China-Japan Friendship Hospital
Beijing, Beijing Municipality, 100029, China
Location status: Recruiting
Recruiting
NCT Number: NCT06655090
Fibrotic Interstitial Lung Disease Early Recognition and Strategic Therapy Study in China
This project aimed to: 1) construct a cohort of no less than 10000 cases of f-ILD (including pneumoconiosis ≥3000 cases) with continuous regular follow-up to reveal the clinical phenotypes closely related to the development, progression and prognosis of pulmonary fibrosis; 2) systematically evaluate the safety and effectiveness of frozen lung biopsy, surgical lung biopsy/thoracoscopic lung biopsy and other techniques, and to optimize the histological diagnosis method of f-ILD; 3) construct a set of artificial intelligence (AI) evaluation system for quantitative evaluation of pulmonary fibrosis and its severity, and develop application software; 4) excavate and verify important molecular targets for the formation of pulmonary fibrosis and identify biomarkers; 5) combined with clinical phenotype, imaging, pathology and biomarkers to establish f-ILD early recognition and progress model, intervention strategies, guidelines and consensus, and applicated nationwide.
Recruiting
Interested in participating?
Request InfoKey information
Conditions
Sex eligibility
All sexes
Study type
Observational
Primary location
Who can participate
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
- Diagnosed as ILD
Exclusion criteria
- Lack of chest CT
- Patients refused to participant
Treatment and study plan
Primary outcomes
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Clinical diagnostic protocol of ILD tissue biopsy
Time frame: 3 years
Report on the reliability and safety assessment of TBLC and SLB diagnostics.
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Predict model
Time frame: 3 years
The contents were based on the f-ILD cohort, combined with clinical, imaging, pathological, lung function and biomarker analysis, and constructed a multidimensional model for the early recognition and progression of f-ILD.
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Severity of fibrosis in HRCT assessed by AI system in patients with ILD
Time frame: 3 years
Explore the diversity of abnormal image performance in patients with f-ILD, and extract multidimensional information based on deep learning and other methods. Realize the intelligent quantitative analysis of the severity of fibrosis.
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F-ILD cohort
Time frame: 6 years
The researchers used inclusion/exclusion criteria for screening, and collected the demographic information, clinical symptoms and signs, laboratory tests, treatment, survival and other conditions of the patients who agreed to participate in the program and signed the informed consent, and collected biological specimens.
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Important molecular targets and biomarkers identified by multi-omics
Time frame: 3 years
Single cell map of lung tissue in the early stage of ILD, key molecular targets and biomarkers for the development and progression of pulmonary fibrosis.
Study contacts
Contact information is provided by the study sponsor or research team.
Sponsors and collaborators
Lead sponsor
Dai Huaping
Other
Collaborators
- Infervision Medical Technology Company Limited
- Institute of Basic Medical Sciences CAMS
- Jilin University
- Nankai University
- National Institute for Occupational Health and Poison Control
- Shanghai Chest Hospital
- Shanghai Pulmonary Hospital, Shanghai, China
- The First Affiliated Hospital of Guangzhou Medical University
- Tongji Hospital
Registry information
Important dates
- Study start
- 2021
- Primary completion
- 2024
- Study completion
- 2027
- First posted
- Oct 23, 2024
- Registry last updated
- Oct 23, 2024
OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.
View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.
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