Single Time Point Prediction as Earlier Diagnosis of Progressive Pulmonary Fibrosis

Part of paid clinical trials in Los Angeles, California.

Sponsor
University of California, Los Angeles
Study ID
NCT06162884
Status
Recruiting
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Conditions

Eligibility Criteria

Sex
ALL
Age
18 Years - N/A
Healthy Volunteers
Not accepted

Study Details

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.

Key Dates

Start date
Nov 6, 2024
Status verified
Jun 2025
Primary completion
Nov 22, 2027
Completion
Aug 19, 2028

Study Design

Enrollment
200 participants (estimated)

Arms

  • Arm: STP >=30%
    STP score is 30% or greater than 30% in whole lung at baseline inspirational HRCT scan. STP score is an AL/ML 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.
  • Arm: STP < 30%
    STP score is less than 30% in whole lung at baseline inspirational HRCT scan.

Primary Outcome Measure

Progression Free Survival (PFS) between the two arms by Single Time point Prediction (STP) score [ Time Frame: From date of randomization until the date of first documented progression or date of death from any cause, whichever came first, assessed up to 2 years ]

Central Contacts

Locations (1)

FacilityCityStateZIPSite coordinators
UCLALos AngelesCalifornia90024
Samuel Weigt, MD

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By research site
UCLA· Los Angeles, CA

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