Interstitial Lung Disease
Conditions
Keywords
Lung Diseases, Interstitial Lung Disease, Multidetector Computed Tomography, Deep Learning
Brief summary
In interstitial lung disease (ILD), the extent of ILD on chest computed tomography (CT) is recognized as an important prognostic factor. Automated tools are now available to quantify ILD, but there is a lack of data on the reproducibility of this measurement and therefore its accuracy. Therefore, the purpose of this study is to evaluate the variability of automated ILD quantification on chest CT. Reproducibility will be assessed by repeating chest CT scans and using different tools to measure the extent of disease.
Detailed description
In interstitial lung disease (ILD), the extent of ILD on chest computed tomography (CT) is recognized as an important prognostic factor. In recent years, several tools based on texture analysis or deep learning methods have been developed to provide rapid and accurate automated quantification of ILD extent. An advantage of automated scoring methods is that they theoretically offer perfect repeatability of measurement. However, this is only true if the measurement is repeated on the same images. Several parameters can alter the appearance of the lungs on scanner images, such as the degree of inspiration or the reconstruction kernel used to reconstruct the images. There is a lack of data on the reproducibility of the whole process of automated ILD quantification and therefore its accuracy. Therefore, the purpose of this study is to evaluate the variability of automated ILD quantification on chest CT. Reproducibility will be assessed by repeating chest CT scans and using different tools to measure the extent of disease in patients with ILD from 2 institutions. Chest CT will be repeated the same day. This will allow assessment of the variability of automated measurement of ILD extent between 2 CT scans performed on the same day and when using different software. It will also allow to assess the variability of lung volume between the 2 CT scans and the effect of disease (idiopathic pulmonary fibrosis or connective tissue disease-related ILD) on the reproducibility of ILD quantification.
Interventions
DIAGNOSTIC_TESTChest CT
Repeated chest CT for automated ILD quantification
Sponsors
URC-CIC Paris Descartes Necker Cochin
Assistance Publique - Hôpitaux de Paris
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
OTHER
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Age ≥ 18 years * Known interstitial lung disease as part of idiopathic pulmonary fibrosis or connective tissue disease * Followed in one of the participating hospitals * Requiring a chest CT as part of a scheduled assessment * Affiliated to a French national social security * Informed consent
Exclusion criteria
* Acute exacerbation of ILD * Pregnancy * Inability to hold an apnea for 10 seconds * Patients in the exclusion period after a previous research * Need for additional procubitus or expiratory images
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Automated Interstitial Lung Disease (ILD) quantification | Day of inclusion | Reproducibility of ILD quantification on chest CT (as a percentage of lung volume) between two successive scan acquisitions. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Automated Interstitial Lung Disease (ILD) quantification | Day of inclusion | Reproducibility of disease extent measured with different software on the same acquisition |
| Lung volume | Day of inclusion | Reproducibility of total lung volume measured on the scanner between two successive acquisitions |
Countries
France
Contacts
Primary ContactGuillaume CHASSAGNON, MD, PhD
Backup ContactMarie BENHAMMANI-GODARD
Outcome results
None listed