ILD Challenges

  • Complex Disease Process – ILD is a complex group of over 200 forms of disease.  Trial sponsors can leverage precision imaging biomarkers to better distinguish ILD phenotypes and assess treatment response across airways, tissue, function, and vasculature–all of which are insightful in measuring treatment response.
  • 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.
  • Challenging Imaging Operations – Quantitative imaging provides exceptional value in ILD/IPF trials; however, precision imaging can be especially challenging for trial sites. VIDA’s trial management platform orchestrates trial imaging operations to empower successful sites and high quality data.

ILD Biomarkers

VIDA Lung Intelligence for ILD Clinical Trials

In “The Modern Art of Reading Computed Tomography Images of the Lungs” by Prof. Herth and colleagues, 11 specific QCT biomarkers are identified for ILD, all of which (and more) are provided by VIDA from a single study. In the years since this paper was published, several additional biomarkers for ILD have been developed by VIDA, including machine-learning based texture analysis. Together with collaboration partners, VIDA is proud to lead the way in development and validation of new imaging biomarkers for ILD. For more information on these biomarkers in the form of an information sheet, 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.

VIDA is proud to be a member of the Open Source Imaging Consortium (OSIC), a cooperative effort between academia, industry and patient advocacy groups with a shared mission to accelerate progress against ILD/IPF.

ILD Studies Utilizing VIDA’s Precision Imaging

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Newell, J. D., Tschirren, J., Peterson, S., Beinlich, M. & Sieren, J. Quantitative CT of Interstitial Lung Disease. Semin Roentgenol 54, 73–79 (2019).
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Choi, B. et al. Regional distribution of high-attenuation areas on chest computed tomography in the Multi-Ethnic Study of Atherosclerosis. ERJ Open Research 6, (2020).
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Herth, F. J. F. et al. The Modern Art of Reading Computed Tomography Images of the Lungs: Quantitative CT. Respiration 95, 8–17 (2018).
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Podolanczuk, A. J. et al. High Attenuation Areas on Chest CT in Community-Dwelling Adults: The MESA Study. Eur Respir J 48, 1442–1452 (2016).
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Li, Y. Z., Jin, G. Y., Chae, K. J. & Han, Y. M. Quantitative Assessment of Airway Changes in Fibrotic Interstitial Lung Abnormality Patients by Chest CT According to Cumulative Cigarette Smoking. Tomography 8, 1024–1032 (2022).
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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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Manichaikul, A. et al. Genome-wide association study of subclinical interstitial lung disease in MESA. Respir Res 18, 97 (2017).
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Bernstein, E. J. et al. Rheumatoid arthritis-associated autoantibodies and subclinical interstitial lung disease: the Multi-Ethnic Study of Atherosclerosis. Thorax 71, 1082–1090 (2016).
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Johnson, C. et al. Smoking and Subclinical ILD in RA versus the Multi-Ethnic Study of Atherosclerosis. PLOS ONE 11, e0153024 (2016).
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Xu, Y. et al. Effect of mixing scanner types and reconstruction kernels on the characterization of lung parenchymal pathologies: emphysema, interstitial pulmonary fibrosis and normal non-smokers. 6143, 206–214 (2006).
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Assessment, U. E. N. C. for E. Computer-based objective quantitative assessment of pulmonary parenchyma via X-ray CT. (2009).
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Xu, Y. et al. Computer-aided classification of interstitial lung diseases via MDCT: 3D adaptive multiple feature method (3D AMFM). Acad Radiol 13, 969–978 (2006).
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Vasilescu, D. M. et al. Microscopic Validation of Deep Learning Based Texture Classification of Idiopathic Pulmonary Fibrosis Patterns Visible on Thoracic Computed Tomography. in D29. TRANSLATIONAL RESEARCH IN IPF A6489–A6489 (American Thoracic Society, 2023). http://doi.org/10.1164/ajrccm-conference.2023.207.1_MeetingAbstracts.A6489.
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Castillo-Saldana, D., Hague, C. J., Coxson, H. O. & Ryerson, C. J. Using Quantitative Computed Tomographic Imaging to Understand Chronic Obstructive Pulmonary Disease and Fibrotic Interstitial Lung Disease: State of the Art and Future Directions. Journal of Thoracic Imaging 35, 246–254 (2020).
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Saldana, D. C. et al. Association of Computed Tomography Densitometry with Disease Severity, Functional Decline, and Survival in Systemic Sclerosis-associated Interstitial Lung Disease. Annals ATS 17, 813–820 (2020).