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

NCT ID: NCT04430491

Last Updated: 2020-06-12

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

COMPLETED

Total Enrollment

300 participants

Study Classification

OBSERVATIONAL

Study Start Date

2005-01-01

Study Completion Date

2017-07-01

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

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.

Detailed Description

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

Conditions

See the medical conditions and disease areas that this research is targeting or investigating.

Interstitial Lung Disease Idiopathic Pulmonary Fibrosis Usual Interstitial Pneumonia

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Study Groups

Review each arm or cohort in the study, along with the interventions and objectives associated with them.

Training dataset

No interventions

radiomics

Intervention Type DIAGNOSTIC_TEST

The high-throughput extraction of large amounts of quantitative image features from medical images

Validation dataset

No interventions

radiomics

Intervention Type DIAGNOSTIC_TEST

The high-throughput extraction of large amounts of quantitative image features from medical images

Interventions

Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.

radiomics

The high-throughput extraction of large amounts of quantitative image features from medical images

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.

Inclusion Criteria

* UIP with final diagnosis in biopsy
* ILDs with final diagnosis in biopsy

Exclusion Criteria

* patients with no biopsy confirmation
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

Meet the organizations funding or collaborating on the study and learn about their roles.

Université Libre de Bruxelles

OTHER

Sponsor Role collaborator

Maastricht University

OTHER

Sponsor Role lead

Responsible Party

Identify the individual or organization who holds primary responsibility for the study information submitted to regulators.

Responsibility Role SPONSOR

Locations

Explore where the study is taking place and check the recruitment status at each participating site.

Maastricht University

Maastricht, Limburg, Netherlands

Site Status

Countries

Review the countries where the study has at least one active or historical site.

Netherlands

Other Identifiers

Review additional registry numbers or institutional identifiers associated with this trial.

RESPIRE_2019

Identifier Type: -

Identifier Source: org_study_id

More Related Trials

Additional clinical trials that may be relevant based on similarity analysis.

Genentech Xenon MRI Idiopathic Pulmonary Fibrosis
NCT04071769 ACTIVE_NOT_RECRUITING PHASE2
Image and Model Based Analysis of Lung Disease
NCT03764163 COMPLETED EARLY_PHASE1
Genentech Validation Tool for Pulmonary Fibrosis
NCT04676594 ACTIVE_NOT_RECRUITING
129 Xenon MRI in Chronic Lung Disease
NCT02723500 RECRUITING NA
Functional Applications of Hyperpolarized 129Xe MRI
NCT01697332 TERMINATED PHASE1/PHASE2