<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/NCT04441580</url>
  </required_header>
  <id_info>
    <org_study_id>GENIAL-CO 2.0 del 09/01/2020</org_study_id>
    <nct_id>NCT04441580</nct_id>
  </id_info>
  <brief_title>Assessing the Additional Neoplasia Yield of Computer-aided Colonoscopy in a Screening Setting</brief_title>
  <acronym>GENIAL-CO</acronym>
  <official_title>Resa Diagnostica Aggiuntiva Dell'Intelligenza Artificiale Nella Colonscopia (GENIAL COLONOSCOPY), Per lo Screening Del Carcinoma Colorettale</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Fondazione Poliambulanza Istituto Ospedaliero</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>Fondazione Poliambulanza Istituto Ospedaliero</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>
      Even if colonoscopy is considered the reference standard for the detection of colonic&#xD;
      neoplasia, polyps are still missed. The risk of early post-colonoscopy cancer appeared to be&#xD;
      independently predicted by a relatively low polyp/adenoma detection rate. When considering&#xD;
      the very high prevalence of advanced neoplasia in the FIT-positive enriched population, the&#xD;
      risk of post-colonoscopy interval cancer due to a suboptimal quality of colonoscopy may be&#xD;
      substantial. Available evidence justifies therefore the implementation of efforts aimed at&#xD;
      improving adenoma detection rate, based on retraining interventions and on the adoption of&#xD;
      innovative technologies, designed to enhance the accuracy of the endoscopic examination.&#xD;
      Artificial intelligence seems to improve the quality of medical diagnosis and treatment. In&#xD;
      the field of gastrointestinal endoscopy, two potential roles of AI in colonoscopy have been&#xD;
      examined so far: automated polyp detection (CADe) and automated polyp histology&#xD;
      characterization (CADx). CADe can minimize the probability of missing a polyp during&#xD;
      colonoscopy, thereby improving the adenoma detection rate (ADR) and potentially decreasing&#xD;
      the incidence of interval cancer. GI Genius is the AI software that will be used in the&#xD;
      present trial and is intended to be used as an adjunct to colonic endoscopy procedures to&#xD;
      help endoscopists to detect in real time mucosal lesions (such as polyps and adenomas,&#xD;
      including those with flat (non-polypoid) morphology) during standard screening and&#xD;
      surveillance endoscopic mucosal evaluations. It is not intended to replace histopathological&#xD;
      sampling as a means of diagnosis.&#xD;
&#xD;
      The objective of this study was to compare the diagnostic yield obtained by using CADe&#xD;
      colonoscopy to the yield obtained by the standard colonoscopy (SC).&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Even if colonoscopy is considered the reference standard for the detection of colonic&#xD;
      neoplasia, polyps are still missed. In large administrative cohort or case-control studies,&#xD;
      the risk of early post-colonoscopy cancer appeared to be independently predicted by a&#xD;
      relatively low polyp/adenoma detection rate. The adenoma detection rate among different&#xD;
      endoscopists has been shown to be strictly related with the risk of post-colonoscopy interval&#xD;
      cancer. When considering the very high prevalence of advanced neoplasia in the FIT-positive&#xD;
      enriched population, the risk of post-colonoscopy interval cancer due to a suboptimal quality&#xD;
      of colonoscopy may be substantial. Available evidence justifies therefore the implementation&#xD;
      of efforts aimed at improving adenoma detection rate, based on retraining interventions and&#xD;
      on the adoption of innovative technologies, designed to enhance the accuracy of the&#xD;
      endoscopic examination.Nowadays, Artificial intelligence (AI) is gaining increased attention&#xD;
      and investigation, since it seems to improve the quality of medical diagnosis and treatment.&#xD;
      In the field of gastrointestinal endoscopy, two potential roles of AI in colonoscopy have&#xD;
      been examined so far: automated polyp detection (CADe) and automated polyp histology&#xD;
      characterization (CADx). CADe can minimize the probability of missing a polyp during&#xD;
      colonoscopy, thereby improving the adenoma detection rate (ADR) and potentially decreasing&#xD;
      the incidence of interval cancer. GI Genius is the AI software that will be used in the&#xD;
      present trial. The software is developed by Medtronic Inc. (Dublin, Ireland) and is intended&#xD;
      to be used as an adjunct to colonic endoscopy procedures to help endoscopists to detect in&#xD;
      real time mucosal lesions (such as polyps and adenomas, including those with flat&#xD;
      (non-polypoid) morphology) during standard screening and surveillance endoscopic mucosal&#xD;
      evaluations. It is not intended to replace histopathological sampling as a means of&#xD;
      diagnosis.&#xD;
&#xD;
      The objective of this study was to compare the diagnostic yield obtained by using CADe&#xD;
      colonoscopy to the yield obtained by the standard colonoscopy (SC). As the risk of&#xD;
      progression is higher for large than for small adenomas the specific contribution of the new&#xD;
      technique in reducing the miss rate of large neoplasms represents an important outcome to be&#xD;
      assessed in the study. In addition, given the suggested association of a higher miss-rate of&#xD;
      serrated and flat lesions with an increased risk of early post-colonoscopy CRC, the benefit&#xD;
      of the new technique in reducing the miss rate of these lesions will be assessed.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Unknown status</overall_status>
  <last_known_status>Recruiting</last_known_status>
  <start_date type="Actual">May 4, 2020</start_date>
  <completion_date type="Anticipated">August 31, 2022</completion_date>
  <primary_completion_date type="Anticipated">August 1, 2022</primary_completion_date>
  <phase>N/A</phase>
  <study_type>Interventional</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <allocation>Randomized</allocation>
    <intervention_model>Parallel Assignment</intervention_model>
    <primary_purpose>Screening</primary_purpose>
    <masking>None (Open Label)</masking>
  </study_design_info>
  <primary_outcome>
    <measure>Rate of advanced adenomas</measure>
    <time_frame>When available the histological report of polyps removed (up to 3 weeks).</time_frame>
    <description>The percentage of patients with adenomas with high-grade displasia in CADe colonoscopy group will be recorded and compared with the rate of patients with adenomas with high-grade displasia in standard colonoscopy group.</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Rate of patients detected with 3 or more adenomas.</measure>
    <time_frame>When available the histological report of polyps removed (up to 3 weeks).</time_frame>
    <description>The percentage of patients with 3 or more adenomas (serrated adenomas will also be considered in the calculation) in CADe colonoscopy group will be compared with the rate of patients with 3 or more adenomas (including serrated adenomas) in standard colonoscopy group.</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>Overall adenoma and polyp detection rate, flat adenoma and serrated polyps/adenomas.</measure>
    <time_frame>When available the histological report of polyps removed (up to 3 weeks).</time_frame>
    <description>The percentage of adenomas, polyps (in general), flat adenoma and serrated polyps/adenoma detected will be recorded and compared between the groups.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Size of lesions detected</measure>
    <time_frame>Immediately after the procedure.</time_frame>
    <description>The size of lesion detected will be measured in millimiters and compared between the groups.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Rate of neoplasia by colonic site</measure>
    <time_frame>Immediately after the procedure.</time_frame>
    <description>The percentage of patients with neoplasia of proximal (right colonic segments) or distal (left colonic segments and rectum) site will be assessed and compared between the groups.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Post-colonoscopy surveilance</measure>
    <time_frame>When available the histological report of polyps removed (up to 3 weeks).</time_frame>
    <description>the time interval, expressed in years, to the next suggested follow-up colonoscopy will be assessed and compared between the groups.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Time of cecal intubation.</measure>
    <time_frame>Immediately after the procedure.</time_frame>
    <description>The time to reach the cecum will be measured in minutes, recorded and compared between the groups.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Withdrawal and total procedure time.</measure>
    <time_frame>Immediately after the procedure.</time_frame>
    <description>The time of withdrawal (from cecum to anus) and of the overall colonoscopy (from anus to anus) will be measured in minutes, recorded and compared between the groups.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Learning curve.</measure>
    <time_frame>3, 6, 9 and 12 months.</time_frame>
    <description>The above-mentioned outcomes will be calculated for each endoscopist at 3, 6, 9 and 12 months.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Patient experience</measure>
    <time_frame>Immediately after the procedure.</time_frame>
    <description>Visual Analogue Scale (VAS) (0 minimum to 10 maximum) will be recorded to assess the pain before and after the colonoscopy and results will be compared between the groups.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Specific contribution of AI</measure>
    <time_frame>Immediately after the procedure.</time_frame>
    <description>Proportion of patients diagnosed with polyps which were detected only by Artificial intelligence</description>
  </secondary_outcome>
  <number_of_arms>2</number_of_arms>
  <enrollment type="Anticipated">600</enrollment>
  <condition>Colonic Neoplasms</condition>
  <condition>Colonic Adenocarcinoma</condition>
  <condition>Polyps of Colon</condition>
  <condition>Rectal Neoplasms</condition>
  <condition>Rectal Adenocarcinoma</condition>
  <arm_group>
    <arm_group_label>Artificial intelligence arm</arm_group_label>
    <arm_group_type>Experimental</arm_group_type>
    <description>Patients undergoing colonoscopy with artificial intelligence.</description>
  </arm_group>
  <arm_group>
    <arm_group_label>standard colonoscopy arm</arm_group_label>
    <arm_group_type>Active Comparator</arm_group_type>
    <description>Patients undergoing colonoscopy with standard colonscopy</description>
  </arm_group>
  <intervention>
    <intervention_type>Device</intervention_type>
    <intervention_name>CADe colonoscopy using GI Genius device</intervention_name>
    <description>We wanted to compare the diagnostic yield obtained by using CADe colonoscopy to the yield obtained by the standard colonoscopy (SC) in a FIT-positive screening population.</description>
    <arm_group_label>Artificial intelligence arm</arm_group_label>
    <arm_group_label>standard colonoscopy arm</arm_group_label>
  </intervention>
  <eligibility>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Patients aged 50 to 69 undergoing colonoscopy examination following a positive fecal&#xD;
             immunochemical test (FIT) performed in the context of a regional mass-screening&#xD;
             program.&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  Patients unwilling or unable to give informed consent.&#xD;
&#xD;
          -  Patients reporting use of anti-platelet agents or anticoagulants precluding removal of&#xD;
             polyps.&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>50 Years</minimum_age>
    <maximum_age>69 Years</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Cristiano Spada, MD, PhD</last_name>
    <role>Principal Investigator</role>
    <affiliation>Fondazione Poliambulanza Istituto Ospedaliero</affiliation>
  </overall_official>
  <overall_contact>
    <last_name>Sebastian Manuel Milluzzo, MD</last_name>
    <phone>+39 0303515373</phone>
    <email>sebastian.m.milluzzo@gmail.com</email>
  </overall_contact>
  <overall_contact_backup>
    <last_name>Paola Cesaro, MD, PhD</last_name>
    <phone>+39 0303515372</phone>
    <email>paola.cesaro@poliambulanza.it</email>
  </overall_contact_backup>
  <location>
    <facility>
      <name>Fondazione Poliambulanza Istituto Ospedaliero</name>
      <address>
        <city>Brescia</city>
        <country>Italy</country>
      </address>
    </facility>
    <status>Recruiting</status>
    <contact>
      <last_name>Sebastian Manuel Milluzzo, MD</last_name>
      <phone>+39 030 3515372</phone>
      <email>sebastian.m.milluzzo@gmail.com</email>
    </contact>
    <contact_backup>
      <last_name>Paola Cesaro, MD, Phd</last_name>
      <phone>+39 030 3515373</phone>
      <email>paola.cesaro@poliambulanza.it</email>
    </contact_backup>
  </location>
  <location_countries>
    <country>Italy</country>
  </location_countries>
  <verification_date>September 2021</verification_date>
  <study_first_submitted>June 9, 2020</study_first_submitted>
  <study_first_submitted_qc>June 18, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">June 22, 2020</study_first_posted>
  <last_update_submitted>September 22, 2021</last_update_submitted>
  <last_update_submitted_qc>September 22, 2021</last_update_submitted_qc>
  <last_update_posted type="Actual">September 27, 2021</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <keyword>artificial intelligence</keyword>
  <keyword>colonoscopy</keyword>
  <keyword>colonic polyps</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Neoplasms</mesh_term>
    <mesh_term>Adenocarcinoma</mesh_term>
    <mesh_term>Rectal Neoplasms</mesh_term>
    <mesh_term>Colonic Neoplasms</mesh_term>
    <mesh_term>Colonic Polyps</mesh_term>
  </condition_browse>
  <!-- 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/NCT04441580
org study id: GENIAL-CO 2.0 del 09/01/2020
nct id: NCT04441580
lead sponsor: 
is fda regulated drug: No
is fda regulated device: No
Even if colonoscopy is considered the reference standard for the detection of colonic
      neoplasia, polyps are still missed. The risk of early post-colonoscopy cancer appeared to be
      independently predicted by a relatively low polyp/adenoma detection rate. When considering
      the very high prevalence of advanced neoplasia in the FIT-positive enriched population, the
      risk of post-colonoscopy interval cancer due to a suboptimal quality of colonoscopy may be
      substantial. Available evidence justifies therefore the implementation of efforts aimed at
      improving adenoma detection rate, based on retraining interventions and on the adoption of
      innovative technologies, designed to enhance the accuracy of the endoscopic examination.
      Artificial intelligence seems to improve the quality of medical diagnosis and treatment. In
      the field of gastrointestinal endoscopy, two potential roles of AI in colonoscopy have been
      examined so far: automated polyp detection (CADe) and automated polyp histology
      characterization (CADx). CADe can minimize the probability of missing a polyp during
      colonoscopy, thereby improving the adenoma detection rate (ADR) and potentially decreasing
      the incidence of interval cancer. GI Genius is the AI software that will be used in the
      present trial and is intended to be used as an adjunct to colonic endoscopy procedures to
      help endoscopists to detect in real time mucosal lesions (such as polyps and adenomas,
      including those with flat (non-polypoid) morphology) during standard screening and
      surveillance endoscopic mucosal evaluations. It is not intended to replace histopathological
      sampling as a means of diagnosis.

      The objective of this study was to compare the diagnostic yield obtained by using CADe
      colonoscopy to the yield obtained by the standard colonoscopy (SC).
Even if colonoscopy is considered the reference standard for the detection of colonic
      neoplasia, polyps are still missed. In large administrative cohort or case-control studies,
      the risk of early post-colonoscopy cancer appeared to be independently predicted by a
      relatively low polyp/adenoma detection rate. The adenoma detection rate among different
      endoscopists has been shown to be strictly related with the risk of post-colonoscopy interval
      cancer. When considering the very high prevalence of advanced neoplasia in the FIT-positive
      enriched population, the risk of post-colonoscopy interval cancer due to a suboptimal quality
      of colonoscopy may be substantial. Available evidence justifies therefore the implementation
      of efforts aimed at improving adenoma detection rate, based on retraining interventions and
      on the adoption of innovative technologies, designed to enhance the accuracy of the
      endoscopic examination.Nowadays, Artificial intelligence (AI) is gaining increased attention
      and investigation, since it seems to improve the quality of medical diagnosis and treatment.
      In the field of gastrointestinal endoscopy, two potential roles of AI in colonoscopy have
      been examined so far: automated polyp detection (CADe) and automated polyp histology
      characterization (CADx). CADe can minimize the probability of missing a polyp during
      colonoscopy, thereby improving the adenoma detection rate (ADR) and potentially decreasing
      the incidence of interval cancer. GI Genius is the AI software that will be used in the
      present trial. The software is developed by Medtronic Inc. (Dublin, Ireland) and is intended
      to be used as an adjunct to colonic endoscopy procedures to help endoscopists to detect in
      real time mucosal lesions (such as polyps and adenomas, including those with flat
      (non-polypoid) morphology) during standard screening and surveillance endoscopic mucosal
      evaluations. It is not intended to replace histopathological sampling as a means of
      diagnosis.

      The objective of this study was to compare the diagnostic yield obtained by using CADe
      colonoscopy to the yield obtained by the standard colonoscopy (SC). As the risk of
      progression is higher for large than for small adenomas the specific contribution of the new
      technique in reducing the miss rate of large neoplasms represents an important outcome to be
      assessed in the study. In addition, given the suggested association of a higher miss-rate of
      serrated and flat lesions with an increased risk of early post-colonoscopy CRC, the benefit
      of the new technique in reducing the miss rate of these lesions will be assessed.
allocation: Randomized
intervention model: Parallel Assignment
primary purpose: Screening
masking: None (Open Label)
measure: Rate of advanced adenomas
time frame: When available the histological report of polyps removed (up to 3 weeks).
description: The percentage of patients with adenomas with high-grade displasia in CADe colonoscopy group will be recorded and compared with the rate of patients with adenomas with high-grade displasia in standard colonoscopy group.
measure: Rate of patients detected with 3 or more adenomas.
time frame: When available the histological report of polyps removed (up to 3 weeks).
description: The percentage of patients with 3 or more adenomas (serrated adenomas will also be considered in the calculation) in CADe colonoscopy group will be compared with the rate of patients with 3 or more adenomas (including serrated adenomas) in standard colonoscopy group.
measure: Overall adenoma and polyp detection rate, flat adenoma and serrated polyps/adenomas.
time frame: When available the histological report of polyps removed (up to 3 weeks).
description: The percentage of adenomas, polyps (in general), flat adenoma and serrated polyps/adenoma detected will be recorded and compared between the groups.
measure: Size of lesions detected
time frame: Immediately after the procedure.
description: The size of lesion detected will be measured in millimiters and compared between the groups.
measure: Rate of neoplasia by colonic site
time frame: Immediately after the procedure.
description: The percentage of patients with neoplasia of proximal (right colonic segments) or distal (left colonic segments and rectum) site will be assessed and compared between the groups.
measure: Post-colonoscopy surveilance
time frame: When available the histological report of polyps removed (up to 3 weeks).
description: the time interval, expressed in years, to the next suggested follow-up colonoscopy will be assessed and compared between the groups.
measure: Time of cecal intubation.
time frame: Immediately after the procedure.
description: The time to reach the cecum will be measured in minutes, recorded and compared between the groups.
measure: Withdrawal and total procedure time.
time frame: Immediately after the procedure.
description: The time of withdrawal (from cecum to anus) and of the overall colonoscopy (from anus to anus) will be measured in minutes, recorded and compared between the groups.
measure: Learning curve.
time frame: 3, 6, 9 and 12 months.
description: The above-mentioned outcomes will be calculated for each endoscopist at 3, 6, 9 and 12 months.
measure: Patient experience
time frame: Immediately after the procedure.
description: Visual Analogue Scale (VAS) (0 minimum to 10 maximum) will be recorded to assess the pain before and after the colonoscopy and results will be compared between the groups.
measure: Specific contribution of AI
time frame: Immediately after the procedure.
description: Proportion of patients diagnosed with polyps which were detected only by Artificial intelligence
arm group label: Artificial intelligence arm
arm group type: Experimental
description: Patients undergoing colonoscopy with artificial intelligence.
arm group label: standard colonoscopy arm
arm group type: Active Comparator
description: Patients undergoing colonoscopy with standard colonscopy
intervention type: Device
intervention name: CADe colonoscopy using GI Genius device
description: We wanted to compare the diagnostic yield obtained by using CADe colonoscopy to the yield obtained by the standard colonoscopy (SC) in a FIT-positive screening population.
arm group label: Artificial intelligence arm
arm group label: standard colonoscopy arm
criteria: 
gender: All
minimum age: 50 Years
maximum age: 69 Years
healthy volunteers: No
last name: Cristiano Spada, MD, PhD
role: Principal Investigator
affiliation: Fondazione Poliambulanza Istituto Ospedaliero
last name: Sebastian Manuel Milluzzo, MD
phone: +39 0303515373
email: sebastian.m.milluzzo@gmail.com
last name: Paola Cesaro, MD, PhD
phone: +39 0303515372
email: paola.cesaro@poliambulanza.it
facility: 
status: Recruiting
contact: 
contact backup: 
country: Italy
responsible party type: Sponsor
mesh term: Neoplasms
mesh term: Adenocarcinoma
mesh term: Rectal Neoplasms
mesh term: Colonic Neoplasms
mesh term: Colonic Polyps