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| Name | Class |
|---|---|
| Université Libre de Bruxelles | OTHER |
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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Training dataset | No interventions |
| |
| Validation dataset | No interventions |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| radiomics | Diagnostic Test | The high-throughput extraction of large amounts of quantitative image features from medical images |
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| Measure | Description | Time Frame |
|---|---|---|
| IPF classifier | Model based on Radiomic that can differentiate IPF from ILDs. | Up to 30 weeks |
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Inclusion Criteria:
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Patients referred to the hospital for lung disease. Two cohorts were included. One from Belgium, and a second one from the US.
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Maastricht University | Maastricht | Limburg | 6229ER | Netherlands |
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| ID | Term |
|---|---|
| D017563 | Lung Diseases, Interstitial |
| D054990 | Idiopathic Pulmonary Fibrosis |
| ID | Term |
|---|---|
| D008171 | Lung Diseases |
| D012140 | Respiratory Tract Diseases |
| D011658 | Pulmonary Fibrosis |
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| ID | Term |
|---|---|
| D000097188 | Radiomics |
| ID | Term |
|---|---|
| D003952 | Diagnostic Imaging |
| D019937 | Diagnostic Techniques and Procedures |
| D003933 | Diagnosis |
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