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

Completed

• Conditions: Interstitial Lung Disease; Usual Interstitial Pneumonia; Idiopathic Pulmonary Fibrosis
• Interventions: Diagnostic Test: radiomics

• Registration Number: NCT04430491
• Lead Sponsor: Maastricht University

Brief Summary

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

Not available

Study Timeline

• Study Start: 2005-01-01
• Primary Completion: 2017-01-01
• Study Completion: 2017-07-01
• Status Verified: 2020-05
• Study First Submitted: 2020-05-01
• Study First Submitted QC: 2020-06-11
• Last Update Submitted: 2020-06-11
• Study First Posted: 2020-06-12
• Last Update Posted: 2020-06-12

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 300

Inclusion Criteria

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

Exclusion Criteria

• patients with no biopsy confirmation

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Training dataset	radiomics	No interventions
Validation dataset	radiomics	No interventions

Primary Outcome Measures

Name	Time	Method
IPF classifier	Up to 30 weeks	Model based on Radiomic that can differentiate IPF from ILDs.

Trial Locations

Locations (1)

Maastricht University; 🇳🇱 Maastricht, Limburg, Netherlands