[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100528600":3},{"organization":4,"armGroups":7,"interventions":10,"overallOfficials":15,"centralContacts":23,"locations":33,"responsibleParty":50,"collaborators":53,"id":57,"slug":58,"hasResults":59,"nctId":60,"briefTitle":61,"officialTitle":62,"acronym":63,"eligibilityCriteria":64,"healthyVolunteers":59,"sex":65,"minAge":66,"maxAge":10,"enrollmentInfo":67,"targetDuration":10,"studyType":70,"phases":10,"briefSummary":71,"conditions":72,"keywords":74,"overallStatus":36,"whyStopped":10,"lastUpdateSubmitDate":79,"lastUpdatePostDateStruct":80,"startDateStruct":83,"completionDateStruct":85,"leadSponsor":87,"locationsCount":88},{"fullName":5,"class":6},"University of California, Los Angeles","OTHER",[8,12],{"label":9,"type":10,"description":11,"interventionNames":10},"STP >=30%",null,"STP score is 30% or greater than 30% in whole lung at baseline inspirational HRCT scan. STP score is an AL\u002FML derived score using radiomic patterns of lung parenchyma to identify the spatial location of likely progressed in the short-term follow up. The higher score is the worse expected outcome.",{"label":13,"type":10,"description":14,"interventionNames":10},"STP \u003C 30%","STP score is less than 30% in whole lung at baseline inspirational HRCT scan.",[16,20],{"name":17,"affiliation":18,"role":19},"Samuel Weigt, MD","UCLA Division of Pulmonary, Critical Care, and Hospitals","PRINCIPAL_INVESTIGATOR",{"name":21,"affiliation":22,"role":19},"Jonathan Goldin, MD","Radiological Sciences at the University of California, Los Angeles",[24,29],{"name":25,"role":26,"phone":27,"phoneExt":10,"email":28},"Grace Hyun Kim, PhD","CONTACT","(310) 481-7594","GraceKim@mednet.ucla.edu",{"name":30,"role":26,"phone":31,"phoneExt":10,"email":32},"Claudia L Perdomo, AS","310-267-4707","cperdomo@mednet.ucla.edu",[34],{"facility":35,"status":36,"city":37,"state":38,"zip":39,"country":40,"countryCode":41,"cosmosGeoPoint":42,"geoPoint":47,"contacts":48},"UCLA","RECRUITING","Los Angeles","California","90024","United States","US",{"type":43,"coordinates":44},"Point",[45,46],-118.24368,34.05223,{"lat":46,"lon":45},[49],{"name":17,"role":26,"phone":10,"phoneExt":10,"email":10},{"type":19,"investigatorFullName":51,"investigatorTitle":52,"investigatorAffiliation":5,"oldNameTitle":10,"oldOrganization":10},"Grace Hyun Kim","Principal Investigator",[54],{"name":55,"class":56},"Boehringer Ingelheim","INDUSTRY","100528600","single-time-point-prediction-as-earlier-diagnosis-of-progressive-pulmonary-fibrosis-100528600",false,"NCT06162884","Single Time Point Prediction as Earlier Diagnosis of Progressive Pulmonary Fibrosis","Imaging Signature of Progressive Pulmonary Fibrosis in Idiopathic Pulmonary Fibrosis and Non-IPF Interstitial Lung Diseases","IS-PPF","IPF Inclusion Criteria:\n\n* Established a diagnosis (within 5 years) of IPF by enrolling center as defined by ATS\u002FERS\u002FJRS\u002FALAT criteria\n* Age over or equal to 40 years old\n* No history of lung transplant\n* FVC % predicted \\>= 45%\n* DLCO % predicted \\>=25%\n* 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.\n\nNon-IPF ILD Inclusion Criteria:\n\n* Established a diagnosis (within 5 years) of non-IPF ILD by enrolling center.\n* Age over or equal to 18 years old\n* Presence of chronic fibrosis ILD defined as architectural distortions with reticulation and the presence of traction bronchiectasis by visual assessment: (1) estimating visually \\>5% in whole lung, or (2) mild pulmonary fibrosis and \\\u003C5% in whole lung (i.e., early non-IPF-ILD identified by a pulmonologist).\n* Patients treated with immunosuppressive agents (other than corticosteroids) for an underlying systemic disease need to be on a stable treatment for at least 12 weeks prior to screening\n* FVC % predicted \\>= 45%\n* DLCO % predicted \\>=25%\n* 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\n\nExclusion Criteria:\n\n* Planned to participate in an intervention trial within the next 6 months\n* Currently listed for lung transplantation at the time of enrollment\n* 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\n* 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\n* Prebronchodilator Forced Expiratory Volume in 1 second (FEV1)\u002FForced vital capacity (FVC) \\\u003C0.7 at as screening\n* Exclusion of co-morbidities: congestive heart failure (stroke, deep vein thrombosis, pulmonary embolism, myocardial infarction), current virus-associated community acquired pneumonia, smoking-related chronic obstructive lung disease with FEV1 \\\u003C70%, history of lung cancer, history of other cancer treated within the past 4 years for IPF and 5 years for non-IPF ILD (excluding basal cell carcinoma of skin).\n\nHRCT data from subjects with combined pulmonary fibrosis and emphysema (CPFE) can be collected.\n\nMajor Discontinuing Criteria in this study\n\n* lung transplant after baseline or death\n* withdraw of consent or transition to another care center","ALL","18 Years",{"count":68,"type":69},200,"ESTIMATED","OBSERVATIONAL","This study is a prospective observational study for subjects with idiopathic pulmonary fibrosis (IPF) or non-IPF interstitial lung diseases (ILD).\n\nThe 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\u002F machine learning (AI\u002FML) using the radiomic features from a HRCT scan that quantifies the imaging patterns of short-term predictive worsening.",[73],"Pulmonary Fibrosis",[75,76,77,78],"imaging outcome","Single Timepoint Prediction","AI\u002Fmachine learning","progressive ILD","2026-06-16",{"date":81,"type":82},"2026-06-18","ACTUAL",{"date":84,"type":82},"2024-11-06",{"date":86,"type":69},"2029-08-19",{"name":5,"class":6},1]