An In Silico Trial to Evaluate Prospectively the Performance of a Radiomics Algorithm for UIP Compared to Medical Doctors
NCT ID: NCT05784207
Last Updated: 2023-03-24
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
145 participants
OBSERVATIONAL
2017-06-01
2021-12-30
Brief Summary
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Detailed Description
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Conditions
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Study Design
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COHORT
RETROSPECTIVE
Study Groups
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IPF/UIP_CT based
patients with an ILD and a pathological UIP pattern and a final diagnosis of IPF
Radiomics model to classify between IPF with UIP pattern and ILDs without UIP pattern
The aim is to evaluate the performance of AI with the performance of doctors
IPF/UIP_Biopsy based
patients with a final diagnosis of IPF but a less typical HRCT pattern( lung biopsy required for the diagnosis)
Radiomics model to classify between IPF with UIP pattern and ILDs without UIP pattern
The aim is to evaluate the performance of AI with the performance of doctors
ILD but not IPF and prove by biopsy not UIP
patients with an ILD and a pathological non-UIP pattern
Radiomics model to classify between IPF with UIP pattern and ILDs without UIP pattern
The aim is to evaluate the performance of AI with the performance of doctors
Normal
Normal healthy patients
Radiomics model to classify between IPF with UIP pattern and ILDs without UIP pattern
The aim is to evaluate the performance of AI with the performance of doctors
Interventions
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Radiomics model to classify between IPF with UIP pattern and ILDs without UIP pattern
The aim is to evaluate the performance of AI with the performance of doctors
Eligibility Criteria
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Inclusion Criteria
Exclusion Criteria
* images containing metal or motion artifacts
* Images reconstructed with a slice thickness larger than 1.5 mm
ALL
Yes
Sponsors
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Maastricht University
OTHER
Responsible Party
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Locations
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Maastricht University
Maastricht, Limburg, Netherlands
Countries
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Other Identifiers
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ISTRU
Identifier Type: -
Identifier Source: org_study_id
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