To Evaluate the Use of Radiomics to Classify Between Idiopathic Pulmonary Fibrosis and Interstitial Lung Disease

June 11, 2020 updated by: Maastricht University

Multi-center Validation of a Radiomics Based Model for the Diagnosis of Idiopathic Pulmonary Fibrosis

To investigate the ability of machine learning models based on radiomic features extracted from thin-section CT images to differentiate IPF patients from non-IPF interstitial lung diseases.

Study Overview

Status

Completed

Conditions

Intervention / Treatment

Study Type

Observational

Enrollment (Actual)

300

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Netherlands
    • Limburg
      • Maastricht, Limburg, Netherlands, 6229ER
        • Maastricht University

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.

Eligibility Criteria

Ages Eligible for Study

18 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Sampling Method

Non-Probability Sample

Study Population

Patients referred to the hospital for lung disease. Two cohorts were included. One from Belgium, and a second one from the US.

Description

Inclusion Criteria:

  • UIP with final diagnosis in biopsy
  • ILDs with final diagnosis in biopsy

Exclusion Criteria:

  • patients with no biopsy confirmation

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Observational Models: Cohort
  • Time Perspectives: Retrospective

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Training dataset
No interventions
Diagnostic test: radiomics
The high-throughput extraction of large amounts of quantitative image features from medical images
Validation dataset
No interventions
Diagnostic test: radiomics
The high-throughput extraction of large amounts of quantitative image features from medical images

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
IPF classifier
Time Frame: Up to 30 weeks
Model based on Radiomic that can differentiate IPF from ILDs.
Up to 30 weeks

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Maastricht University

Collaborators

Université Libre de Bruxelles

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 Major Dates

Study Start (Actual)

January 1, 2005

Primary Completion (Actual)

January 1, 2017

Study Completion (Actual)

July 1, 2017

Study Registration Dates

First Submitted

May 1, 2020

First Submitted That Met QC Criteria

June 11, 2020

First Posted (Actual)

June 12, 2020

Study Record Updates

Last Update Posted (Actual)

June 12, 2020

Last Update Submitted That Met QC Criteria

June 11, 2020

Last Verified

May 1, 2020

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • RESPIRE_2019

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

Undecided

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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