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.
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%
Planned to participate in an intervention trial within the next 6 months
Currently listed for lung transplantation at the time of enrollment
Malignancy, treated or untreated, other than malignancy unlikely to affect prognosis in the next 3 years such as skin cancer or non-metastatic prostate cancer within the past 5 years
Any clinically significant co-morbidity, which in the view of investigator, is likely to contribute to mortality or ability to perform PFT's in the next 2 years
University of California, Los Angeles
Lead sponsor
Boehringer Ingelheim
Collaborator