<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/NCT03822390</url>
  </required_header>
  <id_info>
    <org_study_id>W18_422</org_study_id>
    <nct_id>NCT03822390</nct_id>
  </id_info>
  <brief_title>Diagnostic Performance of a Convolutional Neural Network for Diminutive Colorectal Polyp Recognition</brief_title>
  <acronym>POLAR</acronym>
  <official_title>Diagnostic Performance of a Convolutional Neural Network for Diminutive Colorectal Polyp Recognition. A Multicentre, Prospective Observational Study</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>Bergman Clinics</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>Medical Centre Leeuwarden</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)</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>
      Rationale: Diminutive colorectal polyps (1-5mm in size) have a high prevalence and very low&#xD;
      risk of harbouring cancer. Current practice is to send all these polyps for histopathological&#xD;
      assessment by the pathologist. If an endoscopist would be able to correctly predict the&#xD;
      histology of these diminutive polyps during colonoscopy, histopathological examination could&#xD;
      be omitted and practise could become more time- and cost-effective. Studies have shown that&#xD;
      prediction of histology by the endoscopist remains dependent on training and experience and&#xD;
      varies greatly between endoscopists, even after systematic training. Computer aided diagnosis&#xD;
      (CAD) based on convolutional neural networks (CNN) may facilitate endoscopists in diminutive&#xD;
      polyp differentiation. Up to date, studies comparing the diagnostic performance of CAD-CNN to&#xD;
      a group of endoscopists performing optical diagnosis during real-time colonoscopy are&#xD;
      lacking.&#xD;
&#xD;
      Objective: To develop a CAD-CNN system that is able to differentiate diminutive polyps during&#xD;
      colonoscopy with high accuracy and to compare the performance of this system to a group of&#xD;
      endoscopist performing optical diagnosis, with the histopathology as the gold standard.&#xD;
&#xD;
      Study design: Multicentre, prospective, observational trial. Study population: Consecutive&#xD;
      patients who undergo screening colonoscopy (phase 2)&#xD;
&#xD;
      Main study parameters/endpoints: The accuracy of optical diagnosis of diminutive colorectal&#xD;
      polyps (1-5mm) by CAD-CNN system compared with the accuracy of the endoscopists.&#xD;
      Histopathology is used as the gold standard.&#xD;
    </textblock>
  </brief_summary>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">October 16, 2018</start_date>
  <completion_date type="Actual">October 16, 2021</completion_date>
  <primary_completion_date type="Actual">October 16, 2021</primary_completion_date>
  <study_type>Observational</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <observational_model>Cohort</observational_model>
    <time_perspective>Prospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>The accuracy of the CAD-CNN system for predicting histology of diminutive colorectal polyps (1-5mm) compared with the accuracy of the prediction of the endoscopist. Both the CAD-CNN system and the endoscopist will use NBI for their predictions.</measure>
    <time_frame>2 year</time_frame>
    <description>Accuracy is defined as the percentage of correctly predicted optical diagnoses of the CAD-CNN system and / or endoscopist compared to the gold standard pathology. For the calculation of the accuracy, adenomas and SSLs will be dichotomized as neoplastic polyps, while HPs are considered non-neoplastic</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>The mean duration in seconds of the CAD-CNN system to make a per polyp diagnosis.</measure>
    <time_frame>2 year</time_frame>
    <description>The mean duration in seconds of the CAD-CNN system to make a per polyp diagnosis.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>The mean number of attempts of the CAD-CNN to make a diagnosis per polyp</measure>
    <time_frame>2 year</time_frame>
    <description>The mean number of attempts of the CAD-CNN to make a diagnosis per polyp</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>The ratio of unsuccessful diagnosis from all diagnosis of the CAD-CNN system. An unsuccessful diagnosis/failure of the CAD-CNN system is defined as more than 3 unsuccessful attempts</measure>
    <time_frame>2 year</time_frame>
    <description>The ratio of unsuccessful diagnosis from all diagnosis of the CAD-CNN system. An unsuccessful diagnosis/failure of the CAD-CNN system is defined as more than 3 unsuccessful attempts</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>The number of diminutive polyps per colonoscopy that is resected and discarded without histopathological analysis with optical diagnosis strategy (the CAD-CNN system or endoscopist)</measure>
    <time_frame>2 year</time_frame>
    <description>The number of diminutive polyps per colonoscopy that is resected and discarded without histopathological analysis with optical diagnosis strategy (the CAD-CNN system or endoscopist)</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>The percentage of colonoscopies in which diminutive polyps are characterized based on optical diagnosis, removed and discarded without histopathological evaluation (i.e. proportion of polyps assessed with high confidence)</measure>
    <time_frame>2 year</time_frame>
    <description>The percentage of colonoscopies in which diminutive polyps are characterized based on optical diagnosis, removed and discarded without histopathological evaluation (i.e. proportion of polyps assessed with high confidence)</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>The percentage of colonoscopies in which the surveillance interval is based on the optical diagnosis of the CAD-CNN system and the patient can be directly informed of the surveillance interval after colonoscopy</measure>
    <time_frame>2 year</time_frame>
    <description>The percentage of colonoscopies in which the surveillance interval is based on the optical diagnosis of the CAD-CNN system and the patient can be directly informed of the surveillance interval after colonoscopy</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>The percentage of colonoscopies in which diminutive hyperplastic polyps in the rectosigmoid are left in situ.</measure>
    <time_frame>2 year</time_frame>
    <description>The percentage of colonoscopies in which diminutive hyperplastic polyps in the rectosigmoid are left in situ.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>The diagnostic sensitivity for optical diagnosis of the CAD-CNN system and the endoscopists</measure>
    <time_frame>2 year</time_frame>
    <description>The diagnostic sensitivity for optical diagnosis of the CAD-CNN system and the endoscopists</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>The diagnostic sensitiviy for optical diagnosis of the CAD-CNN system and the endoscopists</measure>
    <time_frame>2 year</time_frame>
    <description>The diagnostic sensitiviy for optical diagnosis of the CAD-CNN system and the endoscopists</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>The accuracy rates on a per polyp basis</measure>
    <time_frame>2 year</time_frame>
    <description>Accuracy on a polyp basis is defined as the percentage of correctly predicted optical diagnoses of the CAD-CNN system and / or endoscopist compared to the gold standard pathology. For the calculation of the accuracy on a polyp basis, adenomas, SSLs and HPs are considered different subtypes.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Agreement between recommended surveillance intervals, based on optical diagnosis of diminutive polyps with high confidence, compared to surveillance recommendations based on histology of all polyps</measure>
    <time_frame>2 year</time_frame>
    <description>Agreement between recommended surveillance intervals, based on optical diagnosis of diminutive polyps with high confidence, compared to surveillance recommendations based on histology of all polyps</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>The diagnostic specificity for optical diagnosis of the CAD-CNN system and the endoscopists</measure>
    <time_frame>2 year</time_frame>
    <description>The diagnostic specificity for optical diagnosis of the CAD-CNN system and the endoscopists</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>The diagnostic PPV for optical diagnosis of the CAD-CNN system and the endoscopists</measure>
    <time_frame>2 year</time_frame>
    <description>The diagnostic PPV for optical diagnosis of the CAD-CNN system and the endoscopists</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>The diagnostic NPV for optical diagnosis of the CAD-CNN system and the endoscopists</measure>
    <time_frame>2 year</time_frame>
    <description>The diagnostic NPV for optical diagnosis of the CAD-CNN system and the endoscopists</description>
  </secondary_outcome>
  <number_of_groups>1</number_of_groups>
  <enrollment type="Actual">292</enrollment>
  <condition>Artificial Intelligence</condition>
  <condition>Colorectal Polyp</condition>
  <arm_group>
    <arm_group_label>Patients</arm_group_label>
    <description>Patients older than 18 years undergoing colonoscopy in one the participating centres.</description>
  </arm_group>
  <intervention>
    <intervention_type>Device</intervention_type>
    <intervention_name>CAD-CNN system</intervention_name>
    <description>The CAD-CNN system will be trained in predicting the histology of diminutive polyps. Before training, the dataset will be split up into a training set and a test set. To ensure a completely independent test and training set there will be no overlap between patients (i.e. if polyps from a patient A is present in the training set it cannot be in the test set as well).</description>
    <arm_group_label>Patients</arm_group_label>
  </intervention>
  <eligibility>
    <study_pop>
      <textblock>
        Phase 1APatients that underwent colonoscopy between 2011-2018 in the Bergman Clinics&#xD;
        Amsterdam, in the context of the Dutch bowel cancer screening or surveillance program or&#xD;
        because of symptoms.&#xD;
&#xD;
        Phase 1B Patients older than 18 years that underwent colonoscopy in one of the&#xD;
        participating centres.&#xD;
&#xD;
        Phase 2 All patients older than 18 years old undergoing screenings colonoscopy in one of&#xD;
        the participating centres.&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Phase 1A -&#xD;
&#xD;
        - Patients with one polyp subtype (based on histology)&#xD;
&#xD;
        Phase 1B Patients older than 18 years that underwent colonoscopy in one of the&#xD;
        participating centres.&#xD;
&#xD;
        Phase 2:- Validation CAD-CNN system&#xD;
&#xD;
        Inclusion Criteria:&#xD;
&#xD;
        All patients older than 18 years old undergoing screenings colonoscopy in one of the&#xD;
        participating centres.&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  Diagnosis of inflammatory bowel disease, Lynch syndrome or (serrated) polyposis&#xD;
             syndrome.&#xD;
&#xD;
          -  Boston Bowel Preparation Scale (BBPS) &lt;2 in one of the colon segments&#xD;
&#xD;
          -  Patients who are unwilling or unable to give informed consent&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
  </eligibility>
  <overall_official>
    <last_name>Evelien NA Dekker, Msc</last_name>
    <role>Principal Investigator</role>
    <affiliation>Amsterdam UMC, location VUmc</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>Academic Medical Centre</name>
      <address>
        <city>Amsterdam</city>
        <state>Noord-Holland</state>
        <zip>1105AZ</zip>
        <country>Netherlands</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>Netherlands</country>
  </location_countries>
  <verification_date>December 2021</verification_date>
  <study_first_submitted>January 21, 2019</study_first_submitted>
  <study_first_submitted_qc>January 29, 2019</study_first_submitted_qc>
  <study_first_posted type="Actual">January 30, 2019</study_first_posted>
  <last_update_submitted>December 9, 2021</last_update_submitted>
  <last_update_submitted_qc>December 9, 2021</last_update_submitted_qc>
  <last_update_posted type="Actual">December 29, 2021</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)</investigator_affiliation>
    <investigator_full_name>Prof. Evelien Dekker, MD, PhD</investigator_full_name>
    <investigator_title>Prof. E. Dekker, MD, PhD</investigator_title>
  </responsible_party>
  <keyword>Artificial Intelligence</keyword>
  <keyword>Computer aided diagnosis</keyword>
  <keyword>Diminutive colorectal polyps</keyword>
  <keyword>Optical diagnosis</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Polyps</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>Undecided</sharing_ipd>
    <ipd_description>There is not yet a plan to share data. However, patients will be asked informed consent with the possibility to share the data.</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/NCT03822390
org study id: W18_422
nct id: NCT03822390
lead sponsor: 
collaborator: 
collaborator: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
Rationale: Diminutive colorectal polyps (1-5mm in size) have a high prevalence and very low
      risk of harbouring cancer. Current practice is to send all these polyps for histopathological
      assessment by the pathologist. If an endoscopist would be able to correctly predict the
      histology of these diminutive polyps during colonoscopy, histopathological examination could
      be omitted and practise could become more time- and cost-effective. Studies have shown that
      prediction of histology by the endoscopist remains dependent on training and experience and
      varies greatly between endoscopists, even after systematic training. Computer aided diagnosis
      (CAD) based on convolutional neural networks (CNN) may facilitate endoscopists in diminutive
      polyp differentiation. Up to date, studies comparing the diagnostic performance of CAD-CNN to
      a group of endoscopists performing optical diagnosis during real-time colonoscopy are
      lacking.

      Objective: To develop a CAD-CNN system that is able to differentiate diminutive polyps during
      colonoscopy with high accuracy and to compare the performance of this system to a group of
      endoscopist performing optical diagnosis, with the histopathology as the gold standard.

      Study design: Multicentre, prospective, observational trial. Study population: Consecutive
      patients who undergo screening colonoscopy (phase 2)

      Main study parameters/endpoints: The accuracy of optical diagnosis of diminutive colorectal
      polyps (1-5mm) by CAD-CNN system compared with the accuracy of the endoscopists.
      Histopathology is used as the gold standard.
observational model: Cohort
time perspective: Prospective
measure: The accuracy of the CAD-CNN system for predicting histology of diminutive colorectal polyps (1-5mm) compared with the accuracy of the prediction of the endoscopist. Both the CAD-CNN system and the endoscopist will use NBI for their predictions.
time frame: 2 year
description: Accuracy is defined as the percentage of correctly predicted optical diagnoses of the CAD-CNN system and / or endoscopist compared to the gold standard pathology. For the calculation of the accuracy, adenomas and SSLs will be dichotomized as neoplastic polyps, while HPs are considered non-neoplastic
measure: The mean duration in seconds of the CAD-CNN system to make a per polyp diagnosis.
time frame: 2 year
description: The mean duration in seconds of the CAD-CNN system to make a per polyp diagnosis.
measure: The mean number of attempts of the CAD-CNN to make a diagnosis per polyp
time frame: 2 year
description: The mean number of attempts of the CAD-CNN to make a diagnosis per polyp
measure: The ratio of unsuccessful diagnosis from all diagnosis of the CAD-CNN system. An unsuccessful diagnosis/failure of the CAD-CNN system is defined as more than 3 unsuccessful attempts
time frame: 2 year
description: The ratio of unsuccessful diagnosis from all diagnosis of the CAD-CNN system. An unsuccessful diagnosis/failure of the CAD-CNN system is defined as more than 3 unsuccessful attempts
measure: The number of diminutive polyps per colonoscopy that is resected and discarded without histopathological analysis with optical diagnosis strategy (the CAD-CNN system or endoscopist)
time frame: 2 year
description: The number of diminutive polyps per colonoscopy that is resected and discarded without histopathological analysis with optical diagnosis strategy (the CAD-CNN system or endoscopist)
measure: The percentage of colonoscopies in which diminutive polyps are characterized based on optical diagnosis, removed and discarded without histopathological evaluation (i.e. proportion of polyps assessed with high confidence)
time frame: 2 year
description: The percentage of colonoscopies in which diminutive polyps are characterized based on optical diagnosis, removed and discarded without histopathological evaluation (i.e. proportion of polyps assessed with high confidence)
measure: The percentage of colonoscopies in which the surveillance interval is based on the optical diagnosis of the CAD-CNN system and the patient can be directly informed of the surveillance interval after colonoscopy
time frame: 2 year
description: The percentage of colonoscopies in which the surveillance interval is based on the optical diagnosis of the CAD-CNN system and the patient can be directly informed of the surveillance interval after colonoscopy
measure: The percentage of colonoscopies in which diminutive hyperplastic polyps in the rectosigmoid are left in situ.
time frame: 2 year
description: The percentage of colonoscopies in which diminutive hyperplastic polyps in the rectosigmoid are left in situ.
measure: The diagnostic sensitivity for optical diagnosis of the CAD-CNN system and the endoscopists
time frame: 2 year
description: The diagnostic sensitivity for optical diagnosis of the CAD-CNN system and the endoscopists
measure: The diagnostic sensitiviy for optical diagnosis of the CAD-CNN system and the endoscopists
time frame: 2 year
description: The diagnostic sensitiviy for optical diagnosis of the CAD-CNN system and the endoscopists
measure: The accuracy rates on a per polyp basis
time frame: 2 year
description: Accuracy on a polyp basis is defined as the percentage of correctly predicted optical diagnoses of the CAD-CNN system and / or endoscopist compared to the gold standard pathology. For the calculation of the accuracy on a polyp basis, adenomas, SSLs and HPs are considered different subtypes.
measure: Agreement between recommended surveillance intervals, based on optical diagnosis of diminutive polyps with high confidence, compared to surveillance recommendations based on histology of all polyps
time frame: 2 year
description: Agreement between recommended surveillance intervals, based on optical diagnosis of diminutive polyps with high confidence, compared to surveillance recommendations based on histology of all polyps
measure: The diagnostic specificity for optical diagnosis of the CAD-CNN system and the endoscopists
time frame: 2 year
description: The diagnostic specificity for optical diagnosis of the CAD-CNN system and the endoscopists
measure: The diagnostic PPV for optical diagnosis of the CAD-CNN system and the endoscopists
time frame: 2 year
description: The diagnostic PPV for optical diagnosis of the CAD-CNN system and the endoscopists
measure: The diagnostic NPV for optical diagnosis of the CAD-CNN system and the endoscopists
time frame: 2 year
description: The diagnostic NPV for optical diagnosis of the CAD-CNN system and the endoscopists
arm group label: Patients
description: Patients older than 18 years undergoing colonoscopy in one the participating centres.
intervention type: Device
intervention name: CAD-CNN system
description: The CAD-CNN system will be trained in predicting the histology of diminutive polyps. Before training, the dataset will be split up into a training set and a test set. To ensure a completely independent test and training set there will be no overlap between patients (i.e. if polyps from a patient A is present in the training set it cannot be in the test set as well).
arm group label: Patients
study pop: 
sampling method: Probability Sample
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
last name: Evelien NA Dekker, Msc
role: Principal Investigator
affiliation: Amsterdam UMC, location VUmc
facility: 
country: Netherlands
responsible party type: Principal Investigator
investigator affiliation: Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)
investigator full name: Prof. Evelien Dekker, MD, PhD
investigator title: Prof. E. Dekker, MD, PhD
mesh term: Polyps
sharing ipd: Undecided
ipd description: There is not yet a plan to share data. However, patients will be asked informed consent with the possibility to share the data.