A comparative analysis of chronic obstructive pulmonary disease using machine learning, and deep learning

Chronic obstructive pulmonary disease (COPD) is a general clinical issue in numerous countries considered the fifth reason for inability and the third reason for mortality on a global scale within 2021. From recent reviews, a deep convolutional neural network (CNN) is used in the primary analysis of the deadly COPD, which uses the computed tomography (CT) images procured from the deep learning tools. Detection and analysis of COPD using several image processing techniques, deep learning models, and machine learning models are notable contributions to this review. This research aims to cover the detailed findings on pulmonary diseases or lung diseases, their causes, and symptoms, which will help treat infections with high performance and a swift response. The articles selected have more than 80% accuracy and are tabulated and analyzed for sensitivity, specificity, and area under the curve (AUC) using different methodologies. This research focuses on the various tools and techniques used in COPD analysis and eventually provides an overview of COPD with coronavirus disease 2019 (COVID-19) symptoms.

Quantitative lung CT analysis for the study and diagnosis of Chronic Obstructive Pulmonary Disease

The importance of medical imaging in the research of Chronic Obstructive Pulmonary Dis- ease (COPD) has risen over the last decades. COPD affects the pulmonary system through two competing mechanisms; emphysema and small airways disease. The relative contribu- tion of each component varies widely across patients whilst they can also evolve regionally in the lung. Patients can also be susceptible to exacerbations, which can dramatically ac- celerate lung function decline. Diagnosis of COPD is based on lung function tests, which measure airflow limitation. There is a growing consensus that this is inadequate in view of the complexities of COPD. Computed Tomography (CT) facilitates direct quantification of the pathological changes that lead to airflow limitation and can add to our understanding of the disease progression of COPD. There is a need to better capture lung pathophysiology whilst understanding regional aspects of disease progression. This has motivated the work presented in this thesis. Two novel methods are proposed to quantify the severity of COPD from CT by analysing the global distribution of features sampled locally in the lung. They can be exploited in the classification of lung CT images or to uncover potential trajectories of disease progression. A novel lobe segmentation algorithm is presented that is based on a probabilistic segmen- tation of the fissures whilst also constructing a groupwise fissure prior. In combination with the local sampling methods, a pipeline of analysis was developed that permits a re- gional analysis of lung disease. This was applied to study exacerbation susceptible COPD. Lastly, the applicability of performing disease progression modelling to study COPD has been shown. Two main subgroups of COPD were found, which are consistent with current clinical knowledge of COPD subtypes. This research may facilitate precise phenotypic characterisation of COPD from CT, which will increase our understanding of its natural history and associated heterogeneities. This will be instrumental in the precision medicine of COPD

Unsupervised and Weakly-Supervised Learning of Localized Texture Patterns of Lung Diseases on Computed Tomography

Computed tomography (CT) imaging enables in vivo assessment of lung parenchyma and several lung diseases. CT scans are key in particular for the diagnosis of 1) chronic obstructive pulmonary disease (COPD), which is the fourth leading cause of death worldwide, and largely overlaps with pulmonary emphysema; and 2) lung cancer, which is the first leading cause of cancer-related death, and manifests in its early stage with the presence of lung nodules. Most lung CT image analysis methods to-date have relied on supervised learning requiring manually annotated local regions of interest (ROIs), which are slow and labor-intensive to obtain. Machine learning models requiring less or no manual annotations are important for a sustainable development of computer-aided diagnosis (CAD) systems. This thesis focused on exploiting CT scans for lung disease characterization via two learning strategies: 1) fully unsupervised learning on a very large amount of unannotated image patches to discover novel lung texture patterns for pulmonary emphysema; and 2) weakly-supervised learning to generate voxel-level localization of lung nodules from CT whole-slice labels. In the first part of this thesis, we proposed an original unsupervised approach to learn emphysema-specific radiological texture patterns. We have designed dedicated spatial and texture features and a two-stage learning strategy incorporating clustering and graph partitioning. Learning was performed on a cohort of 2,922 high-resolution full-lung CT scans, which included a high prevalence of smokers and COPD subjects. Experiments lead to discovering 10 highly-reproducible spatially-informed lung texture patterns and 6 quantitative emphysema subtypes (QES). Our discovered QES were associated independently with distinct risk of symptoms, physiological changes, exacerbations and mortality. Genome-wide association studies identified loci associated with four subtypes. Then we designed a deep-learning approach, using unsupervised domain adaptation with adversarial training, to label the QES on cardiac CT scans, which included approximately 70% of the lung. Our proposed method accounted for the differences in CT image qualities, and enabled us to study the progression of QES on a cohort of 17,039 longitudinal cardiac and full-lung CT scans. Overall, the discovered QES provide novel emphysema sub-phenotyping that may facilitate future study of emphysema development, understanding the stages of COPD and the design of personalized therapies. In the second part of the thesis, we have designed a deep-learning method for lung nodule detection with weak labels, using classification convolutional neural networks (CNNs) with skip-connections to generate high-quality discriminative class activation maps, and a novel candidate screening framework to reduce the number of false positives. Given that the vast majority of annotated nodules are benign, we further exploited a data augmentation framework with a generative adversarial network (GAN) to address the issue of data imbalance for lung cancer prediction. Our weakly-supervised lung nodule detection on 1,000s CT scans achieved competitive performance compared to a fully-supervised method, while requiring 100 times less annotations. Our data augmentation framework enabled synthesizing nodules with high fidelity in specified categories, and is beneficial for predicting nodule malignancy scores and hence improving the accuracy / reliability of lung cancer screening

Cardio-oncology: new insights into association and interaction between cardiovascular disease and cancer

Cardiovascular disease (CVD) and cancer stand as the top leading causes of morbidity and mortality. The emerging field of Cardio-Oncology has unveiled their intricate connection, which arises from the cardiotoxicity of cancer treatments, common risk factors, and the potential for cardiac dysfunction to accelerate cancer progression. Consequently, there's a growing academic interest and clinical importance to investigate the link between these conditions and the underlying mechanism. In this thesis, we studied the CVD-cancer association from various perspectives. We assessed tumour biomarkers in heart failure (HF) patients, revealing that several biomarkers significantly correlate with adverse HF outcomes, indicating shared pathophysiological processes.Additionally, our investigation into clonal haematopoiesis of indeterminate potential (CHIP) showed that CHIP primarily associates with incident HF in individuals < 65 years, which underscores the importance of early detection and prevention of CHIP. We also explored the impact of HF on tumour growth and could show that this varies among different cancer types. Notably, HF didn't promote renal cancer growth in our study, cautioning against broad generalizations about HF-cancer interaction.Furthermore, we provided multi-omics characterization of myocardial tissue in three different HF mouse models (MI, TAC and PLN-R14Δ/Δ), investigated myostatin inhibition in cardiac pressure-overloaded mice, and summarized findings on shared risk factors, mechanisms, and pathophysiological signalling pathways linking cancer with other multifactorial diseases (CVDs, CKD, COPD and MAFLD).In conclusion, this thesis contributes valuable insights to the Cardio-Oncology field, enhancing clinicians' awareness of the potential risks associated with co-morbid HF/cancer, and facilitating safe and efficacious medication use in clinical practice

Effects of municipal smoke-free ordinances on secondhand smoke exposure in the Republic of Korea

ObjectiveTo reduce premature deaths due to secondhand smoke (SHS) exposure among non-smokers, the Republic of Korea (ROK) adopted changes to the National Health Promotion Act, which allowed local governments to enact municipal ordinances to strengthen their authority to designate smoke-free areas and levy penalty fines. In this study, we examined national trends in SHS exposure after the introduction of these municipal ordinances at the city level in 2010.MethodsWe used interrupted time series analysis to assess whether the trends of SHS exposure in the workplace and at home, and the primary cigarette smoking rate changed following the policy adjustment in the national legislation in ROK. Population-standardized data for selected variables were retrieved from a nationally representative survey dataset and used to study the policy action’s effectiveness.ResultsFollowing the change in the legislation, SHS exposure in the workplace reversed course from an increasing (18% per year) trend prior to the introduction of these smoke-free ordinances to a decreasing (−10% per year) trend after adoption and enforcement of these laws (β2 = 0.18, p-value = 0.07; β3 = −0.10, p-value = 0.02). SHS exposure at home (β2 = 0.10, p-value = 0.09; β3 = −0.03, p-value = 0.14) and the primary cigarette smoking rate (β2 = 0.03, p-value = 0.10; β3 = 0.008, p-value = 0.15) showed no significant changes in the sampled period. Although analyses stratified by sex showed that the allowance of municipal ordinances resulted in reduced SHS exposure in the workplace for both males and females, they did not affect the primary cigarette smoking rate as much, especially among females.ConclusionStrengthening the role of local governments by giving them the authority to enact and enforce penalties on SHS exposure violation helped ROK to reduce SHS exposure in the workplace. However, smoking behaviors and related activities seemed to shift to less restrictive areas such as on the streets and in apartment hallways, negating some of the effects due to these ordinances. Future studies should investigate how smoke-free policies beyond public places can further reduce the SHS exposure in ROK

Smoking cessation : a report of the Surgeon General

Tobacco smoking is the leading cause of preventable disease, disability, and death in the United States (U.S. Department of Health and Human Services [USDHHS] 2014). Smoking harms nearly every organ in the body and costs the United States billions of dollars in direct medical costs each year (USDHHS 2014). Although considerable progress has been made in reducing cigarette smoking since the first U.S. Surgeon General\u2019s report was released in 1964 (USDHHS 2014), in 2018, 13.7% of U.S. adults (34.2 million people) were still current cigarette smokers (Creamer et al. 2019). One of the main reasons smokers keep smoking is nicotine (USDHHS 1988). Nicotine, a drug found naturally in the tobacco plant, is highly addictive, as with such drugs as cocaine and heroin; acti- vates the brain\u2019s reward circuits; and reinforces repeated nicotine exposure (USDHHS 1988, 2010, 2014; National Institute on Drug Abuse [NIDA] 2018).The majority of cigarette smokers (68%) want to quit smoking completely (Babb et al. 2017). The 1990 Surgeon General\u2019s report, The Health Benefits of Smoking Cessation, was the last Surgeon General\u2019s report to focus on cur- rent research on smoking cessation and to predominantly review the health benefits of quitting smoking (USDHHS 1990). Because of limited data at that time, the 1990 report did not review the determinants, processes, or outcomes of attempts at smoking cessation. Pharmacotherapy for smoking cessation was not introduced until the 1980s. Additionally, behavioral and other counseling approaches were slow to develop and not widely available at the time of the 1990 report because few were covered under health insurance, and programs such as group counseling ses- sions were hard for smokers to access, even by those who were motivated to quit (Fiore et al. 1990).The purpose of this report is to update and expand the 1990 Surgeon General\u2019s report based on new scien- tific evidence about smoking cessation. Since 1990, the scientific literature has expanded greatly on the deter- minants and processes of smoking cessation, informing the development of interventions that promote cessa- tion and help smokers quit (Fiore et al. 2008; Schlam and Baker 2013). This knowledge and other major develop- ments have transformed the landscape of smoking ces- sation in the United States. This report summarizes this enhanced knowledge and specifically reviews patterns and trends of smoking cessation; biologic mechanisms; various health benefits; overall morbidity, mortality, and economic benefits; interventions; and strategies that pro- mote smoking cessation.Suggested citation: U.S. Department of Health and Human Services. Smoking Cessation. A Report of the Surgeon General. Atlanta, GA: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health, 2020.2020-cessation-sgr-full-report.pdf2020713