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Single Time Point Prediction as Earlier Diagnosis of Progressive Pulmonary Fibrosis

NCT06162884 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 200

Last updated 2025-06-15

No results posted yet for this study

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.

Conditions

Sponsors & Collaborators

Principal Investigators

  • Samuel Weigt, MD · UCLA Division of Pulmonary, Critical Care, and Hospitals

  • Jonathan Goldin, MD · Radiological Sciences at the University of California, Los Angeles

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-11-06
Primary Completion
2027-11-22
Completion
2028-08-19

Countries

  • United States

Study Locations

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Entities

Diseases
Companies

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06162884 on ClinicalTrials.gov