xml: <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/NCT06016335</url>
    </required_header>
    <id_info>
      <org_study_id>2022.0234</org_study_id>
      <secondary_id>NL80426.029.22</secondary_id>
      <nct_id>NCT06016335</nct_id>
    </id_info>
    <brief_title>MRI-based Synthetic CT Images of the Head and Neck</brief_title>
    <official_title>Artificial Intelligence Driven Synthetic CT to Substitute CT Scans of the Head and Neck Region</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Amsterdam UMC, location VUmc</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
      <collaborator>
        <agency>MRIguidance B.V.</agency>
        <agency_class>Industry</agency_class>
      </collaborator>
    </sponsors>
    <source>Amsterdam UMC, location VUmc</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>
        In case of surgical procedures in the head and neck region, MRI in combination with CT of the&#xD;
        bone is often the standard modality to visualise bony landmarks for planning, navigation and&#xD;
        risk assessment. An important downside of a CT scan is the associated radiation exposure,&#xD;
        especially in children. An additional downside is the sedation or general anaesthesia needed&#xD;
        for both the MRI and CT scan session in very young children. These downsides could be removed&#xD;
        if the CT scan can be substituted by an MRI sequence that can provide the same information as&#xD;
        CT. This project aims to determine the feasibility of recreating CT like images of the&#xD;
        craniofacial bones from MRI images using machine learning techniques.&#xD;
      </textblock>
    </brief_summary>
    <overall_status>Enrolling by invitation</overall_status>
    <start_date type="Actual">September 22, 2022</start_date>
    <completion_date type="Anticipated">December 2023</completion_date>
    <primary_completion_date type="Anticipated">December 2023</primary_completion_date>
    <phase>N/A</phase>
    <study_type>Interventional</study_type>
    <has_expanded_access>No</has_expanded_access>
    <study_design_info>
      <allocation>Non-Randomized</allocation>
      <intervention_model>Parallel Assignment</intervention_model>
      <intervention_model_description>For all participants the same research activities will be performed (namely CT and MRI). The resulting paired MRI and CT scans will then be divided into a training set and a test set.</intervention_model_description>
      <primary_purpose>Diagnostic</primary_purpose>
      <masking>None (Open Label)</masking>
    </study_design_info>
    <primary_outcome>
      <measure>Geometrical accuracy.</measure>
      <time_frame>Within one year after scans have been obtained.</time_frame>
      <description>Geometrical accuracy of the bone morphology by determining the mean surface distance in mm between the cortical edges on synthetic CT and on true CT.</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Radiodensity accuracy.</measure>
      <time_frame>Within one year after scans have been obtained.</time_frame>
      <description>Accuracy of the voxelwise radiodensity in Hounsfield Units and accuracy of the radiodensity contrast.</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Visibility of landmarks.</measure>
      <time_frame>Within one year after scans have been obtained.</time_frame>
      <description>Accuracy of the visibility of clinically relevant anatomical landmarks on the synthetic CT images compared to the corresponding true CT images in the adult population, rated by experienced physicians on a 4-point Likert scale (1 = not visible, 4 = very well visible).</description>
    </primary_outcome>
    <secondary_outcome>
      <measure>Usefulness.</measure>
      <time_frame>Within one year after scans have been obtained.</time_frame>
      <description>Evaluation of potential usefulness of the synthetic CT images for surgical planning, surgical navigation and diagnostic purposes, as evaluated by experienced physicians and dichotomised into &quot;useful&quot; or &quot;not useful&quot;.</description>
    </secondary_outcome>
    <number_of_arms>2</number_of_arms>
    <enrollment type="Anticipated">80</enrollment>
    <condition>Imaging of Bony Structures of the Head (Various Conditions)</condition>
    <condition>Hearing Loss</condition>
    <condition>Cholesteatoma</condition>
    <condition>Sinusitis</condition>
    <condition>Head and Neck Tumor</condition>
    <arm_group>
      <arm_group_label>Training</arm_group_label>
      <arm_group_type>Other</arm_group_type>
      <description>Data from 25-35 participants will be used to train an algorithm to generate synthetic CT images from MRI scans.</description>
    </arm_group>
    <arm_group>
      <arm_group_label>Testing</arm_group_label>
      <arm_group_type>Other</arm_group_type>
      <description>Data from remaining participants will be used to test the synthetic CT algorithm, by comparing true CT scans to synthetic CT scans made from MRI.</description>
    </arm_group>
    <intervention>
      <intervention_type>Diagnostic Test</intervention_type>
      <intervention_name>CT scan</intervention_name>
      <description>Participants receive a CT scan of the head as part of their regular care. A larger part of the head will be scanned than for standard care.</description>
      <arm_group_label>Testing</arm_group_label>
      <arm_group_label>Training</arm_group_label>
    </intervention>
    <intervention>
      <intervention_type>Diagnostic Test</intervention_type>
      <intervention_name>MRI scan</intervention_name>
      <description>Participants receive an MRI scan, specifically for the purpose of the study.</description>
      <arm_group_label>Testing</arm_group_label>
      <arm_group_label>Training</arm_group_label>
    </intervention>
    <intervention>
      <intervention_type>Other</intervention_type>
      <intervention_name>Synthetic CT scan</intervention_name>
      <description>Synthetic CT scans will be generated from MRI scans, using the trained machine learning algorithm.</description>
      <arm_group_label>Testing</arm_group_label>
    </intervention>
    <eligibility>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  Patients from the outpatient ENT (Ear, Nose, Throat)-clinic.&#xD;
  &#xD;
            -  Aged 18 years or older.&#xD;
  &#xD;
            -  Referred for CT scan of the mastoid, sinonasal complex or face.&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  Pregnancy.&#xD;
  &#xD;
            -  Contra-indications for MRI or CT.&#xD;
  &#xD;
            -  Unwillingness to be informed about possibly clinically relevant, incidental findings&#xD;
               from the MRI examination.&#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>Paul Merkus, MD PhD</last_name>
      <role>Principal Investigator</role>
      <affiliation>Amsterdam UMC, location VUmc</affiliation>
    </overall_official>
    <location>
      <facility>
        <name>Amsterdam University Medical Center</name>
        <address>
          <city>Amsterdam</city>
          <country>Netherlands</country>
        </address>
      </facility>
    </location>
    <location_countries>
      <country>Netherlands</country>
    </location_countries>
    <verification_date>August 2023</verification_date>
    <study_first_submitted>August 7, 2023</study_first_submitted>
    <study_first_submitted_qc>August 25, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">August 29, 2023</study_first_posted>
    <last_update_submitted>August 25, 2023</last_update_submitted>
    <last_update_submitted_qc>August 25, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">August 29, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Principal Investigator</responsible_party_type>
      <investigator_affiliation>Amsterdam UMC, location VUmc</investigator_affiliation>
      <investigator_full_name>Paul Merkus</investigator_full_name>
      <investigator_title>Prof. Dr.</investigator_title>
    </responsible_party>
    <keyword>MRI</keyword>
    <keyword>Synthetic CT</keyword>
    <keyword>Artificial Intelligence</keyword>
    <keyword>Craniofacial</keyword>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Sinusitis</mesh_term>
      <mesh_term>Head and Neck Neoplasms</mesh_term>
      <mesh_term>Hearing Loss</mesh_term>
      <mesh_term>Cholesteatoma</mesh_term>
    </condition_browse>
    <patient_data>
      <sharing_ipd>No</sharing_ipd>
    </patient_data>
    <!-- Results have not yet been posted for this study                                          -->
  </clinical_study>
  
  
text: 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/NCT06016335
  org study id: 2022.0234
  secondary id: NL80426.029.22
  nct id: NCT06016335
  lead sponsor: 
  collaborator: 
  has dmc: No
  is fda regulated drug: No
  is fda regulated device: No
  In case of surgical procedures in the head and neck region, MRI in combination with CT of the
        bone is often the standard modality to visualise bony landmarks for planning, navigation and
        risk assessment. An important downside of a CT scan is the associated radiation exposure,
        especially in children. An additional downside is the sedation or general anaesthesia needed
        for both the MRI and CT scan session in very young children. These downsides could be removed
        if the CT scan can be substituted by an MRI sequence that can provide the same information as
        CT. This project aims to determine the feasibility of recreating CT like images of the
        craniofacial bones from MRI images using machine learning techniques.
  allocation: Non-Randomized
  intervention model: Parallel Assignment
  intervention model description: For all participants the same research activities will be performed (namely CT and MRI). The resulting paired MRI and CT scans will then be divided into a training set and a test set.
  primary purpose: Diagnostic
  masking: None (Open Label)
  measure: Geometrical accuracy.
  time frame: Within one year after scans have been obtained.
  description: Geometrical accuracy of the bone morphology by determining the mean surface distance in mm between the cortical edges on synthetic CT and on true CT.
  measure: Radiodensity accuracy.
  time frame: Within one year after scans have been obtained.
  description: Accuracy of the voxelwise radiodensity in Hounsfield Units and accuracy of the radiodensity contrast.
  measure: Visibility of landmarks.
  time frame: Within one year after scans have been obtained.
  description: Accuracy of the visibility of clinically relevant anatomical landmarks on the synthetic CT images compared to the corresponding true CT images in the adult population, rated by experienced physicians on a 4-point Likert scale (1 = not visible, 4 = very well visible).
  measure: Usefulness.
  time frame: Within one year after scans have been obtained.
  description: Evaluation of potential usefulness of the synthetic CT images for surgical planning, surgical navigation and diagnostic purposes, as evaluated by experienced physicians and dichotomised into "useful" or "not useful".
  arm group label: Training
  arm group type: Other
  description: Data from 25-35 participants will be used to train an algorithm to generate synthetic CT images from MRI scans.
  arm group label: Testing
  arm group type: Other
  description: Data from remaining participants will be used to test the synthetic CT algorithm, by comparing true CT scans to synthetic CT scans made from MRI.
  intervention type: Diagnostic Test
  intervention name: CT scan
  description: Participants receive a CT scan of the head as part of their regular care. A larger part of the head will be scanned than for standard care.
  arm group label: Testing
  arm group label: Training
  intervention type: Diagnostic Test
  intervention name: MRI scan
  description: Participants receive an MRI scan, specifically for the purpose of the study.
  arm group label: Testing
  arm group label: Training
  intervention type: Other
  intervention name: Synthetic CT scan
  description: Synthetic CT scans will be generated from MRI scans, using the trained machine learning algorithm.
  arm group label: Testing
  criteria: 
  gender: All
  minimum age: 18 Years
  maximum age: N/A
  healthy volunteers: No
  last name: Paul Merkus, MD PhD
  role: Principal Investigator
  affiliation: Amsterdam UMC, location VUmc
  facility: 
  country: Netherlands
  responsible party type: Principal Investigator
  investigator affiliation: Amsterdam UMC, location VUmc
  investigator full name: Paul Merkus
  investigator title: Prof. Dr.
  mesh term: Sinusitis
  mesh term: Head and Neck Neoplasms
  mesh term: Hearing Loss
  mesh term: Cholesteatoma
  sharing ipd: No