<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/NCT03305380</url>
  </required_header>
  <id_info>
    <org_study_id>BMS Radiomics</org_study_id>
    <nct_id>NCT03305380</nct_id>
  </id_info>
  <brief_title>Radiomics to Identify Patients at Risk for Developing Pneumonitis, Differentiate Immune Checkpoint Inhibitor-induced Pneumonitis From Other Lung Inflammation and Distinguish Tumour Pseudo-progression From Real Tumour Growth</brief_title>
  <official_title>Radiomics to 1. Identify Patients at Risk for Developing Pneumonitis, 2. Differentiate Immune Checkpoint Inhibitor-induced Pneumonitis From Other Lung Inflammation and 3. Distinguish Tumour Pseudo-progression From Real Tumour Growth, in Patients With Non-small Cell Lung Cancer Treated With Anti-PD1 or Anti-PD-L1</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Maastricht Radiation Oncology</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>Maastricht University Medical Center</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>Zuyderland Medical Centre</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>Maastricht Radiation Oncology</source>
  <oversight_info>
    <has_dmc>No</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>
      The investigators will develop a radiomics signature for immune checkpoint-induced&#xD;
      pneumonitis in 40 patients with a pulmonary event under anti-PD1 or anti-PD-L1 (cases) and 40&#xD;
      patients without a pulmonary event under anti-PD1 or anti-PD-L1 (controls).&#xD;
&#xD;
      On the basis of the case-control study of patients treated with anti-PD1 or anti-PD-L1, they&#xD;
      will further optimise the model using reinforcement machine learning. The model will then be&#xD;
      validated in 300 prospective patients.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Preliminary analyses on a dataset showed a clear distinction in radiomics features for&#xD;
      patients with and without pneumonitis from anti-PD1 or anti-PD-L1. Prior experience of the&#xD;
      investigators of training and validating radiomics signatures combined with their preliminary&#xD;
      exploratory results presented here, will be used to develop a radiomics signature for immune&#xD;
      checkpoint-induced pneumonitis in 40 patients with a pulmonary event under anti-PD1 or&#xD;
      anti-PD-L1 (cases) and 40 patients without a pulmonary event under anti-PD1 or anti-PD-L1&#xD;
      (controls).&#xD;
&#xD;
      On the basis of the case-control study of patients treated with anti-PD1 or anti-PD-L1, the&#xD;
      investigators will be able to further optimise the model using reinforcement machine&#xD;
      learning. The model will then be validated in 300 prospective patients.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">September 1, 2017</start_date>
  <completion_date type="Actual">April 1, 2021</completion_date>
  <primary_completion_date type="Actual">January 1, 2021</primary_completion_date>
  <study_type>Observational [Patient Registry]</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <observational_model>Other</observational_model>
    <time_perspective>Other</time_perspective>
  </study_design_info>
  <target_duration>1 Day</target_duration>
  <primary_outcome>
    <measure>Cause of pneumonitis</measure>
    <time_frame>6 months</time_frame>
    <description>Determining cause of the pneumonitis by medical status of the patient</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>Predictive accuracy of radiomics for determining the cause of pneumonitis</measure>
    <time_frame>6 months</time_frame>
    <description>Three subgroups of immune checkpoint induced pneumonitis:&#xD;
Immune checkpoint-induced pneumonitis from tumour progression&#xD;
Immune checkpoint-induced pneumonitis from other types of pneumonitis&#xD;
Patients with interstitial lung disease that are at risk to develop immune checkpoint-induced pneumonitis and those who are not.&#xD;
Radiomics will be used to predict the cause of pneumonitis</description>
  </secondary_outcome>
  <number_of_groups>2</number_of_groups>
  <enrollment type="Actual">637</enrollment>
  <condition>Stage IV Non-small Cell Lung Cancer</condition>
  <arm_group>
    <arm_group_label>Patients with a pulmonary event</arm_group_label>
    <description>(under anti-PD1 or anti-PD-L1) This is the first group of the retrospective part of the study.</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Patients without a pulmonary event</arm_group_label>
    <description>(under anti-PD1 or anti-PD-L1) This is the second group of the retrospective part of the study.</description>
  </arm_group>
  <intervention>
    <intervention_type>Other</intervention_type>
    <intervention_name>No interventions</intervention_name>
    <description>As this is a patient registry, there are no interventions.</description>
    <arm_group_label>Patients with a pulmonary event</arm_group_label>
    <arm_group_label>Patients without a pulmonary event</arm_group_label>
  </intervention>
  <eligibility>
    <study_pop>
      <textblock>
        Patients who receive standard anti-PD1 or anti-PD-L1 treatment in routine clinical practice&#xD;
        for first or second line stage IV non-small cell lung cancer&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Patients who receive standard anti-PD1 or anti-PD-L1 treatment in routine clinical&#xD;
             practice for first or second line stage IV non-small cell lung cancer&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  The opposite of the above&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Dirk De Ruysscher, MD, PhD</last_name>
    <role>Principal Investigator</role>
    <affiliation>Maastro Clinic, The Netherlands</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>Zuyderland Medical Center</name>
      <address>
        <city>Heerlen</city>
        <zip>6419 PC</zip>
        <country>Netherlands</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>MUMC+</name>
      <address>
        <city>Maastricht</city>
        <zip>6229 HX</zip>
        <country>Netherlands</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>Netherlands</country>
  </location_countries>
  <verification_date>September 2021</verification_date>
  <study_first_submitted>September 21, 2017</study_first_submitted>
  <study_first_submitted_qc>October 3, 2017</study_first_submitted_qc>
  <study_first_posted type="Actual">October 10, 2017</study_first_posted>
  <last_update_submitted>September 15, 2021</last_update_submitted>
  <last_update_submitted_qc>September 15, 2021</last_update_submitted_qc>
  <last_update_posted type="Actual">September 16, 2021</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <keyword>anti-PD1</keyword>
  <keyword>anti-PD-L1</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Pneumonia</mesh_term>
    <mesh_term>Lung Neoplasms</mesh_term>
    <mesh_term>Carcinoma, Non-Small-Cell Lung</mesh_term>
    <mesh_term>Inflammation</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>No</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/NCT03305380
org study id: BMS Radiomics
nct id: NCT03305380
lead sponsor: 
collaborator: 
collaborator: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
The investigators will develop a radiomics signature for immune checkpoint-induced
      pneumonitis in 40 patients with a pulmonary event under anti-PD1 or anti-PD-L1 (cases) and 40
      patients without a pulmonary event under anti-PD1 or anti-PD-L1 (controls).

      On the basis of the case-control study of patients treated with anti-PD1 or anti-PD-L1, they
      will further optimise the model using reinforcement machine learning. The model will then be
      validated in 300 prospective patients.
Preliminary analyses on a dataset showed a clear distinction in radiomics features for
      patients with and without pneumonitis from anti-PD1 or anti-PD-L1. Prior experience of the
      investigators of training and validating radiomics signatures combined with their preliminary
      exploratory results presented here, will be used to develop a radiomics signature for immune
      checkpoint-induced pneumonitis in 40 patients with a pulmonary event under anti-PD1 or
      anti-PD-L1 (cases) and 40 patients without a pulmonary event under anti-PD1 or anti-PD-L1
      (controls).

      On the basis of the case-control study of patients treated with anti-PD1 or anti-PD-L1, the
      investigators will be able to further optimise the model using reinforcement machine
      learning. The model will then be validated in 300 prospective patients.
observational model: Other
time perspective: Other
measure: Cause of pneumonitis
time frame: 6 months
description: Determining cause of the pneumonitis by medical status of the patient
measure: Predictive accuracy of radiomics for determining the cause of pneumonitis
time frame: 6 months
description: Three subgroups of immune checkpoint induced pneumonitis:
Immune checkpoint-induced pneumonitis from tumour progression
Immune checkpoint-induced pneumonitis from other types of pneumonitis
Patients with interstitial lung disease that are at risk to develop immune checkpoint-induced pneumonitis and those who are not.
Radiomics will be used to predict the cause of pneumonitis
arm group label: Patients with a pulmonary event
description: (under anti-PD1 or anti-PD-L1) This is the first group of the retrospective part of the study.
arm group label: Patients without a pulmonary event
description: (under anti-PD1 or anti-PD-L1) This is the second group of the retrospective part of the study.
intervention type: Other
intervention name: No interventions
description: As this is a patient registry, there are no interventions.
arm group label: Patients with a pulmonary event
arm group label: Patients without a pulmonary event
study pop: 
sampling method: Probability Sample
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
healthy volunteers: No
last name: Dirk De Ruysscher, MD, PhD
role: Principal Investigator
affiliation: Maastro Clinic, The Netherlands
facility: 
facility: 
country: Netherlands
responsible party type: Sponsor
mesh term: Pneumonia
mesh term: Lung Neoplasms
mesh term: Carcinoma, Non-Small-Cell Lung
mesh term: Inflammation
sharing ipd: No