Pulmonary Biomechanics in COPD: Imaging Techniques and Clinical Applications.

The respiratory system depends on complex biomechanical processes to enable gas exchange. The mechanical properties of the lung parenchyma, airways, vasculature, and surrounding structures play an essential role in overall ventilation efficacy. These complex biomechanical processes however are significantly altered in chronic obstructive pulmonary disease (COPD) due to emphysematous destruction of lung parenchyma, chronic airway inflammation, and small airway obstruction.

Machine learning based detection of true ventilatory restriction.

Rationale: Spirometry is only 50 % accurate for the detection of true ventilatory restriction, necessitating additional lung volume tests.

Objective: To develop a detection tool for true lung restriction using spirometry and patient demographics.

Methods: We analyzed spirometry and lung volume data from 21,062 participants.

Light Convolutional Neural Network to Detect Chronic Obstructive Pulmonary Disease (COPDxNet): A Multicenter Model Development and External Validation Study.

Background: Approximately 70% of adults with chronic obstructive pulmonary disease (COPD) remain undiagnosed. Opportunistic screening using chest computed tomography (CT) scans, commonly acquired in clinical practice, may be used to improve COPD detection through simple, clinically applicable deep-learning models. We developed a lightweight, convolutional neural network (COPDxNet) that utilizes minimally processed chest CT scans to detect COPD.

Imaging Endpoints for Biologic Therapy in Chronic Obstructive Pulmonary Disease.

Chronic obstructive pulmonary disease (COPD) is a leading cause of respiratory morbidity and mortality. The disease is characterized by exacerbations, which result in high symptom burden and accelerated disease progression. A subset of patients exhibit a predominant type 2 inflammatory endotype, which is associated with increased risk of exacerbation and higher responsiveness to anti-inflammatory therapy.

Seeing is Believing-On the Utility of CT in Phenotyping COPD.

Chronic obstructive pulmonary disease (COPD) is a heterogeneous condition with complicated structural and functional impairments. For decades now, chest computed tomography (CT) has been used to quantify various abnormalities related to COPD. More recently, with the newer data-driven approaches, biomarker development and validation have evolved rapidly.

Association of Lung Quantitative CT Scan Textures With Systemic Inflammation and Mortality in COPD.

Background: COPD is characterized by persistent inflammation that is responsible for remodeling the bronchovascular bundles (BVBs), which may lead to poor quality of life. Quantitative CT (QCT) scan textures of the lung can capture local disease patterns of inflammation and related respiratory morbidity.

Research Question: Are BVB textures, obtained from the adaptive multiple feature method, associated with systemic inflammation, morbidity, and mortality in COPD?

Study Design And Methods: We analyzed data from the Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS; n = 2,981) and the Genetic Epidemiology of COPD (COPDGene) study (n = 10,305).

Deep Learning Estimation of Small Airway Disease from Inspiratory Chest Computed Tomography: Clinical Validation, Repeatability, and Associations with Adverse Clinical Outcomes in Chronic Obstructive Pulmonary Disease.

Quantifying functional small airway disease (fSAD) requires additional expiratory computed tomography (CT) scans, limiting clinical applicability. Artificial intelligence (AI) could enable fSAD quantification from chest CT scans at total lung capacity (TLC) alone (fSAD). To evaluate an AI model for estimating fSAD, compare it with dual-volume parametric response mapping fSAD (fSAD), and assess its clinical associations and repeatability in chronic obstructive pulmonary disease (COPD).

CT Radiomics Features Predict Change in Lung Density and Rate of Emphysema Progression.

Rationale Emphysema progression is heterogeneous. Predicting temporal changes in lung density and detecting rapid progressors may facilitate selection of individuals for targeted therapies. Objective To test whether computed tomography (CT) radiomics can be used to predict changes in lung density and detect rapid progressors.

Association of Ground-Glass Opacities with Systemic Inflammation and Progression of Emphysema.

Ground-glass opacities (GGOs) in the absence of interstitial lung disease are understudied. To assess the association of GGOs with white blood cells (WBCs) and progression of quantified chest computed tomography emphysema. We analyzed data of participants in the SPIROMICS study (Subpopulations and Intermediate Outcome Measures in COPD Study).

LungViT: Ensembling Cascade of Texture Sensitive Hierarchical Vision Transformers for Cross-Volume Chest CT Image-to-Image Translation.

Chest computed tomography (CT) at inspiration is often complemented by an expiratory CT to identify peripheral airways disease. Additionally, co-registered inspiratory-expiratory volumes can be used to derive various markers of lung function. Expiratory CT scans, however, may not be acquired due to dose or scan time considerations or may be inadequate due to motion or insufficient exhale; leading to a missed opportunity to evaluate underlying small airways disease.

Radiomics for Improved Detection of Chronic Obstructive Pulmonary Disease in Low-Dose and Standard-Dose Chest CT Scans.

Background Approximately half of adults with chronic obstructive pulmonary disease (COPD) remain undiagnosed. Chest CT scans are frequently acquired in clinical practice and present an opportunity to detect COPD. Purpose To assess the performance of radiomics features in COPD diagnosis using standard-dose and low-dose CT models.

Direct estimation of regional lung volume change from paired and single CT images using residual regression neural network.

Background: Chest computed tomography (CT) enables characterization of pulmonary diseases by producing high-resolution and high-contrast images of the intricate lung structures. Deformable image registration is used to align chest CT scans at different lung volumes, yielding estimates of local tissue expansion and contraction.

Purpose: We investigated the utility of deep generative models for directly predicting local tissue volume change from lung CT images, bypassing computationally expensive iterative image registration and providing a method that can be utilized in scenarios where either one or two CT scans are available.

Predicting severe chronic obstructive pulmonary disease exacerbations using quantitative CT: a retrospective model development and external validation study.

Background: Quantitative CT is becoming increasingly common for the characterisation of lung disease; however, its added potential as a clinical tool for predicting severe exacerbations remains understudied. We aimed to develop and validate quantitative CT-based models for predicting severe chronic obstructive pulmonary disease (COPD) exacerbations.

Methods: We analysed the Subpopulations and Intermediate Outcome Measures In COPD Study (SPIROMICS) cohort, a multicentre study done at 12 clinical sites across the USA, of individuals aged 40-80 years from four strata: individuals who never smoked, individuals who smoked but had normal spirometry, individuals who smoked and had mild to moderate COPD, and individuals who smoked and had severe COPD.

Robust Measures of Image-Registration-Derived Lung Biomechanics in SPIROMICS.

Chronic obstructive pulmonary disease (COPD) is an umbrella term used to define a collection of inflammatory lung diseases that cause airflow obstruction and severe damage to the lung parenchyma. This study investigated the robustness of image-registration-based local biomechanical properties of the lung in individuals with COPD as a function of Global Initiative for Chronic Obstructive Lung Disease (GOLD) stage. Image registration was used to estimate the pointwise correspondences between the inspiration (total lung capacity) and expiration (residual volume) computed tomography (CT) images of the lung for each subject.

ATLANTIS - Attractor Landscape Analysis Toolbox for Cell Fate Discovery and Reprogramming.

Boolean modelling of biological networks is a well-established technique for abstracting dynamical biomolecular regulation in cells. Specifically, decoding linkages between salient regulatory network states and corresponding cell fate outcomes can help uncover pathological foundations of diseases such as cancer. Attractor landscape analysis is one such methodology which converts complex network behavior into a landscape of network states wherein each state is represented by propensity of its occurrence.