xml: <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/NCT05968157</url>
    </required_header>
    <id_info>
      <org_study_id>STUDY000000485</org_study_id>
      <nct_id>NCT05968157</nct_id>
    </id_info>
    <brief_title>MIRAI-MRI: Comparing Screening MRI for Patients at High Risk for Breast Cancer Identified by Mirai and Tyrer-Cuzick</brief_title>
    <official_title>MIRAI-MRI: Comparing Screening MRI for Patients at High Risk for Breast Cancer Identified by Mirai and Tyrer-Cuzick</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>University of Massachusetts, Worcester</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
    </sponsors>
    <source>University of Massachusetts, Worcester</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>
        Accurate risk assessment is essential for the success of population screening programs and&#xD;
        early detection efforts in breast cancer. Mirai is a new deep learning model based on full&#xD;
        resolution mammograms.&#xD;
  &#xD;
        Mirai is a mammography-based deep learning model designed to predict risk at multiple&#xD;
        timepoints, leverage potentially missing risk factor information, and produce predictions&#xD;
        that are consistent across mammography machines. Mirai was trained on a large dataset from&#xD;
        Massachusetts General Hospital (MGH) in the United States and found to be significantly more&#xD;
        accurate than the Tyrer-Cuzick model, a current clinical standard.&#xD;
  &#xD;
        The primary aim of this study is to prospectively quantify the clinical benefit (i.e. MRI/CEM&#xD;
        cancer detection rate) of Mirai-based guidelines and to compare them to the current standard&#xD;
        of care.&#xD;
  &#xD;
          1. Conduct a prospective study where patients who are identified as high risk by Mirai&#xD;
             guidelines are invited to receive supplemental MRI within 12 months.&#xD;
  &#xD;
          2. Compare cancer outcomes between patients only identified as high risk by Mirai and&#xD;
             patients identified as high risk by existing guidelines The secondary aim is to study&#xD;
             the impact of new guidelines by race and ethnicity, to ensure equitable improvements in&#xD;
             cancer screening.&#xD;
      </textblock>
    </brief_summary>
    <overall_status>Not yet recruiting</overall_status>
    <start_date type="Anticipated">August 1, 2023</start_date>
    <completion_date type="Anticipated">January 1, 2025</completion_date>
    <primary_completion_date type="Anticipated">September 1, 2024</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>Screening</primary_purpose>
      <masking>None (Open Label)</masking>
    </study_design_info>
    <primary_outcome>
      <measure>CDR Mirai Assessment versus CDR Traditional High Risk Screening</measure>
      <time_frame>1.5 years (duration of patient recruitment and outcome data collection)</time_frame>
      <description>Cancer detection rate from breast MRI following Mirai assessment of high risk on a screening mammogram performed less than 1 year ago and compared with established CDR in traditional high risk screening.</description>
    </primary_outcome>
    <secondary_outcome>
      <measure>Cancer development within study population versus general population of average risk women</measure>
      <time_frame>1.5 years (duration of patient recruitment and outcome data collection)</time_frame>
      <description>On subsequent follow-up with standard of care, assessment of what percentage of the study population develops breast cancer as compared to the general population of women at average risk of breast cancer.</description>
    </secondary_outcome>
    <number_of_arms>1</number_of_arms>
    <enrollment type="Anticipated">500</enrollment>
    <condition>Breast Cancer</condition>
    <arm_group>
      <arm_group_label>Breast MRI Screening for High Risk Patients</arm_group_label>
      <arm_group_type>Other</arm_group_type>
      <description>Breast MRI will be recommended for patients who are deemed high risk by either the traditional model (Tyrer Cusick) or the Mirai model.</description>
    </arm_group>
    <intervention>
      <intervention_type>Procedure</intervention_type>
      <intervention_name>Breast MRI</intervention_name>
      <description>Patients who are identified as high risk for breast cancer by Mirai guidelines are invited to receive supplemental MRI. In addition, the patients eligible for MRI screening according to other guidelines will also be screened to collect additional comparison data. Cancer outcomes will then be compared between patients only identified as high risk by Mirai and patients identified as high risk by existing guidelines.</description>
      <arm_group_label>Breast MRI Screening for High Risk Patients</arm_group_label>
    </intervention>
    <eligibility>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  Women who were identified as high risk on the retrospective study (dating from&#xD;
               2017-2023) using MIRAI will be recruited and consented for the prospective study&#xD;
  &#xD;
            -  Women over 40 years of age identified as high risk according to traditional guidelines&#xD;
               will also be potentially eligible for this study&#xD;
  &#xD;
            -  Following consent and enrollment in the study, a participant will subsequently receive&#xD;
               the following:&#xD;
  &#xD;
                 1. These patients will be invited to receive a supplemental MRI examination&#xD;
                    currently considered the most sensitive test for breast cancer detection.&#xD;
  &#xD;
                 2. Any positive diagnosis on MRI will be followed by biopsy to confirm 'truth&quot; of&#xD;
                    diagnosis.&#xD;
  &#xD;
            -  To be selected, a given record must include the following:&#xD;
  &#xD;
                 1. A report of a routine screening mammogram or diagnostic mammogram, and&#xD;
                    availability of the DICOM images from that report with the PACS system.&#xD;
  &#xD;
                 2. Reports of all follow up screening and diagnostic studies documented on PACS.&#xD;
  &#xD;
                 3. Some may have interventional procedures (as long as all of these are done at one&#xD;
                    of Umass sites) and documentation of these biopsy results in the hospitals EHR.&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  Under age 40. Women under 40 years are not routinely xrayed with a mammogram.&#xD;
  &#xD;
            -  Xray breast cancer screening imaging study that has artifacts, corruption, or other&#xD;
               image quality degradation.&#xD;
  &#xD;
            -  Pregnant patients because they do not routinely receive screening mammogram&#xD;
  &#xD;
            -  Adult male patients with breast cancer&#xD;
        </textblock>
      </criteria>
      <gender>Female</gender>
      <minimum_age>40 Years</minimum_age>
      <maximum_age>N/A</maximum_age>
      <healthy_volunteers>No</healthy_volunteers>
    </eligibility>
    <overall_official>
      <last_name>Mohammed Shazeeb, PhD</last_name>
      <role>Principal Investigator</role>
      <affiliation>UMass Chan Medical School</affiliation>
    </overall_official>
    <overall_contact>
      <last_name>Sara Schiller, MPH</last_name>
      <phone>7744417731</phone>
      <email>sara.schiller1@umassmed.edu</email>
    </overall_contact>
    <location>
      <facility>
        <name>UMass Medical School</name>
        <address>
          <city>Worcester</city>
          <state>Massachusetts</state>
          <zip>01655</zip>
          <country>United States</country>
        </address>
      </facility>
      <contact>
        <last_name>Mohammed Shazeeb, PhD</last_name>
        <phone>508-856-4255</phone>
        <email>mohammed.shazeeb@umassmed.edu</email>
      </contact>
    </location>
    <location_countries>
      <country>United States</country>
    </location_countries>
    <verification_date>August 2023</verification_date>
    <study_first_submitted>July 21, 2023</study_first_submitted>
    <study_first_submitted_qc>July 21, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">August 1, 2023</study_first_posted>
    <last_update_submitted>August 4, 2023</last_update_submitted>
    <last_update_submitted_qc>August 4, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">August 8, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Principal Investigator</responsible_party_type>
      <investigator_affiliation>University of Massachusetts, Worcester</investigator_affiliation>
      <investigator_full_name>Mohammad Salman Shazeeb</investigator_full_name>
      <investigator_title>Director - Image Processing &amp; Analysis Core; Director of Preclinical MRI &amp; Co-Director of Scientific Affairs (Advanced MRI Center); UMass Chan Medical School</investigator_title>
    </responsible_party>
    <keyword>Breast Cancer</keyword>
    <keyword>Screening</keyword>
    <keyword>Artificial Intelligence</keyword>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Breast Neoplasms</mesh_term>
    </condition_browse>
    <patient_data>
      <sharing_ipd>No</sharing_ipd>
    </patient_data>
    <!-- Results have not yet been posted for this study                                          -->
  </clinical_study>
  
  
text: 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/NCT05968157
  org study id: STUDY000000485
  nct id: NCT05968157
  lead sponsor: 
  has dmc: No
  is fda regulated drug: No
  is fda regulated device: No
  Accurate risk assessment is essential for the success of population screening programs and
        early detection efforts in breast cancer. Mirai is a new deep learning model based on full
        resolution mammograms.
  
        Mirai is a mammography-based deep learning model designed to predict risk at multiple
        timepoints, leverage potentially missing risk factor information, and produce predictions
        that are consistent across mammography machines. Mirai was trained on a large dataset from
        Massachusetts General Hospital (MGH) in the United States and found to be significantly more
        accurate than the Tyrer-Cuzick model, a current clinical standard.
  
        The primary aim of this study is to prospectively quantify the clinical benefit (i.e. MRI/CEM
        cancer detection rate) of Mirai-based guidelines and to compare them to the current standard
        of care.
  
          1. Conduct a prospective study where patients who are identified as high risk by Mirai
             guidelines are invited to receive supplemental MRI within 12 months.
  
          2. Compare cancer outcomes between patients only identified as high risk by Mirai and
             patients identified as high risk by existing guidelines The secondary aim is to study
             the impact of new guidelines by race and ethnicity, to ensure equitable improvements in
             cancer screening.
  allocation: N/A
  intervention model: Single Group Assignment
  primary purpose: Screening
  masking: None (Open Label)
  measure: CDR Mirai Assessment versus CDR Traditional High Risk Screening
  time frame: 1.5 years (duration of patient recruitment and outcome data collection)
  description: Cancer detection rate from breast MRI following Mirai assessment of high risk on a screening mammogram performed less than 1 year ago and compared with established CDR in traditional high risk screening.
  measure: Cancer development within study population versus general population of average risk women
  time frame: 1.5 years (duration of patient recruitment and outcome data collection)
  description: On subsequent follow-up with standard of care, assessment of what percentage of the study population develops breast cancer as compared to the general population of women at average risk of breast cancer.
  arm group label: Breast MRI Screening for High Risk Patients
  arm group type: Other
  description: Breast MRI will be recommended for patients who are deemed high risk by either the traditional model (Tyrer Cusick) or the Mirai model.
  intervention type: Procedure
  intervention name: Breast MRI
  description: Patients who are identified as high risk for breast cancer by Mirai guidelines are invited to receive supplemental MRI. In addition, the patients eligible for MRI screening according to other guidelines will also be screened to collect additional comparison data. Cancer outcomes will then be compared between patients only identified as high risk by Mirai and patients identified as high risk by existing guidelines.
  arm group label: Breast MRI Screening for High Risk Patients
  criteria: 
  gender: Female
  minimum age: 40 Years
  maximum age: N/A
  healthy volunteers: No
  last name: Mohammed Shazeeb, PhD
  role: Principal Investigator
  affiliation: UMass Chan Medical School
  last name: Sara Schiller, MPH
  phone: 7744417731
  email: sara.schiller1@umassmed.edu
  facility: 
  contact: 
  country: United States
  responsible party type: Principal Investigator
  investigator affiliation: University of Massachusetts, Worcester
  investigator full name: Mohammad Salman Shazeeb
  investigator title: Director - Image Processing & Analysis Core; Director of Preclinical MRI & Co-Director of Scientific Affairs (Advanced MRI Center); UMass Chan Medical School
  mesh term: Breast Neoplasms
  sharing ipd: No