An Architecture for Computer-Aided Detection and Radiologic Measurement of Lung Nodules in Clinical Trials

Computer tomography (CT) imaging plays an important role in cancer detection and quantitative assessment in clinical trials. High-resolution imaging studies on large cohorts of patients generate vast data sets, which are infeasible to analyze through manual interpretation

Focal Spot, Fall/Winter 2000

https://digitalcommons.wustl.edu/focal_spot_archives/1086/thumbnail.jp

Lung cancer screening

Randomised controlled trials, including the National Lung Screening Trial (NLST) and the NELSON trial, have shown reduced mortality with lung cancer screening with low-dose CT compared with chest radiography or no screening. Although research has provided clarity on key issues of lung cancer screening, uncertainty remains about aspects that might be critical to optimise clinical effectiveness and cost-effectiveness. This Review brings together current evidence on lung cancer screening, including an overview of clinical trials, considerations regarding the identification of individuals who benefit from lung cancer screening, management of screen-detected findings, smoking cessation interventions, cost-effectiveness, the role of artificial intelligence and biomarkers, and current challenges, solutions, and opportunities surrounding the implementation of lung cancer screening programmes from an international perspective. Further research into risk models for patient selection, personalised screening intervals, novel biomarkers, integrated cardiovascular disease and chronic obstructive pulmonary disease assessments, smoking cessation interventions, and artificial intelligence for lung nodule detection and risk stratification are key opportunities to increase the efficiency of lung cancer screening and ensure equity of access.</p

Implementing streamlined radiology reporting and clinical results management in low-dose CT screening for lung cancer

Lung cancer kills more people in the UK than any other cancer. Mortality rates are poor, with fewer than 10% of people alive 10 years after diagnosis. Lung Cancer Screening (LCS) with low-dose CT (LDCT) is effective at reducing lung cancer mortality when employed in at-risk populations; because of this, in the US, LCS has been implemented as a national programme. The UK does not currently screen for lung cancer, but in 2019 NHS England announced a pilot scheme to implement lung health checks (LHC) in areas with the poorest lung cancer outcomes. Despite these advances in LCS in the UK, there are outstanding questions about how LCS could be implemented safely and effectively, which this thesis, based on experience and data from the SUMMIT Study, aims to investigate. To provide screening safely, implementation of any study or programme must focus on maintaining a favourable cost to benefit ratio. This is particularly true in LCS where high false positive and overdiagnosis rates, as well as considerable levels of incidental findings, lead to possible psychological stress, needless investigations and interventions, making provision challenging to both screenees and healthcare providers. The SUMMIT Study investigates how to deliver evidence-based LCS in a large population (25,000), and this thesis in particular focusses on how LCS can be streamlined through proformatisation of radiological data collection, clinical actioning of results and standardised communication with general practitioners (GPs) and participants. This thesis explains the approach to managing pulmonary nodules and incidental findings detected at LDCT in SUMMIT, and how these findings are collected, triaged, and communicated in a way that is both efficient and safe. Early data from SUMMIT is presented to understand how evidence-based proformas may enable streamlined clinical management, data collection and results communications, while decreasing the burden on healthcare professionals and participants alike

Possible Bias in Supervised Deep Learning Algorithms for CT Lung Nodule Detection and Classification

Artificial Intelligence (AI) algorithms for automatic lung nodule detection and classification can assist radiologists in their daily routine of chest CT evaluation. Even though many AI algorithms for these tasks have already been developed, their implementation in the clinical workflow is still largely lacking. Apart from the significant number of false-positive findings, one of the reasons for that is the bias that these algorithms may contain. In this review, different types of biases that may exist in chest CT AI nodule detection and classification algorithms are listed and discussed. Examples from the literature in which each type of bias occurs are presented, along with ways to mitigate these biases. Different types of biases can occur in chest CT AI algorithms for lung nodule detection and classification. Mitigation of them can be very difficult, if not impossible to achieve completely

Focal Spot, Winter 2008/2009

https://digitalcommons.wustl.edu/focal_spot_archives/1110/thumbnail.jp

Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases

Cardiothoracic and pulmonary diseases are a significant cause of mortality and morbidity worldwide. The COVID-19 pandemic has highlighted the lack of access to clinical care, the overburdened medical system, and the potential of artificial intelligence (AI) in improving medicine. There are a variety of diseases affecting the cardiopulmonary system including lung cancers, heart disease, tuberculosis (TB), etc., in addition to COVID-19-related diseases. Screening, diagnosis, and management of cardiopulmonary diseases has become difficult owing to the limited availability of diagnostic tools and experts, particularly in resource-limited regions. Early screening, accurate diagnosis and staging of these diseases could play a crucial role in treatment and care, and potentially aid in reducing mortality. Radiographic imaging methods such as computed tomography (CT), chest X-rays (CXRs), and echo ultrasound (US) are widely used in screening and diagnosis. Research on using image-based AI and machine learning (ML) methods can help in rapid assessment, serve as surrogates for expert assessment, and reduce variability in human performance. In this Special Issue, “Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases”, we have highlighted exemplary primary research studies and literature reviews focusing on novel AI/ML methods and their application in image-based screening, diagnosis, and clinical management of cardiopulmonary diseases. We hope that these articles will help establish the advancements in AI

Focal Spot, Spring/Summer 2010

https://digitalcommons.wustl.edu/focal_spot_archives/1114/thumbnail.jp

Arthritis Rheumatol

Objective:Compare serum anti-malondialdehyde acetaldehyde (MAA) antibodies and MAA expression in lung tissues of patients with rheumatoid arthritis (RA)-associated interstitial lung disease (ILD) with controls.Methods:Anti-MAA antibody (IgA, IgM, IgG) concentrations were measured in validated RA-ILD cases and compared to RA patients with chronic obstructive pulmonary disease (COPD) and RA patients without lung disease. Associations of anti-MAA antibody with RA-ILD was assessed using multivariable logistic regression. Lung tissues from patients with RA-ILD, other ILD, emphysema, and controls (n=3/group) were stained for MAA, citrulline, macrophages (CD68), T cells (CD3), B cells (CD19/CD27), and extracellular matrix proteins (type-II collagen, fibronectin, vimentin). Tissue expression and co-localization with MAA was quantified and compared.Results:Among 1823 RA patients, 90 had prevalent RA-ILD. Serum IgA and IgM anti-MAA antibody concentrations were higher in RA-ILD than RA+COPD or RA alone (p=0.005). After adjusting for covariates, the highest quartiles of IgA (OR 2.09; 95% CI 1.11-3.90) and IgM (OR 2.23; 95% CI 1.19-4.15) anti-MAA antibody were significantly associated with the presence of RA-ILD. MAA expression was greater in RA-ILD lung tissues than all other groups (p<0.001) and co-localized with citrulline (r=0.79), CD19+ B cells (r=0.78), and extracellular matrix proteins (type-II collagen [r=0.72] and vimentin [r=0.77]) to the greatest degree in RA-ILD.Conclusion:Serum IgA and IgM anti-MAA antibody is associated with ILD among RA patients. MAA is highly expressed in lung tissues in RA-ILD where it co-localizes with other RA autoantigens, autoreactive B cells, and extracellular matrix proteins, underscoring its potential role in RA-ILD pathogenesis.CX000896/US Department of Veterans AffairsInternational/Internal Medicine Scientist Development Award, and Mentored Scholars ProgramInternational/R01 ES019325/ES/NIEHS NIH HHSUnited States/U54 OH010162/OH/NIOSH CDC HHSUnited States/U54 GM115458/GM/NIGMS NIH HHSUnited States/University of Nebraska Medical Center Physician-Scientist Training ProgramInternational/2020-09-01T00:00:00Z30933423PMC671704110782vault:4065