<clinical_study>
  <!-- This xml conforms to an XML Schema at:
    https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
  <required_header>
    <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
    <link_text>Link to the current ClinicalTrials.gov record.</link_text>
    <url>https://clinicaltrials.gov/ct2/show/NCT04430491</url>
  </required_header>
  <id_info>
    <org_study_id>RESPIRE_2019</org_study_id>
    <nct_id>NCT04430491</nct_id>
  </id_info>
  <brief_title>To Evaluate the Use of Radiomics to Classify Between Idiopathic Pulmonary Fibrosis and Interstitial Lung Disease</brief_title>
  <official_title>Multi-center Validation of a Radiomics Based Model for the Diagnosis of Idiopathic Pulmonary Fibrosis</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Maastricht University</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>Université Libre de Bruxelles</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>Maastricht University</source>
  <oversight_info>
    <has_dmc>Yes</has_dmc>
    <is_fda_regulated_drug>No</is_fda_regulated_drug>
    <is_fda_regulated_device>No</is_fda_regulated_device>
  </oversight_info>
  <brief_summary>
    <textblock>
      To investigate the ability of machine learning models based on radiomic features extracted&#xD;
      from thin-section CT images to differentiate IPF patients from non-IPF interstitial lung&#xD;
      diseases.&#xD;
    </textblock>
  </brief_summary>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">January 1, 2005</start_date>
  <completion_date type="Actual">July 1, 2017</completion_date>
  <primary_completion_date type="Actual">January 1, 2017</primary_completion_date>
  <study_type>Observational</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <observational_model>Cohort</observational_model>
    <time_perspective>Retrospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>IPF classifier</measure>
    <time_frame>Up to 30 weeks</time_frame>
    <description>Model based on Radiomic that can differentiate IPF from ILDs.</description>
  </primary_outcome>
  <number_of_groups>2</number_of_groups>
  <enrollment type="Actual">300</enrollment>
  <condition>Interstitial Lung Disease</condition>
  <condition>Idiopathic Pulmonary Fibrosis</condition>
  <condition>Usual Interstitial Pneumonia</condition>
  <arm_group>
    <arm_group_label>Training dataset</arm_group_label>
    <description>No interventions</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Validation dataset</arm_group_label>
    <description>No interventions</description>
  </arm_group>
  <intervention>
    <intervention_type>Diagnostic Test</intervention_type>
    <intervention_name>radiomics</intervention_name>
    <description>The high-throughput extraction of large amounts of quantitative image features from medical images</description>
    <arm_group_label>Training dataset</arm_group_label>
    <arm_group_label>Validation dataset</arm_group_label>
  </intervention>
  <eligibility>
    <study_pop>
      <textblock>
        Patients referred to the hospital for lung disease. Two cohorts were included. One from&#xD;
        Belgium, and a second one from the US.&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  UIP with final diagnosis in biopsy&#xD;
&#xD;
          -  ILDs with final diagnosis in biopsy&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  patients with no biopsy confirmation&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <location>
    <facility>
      <name>Maastricht University</name>
      <address>
        <city>Maastricht</city>
        <state>Limburg</state>
        <zip>6229ER</zip>
        <country>Netherlands</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>Netherlands</country>
  </location_countries>
  <verification_date>May 2020</verification_date>
  <study_first_submitted>May 1, 2020</study_first_submitted>
  <study_first_submitted_qc>June 11, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">June 12, 2020</study_first_posted>
  <last_update_submitted>June 11, 2020</last_update_submitted>
  <last_update_submitted_qc>June 11, 2020</last_update_submitted_qc>
  <last_update_posted type="Actual">June 12, 2020</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <keyword>Lung</keyword>
  <keyword>CT</keyword>
  <keyword>UIP</keyword>
  <keyword>IPF</keyword>
  <keyword>ILDs</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Lung Diseases</mesh_term>
    <mesh_term>Pulmonary Fibrosis</mesh_term>
    <mesh_term>Idiopathic Pulmonary Fibrosis</mesh_term>
    <mesh_term>Lung Diseases, Interstitial</mesh_term>
    <mesh_term>Fibrosis</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>Undecided</sharing_ipd>
  </patient_data>
  <!-- Results have not yet been posted for this study                                          -->
</clinical_study>


download date: ClinicalTrials.gov processed this data on September 20, 2023
link text: Link to the current ClinicalTrials.gov record.
url: https://clinicaltrials.gov/ct2/show/NCT04430491
org study id: RESPIRE_2019
nct id: NCT04430491
lead sponsor: 
collaborator: 
has dmc: Yes
is fda regulated drug: No
is fda regulated device: No
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.
observational model: Cohort
time perspective: Retrospective
measure: IPF classifier
time frame: Up to 30 weeks
description: Model based on Radiomic that can differentiate IPF from ILDs.
arm group label: Training dataset
description: No interventions
arm group label: Validation dataset
description: No interventions
intervention type: Diagnostic Test
intervention name: radiomics
description: The high-throughput extraction of large amounts of quantitative image features from medical images
arm group label: Training dataset
arm group label: Validation dataset
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
healthy volunteers: No
facility: 
country: Netherlands
responsible party type: Sponsor
mesh term: Lung Diseases
mesh term: Pulmonary Fibrosis
mesh term: Idiopathic Pulmonary Fibrosis
mesh term: Lung Diseases, Interstitial
mesh term: Fibrosis
sharing ipd: Undecided