The Role of Quantitative CT and Radiomic Biomarkers for Precision Medicine in Pulmonary Fibrosis
- Disease/Condition
- Idiopathic Pulmonary Fibrosis
- Drug/Device/Intervention
- HRCT
- Study type
- Observational
- Intervention type
- diagnostic test
- Primary sponsor
- University of Virginia
- Phase
- N/A
- Start date
- May 01, 2024
- End date
- May 01, 2029
- Enrollment
- 160 participants
Derivation of DTA in IPF only cases from the PFF-PR and its associations with disease severity and outcomes.,Determine whether known IPF-risk genetic variants are associated with DTA score.,Identify novel genetic variants that associate with DTA score progression.,Determine if DTA or any constituent radiomic features correlate with select plasma proteins.,Determine if DTA or any of constituent radiomic features correlate with transcriptomic,Determine the best combination of markers (DTA, proteins and transcriptome) for machine learning algorithms for AUC evaluation of ROCs on all 3 cohorts.
This observational study involves obtaining 2 chest CT scans; a historical baseline CT within
 ±1 year of enrollment into PRECISIONS, and a follow-up CT (either historical or prospective)
 12 months ± 180 days after the baseline CT. Many IPF patients will have a CT scan every 12
 months for disease monitoring and cancer screening. Participants will have the option to
 share historical CTs only or they can choose to have a research CT done for the follow-up
 scan, if a scan for clinical purposes is not available.
- Registry
- ClinicalTrials.gov
- Trial ID
- NCT06323876
- Type
- Non-Device Trial
Access comprehensive clinical trial information for NCT06323876 through Pure Global AI's free database. This phase not specified trial is sponsored by University of Virginia and is currently Not yet recruiting. The study focuses on Idiopathic Pulmonary Fibrosis. Target enrollment is 160 participants.
This page provides complete trial specifications, intervention details, outcomes, and location information. Pure Global AI offers free access to ClinicalTrials.gov data, helping medical device and pharmaceutical companies navigate clinical research efficiently.