Other Diseases

COPD

COPD Challenges

  • Complex Disease Process – COPD is a massive global health issue, with a prevalence of over 400 million and the 3rd leading cause of death. It is a complex disease with many phenotypes that can complicate clinical trials and clinical care. In a clinical trial measuring the impact of a new COPD therapy, for example, it is critical to understand the patient phenotypes included in the study in an effort to develop more personalized treatments.Mucus plugging is shedding new light on an aspect of the disease process; it has recently been discovered in 31.5% of COPD patients. Trial sponsors can leverage precision imaging biomarkers to better distinguish phenotypes and assess treatment response across airways, tissue, function, and vasculature.
  • Weak Conventional Endpoints – Traditional trial endpoints like pulmonary function tests, the 6-minute walk test, and exacerbations are all useful; however, they are often lagging indicators with limited precision when compared to quantitative imaging measures of lung anatomy and function.
  • Challenging Imaging Operations – Quantitative imaging provides exceptional value in COPD trials; however, precision imaging can be especially challenging for trial sites. Strict adherence to CT protocols, staff training, scanner calibration, data management and other complexities can result in unsuccessful sites or poor/inconsistent data quality. Long onboarding times or dropout sites can add significant cost t o COPD trials.
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“QCT IS NOW CONSIDERED BY MANY AS INDISPENSABLE FOR LONGITUDINAL ANALYSIS AND INTERVENTION TRIALS.”

Felix Herth, MD, PhD, DSC, FCCP, FERP

Chief, Head, Associate Director, Professor
Thoraxklinik, University of Heidelberg, Germany

VIDA Lung Intelligence for COPD Clinical Trials

In “The Modern Art of Reading Computed Tomography Images of the Lungs” by Prof. Herth and colleagues, 23 specific QCT biomarkers are identified for COPD, all of which (and more) are provided by VIDA from a single study. In the four years since this paper was published, several novel biomarkers for COPD, such as total airway count (TAC) and mucus scoring have gained support. Together with collaboration partners, VIDA is proud to lead the way in development and validation of new imaging biomarkers. For more information on these biomarkers, please complete this brief form 👇.

VIDA Intelligence Portal is a respiratory trial imaging orchestration platform designed to ease the imaging operations of a lung trial. Portal applies AI-powered intelligence to automate tasks that are mundane and/or prone to human error. The portal also provides eLearning, quality control, data security, drag and drop data exchange, team communications and much more. The result is clinical trial sites capable of acquiring high quality clinical trial imaging data with ease.

Intelligence services leverage imaging and operational data gold mines to maximize their value for trial sponsors. For example, retrospective data analysis services examine existing datasets to surface valuable new insights. Site performance monitoring gives sponsors dynamic operational dashboards to proactively view the health of trial sites, the data they are submitting, and more. Subject screening services assist sponsors in filtering out candidates who meet exclusion criteria.

COPD Biomarkers

VIDA is proud to be the lead QCT imaging provider for SPIROMICS, a large multi-site COPD study for the purpose of identifying subpopulations and intermediate outcome measures.

COPD Studies Utilizing VIDA’s Precision Imaging

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Chaudhary, M. F. A. et al. Predicting severe chronic obstructive pulmonary disease exacerbations using quantitative CT: a retrospective model development and external validation study. The Lancet Digital Health 5, e83–e92 (2023).
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Woodruff, P. G. et al. Clinical Significance of Symptoms in Smokers with Preserved Pulmonary Function. New England Journal of Medicine 374, 1811–1821 (2016).
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Smith, B. M. et al. Comparison of Spatially Matched Airways Reveals Thinner Airway Walls in COPD. The Multi-Ethnic Study of Atherosclerosis (MESA) COPD Study and the Subpopulations and Intermediate Outcomes in COPD Study (SPIROMICS). Thorax 69, 987–996 (2014).
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Wang, J. M., Ram, S., Labaki, W. W., Han, M. K. & Galbán, C. J. CT-Based Commercial Software Applications: Improving Patient Care Through Accurate COPD Subtyping. Int J Chron Obstruct Pulmon Dis 17, 919–930 (2022).
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Hammond, E. et al. Comparison of Low and Ultra-Low Dose Computed Tomography Protocols For Quantitative Lung and Airway Assessment. Med Phys 44, 4747–4757 (2017).
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Motahari, A. et al. Repeatability of Pulmonary Quantitative CT Measurements in COPD. Am J Respir Crit Care Med rccm.202209-1698PP (2023) http://doi.org/10.1164/rccm.202209-1698PP.
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Smith, B. M. et al. Association of Dysanapsis With Chronic Obstructive Pulmonary Disease Among Older Adults. JAMA 323, 2268–2280 (2020).
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Ritchie, A. I. et al. Structural Predictors of Lung Function Decline in Young Smokers with Normal Spirometry. Am J Respir Crit Care Med (2024) http://doi.org/10.1164/rccm.202307-1203OC.
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Grau, M. et al. Percent Emphysema and Right Ventricular Structure and Function: The Multi-Ethnic Study of Atherosclerosis-Lung and Multi-Ethnic Study of Atherosclerosis-Right Ventricle Studies. CHEST 144, 136–144 (2013).
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Sharma, M., Kirby, M., McCormack, D. G. & Parraga, G. Machine Learning and CT Texture Features in Ex-smokers with no CT Evidence of Emphysema and Mildly Abnormal Diffusing Capacity. Academic Radiology 0, (2023).
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Ross, J. C. et al. Dysanapsis is differentially related to lung function trajectories with distinct structural and functional patterns in COPD and variable risk for adverse outcomes. eClinicalMedicine 68, (2024).
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Armstrong, H. F. et al. Lung function, percent emphysema, and QT duration: The Multi-Ethnic Study of Atherosclerosis (MESA) lung study. Respir Med 123, 1–7 (2017).
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Begum, F. et al. Hemizygous Deletion on Chromosome 3p26.1 Is Associated with Heavy Smoking among African American Subjects in the COPDGene Study. PLOS ONE 11, e0164134 (2016).
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Beiko, T. et al. Serum Proteins Associated with Emphysema Progression in Severe Alpha-1 Antitrypsin Deficiency. Chronic Obstr Pulm Dis 4, 204–216 (2017).
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Choi, S. et al. Differentiation of quantitative CT imaging phenotypes in asthma versus COPD. BMJ Open Respiratory Research 4, e000252 (2017).
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Cosentino, J. et al. Analysis of Asthma-Chronic Obstructive Pulmonary Disease Overlap Syndrome Defined on the Basis of Bronchodilator Response and Degree of Emphysema. Ann Am Thorac Soc 13, 1483–1489 (2016).
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Dilger, S. K. et al. Improved pulmonary nodule classification utilizing quantitative lung parenchyma features. JMI 2, 041004 (2015).
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Tran, C. et al. Luminal mucus plugs are spatially associated with airway wall thickening in severe COPD and asthma: A single-centered, retrospective, observational study. Respiratory Medicine 202, 106982 (2022).
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Smith, B. M. et al. Human airway branch variation and chronic obstructive pulmonary disease. Proceedings of the National Academy of Sciences 115, E974–E981 (2018).
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Washko, G. R., Coxson, H. O., O'Donnell, D. E. & Aaron, S. D. CT imaging of chronic obstructive pulmonary disease: insights, disappointments, and promise. The Lancet Respiratory Medicine 5, 903–908 (2017).
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Belchi, F. et al. Lung Topology Characteristics in patients with Chronic Obstructive Pulmonary Disease. Sci Rep 8, 5341 (2018).
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Bhatt, S. P. et al. Comparison of spirometric thresholds in diagnosing smoking-related airflow obstruction: authors' response. Thorax 69, 1147–1148 (2014).
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Ostridge, K. et al. Using Novel Computed Tomography Analysis to Describe the Contribution and Distribution of Emphysema and Small Airways Disease in Chronic Obstructive Pulmonary Disease. Annals ATS 16, 990–997 (2019).
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Gompelmann, D. et al. The minimal important difference for target lobe volume reduction after endoscopic valve therapy. Int J Chron Obstruct Pulmon Dis 13, 465–472 (2018).
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Yablonskiy, D. A. Air Trapping - Insights from CT and In Vivo Lung Morphometry with Hyperpolarized 3He MRI. American Thoracic Society International Conference Meetings Abstracts American Thoracic Society International Conference Meetings Abstracts.
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de Oliveira, H. G. et al. Combined Bone Marrow‐Derived Mesenchymal Stromal Cell Therapy and One‐Way Endobronchial Valve Placement in Patients with Pulmonary Emphysema: A Phase I Clinical Trial. Stem Cells Transl Med 6, 962–969 (2017).
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Lo Cascio, C. M. et al. Percent Emphysema and Daily Motor Activity Levels in the General Population. Chest 151, 1039–1050 (2017).
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Oelsner, E. C. et al. Association between emphysema-like lung on cardiac computed tomography and mortality in persons without airflow obstruction: a cohort study. Ann Intern Med 161, 863–873 (2014).
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Smith, B. M. et al. Not all measures of hyperinflation are created equal: lung structure and clinical correlates of gas trapping and hyperexpansion in COPD: the Multi-Ethnic Study of Atherosclerosis (MESA) COPD Study. Chest 145, 1305–1315 (2014).
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Martinez, C. H. et al. Age and Small Airway Imaging Abnormalities in Subjects with and without Airflow Obstruction in SPIROMICS. Am J Respir Crit Care Med 195, 464–472 (2017).
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Newell, J. D., Sieren, J. & Hoffman, E. A. Development of quantitative computed tomography lung protocols. J Thorac Imaging 28, 266–271 (2013).
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McAllister, D. et al. Pulmonary Function is Associated with Distal Aortic Calcium, not Proximal Aortic Distensibility. MESA Lung Study. COPD 8, 71–78 (2011).
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Han, M. K. et al. Prevalence and clinical correlates of bronchoreversibility in severe emphysema. Eur Respir J 35, 1048–1056 (2010).
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Martinez, F. J. et al. Sex differences in severe pulmonary emphysema. AM.REV.RESPIR.DIS. 176, 243–252 (2007).
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Burkart, K. M. et al. APOM and High-Density Lipoprotein are associated with Lung Function and Percent Emphysema. Eur Respir J 43, 1003–1017 (2014).
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Powell, R. et al. Genetic ancestry and the relationship of cigarette smoking to lung function and per cent emphysema in four race/ethnic groups: a cross-sectional study. Thorax 68, 634–642 (2013).
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Barr, R. G. et al. Percent Emphysema, Airflow Obstruction, and Impaired Left Ventricular Filling. N Engl J Med 362, 217–227 (2010).
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Krachman, S. L. et al. Effect of Emphysema Severity on the Apnea-Hypopnea Index in Smokers with Obstructive Sleep Apnea. Ann Am Thorac Soc 13, 1129–1135 (2016).
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Parikh, M. A. et al. Angiotensin-Converting Inhibitors and Angiotensin II Receptor Blockers and Longitudinal Change in Percent Emphysema on Computed Tomography. The Multi-Ethnic Study of Atherosclerosis Lung Study. Ann Am Thorac Soc 14, 649–658 (2017).
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Xu, Y., Sonka, M., McLennan, G., Guo, J. & Hoffman, E. A. MDCT-based 3-D texture classification of emphysema and early smoking related lung pathologies. IEEE Trans Med Imaging 25, 464–475 (2006).
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Zach, J. A. et al. Quantitative computed tomography of the lungs and airways in healthy nonsmoking adults. Invest Radiol 47, 596–602 (2012).
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Sieren, J. P. et al. Reference standard and statistical model for intersite and temporal comparisons of CT attenuation in a multicenter quantitative lung study. Med Phys 39, 5757–5767 (2012).
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Putman, R. K. et al. Association Between Interstitial Lung Abnormalities and All-Cause Mortality. JAMA 315, 672–681 (2016).
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Xu, Y., Sonka, M., McLennan, G., Guo, J. & Hoffman, E. Sensitivity and specificity of 3-D texture analysis of lung parenchyma is better than 2-D for discrimination of lung pathology in stage 0 COPD. 5746, 474–485 (2005).
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Hueper, K. et al. Pulmonary Microvascular Blood Flow in Mild Chronic Obstructive Pulmonary Disease and Emphysema. The MESA COPD Study. Am J Respir Crit Care Med 192, 570–580 (2015).
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Kim, V. et al. Clinical and computed tomographic predictors of chronic bronchitis in COPD: a cross sectional analysis of the COPDGene study. Respiratory Research 15, 52 (2014).
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Iyer, K. S., Grout, R. W., Zamba, G. K. & Hoffman, E. A. Repeatability and Sample Size Assessment Associated with Computed Tomography-Based Lung Density Metrics. Chronic Obstr Pulm Dis 1, 97–104 (2014).
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Chen-Mayer, H. H. et al. Standardizing CT lung density measure across scanner manufacturers. Med Phys 44, 974–985 (2017).
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Moslemi, A. et al. Quantitative CT Lung Imaging and Machine Learning Improves Prediction of Emergency Room Visits and Hospitalizations in COPD. Acad Radiol S1076-6332(22)00311–7 (2022) http://doi.org/10.1016/j.acra.2022.05.009.
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Makimoto, K. et al. Comparison of Feature Selection Methods and Machine Learning Classifiers for Predicting Chronic Obstructive Pulmonary Disease Using Texture-Based CT Lung Radiomic Features. Acad Radiol S1076-6332(22)00412–3 (2022) http://doi.org/10.1016/j.acra.2022.07.016.