plots were performed.Results. Sixty-two patients were studied including 15 with lung bullae, 14 with obstructive lung diseases, 12 with restrictive lung disease and 21 healthy subjects. Highest concordance was obtained between BP and CT scanning (CCC 0.95, mean difference -0.35 l) and the lowest, with TLC-DLCOsb(CCC 0.65, difference -1.05 l). TLC measured by BP had a moderate concordance with the PDM (CCC = 0.91, mean difference -0.19 l). The PDM on the other hand had the lowest intra-test repeatability (2.7%) of all tested methods.Conclusions. Lung volumes measured by BP and CT had high concordance in the scenario of varied pulmonary conditions including lung bullae, restrictive and obstructive diseases. The new PDM device, had low intra-test variability, and was easy to perform, with a reasonable concordance with BP.
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Affiliation(s)
- Jacob Zac
- Tobacco and COPD Research Unit, Instituto Nacional de Enfermedades Respiratorias INER, Mexico City, Mexico
| | - Salomon Zac
- Tobacco and COPD Research Unit, Instituto Nacional de Enfermedades Respiratorias INER, Mexico City, Mexico
| | - Rogelio Pérez-Padilla
- Tobacco and COPD Research Unit, Instituto Nacional de Enfermedades Respiratorias INER, Mexico City, Mexico
| | - Arantxa Remigio-Luna
- Tobacco and COPD Research Unit, Instituto Nacional de Enfermedades Respiratorias INER, Mexico City, Mexico
| | | | - Laura Gochicoa-Rangel
- Respiratory Physiology Department, INER Instituto Nacional de Enfermedades Respiratorias, Mexico City, Mexico
| | - Carlos Guzmán-Valderrábano
- Respiratory Physiology Department, INER Instituto Nacional de Enfermedades Respiratorias, Mexico City, Mexico
| | - Ireri Thirión-Romero
- Tobacco and COPD Research Unit, Instituto Nacional de Enfermedades Respiratorias INER, Mexico City, Mexico
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9
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Preclinical MRI Using Hyperpolarized 129Xe. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27238338. [PMID: 36500430 PMCID: PMC9738892 DOI: 10.3390/molecules27238338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 11/15/2022] [Accepted: 11/17/2022] [Indexed: 12/05/2022]
Abstract
Although critical for development of novel therapies, understanding altered lung function in disease models is challenging because the transport and diffusion of gases over short distances, on which proper function relies, is not readily visualized. In this review we summarize progress introducing hyperpolarized 129Xe imaging as a method to follow these processes in vivo. The work is organized in sections highlighting methods to observe the gas replacement effects of breathing (Gas Dynamics during the Breathing Cycle) and gas diffusion throughout the parenchymal airspaces (3). We then describe the spectral signatures indicative of gas dissolution and uptake (4), and how these features can be used to follow the gas as it enters the tissue and capillary bed, is taken up by hemoglobin in the red blood cells (5), re-enters the gas phase prior to exhalation (6), or is carried via the vasculature to other organs and body structures (7). We conclude with a discussion of practical imaging and spectroscopy techniques that deliver quantifiable metrics despite the small size, rapid motion and decay of signal and coherence characteristic of the magnetically inhomogeneous lung in preclinical models (8).
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10
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Li Z, Huang K, Liu L, Zhang Z. Early detection of COPD based on graph convolutional network and small and weakly labeled data. Med Biol Eng Comput 2022; 60:2321-2333. [PMID: 35750976 PMCID: PMC9244127 DOI: 10.1007/s11517-022-02589-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 05/08/2022] [Indexed: 11/25/2022]
Abstract
Chronic obstructive pulmonary disease (COPD) is a common disease with high morbidity and mortality, where early detection benefits the population. However, the early diagnosis rate of COPD is low due to the absence or slight early symptoms. In this paper, a novel method based on graph convolution network (GCN) for early detection of COPD is proposed, which uses small and weakly labeled chest computed tomography image data from the publicly available Danish Lung Cancer Screening Trial database. The key idea is to construct a graph using regions of interest randomly selected from the segmented lung parenchyma and then input it into the GCN model for COPD detection. In this way, the model can not only extract the feature information of each region of interest but also the topological structure information between regions of interest, that is, graph structure information. The proposed GCN model achieves an acceptable performance with an accuracy of 0.77 and an area under a curve of 0.81, which is higher than the previous studies on the same dataset. GCN model also outperforms several state-of-the-art methods trained at the same time. As far as we know, it is also the first time using the GCN model on this dataset for COPD detection.
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Affiliation(s)
- Zongli Li
- Department of Pulmonary and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, 100020, People's Republic of China
- Beijing Institute of Respiratory Medicine, Beijing, 100020, People's Republic of China
- Department of Respiratory, Shijingshan Teaching Hospital of Capital Medical University, Beijing Shijingshan Hospital, Beijing, 100043, People's Republic of China
| | - Kewu Huang
- Department of Pulmonary and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, 100020, People's Republic of China.
- Beijing Institute of Respiratory Medicine, Beijing, 100020, People's Republic of China.
| | - Ligong Liu
- Department of Enterprise Management, China Energy Engineering Corporation Limited, Beijing, 100022, People's Republic of China
| | - Zuoqing Zhang
- Department of Respiratory, Shijingshan Teaching Hospital of Capital Medical University, Beijing Shijingshan Hospital, Beijing, 100043, People's Republic of China
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11
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Emphysema Quantification Using Ultra-Low-Dose Chest CT: Efficacy of Deep Learning-Based Image Reconstruction. MEDICINA (KAUNAS, LITHUANIA) 2022; 58:medicina58070939. [PMID: 35888658 PMCID: PMC9317892 DOI: 10.3390/medicina58070939] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 07/03/2022] [Accepted: 07/14/2022] [Indexed: 11/17/2022]
Abstract
Background and Objectives: Although reducing the radiation dose level is important during diagnostic computed tomography (CT) applications, effective image quality enhancement strategies are crucial to compensate for the degradation that is caused by a dose reduction. We performed this prospective study to quantify emphysema on ultra-low-dose CT images that were reconstructed using deep learning-based image reconstruction (DLIR) algorithms, and compared and evaluated the accuracies of DLIR algorithms versus standard-dose CT. Materials and Methods: A total of 32 patients were prospectively enrolled, and all underwent standard-dose and ultra-low-dose (120 kVp; CTDIvol < 0.7 mGy) chest CT scans at the same time in a single examination. A total of six image datasets (filtered back projection (FBP) for standard-dose CT, and FBP, adaptive statistical iterative reconstruction (ASIR-V) 50%, DLIR-low, DLIR-medium, DLIR-high for ultra-low-dose CT) were reconstructed for each patient. Image noise values, emphysema indices, total lung volumes, and mean lung attenuations were measured in the six image datasets and compared (one-way repeated measures ANOVA). Results: The mean effective doses for standard-dose and ultra-low-dose CT scans were 3.43 ± 0.57 mSv and 0.39 ± 0.03 mSv, respectively (p < 0.001). The total lung volume and mean lung attenuation of five image datasets of ultra-low-dose CT scans, emphysema indices of ultra-low-dose CT scans reconstructed using ASIR-V 50 or DLIR-low, and the image noise of ultra-low-dose CT scans that were reconstructed using DLIR-low were not different from those of standard-dose CT scans. Conclusions: Ultra-low-dose CT images that were reconstructed using DLIR-low were found to be useful for emphysema quantification at a radiation dose of only 11% of that required for standard-dose CT.
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12
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Jungblut L, Sartoretti T, Kronenberg D, Mergen V, Euler A, Schmidt B, Alkadhi H, Frauenfelder T, Martini K. Performance of virtual non-contrast images generated on clinical photon-counting detector CT for emphysema quantification: proof of concept. Br J Radiol 2022; 95:20211367. [PMID: 35357902 PMCID: PMC10996315 DOI: 10.1259/bjr.20211367] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 03/09/2022] [Accepted: 03/22/2022] [Indexed: 12/23/2022] Open
Abstract
OBJECTIVE To evaluate the performance of virtual non-contrast images (VNC) compared to true non-contrast (TNC) images in photon-counting detector computed tomography (PCD-CT) for the evaluation of lung parenchyma and emphysema quantification. METHODS 65 (mean age 73 years; 48 male) consecutive patients who underwent a three-phase (non-contrast, arterial and venous) chest/abdomen CT on a first-generation dual-source PCD-CT were retrospectively included. Scans were performed in the multienergy (QuantumPlus) mode at 120 kV with 70 ml intravenous contrast agent at an injection rate of 4 ml s-1. VNC were reconstructed from the arterial (VNCart) and venous phase (VNCven). TNC and VNC images of the lung were assessed quantitatively by calculating the global noise index (GNI) and qualitatively by two independent, blinded readers (overall image quality and emphysema assessment). Emphysema quantification was performed using a commercially available software tool at a threshold of -950 HU for all data sets. TNC images served as reference standard for emphysema quantification. Low attenuation values (LAV) were compared in a Bland-Altman plot. RESULTS GNI was similar in VNCart (103.0 ± 30.1) and VNCven (98.2 ± 22.2) as compared to TNC (100.9 ± 19.0, p = 0.546 and p = 0.272, respectively). Subjective image quality (emphysema assessment and overall image quality) was highest for TNC (p = 0.001), followed by VNCven and VNCart. Both, VNCart and VNCven showed no significant difference in emphysema quantification as compared to TNC (p = 0.409 vs. p = 0.093; respectively). CONCLUSION Emphysema evaluation is feasible using virtual non-contrast images from PCD-CT. ADVANCES IN KNOWLEDGE Emphysema quantification is feasible and accurate using VNC images in PCD-CT. Based on these findings, additional TNC scans for emphysema quantification could be omitted in the future.
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Affiliation(s)
- Lisa Jungblut
- Institute of Diagnostic and Interventional Radiology,
University Hospital Zurich, University of Zurich,
Zurich, Switzerland
| | - Thomas Sartoretti
- Institute of Diagnostic and Interventional Radiology,
University Hospital Zurich, University of Zurich,
Zurich, Switzerland
| | - Daniel Kronenberg
- Institute of Diagnostic and Interventional Radiology,
University Hospital Zurich, University of Zurich,
Zurich, Switzerland
| | - Victor Mergen
- Institute of Diagnostic and Interventional Radiology,
University Hospital Zurich, University of Zurich,
Zurich, Switzerland
| | - Andre Euler
- Institute of Diagnostic and Interventional Radiology,
University Hospital Zurich, University of Zurich,
Zurich, Switzerland
| | - Bernhard Schmidt
- Siemens Healthcare GmbH, Computed Tomography,
Forchheim, Germany
| | - Hatem Alkadhi
- Institute of Diagnostic and Interventional Radiology,
University Hospital Zurich, University of Zurich,
Zurich, Switzerland
| | - Thomas Frauenfelder
- Institute of Diagnostic and Interventional Radiology,
University Hospital Zurich, University of Zurich,
Zurich, Switzerland
| | - Katharina Martini
- Institute of Diagnostic and Interventional Radiology,
University Hospital Zurich, University of Zurich,
Zurich, Switzerland
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13
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Choi H, Kim H, Jin KN, Jeong YJ, Chae KJ, Lee KH, Yong HS, Gil B, Lee HJ, Lee KY, Jeon KN, Yi J, Seo S, Ahn C, Lee J, Oh K, Goo JM. A Challenge for Emphysema Quantification Using a Deep Learning Algorithm With Low-dose Chest Computed Tomography. J Thorac Imaging 2022; 37:253-261. [PMID: 35749623 DOI: 10.1097/rti.0000000000000647] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
PURPOSE We aimed to identify clinically relevant deep learning algorithms for emphysema quantification using low-dose chest computed tomography (LDCT) through an invitation-based competition. MATERIALS AND METHODS The Korean Society of Imaging Informatics in Medicine (KSIIM) organized a challenge for emphysema quantification between November 24, 2020 and January 26, 2021. Seven invited research teams participated in this challenge. In total, 558 pairs of computed tomography (CT) scans (468 pairs for the training set, and 90 pairs for the test set) from 9 hospitals were collected retrospectively or prospectively. CT acquisition followed the hospitals' protocols to reflect the real-world clinical setting. Using the training set, each team developed an algorithm that generated converted LDCT by changing the pixel values of LDCT to simulate those of standard-dose CT (SDCT). The agreement between SDCT and LDCT was evaluated using the intraclass correlation coefficient (ICC; 2-way random effects, absolute agreement, and single rater) for the percentage of low-attenuated area below -950 HU (LAA-950 HU), κ value for emphysema categorization (LAA-950 HU, <5%, 5% to 10%, and ≥10%) and cosine similarity of LAA-950 HU. RESULTS The mean LAA-950 HU of the test set was 14.2%±10.5% for SDCT, 25.4%±10.2% for unconverted LDCT, and 12.9%±10.4%, 11.7%±10.8%, and 12.4%±10.5% for converted LDCT (top 3 teams). The agreement between the SDCT and converted LDCT of the first-place team was 0.94 (95% confidence interval: 0.90, 0.97) for ICC, 0.71 (95% confidence interval: 0.58, 0.84) for categorical agreement, and 0.97 (interquartile range: 0.94 to 0.99) for cosine similarity. CONCLUSIONS Emphysema quantification with LDCT was feasible through deep learning-based CT conversion strategies.
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Affiliation(s)
- Hyewon Choi
- Department of Radiology, Chung-Ang University Hospital, Chung-Ang University College of Medicine
| | - Hyungjin Kim
- Department of Radiology, Seoul National University College of Medicine
| | - Kwang Nam Jin
- Department of Radiology, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul
| | - Yeon Joo Jeong
- Department of Radiology and Biomedical Research Institute, Pusan National University Hospital, Busan
| | - Kum Ju Chae
- Department of Radiology, Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju
| | - Kyung Hee Lee
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do
| | - Hwan Seok Yong
- Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine
| | - Bomi Gil
- Department of Radiology, College of Medicine, The Catholic University of Korea
| | - Hye-Jeong Lee
- Department of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine
| | - Ki Yeol Lee
- Department of Radiology, Korea University College of Medicine
| | - Kyung Nyeo Jeon
- Department of Radiology, Gyeongsang National University, Jinju, Korea
| | | | | | | | | | - Kyuhyup Oh
- Bio Medical Research Center, Korea Testing Laboratory
| | - Jin Mo Goo
- Department of Radiology, Seoul National University College of Medicine
- Cancer Research Institute, Seoul National University, Seoul
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14
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Multiple instance learning for lung pathophysiological findings detection using CT scans. Med Biol Eng Comput 2022; 60:1569-1584. [PMID: 35386027 DOI: 10.1007/s11517-022-02526-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 01/17/2022] [Indexed: 10/18/2022]
Abstract
Lung diseases affect the lives of billions of people worldwide, and 4 million people, each year, die prematurely due to this condition. These pathologies are characterized by specific imagiological findings in CT scans. The traditional Computer-Aided Diagnosis (CAD) approaches have been showing promising results to help clinicians; however, CADs normally consider a small part of the medical image for analysis, excluding possible relevant information for clinical evaluation. Multiple Instance Learning (MIL) approach takes into consideration different small pieces that are relevant for the final classification and creates a comprehensive analysis of pathophysiological changes. This study uses MIL-based approaches to identify the presence of lung pathophysiological findings in CT scans for the characterization of lung disease development. This work was focus on the detection of the following: Fibrosis, Emphysema, Satellite Nodules in Primary Lesion Lobe, Nodules in Contralateral Lung and Ground Glass, being Fibrosis and Emphysema the ones with more outstanding results, reaching an Area Under the Curve (AUC) of 0.89 and 0.72, respectively. Additionally, the MIL-based approach was used for EGFR mutation status prediction - the most relevant oncogene on lung cancer, with an AUC of 0.69. The results showed that this comprehensive approach can be a useful tool for lung pathophysiological characterization.
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15
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Li Z, Liu L, Zhang Z, Yang X, Li X, Gao Y, Huang K. A Novel CT-Based Radiomics Features Analysis for Identification and Severity Staging of COPD. Acad Radiol 2022; 29:663-673. [PMID: 35151548 DOI: 10.1016/j.acra.2022.01.004] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Revised: 12/22/2021] [Accepted: 01/05/2022] [Indexed: 12/12/2022]
Abstract
RATIONALE AND OBJECTIVES To evaluate the role of radiomics based on Chest Computed Tomography (CT) in the identification and severity staging of chronic obstructive pulmonary disease (COPD). MATERIALS AND METHODS This retrospective analysis included 322 participants (249 COPD patients and 73 control subjects). In total, 1395 chest CT-based radiomics features were extracted from each participant's CT images. Three feature selection methods, including variance threshold, Select K Best method, and least absolute shrinkage and selection operator (LASSO), and two classification methods, including support vector machine (SVM) and logistic regression (LR), were used as identification and severity classification of COPD. Performance was compared by AUC, accuracy, sensitivity, specificity, precision, and F1-score. RESULTS 38 and 10 features were selected to construct radiomics models to detect and stage COPD, respectively. For COPD identification, SVM classifier achieved AUCs of 0.992 and 0.970, while LR classifier achieved AUCs of 0.993 and 0.972 in the training set and test set, respectively. For the severity staging of COPD, the mentioned two machine learning classifiers can better differentiate less severity (GOLD1 + GOLD2) group from greater severity (GOLD3 + GOLD4) group. The AUCs of SVM and LR is 0.907 and 0.903 in the training set, and that of 0.799 and 0.797 in the test set. CONCLUSION The present study showed that the novel radiomics approach based on chest CT images that can be used for COPD identification and severity classification, and the constructed radiomics model demonstrated acceptable performance.
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Affiliation(s)
- Zongli Li
- Department of Pulmonary and Critical Care Medicine (Z.L., K.H.), Beijing Chao-Yang Hospital, Capital Medical University, No 8 Gongti South Road, Beijing, 100020, People's Republic of China; Department of Pulmonary and Critical Care Medicine (Z.L., K.H.), Beijing Institute of Respiratory Medicine, Beijing, People's Republic of China; Department of Pulmonary and Critical Care Medicine (Z.L., Z.Z.), Shijingshan Teaching Hospital of Capital Medical University, Beijing Shijingshan Hospital, Beijing, China; Department of Enterprise, Beijing e-Hualu Information Technology Corporation Limited (L. L.), Beijing, China; Dongsheng Science and Technology Park (X.Y.), Huiying Medical Technology Co., Ltd, Beijing, China; Department of Respiratory (X.L.), Third Affiliated Hospital of Jinzhou Medical University, Jinzhou, Liaoning, China; Department of Radiology (Y.G.), Beijing Chao-Yang Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Ligong Liu
- Department of Pulmonary and Critical Care Medicine (Z.L., K.H.), Beijing Chao-Yang Hospital, Capital Medical University, No 8 Gongti South Road, Beijing, 100020, People's Republic of China; Department of Pulmonary and Critical Care Medicine (Z.L., K.H.), Beijing Institute of Respiratory Medicine, Beijing, People's Republic of China; Department of Pulmonary and Critical Care Medicine (Z.L., Z.Z.), Shijingshan Teaching Hospital of Capital Medical University, Beijing Shijingshan Hospital, Beijing, China; Department of Enterprise, Beijing e-Hualu Information Technology Corporation Limited (L. L.), Beijing, China; Dongsheng Science and Technology Park (X.Y.), Huiying Medical Technology Co., Ltd, Beijing, China; Department of Respiratory (X.L.), Third Affiliated Hospital of Jinzhou Medical University, Jinzhou, Liaoning, China; Department of Radiology (Y.G.), Beijing Chao-Yang Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Zuoqing Zhang
- Department of Pulmonary and Critical Care Medicine (Z.L., K.H.), Beijing Chao-Yang Hospital, Capital Medical University, No 8 Gongti South Road, Beijing, 100020, People's Republic of China; Department of Pulmonary and Critical Care Medicine (Z.L., K.H.), Beijing Institute of Respiratory Medicine, Beijing, People's Republic of China; Department of Pulmonary and Critical Care Medicine (Z.L., Z.Z.), Shijingshan Teaching Hospital of Capital Medical University, Beijing Shijingshan Hospital, Beijing, China; Department of Enterprise, Beijing e-Hualu Information Technology Corporation Limited (L. L.), Beijing, China; Dongsheng Science and Technology Park (X.Y.), Huiying Medical Technology Co., Ltd, Beijing, China; Department of Respiratory (X.L.), Third Affiliated Hospital of Jinzhou Medical University, Jinzhou, Liaoning, China; Department of Radiology (Y.G.), Beijing Chao-Yang Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Xuhong Yang
- Department of Pulmonary and Critical Care Medicine (Z.L., K.H.), Beijing Chao-Yang Hospital, Capital Medical University, No 8 Gongti South Road, Beijing, 100020, People's Republic of China; Department of Pulmonary and Critical Care Medicine (Z.L., K.H.), Beijing Institute of Respiratory Medicine, Beijing, People's Republic of China; Department of Pulmonary and Critical Care Medicine (Z.L., Z.Z.), Shijingshan Teaching Hospital of Capital Medical University, Beijing Shijingshan Hospital, Beijing, China; Department of Enterprise, Beijing e-Hualu Information Technology Corporation Limited (L. L.), Beijing, China; Dongsheng Science and Technology Park (X.Y.), Huiying Medical Technology Co., Ltd, Beijing, China; Department of Respiratory (X.L.), Third Affiliated Hospital of Jinzhou Medical University, Jinzhou, Liaoning, China; Department of Radiology (Y.G.), Beijing Chao-Yang Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Xuanyi Li
- Department of Pulmonary and Critical Care Medicine (Z.L., K.H.), Beijing Chao-Yang Hospital, Capital Medical University, No 8 Gongti South Road, Beijing, 100020, People's Republic of China; Department of Pulmonary and Critical Care Medicine (Z.L., K.H.), Beijing Institute of Respiratory Medicine, Beijing, People's Republic of China; Department of Pulmonary and Critical Care Medicine (Z.L., Z.Z.), Shijingshan Teaching Hospital of Capital Medical University, Beijing Shijingshan Hospital, Beijing, China; Department of Enterprise, Beijing e-Hualu Information Technology Corporation Limited (L. L.), Beijing, China; Dongsheng Science and Technology Park (X.Y.), Huiying Medical Technology Co., Ltd, Beijing, China; Department of Respiratory (X.L.), Third Affiliated Hospital of Jinzhou Medical University, Jinzhou, Liaoning, China; Department of Radiology (Y.G.), Beijing Chao-Yang Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Yanli Gao
- Department of Pulmonary and Critical Care Medicine (Z.L., K.H.), Beijing Chao-Yang Hospital, Capital Medical University, No 8 Gongti South Road, Beijing, 100020, People's Republic of China; Department of Pulmonary and Critical Care Medicine (Z.L., K.H.), Beijing Institute of Respiratory Medicine, Beijing, People's Republic of China; Department of Pulmonary and Critical Care Medicine (Z.L., Z.Z.), Shijingshan Teaching Hospital of Capital Medical University, Beijing Shijingshan Hospital, Beijing, China; Department of Enterprise, Beijing e-Hualu Information Technology Corporation Limited (L. L.), Beijing, China; Dongsheng Science and Technology Park (X.Y.), Huiying Medical Technology Co., Ltd, Beijing, China; Department of Respiratory (X.L.), Third Affiliated Hospital of Jinzhou Medical University, Jinzhou, Liaoning, China; Department of Radiology (Y.G.), Beijing Chao-Yang Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Kewu Huang
- Department of Pulmonary and Critical Care Medicine (Z.L., K.H.), Beijing Chao-Yang Hospital, Capital Medical University, No 8 Gongti South Road, Beijing, 100020, People's Republic of China; Department of Pulmonary and Critical Care Medicine (Z.L., K.H.), Beijing Institute of Respiratory Medicine, Beijing, People's Republic of China; Department of Pulmonary and Critical Care Medicine (Z.L., Z.Z.), Shijingshan Teaching Hospital of Capital Medical University, Beijing Shijingshan Hospital, Beijing, China; Department of Enterprise, Beijing e-Hualu Information Technology Corporation Limited (L. L.), Beijing, China; Dongsheng Science and Technology Park (X.Y.), Huiying Medical Technology Co., Ltd, Beijing, China; Department of Respiratory (X.L.), Third Affiliated Hospital of Jinzhou Medical University, Jinzhou, Liaoning, China; Department of Radiology (Y.G.), Beijing Chao-Yang Hospital, Capital Medical University, Beijing, People's Republic of China.
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16
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Silva F, Pereira T, Neves I, Morgado J, Freitas C, Malafaia M, Sousa J, Fonseca J, Negrão E, Flor de Lima B, Correia da Silva M, Madureira AJ, Ramos I, Costa JL, Hespanhol V, Cunha A, Oliveira HP. Towards Machine Learning-Aided Lung Cancer Clinical Routines: Approaches and Open Challenges. J Pers Med 2022; 12:480. [PMID: 35330479 PMCID: PMC8950137 DOI: 10.3390/jpm12030480] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 02/28/2022] [Accepted: 03/10/2022] [Indexed: 12/15/2022] Open
Abstract
Advancements in the development of computer-aided decision (CAD) systems for clinical routines provide unquestionable benefits in connecting human medical expertise with machine intelligence, to achieve better quality healthcare. Considering the large number of incidences and mortality numbers associated with lung cancer, there is a need for the most accurate clinical procedures; thus, the possibility of using artificial intelligence (AI) tools for decision support is becoming a closer reality. At any stage of the lung cancer clinical pathway, specific obstacles are identified and "motivate" the application of innovative AI solutions. This work provides a comprehensive review of the most recent research dedicated toward the development of CAD tools using computed tomography images for lung cancer-related tasks. We discuss the major challenges and provide critical perspectives on future directions. Although we focus on lung cancer in this review, we also provide a more clear definition of the path used to integrate AI in healthcare, emphasizing fundamental research points that are crucial for overcoming current barriers.
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Affiliation(s)
- Francisco Silva
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal; (I.N.); (J.M.); (M.M.); (J.S.); (J.F.); (A.C.); (H.P.O.)
- FCUP—Faculty of Science, University of Porto, 4169-007 Porto, Portugal
| | - Tania Pereira
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal; (I.N.); (J.M.); (M.M.); (J.S.); (J.F.); (A.C.); (H.P.O.)
| | - Inês Neves
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal; (I.N.); (J.M.); (M.M.); (J.S.); (J.F.); (A.C.); (H.P.O.)
- ICBAS—Abel Salazar Biomedical Sciences Institute, University of Porto, 4050-313 Porto, Portugal
| | - Joana Morgado
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal; (I.N.); (J.M.); (M.M.); (J.S.); (J.F.); (A.C.); (H.P.O.)
| | - Cláudia Freitas
- CHUSJ—Centro Hospitalar e Universitário de São João, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
- FMUP—Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal;
| | - Mafalda Malafaia
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal; (I.N.); (J.M.); (M.M.); (J.S.); (J.F.); (A.C.); (H.P.O.)
- FEUP—Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
| | - Joana Sousa
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal; (I.N.); (J.M.); (M.M.); (J.S.); (J.F.); (A.C.); (H.P.O.)
| | - João Fonseca
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal; (I.N.); (J.M.); (M.M.); (J.S.); (J.F.); (A.C.); (H.P.O.)
- FEUP—Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
| | - Eduardo Negrão
- CHUSJ—Centro Hospitalar e Universitário de São João, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
| | - Beatriz Flor de Lima
- CHUSJ—Centro Hospitalar e Universitário de São João, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
| | - Miguel Correia da Silva
- CHUSJ—Centro Hospitalar e Universitário de São João, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
| | - António J. Madureira
- CHUSJ—Centro Hospitalar e Universitário de São João, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
- FMUP—Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal;
| | - Isabel Ramos
- CHUSJ—Centro Hospitalar e Universitário de São João, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
- FMUP—Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal;
| | - José Luis Costa
- FMUP—Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal;
- i3S—Instituto de Investigação e Inovação em Saúde, Universidade do Porto, 4200-135 Porto, Portugal
- IPATIMUP—Institute of Molecular Pathology and Immunology of the University of Porto, 4200-135 Porto, Portugal
| | - Venceslau Hespanhol
- CHUSJ—Centro Hospitalar e Universitário de São João, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
- FMUP—Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal;
| | - António Cunha
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal; (I.N.); (J.M.); (M.M.); (J.S.); (J.F.); (A.C.); (H.P.O.)
- UTAD—University of Trás-os-Montes and Alto Douro, 5001-801 Vila Real, Portugal
| | - Hélder P. Oliveira
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal; (I.N.); (J.M.); (M.M.); (J.S.); (J.F.); (A.C.); (H.P.O.)
- FCUP—Faculty of Science, University of Porto, 4169-007 Porto, Portugal
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Yang K, Yang Y, Kang Y, Liang Z, Wang F, Li Q, Xu J, Tang G, Chen R. The value of radiomic features in chronic obstructive pulmonary disease assessment: a prospective study. Clin Radiol 2022; 77:e466-e472. [DOI: 10.1016/j.crad.2022.02.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 02/17/2022] [Indexed: 12/17/2022]
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Yacoub B, Kabakus IM, Schoepf UJ, Giovagnoli VM, Fischer AM, Wichmann JL, Martinez JD, Sharma P, Rapaka S, Sahbaee P, Hoelzer P, Burt JR, Varga-Szemes A, Emrich T. Performance of an Artificial Intelligence-Based Platform Against Clinical Radiology Reports for the Evaluation of Noncontrast Chest CT. Acad Radiol 2022; 29 Suppl 2:S108-S117. [PMID: 33714665 DOI: 10.1016/j.acra.2021.02.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 02/01/2021] [Accepted: 02/11/2021] [Indexed: 12/20/2022]
Abstract
RATIONALE AND OBJECTIVES Research on implementation of artificial intelligence (AI) in radiology workflows and its impact on reports remains scarce. In this study, we aim to assess if an AI platform would perform better than clinical radiology reports in evaluating noncontrast chest computed tomography (CT) scans. MATERIALS AND METHODS Consecutive patients who had undergone noncontrast chest CT were retrospectively identified. The radiology reports were reviewed in a binary fashion for reporting of pulmonary lesions, pulmonary emphysema, aortic dilatation, coronary artery calcifications (CAC), and vertebral compression fractures (VCF). CT scans were then processed using an AI platform. The reports' findings and the AI results were subsequently compared to a consensus read by two board-certificated radiologists as reference. RESULTS A total of 100 patients (mean age: 64.2 ± 14.8 years; 57% males) were included in this study. Aortic segmentation and calcium quantification failed to be processed by AI in 2 and 3 cases, respectively. AI showed superior diagnostic performance in identifying aortic dilatation (AI: sensitivity: 96.3%, specificity: 81.4%, AUC: 0.89) vs (Reports: sensitivity: 25.9%, specificity: 100%, AUC: 0.63), p <0.001; and CAC (AI: sensitivity: 89.8%, specificity: 100, AUC: 0.95) vs (Reports: sensitivity: 75.4%, specificity: 94.9%, AUC: 0.85), p = 0.005. Reports had better performance than AI in identifying pulmonary lesions (Reports: sensitivity: 97.6%, specificity: 100%, AUC: 0.99) vs (AI: sensitivity: 92.8%, specificity: 82.4%, AUC: 0.88), p = 0.024; and VCF (Reports: sensitivity:100%, specificity: 100%, AUC: 1.0) vs (AI: sensitivity: 100%, specificity: 63.7%, AUC: 0.82), p <0.001. A comparable diagnostic performance was noted in identifying pulmonary emphysema on AI (sensitivity: 80.6%, specificity: 66.7%. AUC: 0.74) and reports (sensitivity: 74.2%, specificity: 97.1%, AUC: 0.86), p = 0.064. CONCLUSION Our results demonstrate that incorporating AI support platforms into radiology workflows can provide significant added value to clinical radiology reporting.
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Affiliation(s)
- Basel Yacoub
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina
| | - Ismail M Kabakus
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina
| | - U Joseph Schoepf
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina.
| | - Vincent M Giovagnoli
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina
| | - Andreas M Fischer
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina; University Hospital Basel, University of Basel, Department of Radiology, Basel, Switzerland
| | - Julian L Wichmann
- University Hospital Frankfurt, Department of Diagnostic and Interventional Radiology, Frankfurt am Main, Germany; Siemens Healthineers, Erlangen, Germany
| | - John D Martinez
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina
| | | | | | | | | | - Jeremy R Burt
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina
| | - Akos Varga-Szemes
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina
| | - Tilman Emrich
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina; University Medical Center Mainz, Department of Diagnostic and Interventional Radiology, Mainz, Germany; German Center for Cardiovascular Research (DZHK), Partner-Site Rhine-Main, Mainz, Germany
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Impact of Contrast Enhancement and Virtual Monoenergetic Image Energy Levels on Emphysema Quantification. Invest Radiol 2022; 57:359-365. [DOI: 10.1097/rli.0000000000000848] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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20
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Wu F, Jiang C, Zhou Y, Zheng Y, Tian H, Li H, Deng Z, Zhao N, Chen H, Ran P. Association of Total Airway Count on Computed Tomography with Pulmonary Function Decline in Early-Stage COPD: A Population-Based Prospective Cohort Study. Int J Chron Obstruct Pulmon Dis 2022; 16:3437-3448. [PMID: 34984001 PMCID: PMC8702985 DOI: 10.2147/copd.s339029] [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: 09/15/2021] [Accepted: 12/06/2021] [Indexed: 11/23/2022] Open
Abstract
Background It has been found that the degree of terminal bronchiole destruction is associated with the severity of COPD. However, total airway count (TAC) of CT-visible and its relationship with COPD lung function severity and pulmonary function decline remains controversial. The present study aimed to determine whether TAC is significantly reduced in early-stage COPD (GOLD stage I–II) compared with healthy control subjects and whether TAC is associated with annual decline in pulmonary function in Chinese patients with early-stage COPD. Methods A total of 176 participants were enrolled in this study, of which 139 participants had undergone at least two spirometry measurements within 7 years (average 5.5 [standard deviation 0.8] years) after baseline data acquisition. CT-visible TAC was measured by summing all airway segments using semi-automated software. Average lumen diameter, average inner area, emphysema index, air trapping, and inspiratory Pi10 were also measured. Multivariable linear analysis was performed to evaluate variables that were significantly related to pulmonary function parameters and to evaluate the correlation between TAC and annual decline in longitudinal pulmonary function. Results Compared with healthy control subjects, CT-visible TAC was significantly reduced by 51% in GOLD II and by 31% in GOLD I after adjustment. TAC had the greatest impact on pre-bronchodilator FEV1, pre-bronchodilator FVC, post-bronchodilator FEV1, and post-bronchodilator FEV1/FVC (both p<0.001) among all CT indicators measured. TAC has the best correlation with inspiratory Pi10 (ρ=−0.751, p<0.001), an evaluation indicator of the degree of airway remodeling. TAC was independently associated with annual decline in pre-bronchodilator FEV1 (p=0.023), post-bronchodilator FEV1 (p=0.018), and post-bronchodilator FEV1/FVC (p<0.001). Conclusion This finding suggests that CT-visible TAC may be an evaluation indicator of the degree of airway remodeling, and was diminished in greater COPD lung function severity, and independently associated with disease progression. Early-stage COPD patients have already occurred lung structural changes and early intervention may be needed to ameliorate the progression of disease. Clinical Trial Registration ChiCTR-OO-14004264.
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Affiliation(s)
- Fan Wu
- National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, People's Republic of China.,Guangzhou Laboratory, Bio-island, Guangzhou, People's Republic of China
| | - Changbin Jiang
- National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, People's Republic of China
| | - Yumin Zhou
- National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, People's Republic of China.,Guangzhou Laboratory, Bio-island, Guangzhou, People's Republic of China
| | - Youlan Zheng
- National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, People's Republic of China
| | - Heshen Tian
- National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, People's Republic of China
| | - Haiqing Li
- National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, People's Republic of China
| | - Zhishan Deng
- National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, People's Republic of China
| | - Ningning Zhao
- National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, People's Republic of China
| | - Huai Chen
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, People's Republic of China
| | - Pixin Ran
- National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, People's Republic of China.,Guangzhou Laboratory, Bio-island, Guangzhou, People's Republic of China
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Early Diagnosis and Real-Time Monitoring of Regional Lung Function Changes to Prevent Chronic Obstructive Pulmonary Disease Progression to Severe Emphysema. J Clin Med 2021; 10:jcm10245811. [PMID: 34945107 PMCID: PMC8708661 DOI: 10.3390/jcm10245811] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 12/07/2021] [Accepted: 12/09/2021] [Indexed: 02/04/2023] Open
Abstract
First- and second-hand exposure to smoke or air pollutants is the primary cause of chronic obstructive pulmonary disease (COPD) pathogenesis, where genetic and age-related factors predispose the subject to the initiation and progression of obstructive lung disease. Briefly, airway inflammation, specifically bronchitis, initiates the lung disease, leading to difficulty in breathing (dyspnea) and coughing as initial symptoms, followed by air trapping and inhibition of the flow of air into the lungs due to damage to the alveoli (emphysema). In addition, mucus obstruction and impaired lung clearance mechanisms lead to recurring acute exacerbations causing progressive decline in lung function, eventually requiring lung transplant and other lifesaving interventions to prevent mortality. It is noteworthy that COPD is much more common in the population than currently diagnosed, as only 16 million adult Americans were reported to be diagnosed with COPD as of 2018, although an additional 14 million American adults were estimated to be suffering from COPD but undiagnosed by the current standard of care (SOC) diagnostic, namely the spirometry-based pulmonary function test (PFT). Thus, the main issue driving the adverse disease outcome and significant mortality for COPD is lack of timely diagnosis in the early stages of the disease. The current treatment regime for COPD emphysema is most effective when implemented early, on COPD onset, where alleviating symptoms and exacerbations with timely intervention(s) can prevent steep lung function decline(s) and disease progression to severe emphysema. Therefore, the key to efficiently combatting COPD relies on early detection. Thus, it is important to detect early regional pulmonary function and structural changes to monitor modest disease progression for implementing timely interventions and effectively eliminating emphysema progression. Currently, COPD diagnosis involves using techniques such as COPD screening questionnaires, PFT, arterial blood gas analysis, and/or lung imaging, but these modalities are limited in their capability for early diagnosis and real-time disease monitoring of regional lung function changes. Hence, promising emerging techniques, such as X-ray phase contrast, photoacoustic tomography, ultrasound computed tomography, electrical impedance tomography, the forced oscillation technique, and the impulse oscillometry system powered by robust artificial intelligence and machine learning analysis capability are emerging as novel solutions for early detection and real time monitoring of COPD progression for timely intervention. We discuss here the scope, risks, and limitations of current SOC and emerging COPD diagnostics, with perspective on novel diagnostics providing real time regional lung function monitoring, and predicting exacerbation and/or disease onset for prognosis-based timely intervention(s) to limit COPD–emphysema progression.
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22
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Similarities in Quantitative Computed Tomography Imaging of the Lung in Severe Asthma with Persistent Airflow Limitation and Chronic Obstructive Pulmonary Disease. J Clin Med 2021; 10:jcm10215058. [PMID: 34768576 PMCID: PMC8584690 DOI: 10.3390/jcm10215058] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 10/19/2021] [Accepted: 10/26/2021] [Indexed: 11/21/2022] Open
Abstract
Background: Severe asthma with persistent airflow limitation (SA-PAL) and chronic obstructive pulmonary disease (COPD) are characterised by irreversible airflow limitation and the remodelling of the airways. The phenotypes of the diseases overlap and may cause diagnostic and therapeutic concerns. Methods: There were 10 patients with SA-PAL, 11 patients with COPD, and 10 healthy volunteers (HV) enrolled in this study. The patients were examined with a 128-multislice scanner at full inspiration. Measurements were taken from the third to ninth bronchial generations. Results: The thickness of the bronchial wall was greater in the SA-PAL than in the COPD group for most bronchial generations (p < 0.05). The mean lung density was the lowest in the SA-PAL group (−846 HU), followed by the COPD group (−836 HU), with no statistical difference between these two groups. The low-attenuation volume percentage (LAV% < −950 HU) was significantly higher in the SA-PAL group (15.8%) and COPD group (10.4%) compared with the HV group (7%) (p = 0.03). Conclusion: Severe asthma with persistent airflow limitation and COPD become similar with time within the functional and morphological dimensions. Emphysema qualities are present in COPD and in SA-PAL patients.
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Role of the Emphysema Index Combined with the Chronic Obstructive Pulmonary Disease Assessment Test Score in the Evaluation of Chronic Obstructive Pulmonary Disease. Can Respir J 2021; 2021:9996305. [PMID: 34691315 PMCID: PMC8528610 DOI: 10.1155/2021/9996305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 09/18/2021] [Indexed: 11/17/2022] Open
Abstract
Background This study aimed to evaluate the efficacy of the emphysema index (EI) in distinguishing chronic bronchitis (CB) from chronic obstructive pulmonary disease (COPD) and its role, combined with the COPD Assessment Test (CAT) score, in the evaluation of COPD. Methods A total of 92 patients with CB and 277 patients with COPD were enrolled in this study. Receiver operating characteristic (ROC) curves were analyzed to evaluate whether the EI can preliminarily distinguish chronic bronchitis from COPD. Considering the heterogeneity of COPD, there might be missed diagnosis of some patients with bronchitis type when differentiating COPD patients only by EI. Therefore, patients with COPD were classified according to the CAT score and EI into four groups: Group 1 (EI < 16%, CAT < 10), Group 2 (EI < 16%, CAT ≥ 10), Group 3 (EI ≥ 16%, CAT < 10), and Group 4 (EI ≥ 16%, CAT ≥ 10). The records of pulmonary function and quantitative computed tomography findings were retrospectively analyzed. Results ROC curve analysis showed that EI = 16.2% was the cutoff value for distinguishing COPD from CB. Groups 1 and 2 exhibited significantly higher maximal voluntary ventilation (MVV) percent predicted (pred), forced expiratory volume in 1 second (FEV1)/forced vital capacity (FVC), maximal midexpiratory flow of 25-75% pred, carbon monoxide-diffusing capacity (DLCO)/alveolar ventilation (VA), FEV1 % pred (p ≤ 0.013), and maximal expiratory flow 50% pred (all p < 0.05) than Group 4. FEV1/FVC and DLCO/VA were significantly lower in Group 3 than in Group 2 (p=0.002 and p < 0.001, respectively). The residual volume/total lung capacity was higher in Group 3 than in Groups 1 and 2 (p < 0.05). Conclusions The combination of EI and CAT was effective in the evaluation of COPD.
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Ebrahimian S, Digumarthy SR, Bizzo B, Primak A, Zimmermann M, Tarbiah MM, Kalra MK, Dreyer KJ. Artificial Intelligence has Similar Performance to Subjective Assessment of Emphysema Severity on Chest CT. Acad Radiol 2021; 29:1189-1195. [PMID: 34657812 DOI: 10.1016/j.acra.2021.09.007] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 09/03/2021] [Accepted: 09/06/2021] [Indexed: 12/12/2022]
Abstract
RATIONALE AND OBJECTIVES To compare an artificial intelligence (AI)-based prototype and subjective grading for predicting disease severity in patients with emphysema. METHODS Our IRB approved HIPAA-compliant study included 113 adults (71±8 years; 47 females, 66 males) who had both non-contrast chest CT and pulmonary function tests performed within a span of 2 months. The disease severity was classified based on the forced expiratory volume in 1 second (FEV1 as % of predicted) into mild, moderate, and severe. 2 thoracic radiologists (RA), blinded to the clinical and AI results, graded severity of emphysema on a 5-point scale suggested by the Fleischner Society for each lobe. The whole lung scores were derived from the summation of lobar scores. Thin-section CT images were processed with the AI-Rad Companion Chest prototype (Siemens Healthineers) to quantify low attenuation areas (LAA < - 950 HU) in whole lung and each lobe separately. Bronchial abnormality was assessed by both radiologists and a fully automated software (Philips Healthcare). RESULTS Both AI (AUC of 0.77; 95% CI: 0.68 - 0.85) and RA (AUC: 0.76, 95% CI: 0.65 - 0.84) emphysema quantification could differentiate mild, moderate, and severe disease based on FEV1. There was a strong positive correlation between AI and RA (r = 0.72 - 0.80; p <0.001). The combination of emphysema and bronchial abnormality quantification from radiologists' and AI assessment could differentiate between different severities with AUC of 0.80 - 0.82 and 0.87, respectively. CONCLUSION The assessed AI-prototypes can predict the disease severity in patients with emphysema with the same predictive value as the radiologists.
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Richter D, Guasti L, Koehler F, Squizzato A, Nistri S, Christodorescu R, Dievart F, Gaudio G, Asteggiano R, Ferrini M. Late phase of COVID-19 pandemic in General Cardiology. A position paper of the ESC Council for Cardiology Practice. ESC Heart Fail 2021; 8:3483-3494. [PMID: 34170086 PMCID: PMC8427022 DOI: 10.1002/ehf2.13466] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 05/06/2021] [Accepted: 05/23/2021] [Indexed: 01/08/2023] Open
Abstract
Cardiovascular (CV) engagement in coronavirus disease 2019 (COVID-19) is a huge determinant of prognosis during the acute phase of the disease. However, little is known about the potential chronic implications of the late phase of COVID-19 and about the appropriate approach to these patients. Heart failure, type 1 and type 2 myocardial infarction, arrhythmias, myocarditis, pulmonary fibrosis, and thrombosis have been shown to be related to severe acute respiratory syndrome coronavirus 2 infection, and a 'long COVID-19' illness has been recognized with fatigue, chest pain, and dyspnoea among the most frequent symptoms reported after discharge from hospital. This paper focuses on some open questions that cardiologists are going to face during the next months in a general cardiology outpatient clinic, in particular how to evaluate a 'post-COVID' patient during follow-up of CV complications of the acute phase and how to manage new CV symptoms that could be the consequence, at least in part, of heart/vessels and/or lung involvement of the previous virus infection. Present symptoms and signs, history of previous CV disease (both preceding COVID-19 and occurring during viral infection), and specific laboratory and imaging measurements during the acute phase may be of interest in focusing on how to approach the clinical evaluation of a post-COVID patient and how to integrate in our standard of care the new information on COVID-19, possibly in a multidisciplinary view. Dealing with the increased COVID-associated CV risk burden and becoming acquainted with potential new e-cardiology approaches aimed at integrating the cardiology practice are relevant new challenges brought by severe acute respiratory syndrome coronavirus 2 infection and its sequelae.
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Affiliation(s)
| | | | - Friedrich Koehler
- Medical Department, Division of Cardiology and Angiology, Centre for Cardiovascular TelemedicineCharité—Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt‐Universität zu Berlin, and Berlin Institute of HealthBerlinGermany
| | | | - Stefano Nistri
- CMSR Veneto Medica—Cardiology ServiceAltavilla VicentinaItaly
| | | | | | | | - Riccardo Asteggiano
- University of Insubria, ASST dei Sette LaghiVareseItaly
- LARC (Laboratorio Analisi e Ricerca Clinica)TurinItaly
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Li T, Zhou HP, Zhou ZJ, Guo LQ, Zhou L. Computed tomography-identified phenotypes of small airway obstructions in chronic obstructive pulmonary disease. Chin Med J (Engl) 2021; 134:2025-2036. [PMID: 34517376 PMCID: PMC8440009 DOI: 10.1097/cm9.0000000000001724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Indexed: 12/02/2022] Open
Abstract
ABSTRACT Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease characteristic of small airway inflammation, obstruction, and emphysema. It is well known that spirometry alone cannot differentiate each separate component. Computed tomography (CT) is widely used to determine the extent of emphysema and small airway involvement in COPD. Compared with the pulmonary function test, small airway CT phenotypes can accurately reflect disease severity in patients with COPD, which is conducive to improving the prognosis of this disease. CT measurement of central airway morphology has been applied in clinical, epidemiologic, and genetic investigations as an inference of the presence and severity of small airway disease. This review will focus on presenting the current knowledge and methodologies in chest CT that aid in identifying discrete COPD phenotypes.
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Affiliation(s)
- Tao Li
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital, Nanjing Medical University, Nanjing, Jiangsu 210029, China
- Department of Respiratory Medicine, Xuzhou First People's Hospital, Xuzhou, Jiangsu 221116, China
| | - Hao-Peng Zhou
- Department of Medicine, Jiangsu University School of Medicine, Zhenjiang, Jiangsu 212013, China
| | - Zhi-Jun Zhou
- Institute of Radio Frequency & Optical Electronics-Integrated Circuits, School of Information and Engineering, Southeast University, Nanjing, Jiangsu 210096, China
| | - Li-Quan Guo
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, China
| | - Linfu Zhou
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital, Nanjing Medical University, Nanjing, Jiangsu 210029, China
- Institute of Integrative Medicine, Nanjing Medical University, Nanjing, Jiangsu 210029, China
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Matsumoto S, Yamada Y, Yamada M, Chubachi S, Yokoyama Y, Matsuoka S, Tanabe A, Niijima Y, Murata M, Fukunaga K, Jinzaki M. Difference in the airway luminal area between the standing and supine positions using upright and conventional computed tomography. Clin Anat 2021; 34:1150-1156. [PMID: 34218460 DOI: 10.1002/ca.23763] [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: 04/25/2021] [Revised: 06/02/2021] [Accepted: 06/21/2021] [Indexed: 11/09/2022]
Abstract
No clinical studies to date have compared the airway luminal area between supine and standing positions. Our aim was therefore to compare the airway luminal area between these two positions on computed tomography (CT) and to determine its correlation with forced expiratory volume in 1 s (FEV1). Thirty-two asymptomatic volunteers underwent both conventional (supine position) and upright (standing position) CT during deep inspiration breath-holding. Pulmonary function tests were conducted on the same day. We measured the airway luminal area on CT in each position. Paired t-tests and Pearson's correlation coefficients were used for statistical analysis. The average luminal areas of the trachea, right and left main bronchi, and average third-generation airway were greater in the standing than the supine position by 3.4%, 6.1%, 5.5%, and 5.2%, respectively. The correlation coefficients between airway luminal areas and FEV1 tended to be higher in the standing than the supine position; this correlation was highest for the average third-generation airway (r = 0.70, P < 0.0001). The airway luminal areas of the trachea, bilateral main bronchi, and average third-generation airway were greater in the standing than the supine position. The average third-generation airway area in the standing position had the highest correlation with FEV1.
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Affiliation(s)
- Shunsuke Matsumoto
- Department of Radiology, Keio University School of Medicine, Tokyo, Japan
| | - Yoshitake Yamada
- Department of Radiology, Keio University School of Medicine, Tokyo, Japan
| | - Minoru Yamada
- Department of Radiology, Keio University School of Medicine, Tokyo, Japan
| | - Shotaro Chubachi
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Yoichi Yokoyama
- Department of Radiology, Keio University School of Medicine, Tokyo, Japan
| | - Shiho Matsuoka
- Department of Clinical Laboratory, Keio University Hospital, Tokyo, Japan
| | - Akiko Tanabe
- Department of Clinical Laboratory, Keio University Hospital, Tokyo, Japan
| | - Yuki Niijima
- Office of Radiation Technology, Keio University Hospital, Tokyo, Japan
| | - Mitsuru Murata
- Department of Laboratory Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Koichi Fukunaga
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Masahiro Jinzaki
- Department of Radiology, Keio University School of Medicine, Tokyo, Japan
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von Wichert P. Bronchial Diseases are Insufficiently Defined with the Term COPD. Int J Chron Obstruct Pulmon Dis 2021; 16:1349-1352. [PMID: 34025120 PMCID: PMC8132574 DOI: 10.2147/copd.s298210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Accepted: 04/28/2021] [Indexed: 11/29/2022] Open
Abstract
This paper discusses the basic thoughts behind the so-called diagnosis of COPD in relation to cause and course of the disease and questions the value of this functional defined terminology. Instead, the terminology should be based on morphology in the broadest sense including all methods suitable to describe pathological processes to understand the cause of bronchial diseases. The diagnosis COPD is only helpful in relation to therapeutic measures.
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Hasenstab KA, Yuan N, Retson T, Conrad DJ, Kligerman S, Lynch DA, Hsiao A. Automated CT Staging of Chronic Obstructive Pulmonary Disease Severity for Predicting Disease Progression and Mortality with a Deep Learning Convolutional Neural Network. Radiol Cardiothorac Imaging 2021; 3:e200477. [PMID: 33969307 DOI: 10.1148/ryct.2021200477] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 01/29/2021] [Accepted: 02/05/2021] [Indexed: 11/11/2022]
Abstract
Purpose To develop a deep learning-based algorithm to stage the severity of chronic obstructive pulmonary disease (COPD) through quantification of emphysema and air trapping on CT images and to assess the ability of the proposed stages to prognosticate 5-year progression and mortality. Materials and Methods In this retrospective study, an algorithm using co-registration and lung segmentation was developed in-house to automate quantification of emphysema and air trapping from inspiratory and expiratory CT images. The algorithm was then tested in a separate group of 8951 patients from the COPD Genetic Epidemiology study (date range, 2007-2017). With measurements of emphysema and air trapping, bivariable thresholds were determined to define CT stages of severity (mild, moderate, severe, and very severe) and were evaluated for their ability to prognosticate disease progression and mortality using logistic regression and Cox regression. Results On the basis of CT stages, the odds of disease progression were greatest among patients with very severe disease (odds ratio [OR], 2.67; 95% CI: 2.02, 3.53; P < .001) and were elevated in patients with moderate disease (OR, 1.50; 95% CI: 1.22, 1.84; P = .001). The hazard ratio of mortality for very severe disease at CT was 2.23 times the normal ratio (95% CI: 1.93, 2.58; P < .001). When combined with Global Initiative for Chronic Obstructive Lung Disease (GOLD) staging, patients with GOLD stage 2 disease had the greatest odds of disease progression when the CT stage was severe (OR, 4.48; 95% CI: 3.18, 6.31; P < .001) or very severe (OR, 4.72; 95% CI: 3.13, 7.13; P < .001). Conclusion Automated CT algorithms can facilitate staging of COPD severity, have diagnostic performance comparable with that of spirometric GOLD staging, and provide further prognostic value when used in conjunction with GOLD staging.Supplemental material is available for this article.© RSNA, 2021See also commentary by Kalra and Ebrahimian in this issue.
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Affiliation(s)
- Kyle A Hasenstab
- Department of Radiology (K.A.H., N.Y., T.R., S.K., A.H.) and Department of Medicine (D.J.C.), University of California San Diego, 9452 Medical Center Dr, La Jolla, CA 92037; Department of Mathematics and Statistics, San Diego State University, San Diego, Calif (K.A.H.); and Department of Radiology, National Jewish Health, Denver, Colo (D.A.L.)
| | - Nancy Yuan
- Department of Radiology (K.A.H., N.Y., T.R., S.K., A.H.) and Department of Medicine (D.J.C.), University of California San Diego, 9452 Medical Center Dr, La Jolla, CA 92037; Department of Mathematics and Statistics, San Diego State University, San Diego, Calif (K.A.H.); and Department of Radiology, National Jewish Health, Denver, Colo (D.A.L.)
| | - Tara Retson
- Department of Radiology (K.A.H., N.Y., T.R., S.K., A.H.) and Department of Medicine (D.J.C.), University of California San Diego, 9452 Medical Center Dr, La Jolla, CA 92037; Department of Mathematics and Statistics, San Diego State University, San Diego, Calif (K.A.H.); and Department of Radiology, National Jewish Health, Denver, Colo (D.A.L.)
| | - Douglas J Conrad
- Department of Radiology (K.A.H., N.Y., T.R., S.K., A.H.) and Department of Medicine (D.J.C.), University of California San Diego, 9452 Medical Center Dr, La Jolla, CA 92037; Department of Mathematics and Statistics, San Diego State University, San Diego, Calif (K.A.H.); and Department of Radiology, National Jewish Health, Denver, Colo (D.A.L.)
| | - Seth Kligerman
- Department of Radiology (K.A.H., N.Y., T.R., S.K., A.H.) and Department of Medicine (D.J.C.), University of California San Diego, 9452 Medical Center Dr, La Jolla, CA 92037; Department of Mathematics and Statistics, San Diego State University, San Diego, Calif (K.A.H.); and Department of Radiology, National Jewish Health, Denver, Colo (D.A.L.)
| | - David A Lynch
- Department of Radiology (K.A.H., N.Y., T.R., S.K., A.H.) and Department of Medicine (D.J.C.), University of California San Diego, 9452 Medical Center Dr, La Jolla, CA 92037; Department of Mathematics and Statistics, San Diego State University, San Diego, Calif (K.A.H.); and Department of Radiology, National Jewish Health, Denver, Colo (D.A.L.)
| | - Albert Hsiao
- Department of Radiology (K.A.H., N.Y., T.R., S.K., A.H.) and Department of Medicine (D.J.C.), University of California San Diego, 9452 Medical Center Dr, La Jolla, CA 92037; Department of Mathematics and Statistics, San Diego State University, San Diego, Calif (K.A.H.); and Department of Radiology, National Jewish Health, Denver, Colo (D.A.L.)
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Machine Learning and Feature Selection Methods for EGFR Mutation Status Prediction in Lung Cancer. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11073273] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The evolution of personalized medicine has changed the therapeutic strategy from classical chemotherapy and radiotherapy to a genetic modification targeted therapy, and although biopsy is the traditional method to genetically characterize lung cancer tumor, it is an invasive and painful procedure for the patient. Nodule image features extracted from computed tomography (CT) scans have been used to create machine learning models that predict gene mutation status in a noninvasive, fast, and easy-to-use manner. However, recent studies have shown that radiomic features extracted from an extended region of interest (ROI) beyond the tumor, might be more relevant to predict the mutation status in lung cancer, and consequently may be used to significantly decrease the mortality rate of patients battling this condition. In this work, we investigated the relation between image phenotypes and the mutation status of Epidermal Growth Factor Receptor (EGFR), the most frequently mutated gene in lung cancer with several approved targeted-therapies, using radiomic features extracted from the lung containing the nodule. A variety of linear, nonlinear, and ensemble predictive classification models, along with several feature selection methods, were used to classify the binary outcome of wild-type or mutant EGFR mutation status. The results show that a comprehensive approach using a ROI that included the lung with nodule can capture relevant information and successfully predict the EGFR mutation status with increased performance compared to local nodule analyses. Linear Support Vector Machine, Elastic Net, and Logistic Regression, combined with the Principal Component Analysis feature selection method implemented with 70% of variance in the feature set, were the best-performing classifiers, reaching Area Under the Curve (AUC) values ranging from 0.725 to 0.737. This approach that exploits a holistic analysis indicates that information from more extensive regions of the lung containing the nodule allows a more complete lung cancer characterization and should be considered in future radiogenomic studies.
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31
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Xu G, Wang Z, Xiong Z, Li M, Luo W, Xu Y, Min T. Correlation Between Pulmonary Sclerosing Pneumocytoma Features and MSCT Imaging Manifestations in 34 Patients: Implications for Precision Medicine. Front Med (Lausanne) 2021; 8:650996. [PMID: 33816530 PMCID: PMC8012803 DOI: 10.3389/fmed.2021.650996] [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] [Received: 01/08/2021] [Accepted: 02/18/2021] [Indexed: 12/25/2022] Open
Abstract
Objective: To identify and analyze the multi-slice computed tomography (MSCT) imaging manifestations and clinicopathological features of PSP to improve the preoperative and intraoperative diagnosis of the disease. Method: This was a retrospective study conducted on the imaging and clinicopathological data of the PSP patients treated in two major hospitals in China from October 2001 to December 2019. The locations of lung lesions, clinical symptoms, surgical complications, MSCT imaging features, and the corresponding relationship with clinicopathological features were assessed. Then, a new diagnostic approach was defined and used to train imaging and pathological doctors (experimental group). Then, the diagnostic accuracy of the experimental group was evaluated in preoperative and intraoperative diagnosis of PSP. Results: Thirty-four PSP cases were analyzed (mean: 51.42; range: 39-69 years old). The peripheral type was more common, while 92% of the lesions located in the middle lobe of the right lung and the lower lobe of bilateral lungs. The shortest lesion edge-pleura distance ranged 0 to 30 mm and 46% of the lesions (16/34) were attached to the pleura, 62% (21/34) located at 0-5 mm, 92% (31/34) within 20 mm from the pleura. Diameters of the lesions ranged 8.58 to 68.41 mm, while most of them were 20-40 mm. All lesions showed enhancement, and 97% (33/34) were unevenly enhanced. PSP volume was negatively correlated with the total degree of enhancement (r = -0.587, p < 0.01), and the volume difference between the obvious enhancement zone and the slight enhancement zone (r = -0.795, p < 0.01). Welt vessel sign was observed in 61.7% (21/34) of cases, and none of welt vessels entered into the lesions. Vascular-like enhancement area inside the lesion showed no significant correlation with the welt vessels outside the lesion, and no case showed entrance of bronchus into the lesion. The trained experimental group showed significantly greater diagnostic accuracy than the control group. In particular, the accuracy rate of intraoperative frozen section diagnosis was 60% higher in the experimental group than the control group. Conclusion: PSP has characteristic imaging manifestations, which can be utilized to improve the preoperative and intraoperative diagnostic coincidence rate of PSP.
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Affiliation(s)
- Gen Xu
- Department of Radiology, First People's Hospital of Pingjiang County, Yueyang, China
| | - Zhaoyu Wang
- Shanghai Hengdao Medical Pathology Diagnostic Center, Shanghai, China
| | - Zeng Xiong
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Manqiu Li
- Department of Pathology, First People's Hospital of Pingjiang County, Yueyang, China
| | - Weijun Luo
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Yong Xu
- Department of Radiology, First People's Hospital of Pingjiang County, Yueyang, China
| | - Tang Min
- Department of Radiology, First People's Hospital of Pingjiang County, Yueyang, China
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32
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Ray E, Culliford D, Kruk H, Gillett K, North M, Astles CM, Hicks A, Johnson M, Lin SX, Orlando R, Thomas M, Jordan RE, Price D, Konstantin M, Wilkinson TMA. Specialist respiratory outreach: a case-finding initiative for identifying undiagnosed COPD in primary care. NPJ Prim Care Respir Med 2021; 31:7. [PMID: 33574260 PMCID: PMC7878732 DOI: 10.1038/s41533-021-00219-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 01/06/2021] [Indexed: 12/18/2022] Open
Abstract
COPD remains largely undiagnosed or is diagnosed late in the course of disease. We report findings of a specialist outreach programme to identify undiagnosed COPD in primary care. An electronic case-finding algorithm identified 1602 at-risk patients from 12 practices who were invited to attend the clinic. Three hundred and eighty-three (23.9%) responded and 288 were enrolled into the study. Forty-eight (16.6%) had undiagnosed mild and 28 (9.7%) had moderate airway obstruction, meeting spirometric diagnostic criteria for COPD. However, at 12 months only 8 suspected COPD patients (10.6%) had received a diagnostic label in their primary care record. This constituted 0.38% of the total patient population, as compared with 0.31% of control practices, p = 0.306. However, if all patients with airway obstruction received a coding of COPD, then the diagnosis rate in the intervention group would have risen by 0.84%. Despite the low take-up and diagnostic yield, this programme suggests that integrated case-finding strategies could improve COPD recognition.
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Affiliation(s)
- Emma Ray
- NIHR ARC Wessex, Faculty of Health Sciences, University of Southampton, Southampton, UK
- University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - David Culliford
- NIHR ARC Wessex, Faculty of Health Sciences, University of Southampton, Southampton, UK
| | - Helen Kruk
- NIHR ARC Wessex, Faculty of Health Sciences, University of Southampton, Southampton, UK
- University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Kate Gillett
- NIHR ARC Wessex, Faculty of Health Sciences, University of Southampton, Southampton, UK
- University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Mal North
- NIHR ARC Wessex, Faculty of Health Sciences, University of Southampton, Southampton, UK
- University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Carla M Astles
- NIHR ARC Wessex, Faculty of Health Sciences, University of Southampton, Southampton, UK
- University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Alexander Hicks
- University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Matthew Johnson
- NIHR ARC Wessex, Faculty of Health Sciences, University of Southampton, Southampton, UK
| | - Sharon Xiaowen Lin
- NIHR ARC Wessex, Faculty of Health Sciences, University of Southampton, Southampton, UK
| | - Rosanna Orlando
- NIHR ARC Wessex, Faculty of Health Sciences, University of Southampton, Southampton, UK
| | - Mike Thomas
- Department of Primary Care and Population Sciences, University of Southampton, Southampton, UK
| | - Rachel E Jordan
- Institute of Applied Health Research, University of Birmingham, Birmingham, 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
| | - Mita Konstantin
- University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Tom M A Wilkinson
- Faculty of Medicine, University of Southampton, Southampton, UK.
- NIHR Biomedical Research Centre, University Hospitals Southampton NHS Foundation Trust, Southampton, UK.
- Wessex Investigational Sciences Hub, University of Southampton Faculty of Medicine, Southampton General Hospital, Southampton, UK.
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33
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Rose D, Ray E, Summers RH, Taylor M, Kruk H, North M, Gillett K, Thomas M, Wilkinson TMA. Case-finding for COPD clinic acceptability to patients in GPs across Hampshire: a qualitative study. NPJ Prim Care Respir Med 2021; 31:4. [PMID: 33542246 PMCID: PMC7862661 DOI: 10.1038/s41533-021-00216-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 11/17/2020] [Indexed: 11/23/2022] Open
Abstract
Despite high mortality and morbidity, COPD remains under-diagnosed. Case-finding strategies are possible, but patients' perspectives are unexplored. Using qualitative methods, we explored the patient perspective of a case-finding intervention among at-risk patients in primary care. Semi-structured telephone interviews were transcribed and thematic analysis utilised. Seven patients without (mean age 64.5 years (58-74), n = 4) and 8 with obstructed spirometry (mean age 63.5 (53-75), n = 4) were interviewed. Themes identified were motives, challenges and concerns regarding attending the clinic. These included wanting to be well; to help with research; concern over negative impact to life from COPD diagnosis; perceived utility of the clinic; quality of information given; staff manner, approachability and knowledge; and perceived effects of the clinic on lifestyle, self-management and symptoms. The intervention was generally deemed useful and reassuring, although shared information was too detailed or irrelevant for some. Several reported positive lifestyle changes, improved symptoms and improved self-management.
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Affiliation(s)
| | - Emma Ray
- NIHR ARC Wessex, Faculty of Health Sciences, University of Southampton, Southampton, UK
- University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Rachael H Summers
- Faculty of Health Sciences, University of Southampton, Southampton, UK
| | - Melinda Taylor
- NIHR ARC Wessex, Faculty of Health Sciences, University of Southampton, Southampton, UK
| | - Helen Kruk
- NIHR ARC Wessex, Faculty of Health Sciences, University of Southampton, Southampton, UK
- University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Mal North
- NIHR ARC Wessex, Faculty of Health Sciences, University of Southampton, Southampton, UK
- University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Kate Gillett
- NIHR ARC Wessex, Faculty of Health Sciences, University of Southampton, Southampton, UK
- University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Mike Thomas
- Department of Primary Care and Population Sciences, University of Southampton, Southampton, UK
| | - Tom M A Wilkinson
- Faculty of Medicine, University of Southampton, Southampton, UK.
- NIHR Biomedical Research Centre, University Hospitals Southampton NHS Foundation Trust, Southampton, UK.
- Wessex Investigational Sciences Hub, University of Southampton Faculty of Medicine, Southampton General Hospital, Southampton, UK.
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A 3D-CNN model with CT-based parametric response mapping for classifying COPD subjects. Sci Rep 2021; 11:34. [PMID: 33420092 PMCID: PMC7794420 DOI: 10.1038/s41598-020-79336-5] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Accepted: 12/08/2020] [Indexed: 12/18/2022] Open
Abstract
Chronic obstructive pulmonary disease (COPD) is a respiratory disorder involving abnormalities of lung parenchymal morphology with different severities. COPD is assessed by pulmonary-function tests and computed tomography-based approaches. We introduce a new classification method for COPD grouping based on deep learning and a parametric-response mapping (PRM) method. We extracted parenchymal functional variables of functional small airway disease percentage (fSAD%) and emphysema percentage (Emph%) with an image registration technique, being provided as input parameters of 3D convolutional neural network (CNN). The integrated 3D-CNN and PRM (3D-cPRM) achieved a classification accuracy of 89.3% and a sensitivity of 88.3% in five-fold cross-validation. The prediction accuracy of the proposed 3D-cPRM exceeded those of the 2D model and traditional 3D CNNs with the same neural network, and was comparable to that of 2D pretrained PRM models. We then applied a gradient-weighted class activation mapping (Grad-CAM) that highlights the key features in the CNN learning process. Most of the class-discriminative regions appeared in the upper and middle lobes of the lung, consistent with the regions of elevated fSAD% and Emph% in COPD subjects. The 3D-cPRM successfully represented the parenchymal abnormalities in COPD and matched the CT-based diagnosis of COPD.
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Watson A, Wilkinson TMA. Respiratory viral infections in the elderly. Ther Adv Respir Dis 2021; 15:1753466621995050. [PMID: 33749408 PMCID: PMC7989115 DOI: 10.1177/1753466621995050] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Accepted: 01/05/2021] [Indexed: 12/12/2022] Open
Abstract
With the global over 60-year-old population predicted to more than double over the next 35 years, caring for this aging population has become a major global healthcare challenge. In 2016 there were over 1 million deaths in >70 year olds due to lower respiratory tract infections; 13-31% of these have been reported to be caused by viruses. Since then, there has been a global COVID-19 pandemic, which has caused over 2.3 million deaths so far; increased age has been shown to be the biggest risk factor for morbidity and mortality. Thus, the burden of respiratory viral infections in the elderly is becoming an increasing unmet clinical need. Particular challenges are faced due to the interplay of a variety of factors including complex multimorbidities, decreased physiological reserve and an aging immune system. Moreover, their atypical presentation of symptoms may lead to delayed necessary care, prescription of additional drugs and prolonged hospital stay. This leads to morbidity and mortality and further nosocomial spread. Clinicians currently have limited access to sensitive detection methods. Furthermore, a lack of effective antiviral treatments means there is little incentive to diagnose and record specific non-COVID-19 viral infections. To meet this unmet clinical need, it is first essential to fully understand the burden of respiratory viruses in the elderly. Doing this through prospective screening research studies for all respiratory viruses will help guide preventative policies and clinical trials for emerging therapeutics. The implementation of multiplex point-of-care diagnostics as a mainstay in all healthcare settings will be essential to understand the burden of respiratory viruses, diagnose patients and monitor outbreaks. The further development of novel targeted vaccinations as well as anti-viral therapeutics and new ways to augment the aging immune system is now also essential.The reviews of this paper are available via the supplemental material section.
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Affiliation(s)
- Alastair Watson
- Faculty of Medicine, Clinical & Experimental Sciences, University of Southampton, Southampton, UK
- NIHR Southampton Biomedical Research Centre, University Hospital Southampton NHS Foundation Trust, Southampton, UK
- Birmingham Medical School, University of Birmingham, Birmingham, UK
| | - Tom M. A. Wilkinson
- Faculty of Medicine, Clinical and Experimental Sciences, Southampton University, Mailpoint 810, Level F, South Block, Southampton General Hospital, Southampton, Hampshire, SO16 6YD, UK
- NIHR Southampton Biomedical Research Centre, University Hospital Southampton NHS Foundation Trust, Southampton, UK
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Day K, Ostridge K, Conway J, Cellura D, Watson A, Spalluto CM, Staples KJ, Thompson B, Wilkinson T. Interrelationships Among Small Airways Dysfunction, Neutrophilic Inflammation, and Exacerbation Frequency in COPD. Chest 2020; 159:1391-1399. [PMID: 33245876 DOI: 10.1016/j.chest.2020.11.018] [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] [Received: 03/19/2020] [Revised: 11/03/2020] [Accepted: 11/08/2020] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Small airways disease (SAD) is a key component of COPD and is a main contributing factor to lung function decline. RESEARCH QUESTION Is SAD a key feature of frequent COPD exacerbators and is this related to airway inflammation? STUDY DESIGN AND METHODS Thirty-nine COPD patients defined as either frequent exacerbator (FE) group (≥ 2 exacerbations/y; n = 17) and infrequent exacerbator (IFE) group (≤ 1 exacerbation/y; n = 22) underwent the forced oscillation technique (resistance at 5 Hz minus 19 Hz [R5-R19], area of reactance [AX]), multiple breath nitrogen washout (conducting airways ventilation heterogeneity, acinar ventilation heterogeneity [Sacin]), plethysmography (ratio of residual volume to total lung capacity), single-breath transfer factor of the lung for carbon monoxide, spirometry (FEV1, FEV1/FVC), and paired inspiratory-expiratory CT scans to ascertain SAD. A subpopulation underwent bronchoscopy to enable enumeration of BAL cell proportions. RESULTS Sacin was significantly higher in the COPD FE group compared with the IFE group (P = .027). In the FE group, markers of SAD were associated strongly with BAL neutrophil proportions, R5-R19 (P = .001, r = 0.795), AX (P = .049, ρ = 0.560), residual volume to total lung capacity ratio (P = .004, r = 0.730), and the mean lung density of the paired CT scans (P = .018, r = 0.639). INTERPRETATION Increased Sacin may be a consequence of previous exacerbations or may highlight a group of patients prone to exacerbations. Measures of SAD were associated strongly with neutrophilic inflammation in the small airways of FE patients, supporting the hypothesis that frequent exacerbations are associated with SAD related to increased cellular inflammation.
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Affiliation(s)
- Kerry Day
- Faculty of Medicine, University of Southampton, Southampton; NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton.
| | - Kristoffer Ostridge
- Faculty of Medicine, University of Southampton, Southampton; NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton; Clinical Development, Research and Early Development, Respiratory & Immunology, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | | | | | | | | | - Karl J Staples
- Faculty of Medicine, University of Southampton, Southampton; NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton
| | - Bruce Thompson
- Swinburne University of Technology, Melbourne, Australia
| | - Tom Wilkinson
- Faculty of Medicine, University of Southampton, Southampton; NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton
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Hao W, Li M, Pang Y, Du W, Huang X. Increased chemokines levels in patients with chronic obstructive pulmonary disease: correlation with quantitative computed tomography metrics. Br J Radiol 2020; 94:20201030. [PMID: 33237823 DOI: 10.1259/bjr.20201030] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
OBJECTIVE We sought to explore the relationships between multiple chemokines with spirometry, inflammatory mediators and CT findings of emphysema, small airways disease and bronchial wall thickness. METHODS All patients with COPD (n = 65) and healthy control subjects (n = 23) underwent high-resolution CT, with image analysis determining the low attenuation area (LAA), ratio of mean lung attenuation on expiratory and inspiratory scans (E/I MLD) and bronchial wall thickness of inner perimeter of a 10-mm diameter airway (Pi10). At enrollment, subjects underwent pulmonary function studies, chemokines and inflammatory mediators measurements. RESULTS Multiple chemokines (CCL2, CCL3, CCL5, CX3CL1, CXCL8, CXCL9, CXCL10, CXCL11 and CXCL12) and inflammatory mediators (MMP-9, MMP-12, IL-18 and neutrophil count) were markedly increased in the serum of COPD patients compared with healthy controls. There were associations between small airway disease (E/I MLD) and CCL11, CXCL8, CXCL10, CXCL11, CXCL12 and CX3CL1. Especially CXCL8 and CX3CL1 are strongly associated with E/I MLD (r = 0.74, p < 0.001; r = 0.76, p < 0.001, respectively). CXCL8, CXCL12 and CX3CL1 were moderately positively correlated with emphysema (%LAA) (r = 0.49, p < 0.05; r = 0.51, p < 0.05; r = 0.54, p < 0.01, respectively). Bronchial wall thickness (Pi10)showed no significant differences between the COPD and healthy controls,,but there was an association between Pi10 and FEV1% in COPD patients (r=-0.420, p = 0.048). Our statistical results showed that there were not any associations between airway wall thickness (Pi10) and chemokines. CONCLUSION Pulmonary chemokines levels are closely associated with the extent of gas trapping, small airways disease and emphysema identified on high-resolution chest CT scan. ADVANCES IN KNOWLEDGE This study combines quantitative CT analysis with multiplex chemokines and inflammatory mediators to identify a new role of pathological changes in COPD.
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Affiliation(s)
- Wendong Hao
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Xi'anJiaotong University, Xi'an, China.,Department of Respiratory Medicine, The Affiliated Hospital of Yan'an University, Yan'an, China
| | - Manxiang Li
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Xi'anJiaotong University, Xi'an, China
| | - Yamei Pang
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Xi'anJiaotong University, Xi'an, China
| | - Weiping Du
- Clinical Laboratory Diagnosis Department, The Affiliated Hospital of Yan'an University, Yan'an, China
| | - Xiaoqi Huang
- Department of Radiology, The Affiliated Hospital of Yan'an University, Yan'an, China
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Ohno Y, Aoyagi K, Takenaka D, Yoshikawa T, Ikezaki A, Fujisawa Y, Murayama K, Hattori H, Toyama H. Machine learning for lung CT texture analysis: Improvement of inter-observer agreement for radiological finding classification in patients with pulmonary diseases. Eur J Radiol 2020; 134:109410. [PMID: 33246272 DOI: 10.1016/j.ejrad.2020.109410] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 10/12/2020] [Accepted: 11/06/2020] [Indexed: 10/23/2022]
Abstract
PURPOSE To evaluate the capability ML-based CT texture analysis for improving interobserver agreement and accuracy of radiological finding assessment in patients with COPD, interstitial lung diseases or infectious diseases. MATERIALS AND METHODS Training cases (n = 28), validation cases (n = 17) and test cases (n = 89) who underwent thin-section CT at a 320-detector row CT with wide volume scan and two 64-detector row CTs with helical scan were enrolled in this study. From 89 CT data, a total of 350 computationally selected ROI including normal lung, emphysema, nodular lesion, ground-glass opacity, reticulation and honeycomb were evaluated by three radiologists as well as by the software. Inter-observer agreements between consensus reading with and without using the software or software alone and standard references determined by consensus of pulmonologists and chest radiologists were determined using κ statistics. Overall distinguishing accuracies were compared among all methods by McNemar's test. RESULTS Agreements for consensus readings obtained with and without the software or the software alone with standard references were determined as significant and substantial or excellent (with the software: κ = 0.91, p < 0.0001; without the software: κ = 0.81, p < 0.0001; the software alone: κ = 0.79, p < 0.0001). Overall differentiation accuracy of consensus reading using the software (94.9 [332/350] %) was significantly higher than that of consensus reading without using the software (84.3 [295/350] %, p < 0.0001) and the software alone (82.3 [288/350] %, p < 0.0001). CONCLUSION ML-based CT texture analysis software has potential for improving interobserver agreement and accuracy for radiological finding assessments in patients with COPD, interstitial lung diseases or infectious diseases.
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Affiliation(s)
- Yoshiharu Ohno
- Department of Radiology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan; Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University School of Medicine, Toyoake, Aichi, Japan; Division of Functional and Diagnostic Imaging Research, Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan.
| | - Kota Aoyagi
- Canon Medical Systems Corporation, Otawara, Tochigi, Japan
| | - Daisuke Takenaka
- Department of Diagnostic Radiology, Hyogo Cancer Center, Akashi, Hyogo, Japan
| | - Takeshi Yoshikawa
- Division of Functional and Diagnostic Imaging Research, Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan; Department of Diagnostic Radiology, Hyogo Cancer Center, Akashi, Hyogo, Japan
| | - Aina Ikezaki
- Canon Medical Systems Corporation, Otawara, Tochigi, Japan
| | | | - Kazuhiro Murayama
- Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
| | - Hidekazu Hattori
- Department of Radiology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
| | - Hiroshi Toyama
- Department of Radiology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
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North M, Bourne S, Green B, Chauhan AJ, Brown T, Winter J, Jones T, Neville D, Blythin A, Watson A, Johnson M, Culliford D, Elkes J, Cornelius V, Wilkinson TMA. A randomised controlled feasibility trial of E-health application supported care vs usual care after exacerbation of COPD: the RESCUE trial. NPJ Digit Med 2020; 3:145. [PMID: 33145441 PMCID: PMC7603326 DOI: 10.1038/s41746-020-00347-7] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Accepted: 09/17/2020] [Indexed: 01/17/2023] Open
Abstract
Exacerbations of COPD are one of the commonest causes of admission and readmission to hospital. The role of digital interventions to support self-management in improving outcomes is uncertain. We conducted an open, randomised controlled trial of a digital health platform application (app) in 41 COPD patients recruited following hospital admission with an acute exacerbation. Subjects were randomised to either receive usual care, including a written self-management plan (n = 21), or the myCOPD app (n = 20) for 90 days. The primary efficacy outcome was recovery rate of symptoms measured by COPD assessment test (CAT) score. Exacerbations, readmission, inhaler technique quality of life and patient activation (PAM) scores were also captured by a blinded team. The app was acceptable in this care setting and was used by 17 of the 20 patients with sustained use over the study period. The treatment effect on the CAT score was 4.49 (95% CI: -8.41, -0.58) points lower in the myCOPD arm. Patients' inhaler technique improved in the digital intervention arm (101 improving to 20 critical errors) compared to usual care (100 to 72 critical errors). Exacerbations tended to be less frequent in the digital arm compared to usual care; 34 vs 18 events. Hospital readmissions risk was numerically lower in the digital intervention arm: OR for readmission 0.383 (95% CI: 0.074, 1.987; n = 35). In this feasibility study of the digital self-management platform myCOPD, the app has proven acceptable to patients to use and use has improved exacerbation recovery rates, with strong signals of lower re-exacerbation and readmission rates over 90 days. myCOPD reduced the number of critical errors in inhaler technique compared to usual care with written self-management. This provides a strong basis for further exploration of the use of app interventions in the context of recently hospitalised patients with COPD and informs the potential design of a large multi-centre trial.
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Affiliation(s)
| | | | - Ben Green
- Portsmouth Hospitals NHS Trust, Portsmouth, UK
| | | | - Tom Brown
- Portsmouth Hospitals NHS Trust, Portsmouth, UK
| | | | - Tom Jones
- Portsmouth Hospitals NHS Trust, Portsmouth, UK
| | - Dan Neville
- Portsmouth Hospitals NHS Trust, Portsmouth, UK
| | | | - Alastair Watson
- NIHR ARC Wessex, University of Southampton, Southampton, UK
- University of Southampton Faculty of Medicine, Southampton, UK
| | | | | | | | | | - Tom M. A. Wilkinson
- my mhealth Limited, Bournemouth, UK
- NIHR ARC Wessex, University of Southampton, Southampton, UK
- University of Southampton Faculty of Medicine, Southampton, UK
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Using Novel Computed Tomography Analysis to Describe the Contribution and Distribution of Emphysema and Small Airways Disease in Chronic Obstructive Pulmonary Disease. Ann Am Thorac Soc 2020; 16:990-997. [PMID: 30892055 DOI: 10.1513/annalsats.201810-669oc] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Rationale: Chronic obstructive pulmonary disease (COPD) is characterized by airflow limitation, caused by emphysema and small airways disease (SAD). Computed tomography (CT) coupled with image analysis enables the quantification of these abnormalities; however, the optimum method for doing so has not been determined.Objectives: This study aims to compare two CT quantitative analysis techniques, disease probability measure (DPM) and parametric response mapping (PRM), and assess their relationship with specific physiological measures of SAD.Methods: Subjects with mild to moderate COPD, never smokers, and healthy ex-smokers were recruited. Each had airway oscillometry and multiple-breath nitrogen washout, measuring peripheral airway resistance, peripheral airway reactance, and acinar airway inhomogeneity. Subjects also had an inspiratory and expiratory chest CT, with DPM and PRM analysis performed by coregistering images and classifying each voxel as normal, emphysema, or nonemphysematous gas trapping related to SAD.Results: Thirty-eight subjects with COPD, 18 never smokers, and 23 healthy ex-smokers were recruited. There were strong associations between DPM and PRM analysis when measuring gas trapping (ρ = 0.87; P < 0.001) and emphysema (ρ = 0.99; P < 0.001). DPM assigned significantly more voxels as emphysema and gas trapped than PRM (P < 0.001). Both techniques showed significantly greater emphysema and gas trapping in subjects with COPD than in never smokers and ex-smokers (P < 0.001). All CT measures had significant associations with peripheral airway resistance and reactance, with disease probability measure of nonemphysematous gas trapping related to SAD having the strongest independent association with peripheral airway resistance (β = 0.42; P = 0.001) and peripheral airway reactance (β = 0.41; P = 0.001). Emphysema measures had the strongest associations with acinar airway inhomogeneity (β = 0.35-0.38).Conclusions: These results provide further validation for the use of DPM/PRM analysis in COPD by demonstrating significant relationships with specific physiological measures of SAD.
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Suzuki M, Kawata N, Abe M, Yokota H, Anazawa R, Matsuura Y, Ikari J, Matsuoka S, Tsushima K, Tatsumi K. Objective quantitative multidetector computed tomography assessments in patients with combined pulmonary fibrosis with emphysema: Relationship with pulmonary function and clinical events. PLoS One 2020; 15:e0239066. [PMID: 32941486 PMCID: PMC7498084 DOI: 10.1371/journal.pone.0239066] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2020] [Accepted: 08/29/2020] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Combined pulmonary fibrosis with emphysema (CPFE) is a clinically meaningful syndrome characterized by coexisting upper-lobe emphysema and lower-lobe interstitial fibrosis. However, ambiguous diagnostic criteria and, particularly, the absence of objective methods to quantify emphysematous/fibrotic lesions in patients with CPFE confound the interpretation of the pathophysiology of this syndrome. We analyzed the relationship between objectively quantified computed tomography (CT) measurements and the results of pulmonary function testing (PFT) and clinical events in CPFE patients. MATERIALS AND METHODS We enrolled 46 CPFE patients who underwent CT and PFT. The extent of emphysematous lesions was obtained by calculating the percent of low attenuation area (%LAA). The extent of fibrotic lesions was calculated as the percent of high attenuation area (%HAA). %LAA and %HAA values were combined to yield the percent of abnormal area (%AA). We assessed the relationships between CT parameters and other clinical indices, including PFT results. Multivariate analysis was performed to examine the association between the CT parameters and clinical events. RESULTS A greater negative correlation with percent predicted diffusing capacity of the lung for carbon monoxide (DLCO %predicted) existed for %AA (r = -0.73, p < 0.001) than for %LAA or %HAA alone. The %HAA value was inversely correlated with percent predicted forced vital capacity (r = -0.48, p < 0.001), percent predicted total lung capacity (r = -0.48, p < 0.01), and DLCO %predicted (r = -0.47, p < 0.01). Multivariate logistic regression analysis found that %AA showed the strongest association with hospitalization events (odds ratio = 1.20, 95% confidence interval = 1.01-1.54, p = 0.029). CONCLUSION Quantitative CT measurements reflected deterioration in pulmonary function and were associated with hospitalization in patients with CPFE. This approach could serve as a useful method to determine the extent of lung morphology, pathophysiology, and the clinical course of patients with CPFE.
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Affiliation(s)
- Masaki Suzuki
- Department of Respirology, Graduate School of Medicine, Chiba University, Chiba-city, Chiba, Japan
- * E-mail:
| | - Naoko Kawata
- Department of Respirology, Graduate School of Medicine, Chiba University, Chiba-city, Chiba, Japan
| | - Mitsuhiro Abe
- Department of Respirology, Graduate School of Medicine, Chiba University, Chiba-city, Chiba, Japan
| | - Hajime Yokota
- Department of Diagnostic Radiology and Radiation Oncology, Graduate School of Medicine, Chiba University Hospital, Chiba-city, Chiba, Japan
| | - Rie Anazawa
- Department of Respirology, Graduate School of Medicine, Chiba University, Chiba-city, Chiba, Japan
| | - Yukiko Matsuura
- Department of Respirology, Graduate School of Medicine, Chiba University, Chiba-city, Chiba, Japan
| | - Jun Ikari
- Department of Respirology, Graduate School of Medicine, Chiba University, Chiba-city, Chiba, Japan
| | - Shin Matsuoka
- Department of Radiology, St. Marianna University School of Medicine, Kawasaki-city, Kanagawa, Japan
| | - Kenji Tsushima
- Department of Pulmonary Medicine, International University of Health and Welfare, School of Medicine, Kozunomori, Narita-city, Chiba, Japan
| | - Koichiro Tatsumi
- Department of Respirology, Graduate School of Medicine, Chiba University, Chiba-city, Chiba, Japan
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Vass L, Fisk M, Lee S, Wilson FJ, Cheriyan J, Wilkinson I. Advances in PET to assess pulmonary inflammation: A systematic review. Eur J Radiol 2020; 130:109182. [DOI: 10.1016/j.ejrad.2020.109182] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 05/27/2020] [Accepted: 07/07/2020] [Indexed: 12/12/2022]
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Freeman AT, Hill D, Newell C, Moyses H, Azim A, Knight D, Presland L, Harvey M, Haitchi HM, Watson A, Staples KJ, Kurukulaaratchy RJ, Wilkinson TMA. Patient perceived barriers to exercise and their clinical associations in difficult asthma. Asthma Res Pract 2020; 6:5. [PMID: 32537235 PMCID: PMC7285728 DOI: 10.1186/s40733-020-00058-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2020] [Accepted: 06/02/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Exercise is recommended in guidelines for asthma management and has beneficial effects on symptom control, inflammation and lung function in patients with sub-optimally controlled asthma. Despite this, physical activity levels in patients with difficult asthma are often impaired. Understanding the barriers to exercise in people with difficult asthma is crucial for increasing their activity, and in implementing successful, disease modifying, and holistic approaches to improve their health. METHODS 62 Patients within the WATCH Difficult Asthma Cohort (Southampton, UK) completed an Exercise Therapy Burden Questionnaire (ETBQ). The results were analyzed with contemporaneous asthma-related data to determine relationships between perceived exercise barriers and asthma and comorbidity characteristics. RESULTS Patients were reflective of a difficult asthma cohort, 66% were female, and 63% were atopic. They had a high BMI (median [inter-quartile range]) of 29.3 [25.5-36.2], age of 53.5 [38.75, 65.25], impaired spirometry with FEV1 73% predicted [59.5, 86.6%] and FEV/FVC ratio of 72 [56.5, 78.0] and poor symptom control, as defined by an Asthma Control Questionnaire (ACQ6) result of 2.4 [1.28, 3.2]. A high perceived barriers to exercise score was significantly correlated with increased asthma symptoms (r = 0.452, p < 0.0001), anxiety (r = 0.375, p = 0.005) and depression (r = 0.363, p = 0.008), poor quality of life (r = 0.345, p = 0.015) and number of rescue oral steroid courses in the past 12 months (r = 0.257, p = 0.048). Lung function, blood eosinophil count, FeNO, Njimegen and SNOT22 scores, BMI and hospitalisations in the previous year were not related to exercise perceptions. CONCLUSION In difficult asthma, perceived barriers to exercise are related to symptom burden and psychological morbidity. Therefore, exercise interventions combined with psychological input such as CBT to restructure thought processes around these perceived barriers may be useful in facilitating adoption of exercise.
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Affiliation(s)
- Anna T. Freeman
- Clinical & Experimental Sciences, University of Southampton Faculty of Medicine, Southampton General Hospital, Southampton, UK
- Wessex Investigational Sciences Hub, University of Southampton Faculty of Medicine, Southampton General Hospital, Southampton, UK
- Southampton NIHR Respiratory Biomedical Research Centre, Southampton General Hospital, Southampton, UK
| | - David Hill
- Clinical & Experimental Sciences, University of Southampton Faculty of Medicine, Southampton General Hospital, Southampton, UK
- Southampton NIHR Respiratory Biomedical Research Centre, Southampton General Hospital, Southampton, UK
| | - Colin Newell
- Southampton NIHR Respiratory Biomedical Research Centre, Southampton General Hospital, Southampton, UK
| | - Helen Moyses
- Clinical & Experimental Sciences, University of Southampton Faculty of Medicine, Southampton General Hospital, Southampton, UK
- Southampton NIHR Respiratory Biomedical Research Centre, Southampton General Hospital, Southampton, UK
| | - Adnan Azim
- Clinical & Experimental Sciences, University of Southampton Faculty of Medicine, Southampton General Hospital, Southampton, UK
- Southampton NIHR Respiratory Biomedical Research Centre, Southampton General Hospital, Southampton, UK
- Asthma, Allergy and Clinical Immunology Department, University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Deborah Knight
- Wessex Investigational Sciences Hub, University of Southampton Faculty of Medicine, Southampton General Hospital, Southampton, UK
- Southampton NIHR Respiratory Biomedical Research Centre, Southampton General Hospital, Southampton, UK
| | - Laura Presland
- Southampton NIHR Respiratory Biomedical Research Centre, Southampton General Hospital, Southampton, UK
| | - Matthew Harvey
- Southampton NIHR Respiratory Biomedical Research Centre, Southampton General Hospital, Southampton, UK
| | - Hans Michael Haitchi
- Clinical & Experimental Sciences, University of Southampton Faculty of Medicine, Southampton General Hospital, Southampton, UK
- Southampton NIHR Respiratory Biomedical Research Centre, Southampton General Hospital, Southampton, UK
- Asthma, Allergy and Clinical Immunology Department, University Hospital Southampton NHS Foundation Trust, Southampton, UK
- Institute for Life Sciences, University of Southampton, Southampton, UK
| | - Alastair Watson
- Clinical & Experimental Sciences, University of Southampton Faculty of Medicine, Southampton General Hospital, Southampton, UK
- Wessex Investigational Sciences Hub, University of Southampton Faculty of Medicine, Southampton General Hospital, Southampton, UK
- Southampton NIHR Respiratory Biomedical Research Centre, Southampton General Hospital, Southampton, UK
| | - Karl J. Staples
- Clinical & Experimental Sciences, University of Southampton Faculty of Medicine, Southampton General Hospital, Southampton, UK
- Wessex Investigational Sciences Hub, University of Southampton Faculty of Medicine, Southampton General Hospital, Southampton, UK
- Southampton NIHR Respiratory Biomedical Research Centre, Southampton General Hospital, Southampton, UK
| | - Ramesh J. Kurukulaaratchy
- Clinical & Experimental Sciences, University of Southampton Faculty of Medicine, Southampton General Hospital, Southampton, UK
- Southampton NIHR Respiratory Biomedical Research Centre, Southampton General Hospital, Southampton, UK
- Asthma, Allergy and Clinical Immunology Department, University Hospital Southampton NHS Foundation Trust, Southampton, UK
- The David Hide Asthma & Allergy Research Centre, St Mary’s Hospital, Newport, Isle of Wight UK
| | - Tom M. A. Wilkinson
- Clinical & Experimental Sciences, University of Southampton Faculty of Medicine, Southampton General Hospital, Southampton, UK
- Wessex Investigational Sciences Hub, University of Southampton Faculty of Medicine, Southampton General Hospital, Southampton, UK
- Southampton NIHR Respiratory Biomedical Research Centre, Southampton General Hospital, Southampton, UK
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Kong CW, Wilkinson TM. Predicting and preventing hospital readmission for exacerbations of COPD. ERJ Open Res 2020; 6:00325-2019. [PMID: 32420313 PMCID: PMC7211949 DOI: 10.1183/23120541.00325-2019] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Accepted: 02/06/2020] [Indexed: 12/17/2022] Open
Abstract
More than a third of patients hospitalised for acute exacerbation of COPD are readmitted to hospital within 90 days. Healthcare professionals and service providers are expected to collaboratively drive efforts to improve hospital readmission rates, which can be challenging due to the lack of clear consensus and guidelines on how best to predict and prevent readmissions. This review identifies these risk factors, highlighting the contribution of multimorbidity, frailty and poor socioeconomic status. Predictive models of readmission that address the multifactorial nature of readmissions and heterogeneity of the disease are reviewed, recognising that in an era of precision medicine, in-depth understanding of the intricate biological mechanisms that heighten the risk of COPD exacerbation and re-exacerbation is needed to derive modifiable biomarkers that can stratify accurately the highest risk groups for targeted treatment. We evaluate conventional and emerging strategies to reduce these potentially preventable readmissions. Here, early recognition of exacerbation symptoms and the delivery of prompt treatment can reduce risk of hospital admissions, while patient education can improve treatment adherence as a key component of self-management strategies. Care bundles are recommended to ensure high-quality care is provided consistently, but evidence for their benefit is limited to date. The search continues for interventions which are effective, sustainable and applicable to a diverse population of patients with COPD exacerbations. Further research into mechanisms that drive exacerbation and affect recovery is crucial to improve our understanding of this complex, highly prevalent disease and to advance the development of more effective treatments.
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Affiliation(s)
- Chia Wei Kong
- Southampton NIHR Respiratory Biomedical Research Unit, University Hospital Southampton, Southampton, UK
- Clinical and Experimental Sciences, Faculty of Medicine, University Hospital Southampton, Southampton, UK
| | - Tom M.A. Wilkinson
- Southampton NIHR Respiratory Biomedical Research Unit, University Hospital Southampton, Southampton, UK
- Clinical and Experimental Sciences, Faculty of Medicine, University Hospital Southampton, Southampton, UK
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Artificial Intelligence-based Fully Automated Per Lobe Segmentation and Emphysema-quantification Based on Chest Computed Tomography Compared With Global Initiative for Chronic Obstructive Lung Disease Severity of Smokers. J Thorac Imaging 2020; 35 Suppl 1:S28-S34. [DOI: 10.1097/rti.0000000000000500] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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Comparison of Artificial Intelligence-Based Fully Automatic Chest CT Emphysema Quantification to Pulmonary Function Testing. AJR Am J Roentgenol 2020; 214:1065-1071. [PMID: 32130041 DOI: 10.2214/ajr.19.21572] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
OBJECTIVE. The purpose of this study was to evaluate an artificial intelligence (AI)-based prototype algorithm for fully automated quantification of emphysema on chest CT compared with pulmonary function testing (spirometry). MATERIALS AND METHODS. A total of 141 patients (72 women, mean age ± SD of 66.46 ± 9.7 years [range, 23-86 years]; 69 men, mean age of 66.72 ± 11.4 years [range, 27-91 years]) who underwent both chest CT acquisition and spirometry within 6 months were retrospectively included. The spirometry-based Tiffeneau index (TI; calculated as the ratio of forced expiratory volume in the first second to forced vital capacity) was used to measure emphysema severity; a value less than 0.7 was considered to indicate airway obstruction. Segmentation of the lung based on two different reconstruction methods was carried out by using a deep convolution image-to-image network. This multilayer convolutional neural network was combined with multilevel feature chaining and depth monitoring. To discriminate the output of the network from ground truth, an adversarial network was used during training. Emphysema was quantified using spatial filtering and attenuation-based thresholds. Emphysema quantification and TI were compared using the Spearman correlation coefficient. RESULTS. The mean TI for all patients was 0.57 ± 0.13. The mean percentages of emphysema using reconstruction methods 1 and 2 were 9.96% ± 11.87% and 8.04% ± 10.32%, respectively. AI-based emphysema quantification showed very strong correlation with TI (reconstruction method 1, ρ = -0.86; reconstruction method 2, ρ = -0.85; both p < 0.0001), indicating that AI-based emphysema quantification meaningfully reflects clinical pulmonary physiology. CONCLUSION. AI-based, fully automated emphysema quantification shows good correlation with TI, potentially contributing to an image-based diagnosis and quantification of emphysema severity.
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Fazleen A, Wilkinson T. Early COPD: current evidence for diagnosis and management. Ther Adv Respir Dis 2020; 14:1753466620942128. [PMID: 32664818 PMCID: PMC7394029 DOI: 10.1177/1753466620942128] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2020] [Accepted: 06/15/2020] [Indexed: 12/24/2022] Open
Abstract
Chronic obstructive pulmonary disease (COPD) affects one-tenth of the world's population and has been identified as a major global unmet health need by the World Health Organisation, which predicts that within 10 years, COPD will become the third leading cause of death. Despite active research, there have been no recent major strides in terms of disease modifying treatment for COPD; smoking cessation remains the only intervention known to alter disease progression and improve mortality. As established COPD is a key driver of disease burden, earlier diagnosis coupled with disease-modifying intervention carries promise as a route to address this global health priority. The concept of early COPD is emerging as an area of focus for research and consideration of new treatment modalities, as it has been hypothesised that intervention at this stage may potentially halt or reverse the disease process. However, at present, a globally accepted criteria for defining early COPD does not exist. Several studies propose small airways disease as the earliest stage in the development of COPD, and this has been demonstrated to be a precursor to development of emphysema and to correlate with subsequent development of airflow obstruction. However, treatment strategies for early disease, which pre-date the development of airflow obstruction, remain uncertain. This review addresses the rationale and current evidence base for the diagnosis and treatment of early COPD and highlights the challenges of implementing trials and clinical pathways to address COPD earlier in the life course, particularly in the absence of a universally accepted definition of COPD.The reviews of this paper are available via the supplemental material section.
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Affiliation(s)
- Aishath Fazleen
- University Hospital Southampton NHS Foundation Trust, Tremona Road, Southampton, Hampshire SO16 6YD, UK
- Faculty of Medicine, University of Southampton, Hampshire, UK
| | - Tom Wilkinson
- University Hospital Southampton NHS Foundation Trust, Tremona Road, Southampton, Hampshire, UK
- Faculty of Medicine, University of Southampton, Hampshire, UK
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Using Quantitative Computed Tomographic Imaging to Understand Chronic Obstructive Pulmonary Disease and Fibrotic Interstitial Lung Disease. J Thorac Imaging 2019; 35:246-254. [DOI: 10.1097/rti.0000000000000440] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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Gawlitza J, Sturm T, Spohrer K, Henzler T, Akin I, Schönberg S, Borggrefe M, Haubenreisser H, Trinkmann F. Predicting Pulmonary Function Testing from Quantified Computed Tomography Using Machine Learning Algorithms in Patients with COPD. Diagnostics (Basel) 2019; 9:diagnostics9010033. [PMID: 30901865 PMCID: PMC6468377 DOI: 10.3390/diagnostics9010033] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2019] [Revised: 03/17/2019] [Accepted: 03/18/2019] [Indexed: 12/24/2022] Open
Abstract
Introduction: Quantitative computed tomography (qCT) is an emergent technique for diagnostics and research in patients with chronic obstructive pulmonary disease (COPD). qCT parameters demonstrate a correlation with pulmonary function tests and symptoms. However, qCT only provides anatomical, not functional, information. We evaluated five distinct, partial-machine learning-based mathematical models to predict lung function parameters from qCT values in comparison with pulmonary function tests. Methods: 75 patients with diagnosed COPD underwent body plethysmography and a dose-optimized qCT examination on a third-generation, dual-source CT with inspiration and expiration. Delta values (inspiration—expiration) were calculated afterwards. Four parameters were quantified: mean lung density, lung volume low-attenuated volume, and full width at half maximum. Five models were evaluated for best prediction: average prediction, median prediction, k-nearest neighbours (kNN), gradient boosting, and multilayer perceptron. Results: The lowest mean relative error (MRE) was calculated for the kNN model with 16%. Similar low MREs were found for polynomial regression as well as gradient boosting-based prediction. Other models led to higher MREs and thereby worse predictive performance. Beyond the sole MRE, distinct differences in prediction performance, dependent on the initial dataset (expiration, inspiration, delta), were found. Conclusion: Different, partially machine learning-based models allow the prediction of lung function values from static qCT parameters within a reasonable margin of error. Therefore, qCT parameters may contain more information than we currently utilize and can potentially augment standard functional lung testing.
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Affiliation(s)
- Joshua Gawlitza
- Institute of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany.
| | - Timo Sturm
- Department of General Management and Information Systems, University of Mannheim, 68131 Mannheim, Germany.
| | - Kai Spohrer
- Department of General Management and Information Systems, University of Mannheim, 68131 Mannheim, Germany.
| | - Thomas Henzler
- Institute of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany.
| | - Ibrahim Akin
- 1st Department of Medicine (Cardiology, Angiology, Pulmonary and Intensive Care), University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany.
- DZHK (German Center for Cardiovascular Research), partner site, 68167 Mannheim, Germany.
| | - Stefan Schönberg
- Institute of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany.
| | - Martin Borggrefe
- 1st Department of Medicine (Cardiology, Angiology, Pulmonary and Intensive Care), University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany.
- DZHK (German Center for Cardiovascular Research), partner site, 68167 Mannheim, Germany.
| | - Holger Haubenreisser
- Institute of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany.
| | - Frederik Trinkmann
- 1st Department of Medicine (Cardiology, Angiology, Pulmonary and Intensive Care), University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany.
- Department of Biomedical Informatics of the Heinrich-Lanz-Center, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany.
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den Harder AM, de Boer E, Lagerweij SJ, Boomsma MF, Schilham AMR, Willemink MJ, Milles J, Leiner T, Budde RPJ, de Jong PA. Emphysema quantification using chest CT: influence of radiation dose reduction and reconstruction technique. Eur Radiol Exp 2018; 2:30. [PMID: 30402740 PMCID: PMC6220000 DOI: 10.1186/s41747-018-0064-3] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2018] [Accepted: 08/06/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Computed tomography (CT) emphysema quantification is affected by both radiation dose (i.e. image noise) and reconstruction technique. At reduced dose, filtered back projection (FBP) results in an overestimation of the amount of emphysema due to higher noise levels, while the use of iterative reconstruction (IR) can result in an underestimation due to reduced noise. The objective of this study was to determine the influence of dose reduction and hybrid IR (HIR) or model-based IR (MIR) on CT emphysema quantification. METHODS Twenty-two patients underwent inspiratory chest CT scan at routine radiation dose and at 45%, 60% and 75% reduced radiation dose. Acquisitions were reconstructed with FBP, HIR and MIR. Emphysema was quantified using the 15th percentile of the attenuation curve and the percentage of voxels below -950 HU. To determine whether the use of a different percentile or HU threshold is more accurate at reduced dose levels and with IR, additional measurements were performed using different percentiles and HU thresholds to determine the optimal combination. RESULTS Dose reduction resulted in a significant overestimation of emphysema, while HIR and MIR resulted in an underestimation. Lower HU thresholds with FBP at reduced dose and higher HU thresholds with HIR and MIR resulted in emphysema percentages comparable to the reference. The 15th percentile quantification method showed similar results as the HU threshold method. CONCLUSIONS This within-patients study showed that CT emphysema quantification is significantly affected by dose reduction and IR. This can potentially be solved by adapting commonly used thresholds.
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Affiliation(s)
| | - Erwin de Boer
- Department of Radiology, Isala hospital, Zwolle, The Netherlands
| | - Suzanne J Lagerweij
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | | | - Arnold M R Schilham
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Martin J Willemink
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | | | - Tim Leiner
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Ricardo P J Budde
- Department of Radiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Pim A de Jong
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
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