Cited by 198Interstitial Lung Diseases and Idiopathic Pulmonary FibrosisChronic Obstructive Pulmonary Disease (COPD) ResearchRadiomics and Machine Learning in Medical Imaging

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Predicting Outcomes in Idiopathic Pulmonary Fibrosis Using Automated Computed Tomographic Analysis

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Jacob, Joseph, Bartholmai, Brian J., Rajagopalan, Srinivasan, van Moorsel, Coline H. M., van Es, Hendrik W., van Beek, Frouke T., Struik, Marjolijn H. L., Kokosi, Maria, Egashira, Ryoko, Brun, Anne Laure, Nair, Arjun, Walsh, Simon L. F., Cross, Gary, Barnett, Joseph, de Lauretis, Angelo, Judge, Eoin P., Desai, Sujal, Karwoski, Ronald, Ourselin, Sebastien, Renzoni, Elisabetta, Maher, Toby M., Altmann, Andre, Wells, Athol U.·2018·American Journal of Respiratory and Critical Care Medicine

DOI 10.1164/rccm.201711-2174ocPMC PMC6222463

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Abstract

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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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: Jacob, Joseph
Bartholmai, Brian J.
Kokosi, Maria
Egashira, Ryoko
Brun, Anne Laure
Nair, Arjun
Walsh, Simon L. F.
Cross, Gary
Barnett, Joseph
Desai, Sujal
Renzoni, Elisabetta
Maher, Toby M.
Wells, Athol U.
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
DOI: 10.1164/rccm.201711-2174oc
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
Licence: academic.oup.com/pages/standard-publication-reuse-rights (vor)
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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Predicting Outcomes in Idiopathic Pulmonary Fibrosis Using Automated Computed Tomographic Analysis

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