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Predicting Outcomes in Idiopathic Pulmonary Fibrosis Using Automated Computed Tomographic Analysis
Open accessAbstract
Rationale: Quantitative computed tomographic (CT) measures of baseline disease severity might identify patients with idiopathic pulmonary fibrosis (IPF) with an increased mortality risk. We evaluated whether quantitative CT variables could act as a cohort enrichment tool in future IPF drug trials. Objectives: To determine whether computer-derived CT measures, specifically measures of pulmonary vessel–related structures (VRSs), can better predict functional decline and survival in IPF and reduce requisite sample sizes in drug trial populations. Methods: Patients with IPF undergoing volumetric noncontrast CT imaging at the Royal Brompton Hospital, London, and St. Antonius Hospital, Utrecht, were examined to identify pulmonary function measures (including FVC) and visual and computer-derived (CALIPER [Computer-Aided Lung Informatics for Pathology Evaluation and Rating] software) CT features predictive of mortality and FVC decline. The discovery cohort comprised 247 consecutive patients, with validation of results conducted in a separate cohort of 284 patients, all fulfilling drug trial entry criteria. Measurements and Main Results: In the discovery and validation cohorts, CALIPER-derived features, particularly VRS scores, were among the strongest predictors of survival and FVC decline. CALIPER results were accentuated in patients with less extensive disease, outperforming pulmonary function measures. When used as a cohort enrichment tool, a CALIPER VRS score greater than 4.4% of the lung was able to reduce the requisite sample size of an IPF drug trial by 26%. Conclusions: Our study has validated a new quantitative CT measure in patients with IPF fulfilling drug trial entry criteria—the VRS score—that outperformed current gold standard measures of outcome. When used for cohort enrichment in an IPF drug trial setting, VRS threshold scores can reduce a required IPF drug trial population size by 25%, thereby limiting prohibitive trial costs. Importantly, VRS scores identify patients in whom antifibrotic medication prolongs life and reduces FVC decline.
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“VRS scores have been able to identify patients with IPF who may reach drug trial endpoints, and this has the potential to significantly impact the requisite sample size in IPF drug trials, reducing it by 26%, with important cost implications.”
From Pulmonary fibrosis: Emerging diagnostic and therapeutic strategies (2023) · cites this paper as Jacob et al., 2018
In the citing paper
“Importantly, an implication of this is that VRS scores can identify patients in whom antifibrotic medication prolongs life and reduces FVC decline (Jacob et al., 2018).”
From Pulmonary fibrosis: Emerging diagnostic and therapeutic strategies (2023) · cites this paper as Jacob et al., 2018
In the citing paper
“By measuring pulmonary VRSs, the Computer-Aided Lung Informatics for Pathology Evaluation and Rating (CALIPER) software can outperform current gold standard measures of outcome in IPF, by strongly predicting survival and likelihood of forced vital capacity (FVC) decline with effects enhanced over functional indices in patients with less extensive disease (Jacob et al., 2017”
From Pulmonary fibrosis: Emerging diagnostic and therapeutic strategies (2023) · cites this paper as Jacob et al., 2018
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Cites
Fitting Linear Mixed-Effects Models Using lme4
Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015) · Journal of Statistical Software · doi:10.18637/jss.v067.i01 · Cited by 86,835
Regression Modeling Strategies
Harrell, F. E. (2001) · Springer series in statistics · doi:10.1007/978-1-4757-3462-1 · Cited by 9,270
Raghu, G., Collard, H. R., Egan, J. J., Martinez, F. J., Behr, J., Brown, K. K., Colby, T. V., Cordier, J.-F., Flaherty, K. R., Lasky, J. A., et al. (2011) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.2009-040gl · Cited by 7,186
Self-citationInterpretative strategies for lung function tests
Pellegrino, R., Viegi, G., Brusasco, V., Crapo, R. O., Burgos, F., Casaburi, R., Coates, A., van der Grinten, C. P. M., Gustafsson, P., Hankinson, J., et al. (2005) · European Respiratory Journal · doi:10.1183/09031936.05.00035205 · Cited by 5,945
Efficacy and Safety of Nintedanib in Idiopathic Pulmonary Fibrosis
Richeldi, L., du Bois, R. M., Raghu, G., Azuma, A., Brown, K. K., Costabel, U., Cottin, V., Flaherty, K. R., Hansell, D. M., Inoue, Y., et al. (2014) · New England Journal of Medicine · doi:10.1056/nejmoa1402584 · Cited by 4,654
A phase 3 trial of pirfenidone in patients with idiopathic pulmonary fibrosis.
King, T. E., Bradford, W. Z., Castro-Bernardini, S., Fagan, E. A., Glaspole, I., Glassberg, M. K., Gorina, E., Hopkins, P. M., Kardatzke, D., Lancaster, L., et al. (2014) · New England Journal of Medicine · doi:10.1056/nejmoa1402582 · Cited by 3,975
Clinical course and prediction of survival in idiopathic pulmonary fibrosis
Ley, B., Collard, H. R., & King, T. E. (2011) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201006-0894ci · Cited by 1,765
Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix
Li, J., & Ji, L. (2005) · Heredity · doi:10.1038/sj.hdy.6800717 · Cited by 1,458
Incidence and Prevalence of Idiopathic Pulmonary Fibrosis
Raghu, G., Weycker, D., Edelsberg, J., Bradford, W. Z., & Oster, G. (2006) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.200602-163oc · Cited by 1,310
Efficacy of a Tyrosine Kinase Inhibitor in Idiopathic Pulmonary Fibrosis
Richeldi, L., Costabel, U., Selman, M., Kim, D. S., Hansell, D. M., Nicholson, A. G., Brown, K. K., Flaherty, K. R., Noble, P. W., Raghu, G., et al. (2011) · New England Journal of Medicine · doi:10.1056/nejmoa1103690 · Cited by 1,157
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Predicting outcomes in rheumatoid arthritis related interstitial lung disease
Jacob, J., Hirani, N., van Moorsel, C. H. M., Rajagopalan, S., Murchison, J. T., van Es, H. W., Bartholmai, B. J., van Beek, F. T., Struik, M. H. L., Stewart, G. A., et al. (2018) · European Respiratory Journal · doi:10.1183/13993003.00869-2018 · Cited by 179
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Metadata
- Authors
- Jacob, J., Bartholmai, B. J., Rajagopalan, S., van Moorsel, C. H. M., van Es, H. W., van Beek, F. T., Struik, M. H. L., Kokosi, M., Egashira, R., Brun, A. L., Nair, A., Walsh, S. L. F., Cross, G., Barnett, J., de Lauretis, A., Judge, E. P., Desai, S., Karwoski, R., Ourselin, S., Renzoni, E., Maher, T. M., Altmann, A., & Wells, A. U.
- Affiliations
- Department of Respiratory Medicine
- Division of Radiology and
- St. Antonius ILD Center of Excellence, Department of Pulmonology, and
- Department of Radiology, St. Antonius Hospital, Nieuwegein, the Netherlands
- University Medical Center Utrecht (NL)
- Interstitial Lung Disease Unit and
- Saga University (JP)
- Hôpital Cochin (FR)
- University College London (GB)
- King's College Hospital NHS Foundation Trust (GB)
- Royal Free London NHS Foundation Trust (GB)
- Division of Pneumology, “Guido Salvini” Hospital, Garbagnate Milanese, Italy
- Aintree University Hospital (GB)
- Royal Brompton & Harefield NHS Foundation Trust (GB)
- Mayo Clinic (US)
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, United Kingdom
- Centre for Medical Image Computing, and
- Corresponding author
- Jacob, Joseph
- ORCID
- Venue
- American Journal of Respiratory and Critical Care Medicine
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-09
- Volume
- 198
- Issue
- 6
- Pages
- 767-776
- ISSN
- 1073-449X (ISSN-L)
- 1535-4970
- PMC
- PMC6222463
- OpenAlex
- W2800281426
- Cited by
- 198
- References
- 41
- Citations by year
- 2026: 10 · 2025: 28 · 2024: 21 · 2023: 34 · 2022: 26 · 2021: 30 · 2020: 17 · 2019: 24 · 2018: 8
- FWCI
- 14.88
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- American Journal of Respiratory and Critical Care Medicine (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Spiral (Imperial College London) (submittedVersion)
- Funders
- Medical Research Council (MR/L016311/1)
- National Institute of Health Research Respiratory Disease Biomedical Research Unit at the Royal Brompton and Harefield NHS Foundation Trust and Imperial College London
- National Institute for Health Research, University College London Hospitals Biomedical Research Centre.
- Topics
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Chronic Obstructive Pulmonary Disease (COPD) Research
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- idiopathic pulmonary fibrosis, quantitative computed tomography, mortality prediction, forced vital capacity decline, disease severity assessment
- MeSH terms
- Aged, Female, Humans, Male, Middle Aged, Prognosis, Respiratory Function Tests, Tomography, X-Ray Computed, Vital Capacity, Idiopathic Pulmonary Fibrosis
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