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/NCT06037343</url>
    </required_header>
    <id_info>
      <org_study_id>2023PI111</org_study_id>
      <nct_id>NCT06037343</nct_id>
    </id_info>
    <brief_title>Deep Learning Image Reconstruction for Abdominal CT of Hepatocellular Carcinoma Compared With 3-TESLA MRI</brief_title>
    <official_title>Deep Learning Image Reconstruction for Abdominal CT of Hepatocellular Carcinoma Compared With 3-TESLA MRI</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Central Hospital, Nancy, France</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
    </sponsors>
    <source>Central Hospital, Nancy, France</source>
    <oversight_info>
      <is_fda_regulated_drug>No</is_fda_regulated_drug>
      <is_fda_regulated_device>No</is_fda_regulated_device>
    </oversight_info>
    <brief_summary>
      <textblock>
        New algorithms for processing CT acquisitions, based on artificial intelligence, have been&#xD;
        reported to improve acquisition quality. Thats' why it's possible to imagine that new scan&#xD;
        post-processing algorithms enable better detection and characterization of hepatocellular&#xD;
        carcinoma lesions than with standard reconstructions. DLIR reconstructions could even match&#xD;
        with MRI detection.&#xD;
  &#xD;
        The aim of the study is to compare the detection and characterization of hepatic lesions&#xD;
        according to the LI-RADS classification in CT with DLIR artificial intelligence&#xD;
        reconstruction, compared with ASIR-V reconstruction and the gold standard of MRI.&#xD;
      </textblock>
    </brief_summary>
    <overall_status>Recruiting</overall_status>
    <start_date type="Actual">February 23, 2023</start_date>
    <completion_date type="Anticipated">October 30, 2023</completion_date>
    <primary_completion_date type="Actual">February 23, 2023</primary_completion_date>
    <study_type>Observational</study_type>
    <has_expanded_access>No</has_expanded_access>
    <study_design_info>
      <observational_model>Case-Only</observational_model>
      <time_perspective>Retrospective</time_perspective>
    </study_design_info>
    <primary_outcome>
      <measure>Lesion size in mm</measure>
      <time_frame>Through study completion, an average of 1 year</time_frame>
    </primary_outcome>
    <primary_outcome>
      <measure>Hypervascular appearance of lesion</measure>
      <time_frame>Through study completion an average of 1 year</time_frame>
      <description>Qualitative measurement: presence or absence of hypervascular lesion</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Hypervascular capsule</measure>
      <time_frame>Through study completion an average of 1 year</time_frame>
      <description>Qualitative measurement : presence or absence of hypervascular capsule</description>
    </primary_outcome>
    <secondary_outcome>
      <measure>Infiltrative nature Classification of the hepatic lesion by LI-RADS in MRI</measure>
      <time_frame>Through study completion an average of 1 year</time_frame>
    </secondary_outcome>
    <enrollment type="Anticipated">50</enrollment>
    <condition>Hepatocellular Carcinoma</condition>
    <eligibility>
      <study_pop>
        <textblock>
          All adults undergoing a CT and a MRI for detection or following of an HCC during the same&#xD;
          week&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Non-Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  undergoing CT and MRI scans in the same week, with protocols dedicated to the&#xD;
               detection of HCC lesions&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  imaging with radiological artefact&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>18 Years</minimum_age>
      <maximum_age>N/A</maximum_age>
    </eligibility>
    <overall_contact>
      <last_name>Valérie LAURENT, MD, PhD</last_name>
      <phone>+33383154181</phone>
      <email>v.laurent@chru-nancy.fr</email>
    </overall_contact>
    <location>
      <facility>
        <name>CHRU de Nancy</name>
        <address>
          <city>Nancy</city>
          <zip>5400</zip>
          <country>France</country>
        </address>
      </facility>
      <status>Recruiting</status>
      <contact>
        <last_name>Valerie Laurent, Md, PhD</last_name>
      </contact>
    </location>
    <location_countries>
      <country>France</country>
    </location_countries>
    <verification_date>September 2023</verification_date>
    <study_first_submitted>August 31, 2023</study_first_submitted>
    <study_first_submitted_qc>September 13, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">September 14, 2023</study_first_posted>
    <last_update_submitted>September 13, 2023</last_update_submitted>
    <last_update_submitted_qc>September 13, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">September 15, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Principal Investigator</responsible_party_type>
      <investigator_affiliation>Central Hospital, Nancy, France</investigator_affiliation>
      <investigator_full_name>Valérie LAURENT</investigator_full_name>
      <investigator_title>Principal Investigator</investigator_title>
    </responsible_party>
    <keyword>Hepatocellular carcinoma</keyword>
    <keyword>DLIR CT</keyword>
    <keyword>ASIR reconstruction</keyword>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Carcinoma</mesh_term>
      <mesh_term>Carcinoma, Hepatocellular</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/NCT06037343
  org study id: 2023PI111
  nct id: NCT06037343
  lead sponsor: 
  is fda regulated drug: No
  is fda regulated device: No
  New algorithms for processing CT acquisitions, based on artificial intelligence, have been
        reported to improve acquisition quality. Thats' why it's possible to imagine that new scan
        post-processing algorithms enable better detection and characterization of hepatocellular
        carcinoma lesions than with standard reconstructions. DLIR reconstructions could even match
        with MRI detection.
  
        The aim of the study is to compare the detection and characterization of hepatic lesions
        according to the LI-RADS classification in CT with DLIR artificial intelligence
        reconstruction, compared with ASIR-V reconstruction and the gold standard of MRI.
  observational model: Case-Only
  time perspective: Retrospective
  measure: Lesion size in mm
  time frame: Through study completion, an average of 1 year
  measure: Hypervascular appearance of lesion
  time frame: Through study completion an average of 1 year
  description: Qualitative measurement: presence or absence of hypervascular lesion
  measure: Hypervascular capsule
  time frame: Through study completion an average of 1 year
  description: Qualitative measurement : presence or absence of hypervascular capsule
  measure: Infiltrative nature Classification of the hepatic lesion by LI-RADS in MRI
  time frame: Through study completion an average of 1 year
  study pop: 
  sampling method: Non-Probability Sample
  criteria: 
  gender: All
  minimum age: 18 Years
  maximum age: N/A
  last name: Valérie LAURENT, MD, PhD
  phone: +33383154181
  email: v.laurent@chru-nancy.fr
  facility: 
  status: Recruiting
  contact: 
  country: France
  responsible party type: Principal Investigator
  investigator affiliation: Central Hospital, Nancy, France
  investigator full name: Valérie LAURENT
  investigator title: Principal Investigator
  mesh term: Carcinoma
  mesh term: Carcinoma, Hepatocellular
  sharing ipd: No