<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/NCT03775811</url>
  </required_header>
  <id_info>
    <org_study_id>HISINVIA</org_study_id>
    <secondary_id>PI17/00894</secondary_id>
    <nct_id>NCT03775811</nct_id>
  </id_info>
  <brief_title>In Vivo Computer-aided Prediction of Polyp Histology on White Light Colonoscopy</brief_title>
  <official_title>In Vivo Computer-aided Prediction of Polyp Histology on White Light Colonoscopy</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Hospital Clinic of Barcelona</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>Instituto de Salud Carlos III</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>Hospital Clinic of Barcelona</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>
      Our group, prior to the present study, developed a handcrafted predictive model based on the&#xD;
      extraction of surface patterns (textons) with a diagnostic accuracy of over 90%24. This&#xD;
      method was validated in a small dataset containing only high-quality images.&#xD;
&#xD;
      Artificial intelligence is expected to improve the accuracy of colorectal polyp optical&#xD;
      diagnosis. We propose a hybrid approach combining a Deep learning (DL) system with polyp&#xD;
      features indicated by clinicians (HybridAI). A pilot in vivo experiment will carried out.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Optical diagnosis aims to predict the histology of a polyp based on its endoscopic features.&#xD;
      This practice could avoid histopathological analysis and reduce the derived costs. Under this&#xD;
      premise, the American Society of Gastrointestinal Endoscopy (ASGE), in its Preservation and&#xD;
      Incorporation of Valuable endoscopic Innovations (PIVI) statement, established a diagnostic&#xD;
      threshold for real-time endoscopic assessment of diminutive polyps. The rationale for its&#xD;
      implementation is that the prevalence of advanced histology in polyps &lt; 5mm is very low&#xD;
      (0.5%).&#xD;
&#xD;
      Several studies have demonstrated that optical diagnosis of small polyps is safe and feasible&#xD;
      in clinical practice and comparable to the current gold standard, histopathology. However,&#xD;
      the accuracy of optical diagnosis has been shown to be insufficient in community-based&#xD;
      practices or in non-expert hands and the diagnosis is even more difficult in diminutive&#xD;
      polyps &lt; 3 mm in which the discrepancy between the endoscopic and pathological diagnosis is&#xD;
      about 15%.&#xD;
&#xD;
      Artificial Intelligence (AI) has emerged as a help tool for polyp characterization.&#xD;
&#xD;
      Aiming to improve optical diagnosis using AI methods, we propose a hybrid approach that&#xD;
      combines DL with characteristics of polyps manually indicated by endoscopists (HybridAI).&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">January 1, 2019</start_date>
  <completion_date type="Actual">December 31, 2022</completion_date>
  <primary_completion_date type="Actual">March 31, 2019</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>Accuracy of the computer-aided system for predicting polyps histology in real clinical practice</measure>
    <time_frame>One year</time_frame>
    <description>The results of the computer-aided system prediction will be compared with the final pathology report, which is the gold standard</description>
  </primary_outcome>
  <enrollment type="Actual">90</enrollment>
  <condition>Colonoscopy</condition>
  <condition>Histology</condition>
  <condition>Computer-aided Diagnosis</condition>
  <condition>Artificial Intelligence</condition>
  <condition>Colorectal Polyp</condition>
  <condition>Adenoma Colon Polyp</condition>
  <condition>Hyperplastic Polyp</condition>
  <intervention>
    <intervention_type>Other</intervention_type>
    <intervention_name>AUTOMATED POLYP CLASSIFICATION</intervention_name>
    <description>COLONIC POLYP HISTOLOGY PREDICTION IN WHITE LIGHT IMAGES COMBINING ARTIFICIAL INTELLIGENCE AND CLINICAL INFORMATION</description>
  </intervention>
  <eligibility>
    <study_pop>
      <textblock>
        All patients with polyps of any size/morphology, detected in a routine or screening&#xD;
        colonoscopy, that are resected endoscopically and recovered for histological analysis will&#xD;
        be included.&#xD;
&#xD;
        The images obtained will be used to expand the database.&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Age &gt; 18 years&#xD;
&#xD;
          -  Approval of participation in the study. Signature of informed consent&#xD;
&#xD;
          -  Patients with at least one polyp of any size/morphology diagnosed in a routine or&#xD;
             screening colonoscopy&#xD;
&#xD;
          -  Endoscopies performed with high definition endoscopes&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  Age &lt;18 years&#xD;
&#xD;
          -  Refusal to participate in the study&#xD;
&#xD;
          -  Polyps partially resected in a previous endoscopy&#xD;
&#xD;
          -  Patients with inflammatory disease&#xD;
&#xD;
          -  Impossibility to wash remains of stool or mucus on the surface of the polyp&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <location>
    <facility>
      <name>Hospital Clínic de Barcelona</name>
      <address>
        <city>Barcelona</city>
        <zip>08036</zip>
        <country>Spain</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>Spain</country>
  </location_countries>
  <reference>
    <citation>Sanchez-Montes C, Sanchez FJ, Bernal J, Cordova H, Lopez-Ceron M, Cuatrecasas M, Rodriguez de Miguel C, Garcia-Rodriguez A, Garces-Duran R, Pellise M, Llach J, Fernandez-Esparrach G. Computer-aided prediction of polyp histology on white light colonoscopy using surface pattern analysis. Endoscopy. 2019 Mar;51(3):261-265. doi: 10.1055/a-0732-5250. Epub 2018 Oct 25.</citation>
    <PMID>30360010</PMID>
  </reference>
  <reference>
    <citation>Bernal J, Histace A, Masana M, Angermann Q, Sanchez-Montes C, Rodriguez de Miguel C, Hammami M, Garcia-Rodriguez A, Cordova H, Romain O, Fernandez-Esparrach G, Dray X, Sanchez FJ. GTCreator: a flexible annotation tool for image-based datasets. Int J Comput Assist Radiol Surg. 2019 Feb;14(2):191-201. doi: 10.1007/s11548-018-1864-x. Epub 2018 Sep 25.</citation>
    <PMID>30255462</PMID>
  </reference>
  <reference>
    <citation>Byrne MF, Chapados N, Soudan F, Oertel C, Linares Perez M, Kelly R, Iqbal N, Chandelier F, Rex DK. Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model. Gut. 2019 Jan;68(1):94-100. doi: 10.1136/gutjnl-2017-314547. Epub 2017 Oct 24.</citation>
    <PMID>29066576</PMID>
  </reference>
  <verification_date>January 2023</verification_date>
  <study_first_submitted>December 11, 2018</study_first_submitted>
  <study_first_submitted_qc>December 11, 2018</study_first_submitted_qc>
  <study_first_posted type="Actual">December 14, 2018</study_first_posted>
  <last_update_submitted>January 17, 2023</last_update_submitted>
  <last_update_submitted_qc>January 17, 2023</last_update_submitted_qc>
  <last_update_posted type="Actual">January 18, 2023</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>Hospital Clinic of Barcelona</investigator_affiliation>
    <investigator_full_name>Ana García-Rodríguez</investigator_full_name>
    <investigator_title>Principal Investigator</investigator_title>
  </responsible_party>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Adenoma</mesh_term>
    <mesh_term>Polyps</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>No</sharing_ipd>
  </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/NCT03775811
org study id: HISINVIA
secondary id: PI17/00894
nct id: NCT03775811
lead sponsor: 
collaborator: 
is fda regulated drug: No
is fda regulated device: No
Our group, prior to the present study, developed a handcrafted predictive model based on the
      extraction of surface patterns (textons) with a diagnostic accuracy of over 90%24. This
      method was validated in a small dataset containing only high-quality images.

      Artificial intelligence is expected to improve the accuracy of colorectal polyp optical
      diagnosis. We propose a hybrid approach combining a Deep learning (DL) system with polyp
      features indicated by clinicians (HybridAI). A pilot in vivo experiment will carried out.
Optical diagnosis aims to predict the histology of a polyp based on its endoscopic features.
      This practice could avoid histopathological analysis and reduce the derived costs. Under this
      premise, the American Society of Gastrointestinal Endoscopy (ASGE), in its Preservation and
      Incorporation of Valuable endoscopic Innovations (PIVI) statement, established a diagnostic
      threshold for real-time endoscopic assessment of diminutive polyps. The rationale for its
      implementation is that the prevalence of advanced histology in polyps < 5mm is very low
      (0.5%).

      Several studies have demonstrated that optical diagnosis of small polyps is safe and feasible
      in clinical practice and comparable to the current gold standard, histopathology. However,
      the accuracy of optical diagnosis has been shown to be insufficient in community-based
      practices or in non-expert hands and the diagnosis is even more difficult in diminutive
      polyps < 3 mm in which the discrepancy between the endoscopic and pathological diagnosis is
      about 15%.

      Artificial Intelligence (AI) has emerged as a help tool for polyp characterization.

      Aiming to improve optical diagnosis using AI methods, we propose a hybrid approach that
      combines DL with characteristics of polyps manually indicated by endoscopists (HybridAI).
observational model: Cohort
time perspective: Prospective
measure: Accuracy of the computer-aided system for predicting polyps histology in real clinical practice
time frame: One year
description: The results of the computer-aided system prediction will be compared with the final pathology report, which is the gold standard
intervention type: Other
intervention name: AUTOMATED POLYP CLASSIFICATION
description: COLONIC POLYP HISTOLOGY PREDICTION IN WHITE LIGHT IMAGES COMBINING ARTIFICIAL INTELLIGENCE AND CLINICAL INFORMATION
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
healthy volunteers: No
facility: 
country: Spain
citation: Sanchez-Montes C, Sanchez FJ, Bernal J, Cordova H, Lopez-Ceron M, Cuatrecasas M, Rodriguez de Miguel C, Garcia-Rodriguez A, Garces-Duran R, Pellise M, Llach J, Fernandez-Esparrach G. Computer-aided prediction of polyp histology on white light colonoscopy using surface pattern analysis. Endoscopy. 2019 Mar;51(3):261-265. doi: 10.1055/a-0732-5250. Epub 2018 Oct 25.
PMID: 30360010
citation: Bernal J, Histace A, Masana M, Angermann Q, Sanchez-Montes C, Rodriguez de Miguel C, Hammami M, Garcia-Rodriguez A, Cordova H, Romain O, Fernandez-Esparrach G, Dray X, Sanchez FJ. GTCreator: a flexible annotation tool for image-based datasets. Int J Comput Assist Radiol Surg. 2019 Feb;14(2):191-201. doi: 10.1007/s11548-018-1864-x. Epub 2018 Sep 25.
PMID: 30255462
citation: Byrne MF, Chapados N, Soudan F, Oertel C, Linares Perez M, Kelly R, Iqbal N, Chandelier F, Rex DK. Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model. Gut. 2019 Jan;68(1):94-100. doi: 10.1136/gutjnl-2017-314547. Epub 2017 Oct 24.
PMID: 29066576
responsible party type: Principal Investigator
investigator affiliation: Hospital Clinic of Barcelona
investigator full name: Ana García-Rodríguez
investigator title: Principal Investigator
mesh term: Adenoma
mesh term: Polyps
sharing ipd: No