RECRUITING

Single Time Point Prediction as Earlier Diagnosis of Progressive Pulmonary Fibrosis

Study Overview

This clinical trial focuses on testing the efficacy of different digital interventions to promote re-engagement in cancer-related long-term follow-up care for adolescent and young adult (AYA) survivors of childhood cancer.

Description

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.

Official Title

Imaging Signature of Progressive Pulmonary Fibrosis in Idiopathic Pulmonary Fibrosis and Non-IPF Interstitial Lung Diseases

Quick Facts

Study Start:2024-10-20
Study Completion:2028-08-19
Study Type:Not specified
Phase:Not Applicable
Enrollment:Not specified
Status:RECRUITING

Study ID

NCT06162884

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Ages Eligible for Study:18 Years
Sexes Eligible for Study:ALL
Accepts Healthy Volunteers:No
Standard Ages:ADULT, OLDER_ADULT
Inclusion CriteriaExclusion Criteria
  1. * Established a diagnosis (within 3 years) of IPF by enrolling center as defined by ATS/ERS/JRS/ALAT criteria
  2. * Age over or equal to 40 years old
  3. * No history of lung transplant
  4. * FVC % predicted \>= 45%
  5. * DLCO % predicted \>=25%
  1. * Planned to participate in an intervention trial within the next 3 months
  2. * Currently listed for lung transplantation at the time of enrollment
  3. * Malignancy, treated or untreated, other than skin cancer or prostate cancer within the past 5 years
  4. * 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 \< 70%, history of lung cancer, history of other cancer treated within the past 4 years (excluding basal cell carcinoma of skin).
  5. * lung transplant after baseline or death
  6. * withdraw of consent or transition to another care center

Contacts and Locations

Study Contact

Grace Hyun Kim, PhD
CONTACT
(310) 481-7594
GraceKim@mednet.ucla.edu
Claudia L Perdomo, AS
CONTACT
310-267-4707
cperdomo@mednet.ucla.edu

Principal Investigator

Samuel Weigt, MD
PRINCIPAL_INVESTIGATOR
UCLA Division of Pulmonary, Critical Care, and Hospitals
Jonathan Goldin, MD
PRINCIPAL_INVESTIGATOR
Radiological Sciences at the University of California, Los Angeles

Study Locations (Sites)

UCLA
Los Angeles, California, 90024
United States

Collaborators and Investigators

Sponsor: University of California, Los Angeles

  • Samuel Weigt, MD, PRINCIPAL_INVESTIGATOR, UCLA Division of Pulmonary, Critical Care, and Hospitals
  • Jonathan Goldin, MD, PRINCIPAL_INVESTIGATOR, Radiological Sciences at the University of California, Los Angeles

Study Record Dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Registration Dates

Study Start Date2024-10-20
Study Completion Date2028-08-19

Study Record Updates

Study Start Date2024-10-20
Study Completion Date2028-08-19

Terms related to this study

Keywords Provided by Researchers

  • imaging outcome
  • Single Timepoint Prediction
  • AI/machine learning
  • progressive ILD

Additional Relevant MeSH Terms

  • Pulmonary Fibrosis