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

Maastricht University300 enrolled

Overview

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 Type

OBSERVATIONAL

Enrollment

300

Conditions

Interstitial Lung Disease Idiopathic Pulmonary Fibrosis Usual Interstitial Pneumonia

Interventions

radiomicsDIAGNOSTIC_TEST

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

Eligibility

Sex: ALLMin age: 18 Years

Medical Language ↔ Plain English

Inclusion Criteria: * UIP with final diagnosis in biopsy * ILDs with final diagnosis in biopsy Exclusion Criteria: * patients with no biopsy confirmation

Locations (1)

Maastricht University

Maastricht, Limburg, Netherlands

Outcomes

Primary Outcomes

IPF classifier

Model based on Radiomic that can differentiate IPF from ILDs.

Time frame: Up to 30 weeks

Data from ClinicalTrials.gov

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