<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/NCT04409340</url>
  </required_header>
  <id_info>
    <org_study_id>2021-8561</org_study_id>
    <nct_id>NCT04409340</nct_id>
  </id_info>
  <brief_title>Viscoelasticity Imaging to Assess Liver Cancer</brief_title>
  <acronym>VisCan</acronym>
  <official_title>Added Value of Shear Wave Viscoelasticity Imaging, Homodyned-K Tissue Imaging and Acoustic Attenuation to Assess Liver Cancer at Ultrasound: a Multiparametric Learning Approach</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Centre hospitalier de l'Université de Montréal (CHUM)</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>Siemens Corporation, Corporate Technology</agency>
      <agency_class>Industry</agency_class>
    </collaborator>
    <collaborator>
      <agency>Fonds de recherche du Québec</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>Centre de Recherche du Centre Hospitalier de l'Université de Montréal</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>Centre hospitalier de l'Université de Montréal (CHUM)</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>
      Ultrasound (US) used for hepatocellular carcinoma (HCC) surveillance suffers from low&#xD;
      sensitivity (60-78%) due to fatty liver, obesity, and diffusely nodular appearance in&#xD;
      cirrhosis. Once a suspicious malignant lesion is detected at US, guidelines recommend&#xD;
      contrast-enhanced US, magnetic resonance imaging (MRI) or computed tomography (CT) scans to&#xD;
      confirm suspicion. The investigators' team has developed innovative quantitative US (QUS)&#xD;
      techniques that have a high potential to improve tissue characterization in terms of&#xD;
      sensitivity and specificity. The investigators hypothesize that advanced QUS providing tumor&#xD;
      viscoelasticity assessment, sub-resolution tissue structure characterization and US&#xD;
      attenuation in the framework of a machine learning classification model can improve HCC&#xD;
      diagnosis compared with standard US.&#xD;
&#xD;
      Early detection through systematic US surveillance translates into curative therapy in a&#xD;
      higher proportion of patients and into improvements in survival rates. Thus, there is an&#xD;
      urgent need to investigate innovative and cost-effective imaging techniques for improving&#xD;
      detection and characterization of HCC. The proposed QUS methods are experimental and will be&#xD;
      validated in this proof-of-concept clinical study. A major impact of this work, for patients&#xD;
      and medical institutions, will be to improve early-stage detection and characterization of&#xD;
      HCC, and offer alternatives in patients with negative or inconclusive conventional US. QUS&#xD;
      are low-cost, non-invasive and non-irradiating imaging modalities available from a single&#xD;
      exam (i.e., no additional imaging session is necessary).&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      RESEARCH QUESTION AND BACKGROUND: Primary liver cancer or hepatocellular carcinoma (HCC) is&#xD;
      the fifth most common cancer in men and the seventh in women and is the second cause of&#xD;
      cancer mortality worldwide. In Canada, HCC is the only cancer for which mortality is&#xD;
      increasing. More than 80% of HCC cases occur in individuals with advanced liver fibrosis&#xD;
      (cirrhosis) due to viral hepatitis infection (B and C), non-alcoholic fatty liver disease&#xD;
      (NAFLD), and alcoholic liver disease. Once cirrhosis is established, there is a significantly&#xD;
      increased risk of developing HCC. Furthermore, HCC is observed in obese diabetic individuals&#xD;
      without cirrhosis, increasing the population of patients at risk with a disease that has high&#xD;
      fatality rate. HCC surveillance is associated with significantly prolonged survival. However,&#xD;
      only 52% of patients undergoing surveillance have early HCCs that are eligible for curative&#xD;
      treatment, whereas remainder of patients have intermediate- or advanced-stage disease&#xD;
      eligible for bridge or palliative treatment only. HCC surveillance is also associated with&#xD;
      significant improvements in early-stage detection, curative-treatment rates, and survival,&#xD;
      even after adjusting for lead-time bias. North American guidelines recommend ultrasound (US)&#xD;
      surveillance every 6 months in at-risk patients who are non-cirrhotic hepatitis B carriers&#xD;
      and cirrhotic. However, a key challenge for US is the low sensitivity (60-78%) for&#xD;
      identifying a lesion due to liver steatosis and cirrhosis. Once a suspicious malignant lesion&#xD;
      is detected at US, current American Association for the Study of Liver Diseases (AASLD)&#xD;
      guidelines recommend contrast-enhanced US, magnetic resonance imaging (MRI) or computed&#xD;
      tomography (CT) scans to confirm suspicion.&#xD;
&#xD;
      GOAL: The long-term reaching goal is to develop US biomarkers of focal liver lesions and&#xD;
      strategies to improve diagnostic sensitivity to HCC while maintaining a high specificity.&#xD;
      This would constitute a major breakthrough because HCC diagnosis currently requires a&#xD;
      combination of US for screening and confirmation using MRI, CT and less often biopsy.&#xD;
&#xD;
      OBJECTIVES: 1) Develop a machine learning model based on QUS for classification of solid&#xD;
      hepatocellular carcinomas identified at US and diagnosed with MRI (or biopsy if required); 2)&#xD;
      Determine if QUS maps can improve visual detection of suspected lesions at US; 3) Compare&#xD;
      performance of QUS- versus MRI-based viscoelastography for lesion characterization.&#xD;
&#xD;
      Hypothesis: the investigators hypothesize that advanced QUS providing tumor viscoelasticity&#xD;
      assessment, sub-resolution tissue structure characterization and US attenuation in the&#xD;
      framework of a machine learning classification model can improve HCC diagnosis compared with&#xD;
      standard US.&#xD;
&#xD;
      METHODOLOGY - Study design: This will be a clinical study with two sequential cohorts: 1) a&#xD;
      training cohort of 100 patients at risk for HCC to optimize QUS biomarkers for classification&#xD;
      of solid liver lesions using MRI and/or biopsy as gold standard clinical references; and 2) a&#xD;
      validation cohort of 100 patients to confirm diagnostic performance.&#xD;
&#xD;
      Data analysis: Random forests machine learning to develop QUS classification models.&#xD;
      Sensitivity and specificity to assess diagnostic accuracy, according to MRI and/or biopsy,&#xD;
      with bootstrapping to obtain confidence intervals with training set. Confirmation of accuracy&#xD;
      on test set. Inter-observer assessment of lesion detectability on clinical B-mode US versus&#xD;
      QUS maps. Comparison of US- and MRI-based elasticity and viscosity according to diagnostic&#xD;
      results.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Recruiting</overall_status>
  <start_date type="Actual">October 5, 2020</start_date>
  <completion_date type="Anticipated">December 22, 2023</completion_date>
  <primary_completion_date type="Anticipated">June 22, 2023</primary_completion_date>
  <study_type>Observational</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <observational_model>Other</observational_model>
    <time_perspective>Cross-Sectional</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>Liver viscoelastography determined by MRI</measure>
    <time_frame>Within 6 weeks of liver echography</time_frame>
    <description>Measure of liver viscoelastography using magnetic resonance Imaging (MRI)</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Liver viscoelastography determined by QUS</measure>
    <time_frame>Within 6 weeks of liver echography</time_frame>
    <description>Measure of liver viscoelastography using quantitative ultrasound (QUS).</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Detection of focal HCC lesions</measure>
    <time_frame>Within 6 weeks of liver echography</time_frame>
    <description>Detection of focal HCC lesions using quantitative ultrasound (QUS).</description>
  </primary_outcome>
  <number_of_groups>2</number_of_groups>
  <enrollment type="Anticipated">200</enrollment>
  <condition>Liver Cancer</condition>
  <arm_group>
    <arm_group_label>Training cohort</arm_group_label>
    <description>All patients enrolled will undergo:&#xD;
Magnetic Resonance Viscoelastography&#xD;
Quantitative ultrasound (QUS)</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Validation cohort</arm_group_label>
    <description>All patients enrolled will undergo:&#xD;
• Quantitative ultrasound (QUS)</description>
  </arm_group>
  <intervention>
    <intervention_type>Device</intervention_type>
    <intervention_name>Magnetic Resonance Viscoelastography</intervention_name>
    <description>Magnetic Resonance Viscoelastography</description>
    <arm_group_label>Training cohort</arm_group_label>
  </intervention>
  <intervention>
    <intervention_type>Device</intervention_type>
    <intervention_name>QUS</intervention_name>
    <description>QUS</description>
    <arm_group_label>Training cohort</arm_group_label>
    <arm_group_label>Validation cohort</arm_group_label>
  </intervention>
  <eligibility>
    <study_pop>
      <textblock>
        The investigators propose a cross-sectional prospective clinical study with two sequential&#xD;
        cohorts: 1) a training cohort of 100 patients at risk for HCC to optimize QUS biomarkers&#xD;
        for classification of solid liver lesions using MRI and/or biopsy as gold standard clinical&#xD;
        references; and 2) a validation cohort of 100 patients to confirm diagnostic performance.&#xD;
        The investigators will recruit patients seen at the hepatology clinic of the University of&#xD;
        Montreal Hospital (CHUM).&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Are at least 18 years old at screening;&#xD;
&#xD;
          -  Able to comprehend and willingness to provide voluntary consent;&#xD;
&#xD;
          -  Are able to have a MRI;&#xD;
&#xD;
          -  Understand French or English;&#xD;
&#xD;
          -  Patients enrolled in a monitoring program or referred for the characterization of a&#xD;
             focal liver lesion;&#xD;
&#xD;
          -  Focal liver lesion is visible during ultrasound screening in B-mode.&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  Are pregnant or trying to become pregnant;&#xD;
&#xD;
          -  Have a weight or girth preventing from entering the MR magnet bore;&#xD;
&#xD;
          -  Are unable to understand or unwilling to provide written informed consent for this&#xD;
             study;&#xD;
&#xD;
          -  Have a contraindication to MRI (pacemaker, insurmountable claustrophobia);&#xD;
&#xD;
          -  Have chronic kidney disease preventing the injection of gadolinium-based contrast&#xD;
             agent.&#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>Guy Cloutier, PhD</last_name>
    <role>Principal Investigator</role>
    <affiliation>Centre hospitalier de l'Université de Montréal (CHUM)</affiliation>
  </overall_official>
  <overall_official>
    <last_name>An Tang, MD, MSc</last_name>
    <role>Principal Investigator</role>
    <affiliation>Centre hospitalier de l'Université de Montréal (CHUM)</affiliation>
  </overall_official>
  <overall_contact>
    <last_name>Guy Cloutier, PhD</last_name>
    <phone>514-890-8000</phone>
    <phone_ext>24703</phone_ext>
    <email>guy.cloutier@umontreal.ca</email>
  </overall_contact>
  <overall_contact_backup>
    <last_name>Louise Allard, PhD</last_name>
    <phone>514-890-8000</phone>
    <phone_ext>24705</phone_ext>
    <email>louise.allard@crchum.qc.ca</email>
  </overall_contact_backup>
  <location>
    <facility>
      <name>Centre hospitalier de l'Université de Montréal (CHUM)</name>
      <address>
        <city>Montréal</city>
        <state>Quebec</state>
        <zip>H2X 0A9</zip>
        <country>Canada</country>
      </address>
    </facility>
    <status>Recruiting</status>
    <contact>
      <last_name>Guy Cloutier, PhD</last_name>
      <phone>514-890-8000</phone>
      <phone_ext>24703</phone_ext>
      <email>guy.cloutier@umontreal.ca</email>
    </contact>
    <contact_backup>
      <last_name>Louise Allard, PhD</last_name>
      <phone>514-890-8000</phone>
      <phone_ext>24705</phone_ext>
      <email>louise.allard@crchum.qc.ca</email>
    </contact_backup>
    <investigator>
      <last_name>Marc Bilodeau, MD</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Hélène Castel, MD</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Jeanne-Marie Giard, MD</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Bich Nguyen, MD</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Marie-Pierre Sylvestre, PhD</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Elijah Van Houten, PhD</last_name>
      <role>Sub-Investigator</role>
    </investigator>
  </location>
  <location_countries>
    <country>Canada</country>
  </location_countries>
  <verification_date>January 2023</verification_date>
  <study_first_submitted>May 27, 2020</study_first_submitted>
  <study_first_submitted_qc>May 27, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">June 1, 2020</study_first_posted>
  <last_update_submitted>January 30, 2023</last_update_submitted>
  <last_update_submitted_qc>January 30, 2023</last_update_submitted_qc>
  <last_update_posted type="Actual">February 1, 2023</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <keyword>quantitative ultrasound</keyword>
  <keyword>Viscoelasticity</keyword>
  <keyword>Homodyned-K imaging</keyword>
  <keyword>Liver cancer</keyword>
  <keyword>Viscoelastography</keyword>
  <keyword>Ultrasound attenuation</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Liver Neoplasms</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>Undecided</sharing_ipd>
    <ipd_description>plan undicided</ipd_description>
  </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/NCT04409340
org study id: 2021-8561
nct id: NCT04409340
lead sponsor: 
collaborator: 
collaborator: 
collaborator: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
Ultrasound (US) used for hepatocellular carcinoma (HCC) surveillance suffers from low
      sensitivity (60-78%) due to fatty liver, obesity, and diffusely nodular appearance in
      cirrhosis. Once a suspicious malignant lesion is detected at US, guidelines recommend
      contrast-enhanced US, magnetic resonance imaging (MRI) or computed tomography (CT) scans to
      confirm suspicion. The investigators' team has developed innovative quantitative US (QUS)
      techniques that have a high potential to improve tissue characterization in terms of
      sensitivity and specificity. The investigators hypothesize that advanced QUS providing tumor
      viscoelasticity assessment, sub-resolution tissue structure characterization and US
      attenuation in the framework of a machine learning classification model can improve HCC
      diagnosis compared with standard US.

      Early detection through systematic US surveillance translates into curative therapy in a
      higher proportion of patients and into improvements in survival rates. Thus, there is an
      urgent need to investigate innovative and cost-effective imaging techniques for improving
      detection and characterization of HCC. The proposed QUS methods are experimental and will be
      validated in this proof-of-concept clinical study. A major impact of this work, for patients
      and medical institutions, will be to improve early-stage detection and characterization of
      HCC, and offer alternatives in patients with negative or inconclusive conventional US. QUS
      are low-cost, non-invasive and non-irradiating imaging modalities available from a single
      exam (i.e., no additional imaging session is necessary).
RESEARCH QUESTION AND BACKGROUND: Primary liver cancer or hepatocellular carcinoma (HCC) is
      the fifth most common cancer in men and the seventh in women and is the second cause of
      cancer mortality worldwide. In Canada, HCC is the only cancer for which mortality is
      increasing. More than 80% of HCC cases occur in individuals with advanced liver fibrosis
      (cirrhosis) due to viral hepatitis infection (B and C), non-alcoholic fatty liver disease
      (NAFLD), and alcoholic liver disease. Once cirrhosis is established, there is a significantly
      increased risk of developing HCC. Furthermore, HCC is observed in obese diabetic individuals
      without cirrhosis, increasing the population of patients at risk with a disease that has high
      fatality rate. HCC surveillance is associated with significantly prolonged survival. However,
      only 52% of patients undergoing surveillance have early HCCs that are eligible for curative
      treatment, whereas remainder of patients have intermediate- or advanced-stage disease
      eligible for bridge or palliative treatment only. HCC surveillance is also associated with
      significant improvements in early-stage detection, curative-treatment rates, and survival,
      even after adjusting for lead-time bias. North American guidelines recommend ultrasound (US)
      surveillance every 6 months in at-risk patients who are non-cirrhotic hepatitis B carriers
      and cirrhotic. However, a key challenge for US is the low sensitivity (60-78%) for
      identifying a lesion due to liver steatosis and cirrhosis. Once a suspicious malignant lesion
      is detected at US, current American Association for the Study of Liver Diseases (AASLD)
      guidelines recommend contrast-enhanced US, magnetic resonance imaging (MRI) or computed
      tomography (CT) scans to confirm suspicion.

      GOAL: The long-term reaching goal is to develop US biomarkers of focal liver lesions and
      strategies to improve diagnostic sensitivity to HCC while maintaining a high specificity.
      This would constitute a major breakthrough because HCC diagnosis currently requires a
      combination of US for screening and confirmation using MRI, CT and less often biopsy.

      OBJECTIVES: 1) Develop a machine learning model based on QUS for classification of solid
      hepatocellular carcinomas identified at US and diagnosed with MRI (or biopsy if required); 2)
      Determine if QUS maps can improve visual detection of suspected lesions at US; 3) Compare
      performance of QUS- versus MRI-based viscoelastography for lesion characterization.

      Hypothesis: the investigators hypothesize that advanced QUS providing tumor viscoelasticity
      assessment, sub-resolution tissue structure characterization and US attenuation in the
      framework of a machine learning classification model can improve HCC diagnosis compared with
      standard US.

      METHODOLOGY - Study design: This will be a clinical study with two sequential cohorts: 1) a
      training cohort of 100 patients at risk for HCC to optimize QUS biomarkers for classification
      of solid liver lesions using MRI and/or biopsy as gold standard clinical references; and 2) a
      validation cohort of 100 patients to confirm diagnostic performance.

      Data analysis: Random forests machine learning to develop QUS classification models.
      Sensitivity and specificity to assess diagnostic accuracy, according to MRI and/or biopsy,
      with bootstrapping to obtain confidence intervals with training set. Confirmation of accuracy
      on test set. Inter-observer assessment of lesion detectability on clinical B-mode US versus
      QUS maps. Comparison of US- and MRI-based elasticity and viscosity according to diagnostic
      results.
observational model: Other
time perspective: Cross-Sectional
measure: Liver viscoelastography determined by MRI
time frame: Within 6 weeks of liver echography
description: Measure of liver viscoelastography using magnetic resonance Imaging (MRI)
measure: Liver viscoelastography determined by QUS
time frame: Within 6 weeks of liver echography
description: Measure of liver viscoelastography using quantitative ultrasound (QUS).
measure: Detection of focal HCC lesions
time frame: Within 6 weeks of liver echography
description: Detection of focal HCC lesions using quantitative ultrasound (QUS).
arm group label: Training cohort
description: All patients enrolled will undergo:
Magnetic Resonance Viscoelastography
Quantitative ultrasound (QUS)
arm group label: Validation cohort
description: All patients enrolled will undergo:
• Quantitative ultrasound (QUS)
intervention type: Device
intervention name: Magnetic Resonance Viscoelastography
description: Magnetic Resonance Viscoelastography
arm group label: Training cohort
intervention type: Device
intervention name: QUS
description: QUS
arm group label: Training cohort
arm group label: Validation cohort
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
healthy volunteers: No
last name: Guy Cloutier, PhD
role: Principal Investigator
affiliation: Centre hospitalier de l'Université de Montréal (CHUM)
last name: An Tang, MD, MSc
role: Principal Investigator
affiliation: Centre hospitalier de l'Université de Montréal (CHUM)
last name: Guy Cloutier, PhD
phone: 514-890-8000
phone ext: 24703
email: guy.cloutier@umontreal.ca
last name: Louise Allard, PhD
phone: 514-890-8000
phone ext: 24705
email: louise.allard@crchum.qc.ca
facility: 
status: Recruiting
contact: 
contact backup: 
investigator: 
investigator: 
investigator: 
investigator: 
investigator: 
investigator: 
country: Canada
responsible party type: Sponsor
mesh term: Liver Neoplasms
sharing ipd: Undecided
ipd description: plan undicided