Chronic obstructive pulmonary disease (COPD) is one of the world’s most common chronic diseases, and consists of chronic bronchitis that involves chronic inflammation of the bronchi, or emphysema that involves destruction of lung alveoli. In COPD patients, the airways become narrowed, and the airflow is irreversibly obstructed. This leads to a limitation of the flow of air to and from the lungs, causing shortness of breath (dyspnea), as well as abnormal inflammatory response in the lung. Nowadays, COPD is often under-diagnosed, as spirometry was not performed until patient has significant symptoms of dyspnea, cough and sputum production. At that stage, the COPD patients may have reached an advanced stage with considerable loss of lung function. Thus, biomarkers are of great interest for research and clinical purposes in COPD, especially for early diagnosis of COPD. In this study, the relationship between plasma levels of different biomarkers, including monocyte chemoattractant protein-1 (MCP)-1 (a primary chemoattractant biomarker), matrix metalloproteinase nine (MMP)-9, vascular endothelial growth factor (VEGF), and hepatocyte growth factor (HGF) (injury and repair biomarkers), and growth differentiation factor 15 (GDF)-15 (a novel biomarker), in 29 healthy ever-smokers and 116 COPD patients was investigated using commercially available enzyme-linked immunosorbent assay (ELISA) kits. We also investigated the correlations between these biomarkers and lung function. There were significant increases in plasma MCP-1, MMP-9, HGF and GDF-15 in COPD patients compared to healthy smokers. Among ever-smokers with or without COPD, plasma MCP-1, MMP-9 and HGF levels were inversely correlated with force expiratory volume in one second![FEV1 (% predicted)] after adjustment for age, smoking status and packyears smoked. Correlation was also found between plasma MCP-1 and HGF, plasma MMP-9 and HGF or GDF-15, plasma HGF and GDF-15 after adjustment for age, smoking status and pack-years smoked. Further multiple linear regression analyses demonstrated that plasma MMP-9 level increased with the COPD GOLD stages. In conclusion, our findings suggest that MMP-9 might be as an important biomarker for COPD initiation and progression. As this study provides only evidence of association rather than of causation, prospective studies are required to assess biological significance of these associations between the plasma biomarkers. / published_or_final_version / Medicine / Master / Master of Medical Sciences

Chronic obstructive pulmonary disease (COPD) and emphysema together are one of the leading causes of death in the United States and worldwide; meanwhile, breast cancer has the highest incidence and second-highest mortality burden of all cancers in women. Imaging markers relevant to each of these conditions are readily identifiable on chest computed tomography (CT): (1) visually-appreciable variants in airway tree structure exist which are associated with increased odds for development of COPD; (2) CT emphysema subtypes (CTES), based on lung texture and spatial features, have been identified by unsupervised clustering and correlate with functional measures and clinical outcomes; (3) dysanapsis, or the ratio of airway caliber to lung volume, is the strongest known predictor of COPD risk, and (4) breast density (i.e., the extent of fibroglandular tissue within the breast) is strongly associated with breast cancer risk. Machine- and deep-learning frameworks present an opportunity to address unmet needs in each of these directions, leveraging the data from large CT cohorts. Application of unsupervised learning approaches serves to discover new, image-based phenotypes. While topologic and geometric variation in the structure of the CT-resolved airway tree are well-described, tree- structural subtypes are not fully characterized. Similarly, while the clinical correlates of CTES have been described in large cohort studies, the association of CTES with structural and functional measures of the lung parenchyma are only partially described, and the time-dependent evolution of emphysematous lung texture has not been studied. Supervised approaches are required to automate CT image assessment, or to estimate CT- based measures from incomplete input data. While dysanapsis can be directly quantified on full- lung CT, the lungs are often only partially imaged in large CT datasets; total lung volume must then be regressed from the observed partial image. Breast density grades, meanwhile, are generally visually assessed, which is laborious to perform at scale. Moreover, current automated methods rely on segmentation followed by intensity thresholding, excluding higher-order features which may contribute to the radiologist assessment. In this thesis, we present a series of machine-learning methods which address each of these gaps in the field, using CT scans from the Multi-Ethnic Study of Atherosclerosis (MESA), the SubPopulations and InteRmediate Outcome Measures in COPD (SPIROMICS) Study, and an institutional chest CT dataset acquired at Columbia University Irving Medical Center. First, we design a novel graph-based clustering framework for identifying tree-structure subtypes in Billera-Holmes-Vogtmann (BHV) tree-space, using the airway trees segmented from the full-lung CT scans of MESA Lung Exam 5. We characterize the behavior of our clustering algorithm on a synthetic dataset, describe the geometric and topological variation across tree-structure clusters, and demonstrate the algorithm’s robustness to perturbation of the input dataset and graph tuning parameter. Second, in MESA Lung Exam 5 CT scans, we quantify the loss of small-diameter airway and pulmonary vessel branches within CTES-labeled lung tissue, demonstrating that depletion of these structures is concentrated within CTES regions, and that the magnitude of this effect is CTES-specific. In a sample of 278 SPIROMICS Visit 1 participants, we find that CTES demonstrate distinct patterns of gas trapping and functional small airways disease (fSAD) on expiratory CT imaging. In the CT scans of SPIROMICS participants imaged at Visit 1 and Visit 5, we update the CTES clustering pipeline to identify longitudinal emphysema patterns (LEPs), which refine CTES by defining subphenotypes informative of time-dependent texture change. Third, we develop a multi-view convolutional neural network (CNN) model to estimate total lung volume (TLV) from cardiac CT scans and lung masks in MESA Lung Exam 5. We demonstrate that our model outperforms regression on imaged lung volume, and is robust to same- day repeated imaging and longitudinal follow-up within MESA. Our model is directly applicable to multiple large-scale cohorts containing cardiac CT and totaling over ten thousand participants. Finally, we design a 3-D CNN model for end-to-end automated breast density assessment on chest CT, trained and evaluated on an institutional chest CT dataset of patients imaged at Columbia University Irving Medical Center. We incorporate ordinal regression frameworks for density grade prediction which outperform binary or multi-class classification objectives, and we demonstrate that model performance on identifying high breast density is comparable to the inter-rater reliability of expert radiologists on this task.

Lau Yin Kei. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 58-79). / Abstracts in English and Chinese. / Acknowledgement --- p.I / Abstract --- p.IV / Abstract in Chinese --- p.VI / Abbreviations --- p.VIII / Introduction --- p.1 / Chapter 1.1 --- Prevalence of COPD and asthma in Hong Kong --- p.1 / Chapter 1.2 --- Players in pathogenesis of COPD --- p.2 / Chapter 1.3 --- Players in pathogenesis of asthma --- p.4 / Chapter 1.4 --- The use of exhaled breath condensate in previous studies --- p.6 / Chapter 1. 5 --- Brief overview of chemokines --- p.8 / Chapter 1.6 --- Objective of this study --- p.12 / Materials and methods --- p.14 / Chapter 2.1 --- Study population --- p.14 / Chapter 2.1.1 --- Patients with COPD and control subjects --- p.14 / Chapter 2.1.2 --- Patients with asthma and control subjects --- p.15 / Chapter 2.2 --- Lung function --- p.15 / Chapter 2.3 --- Dyspnoea score measurement of patients with COPD --- p.16 / Chapter 2.4 --- Classification of patients and asthma severity --- p.16 / Chapter 2.5 --- Skin prick test and blood tests --- p.16 / Chapter 2.6 --- Collection of exhaled breath condensate --- p.17 / Chapter 2.7 --- Measurement of constituent in EBC --- p.17 / Chapter 2.7.1 --- "Measurement of 8-isoprostane, MCP-1 and GROα in patients with COPD and the corresponding control subjects" --- p.17 / Chapter 2.7.2 --- Measurement of eotaxin and MDC of patients with asthma and the corresponding control subjects --- p.18 / Chapter 2.8 --- Reproducibility of exhaled breath constituent --- p.18 / Chapter 2.8.1 --- "Assessment of reproducibility of the exhaled MCP-1, GROα and8- isoprostane measurements" --- p.19 / Chapter 2.8.2 --- Assessment of reproducibility of the exhaled eotaxin and MDC measurement --- p.19 / Chapter 2.9 --- Statistical analysis --- p.19 / Results --- p.21 / Chapter 3.1 --- Patients with COPD and corresponding control subjects --- p.21 / Chapter 3.2 --- Patients with asthma and corresponding control subjects --- p.28 / Discussion --- p.36 / Chapter 4.1 --- "Exhaled 8-isoprostane, GRO-α and MCP-1 of patients with COPD and corresponding control subjects" --- p.36 / Chapter 4.2 --- Exhaled eotaxin and MDC from patients with asthma and corresponding control subjects --- p.43 / Chapter 4.3 --- Technical aspects of EBC assessment --- p.49 / Future prospect --- p.54 / Conclusion --- p.56 / References --- p.58 / Tables and Figures / Table 1. Demographics of the COPD and control subjects --- p.22 / Figure 1. The level of 8-isoprostane in the exhaled breath condensate of COPD and control subjects --- p.23 / Figure 2. The level of GROa in the exhaled breath condensate of COPD and control subjects --- p.25 / "Figure 3 Bland and Altman's Plot of the repeatability of 8-isoprostane, GROa and MCP-1 in the exhaled breath condensate of normal controls" --- p.27 / Table2. Clinical and physiological details of the subjects --- p.29 / Figure 4. Level of eotaxin in exhaled breath condensate of asthma and control subjects --- p.30 / Figure 5 Level of MDC in exhaled breath condensate of asthma and control subjects --- p.31 / Table 3. Levels of eotaxin and MDC in exhaled breath condensate of asthma subjects on different dose of inhaled corticosteroids --- p.33 / Figure 6. Relationship between exhaled breath condensate level of MDC and total serum IgE level --- p.35