Imaging Signature of Progressive Pulmonary Fibrosis in Idiopathic Pulmonary Fibrosis and Non-IPF Interstitial Lung Diseases
University of California, Los Angeles
Summary
This study is a prospective observational study for subjects with idiopathic pulmonary fibrosis (IPF) or non-IPF interstitial lung diseases (ILD). The purpose of this study is to compare whether imaging patterns from high-resolution computed tomography (HRCT) at baseline can predict worsening. Single Time point Prediction (STP) is a score derived from an artificial intelligenc/ machine learning (AI/ML) using the radiomic features from a HRCT scan that quantifies the imaging patterns of short-term predictive worsening.
Description
Primary objective is to predict early for progression in both IPF and non-IPF ILD population using an artificial intelligence (AI)/Machine Learning (ML) algorithm of STP score. The primary interest is to validate STP score in identifying a cohort early for the candidate of anti-fibrotic treatment. The study plans to collect clinical information such as pulmonary function tests (PFT), symptom scores, 6-minute walk tests (6MWT), and radiologic information from HRCT. This study does not intervene with patient's standard medical care. This proposal is a prospective study that will enroll patients…
Eligibility
- Age range
- 18+ years
- Sex
- All
- Healthy volunteers
- No
IPF Inclusion Criteria: * Established a diagnosis (within 5 years) of IPF by enrolling center as defined by ATS/ERS/JRS/ALAT criteria * Age over or equal to 40 years old * No history of lung transplant * FVC % predicted \>= 45% * DLCO % predicted \>=25% * Women of childbearing potential (WOCBP) must be ready and able to use highly effective methods of birth control. WOCBP taking oral contraceptives (OCs) also have to use one barrier method. Non-IPF ILD Inclusion Criteria: * Established a diagnosis (within 5 years) of non-IPF ILD by enrolling center. * Age over or equal to 18 years old * Pre…
Location
- UCLALos Angeles, California