<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/NCT03829423</url>
  </required_header>
  <id_info>
    <org_study_id>4901</org_study_id>
    <nct_id>NCT03829423</nct_id>
  </id_info>
  <brief_title>An Enhanced Artificial Intelligence Breast MRI Interpretation System</brief_title>
  <acronym>IntelliScan</acronym>
  <official_title>A Comparative Single-centre Study to Evaluate an Enhanced Artificial Intelligence Breast MRI Interpretation System in Women Over 20 With Breast Lesions</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Jamil Kanfoud</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>Brunel University London</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>First Option Software Ltd.</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>Teesside University</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>
      Interpretation of breast MR images is a very time-consuming process and places a great burden&#xD;
      on breast radiologists. This project aims to develop a technical solution that addresses this&#xD;
      healthcare challenge by developing a system that is able to automatically interpret breast MR&#xD;
      images in order to aid the radiologist in their diagnosis.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Breast cancer is the most common type of cancer in women worldwide, with nearly 1.7 million&#xD;
      new cases diagnosed in 2015. In the UK, one in five cases of breast cancer results in a&#xD;
      fatality. The IntelliScan project aims to develop a technological solution that addresses a&#xD;
      significant healthcare challenge. IntelliScan will develop a software system that will be&#xD;
      able to interpret breast MR images automatically in order to identify potential breast&#xD;
      cancers.&#xD;
&#xD;
      Regular MRI screening of the breast is offered to women from the age of 20, who are at higher&#xD;
      risk of developing breast cancer. MR image sequences provide a large amount of information to&#xD;
      the radiologist and the interpretation of images is a manual process, which is very time&#xD;
      consuming. The high number of women eligible for MRI screening combined with the amount of&#xD;
      data provided by MRI scans places a great burden on healthcare systems. Therefore,&#xD;
      automatisation of this process would greatly relieve this burden and also has the potential&#xD;
      to provide more accurate diagnoses.&#xD;
&#xD;
      In this first study, the system's user interface as well as the algorithm will be developed&#xD;
      using existing MRI scans. Existing MRI scans with known breast anomalies will be used to&#xD;
      develop the decision-making basis for the algorithm. The system will then be tested using&#xD;
      existing MRI scans without information about possible anomalies and results will be compared&#xD;
      to results from the software system currently in use. In addition, the user-friendliness of&#xD;
      the system's user interface will also be evaluated.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Unknown status</overall_status>
  <last_known_status>Not yet recruiting</last_known_status>
  <start_date type="Anticipated">April 2019</start_date>
  <completion_date type="Anticipated">July 2020</completion_date>
  <primary_completion_date type="Anticipated">January 2020</primary_completion_date>
  <phase>N/A</phase>
  <study_type>Interventional</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <allocation>N/A</allocation>
    <intervention_model>Single Group Assignment</intervention_model>
    <primary_purpose>Diagnostic</primary_purpose>
    <masking>Single (Care Provider)</masking>
    <masking_description>Retrospective breast MRI datasets with all personal patient information removed</masking_description>
  </study_design_info>
  <primary_outcome>
    <measure>Sensitivity/specificity of breast interpretation algorithm</measure>
    <time_frame>1 year</time_frame>
    <description>Sensitivity and specificity of the information provided by the breast interpretation algorithm to be above 90% and 70%, respectively</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>Time required for diagnosis</measure>
    <time_frame>1 year</time_frame>
    <description>The time required to arrive at a diagnosis using IntelliScan should be less than using manual procedures</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>User-friendliness of IntelliScan system</measure>
    <time_frame>1 year</time_frame>
    <description>Obviousness score for categorisation of beast lesions (0 [not obvious] to 100 [extremely obvious]); ease-of-use score for IntelliScan system (0 [not easy to use] to 10 [extremely easy to use])</description>
  </secondary_outcome>
  <enrollment type="Anticipated">1526</enrollment>
  <condition>Breast Cancer</condition>
  <intervention>
    <intervention_type>Diagnostic Test</intervention_type>
    <intervention_name>Breast MRI interpretation</intervention_name>
    <description>Analysis and interpretation of breast MRI sequences by a specially developed breast MRI interpretation algorithm</description>
  </intervention>
  <eligibility>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Breast MRI scans&#xD;
&#xD;
          -  MRI examinations undertaken at partner NHS Trust in the UK&#xD;
&#xD;
          -  MRI examinations undertaken on the MRI system currently installed at partner NHS Trust&#xD;
             site (since 2008)&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  Incomplete breast MRI datasets&#xD;
&#xD;
          -  Breast MRI without lesions&#xD;
&#xD;
          -  Breast lesion on MRI not biopsied&#xD;
      </textblock>
    </criteria>
    <gender>Female</gender>
    <minimum_age>20 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Steve Dennis, B.Sc.</last_name>
    <role>Study Director</role>
    <affiliation>First Option Software</affiliation>
  </overall_official>
  <overall_contact>
    <last_name>Jamil Kanfoud, M.Eng.</last_name>
    <phone>+44(0)01223940</phone>
    <phone_ext>310</phone_ext>
    <email>jamil.kanfoud@brunel.ac.uk</email>
  </overall_contact>
  <overall_contact_backup>
    <last_name>Susann Wolfram, PhD</last_name>
    <phone>+44(0)1223940</phone>
    <phone_ext>341</phone_ext>
    <email>s.wolfram@tees.ac.uk</email>
  </overall_contact_backup>
  <verification_date>February 2019</verification_date>
  <study_first_submitted>January 30, 2019</study_first_submitted>
  <study_first_submitted_qc>February 1, 2019</study_first_submitted_qc>
  <study_first_posted type="Actual">February 4, 2019</study_first_posted>
  <last_update_submitted>February 5, 2019</last_update_submitted>
  <last_update_submitted_qc>February 5, 2019</last_update_submitted_qc>
  <last_update_posted type="Actual">February 6, 2019</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor-Investigator</responsible_party_type>
    <investigator_affiliation>Teesside University</investigator_affiliation>
    <investigator_full_name>Jamil Kanfoud</investigator_full_name>
    <investigator_title>Head of Brunel Innovation Centre</investigator_title>
  </responsible_party>
  <keyword>artificial intelligence</keyword>
  <keyword>magnetic resonance imaging</keyword>
  <keyword>breast cancer</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Breast Neoplasms</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/NCT03829423
org study id: 4901
nct id: NCT03829423
lead sponsor: 
collaborator: 
collaborator: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
Interpretation of breast MR images is a very time-consuming process and places a great burden
      on breast radiologists. This project aims to develop a technical solution that addresses this
      healthcare challenge by developing a system that is able to automatically interpret breast MR
      images in order to aid the radiologist in their diagnosis.
Breast cancer is the most common type of cancer in women worldwide, with nearly 1.7 million
      new cases diagnosed in 2015. In the UK, one in five cases of breast cancer results in a
      fatality. The IntelliScan project aims to develop a technological solution that addresses a
      significant healthcare challenge. IntelliScan will develop a software system that will be
      able to interpret breast MR images automatically in order to identify potential breast
      cancers.

      Regular MRI screening of the breast is offered to women from the age of 20, who are at higher
      risk of developing breast cancer. MR image sequences provide a large amount of information to
      the radiologist and the interpretation of images is a manual process, which is very time
      consuming. The high number of women eligible for MRI screening combined with the amount of
      data provided by MRI scans places a great burden on healthcare systems. Therefore,
      automatisation of this process would greatly relieve this burden and also has the potential
      to provide more accurate diagnoses.

      In this first study, the system's user interface as well as the algorithm will be developed
      using existing MRI scans. Existing MRI scans with known breast anomalies will be used to
      develop the decision-making basis for the algorithm. The system will then be tested using
      existing MRI scans without information about possible anomalies and results will be compared
      to results from the software system currently in use. In addition, the user-friendliness of
      the system's user interface will also be evaluated.
allocation: N/A
intervention model: Single Group Assignment
primary purpose: Diagnostic
masking: Single (Care Provider)
masking description: Retrospective breast MRI datasets with all personal patient information removed
measure: Sensitivity/specificity of breast interpretation algorithm
time frame: 1 year
description: Sensitivity and specificity of the information provided by the breast interpretation algorithm to be above 90% and 70%, respectively
measure: Time required for diagnosis
time frame: 1 year
description: The time required to arrive at a diagnosis using IntelliScan should be less than using manual procedures
measure: User-friendliness of IntelliScan system
time frame: 1 year
description: Obviousness score for categorisation of beast lesions (0 [not obvious] to 100 [extremely obvious]); ease-of-use score for IntelliScan system (0 [not easy to use] to 10 [extremely easy to use])
intervention type: Diagnostic Test
intervention name: Breast MRI interpretation
description: Analysis and interpretation of breast MRI sequences by a specially developed breast MRI interpretation algorithm
criteria: 
gender: Female
minimum age: 20 Years
maximum age: N/A
healthy volunteers: No
last name: Steve Dennis, B.Sc.
role: Study Director
affiliation: First Option Software
last name: Jamil Kanfoud, M.Eng.
phone: +44(0)01223940
phone ext: 310
email: jamil.kanfoud@brunel.ac.uk
last name: Susann Wolfram, PhD
phone: +44(0)1223940
phone ext: 341
email: s.wolfram@tees.ac.uk
responsible party type: Sponsor-Investigator
investigator affiliation: Teesside University
investigator full name: Jamil Kanfoud
investigator title: Head of Brunel Innovation Centre
mesh term: Breast Neoplasms