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

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

Status

Completed

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT04430491

Enrollment

300

Registered

2020-06-12

Start date

2005-01-01

Completion date

2017-07-01

Last updated

2020-06-12

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Idiopathic Pulmonary Fibrosis, Interstitial Lung Disease, Usual Interstitial Pneumonia

Keywords

Lung, CT, UIP, IPF, ILDs

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.

Interventions

DIAGNOSTIC_TESTradiomics

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

Study design

Observational model

COHORT

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to No maximum

Healthy volunteers

Inclusion criteria

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

Exclusion criteria

* patients with no biopsy confirmation

Design outcomes

Primary

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

Countries

Netherlands

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026