This study is not yet accepting patients

Mirai-MRI: Validation of AI Models for Breast Cancer Risk

a study on Cancer Risk Breast Cancer

Summary

Eligibility
for females ages 40-89 (full criteria)
Healthy Volunteers
healthy people welcome
Location
at San Francisco, California and other locations
Dates
study started
study ends around
Principal Investigator
by Maggie Chung, MD
Headshot of Maggie Chung
Maggie Chung

Description

Summary

This is a multi-center, single arm trial to evaluate the cancer detection rate of supplemental screening magnetic resonance imaging (MRI) in participants who are high-risk by Mirai-MRI assessment. Mirai is an accurate cancer risk model based on full-resolution mammograms.

Official Title

Mirai-MRI: Multi-site Prospective Validation of AI Models for Breast Cancer Risk

Details

PRIMARY OBJECTIVE:

  1. To estimate the cancer detection rate (CDR) of supplemental screening breast MRI in participants who are high-risk by the Mirai model

SECONDARY OBJECTIVES:

  1. To estimate the CDR of supplemental screening breast MRI in participants with high Mirai risk and low (<20%) lifetime Tyrer-Cuzick (TC) risk.

II. To compare the CDR of supplemental screening breast MRI in participants with high Mirai risk and high (≥ 20%) lifetime TC risk versus CDR in participants with high Mirai risk and low (<20%) lifetime TC risk.

III. To estimate the positive predictive value (PPV)1, PPV2, and PPV3 of supplemental MRI screening in participants with high Mirai risk.

IV. To estimate the false positive rate (1 - specificity) of supplemental screening MRI in participants with high Mirai risk.

  1. To evaluate the tumor size, stage, grade, histological and molecular subtypes, Ki-67 percentage, and lymph node involvement of cancers detected on supplemental screening MRI in Mirai high risk participants.

VI. To evaluate the association between race/ethnicity, menopausal status, hormonal status, breast density, family history of cancer, and CDR in Mirai high-risk participants.

Keywords

Cancer Risk, Breast Cancer Risk, Screening, BI-RADS, Breast Neoplasms, Magnetic resonance imaging (MRI), Artificial Intelligence (AI), Supplemental MRI

Eligibility

You can join if…

Open to females ages 40-89

  1. All asymptomatic participants assigned female at birth, ages 40-89 years old with screening mammography assessed as Breast Imaging Reporting and Data System (BI-RADS) 1, BI-RADS 2, or initial BI-RADS 0 with subsequent diagnostic mammogram assessed as BI-RADS 1 or BI-RADS 2.
  2. Screening mammogram assessed as high-risk by Mirai (top 3 percentile of 2-year risk).
  3. Availability of a routine screening mammogram report, along with a subsequent diagnostic mammogram report if applicable, and access to the corresponding Digital Imaging and Communications in Medicine (DICOM) images.
  4. Ability to understand and the willingness to sign a written informed consent document.

You CAN'T join if...

  1. Screening mammogram that is assessed as BI-RADS 0 for technical recall.
  2. Contraindications for MRI:
    1. Metallic foreign body in the eye.
    2. MRI unsafe implants and/or medical devices.
    3. Adverse reaction to a (gadolinium-based) contrast agent in the past.
    4. Pregnant women.
    5. Claustrophobia.
    6. Exceeds site specific size and/or weight limit for MRI.
  3. If the participant meets site-specific criteria for screening estimated glomerular filtration rate (eGFR) prior to MRI, participants with severely impaired renal function (GFR < 30 mL/min) will be excluded. According to University of California, San Francisco's policy for MRI with contrast (gadolinium-containing), serum creatinine with calculation of eGFR should be obtained within 6 weeks of the MRI study for participants with any of the following risk factors:
    1. History of "kidney disease" as an adult, including renal tumor or transplant.
    2. Diabetes treated with insulin or other prescribed medications.
    3. Hypertension (high blood pressure) requiring medication.
    4. Multiple myeloma.
    5. Solid organ transplant.
    6. History of severe hepatic disease/liver transplant/pending liver transplant.

Locations

  • UCSF
    San Francisco California 94143 United States
  • University of California, San Diego
    San Diego California 92093 United States
  • University of Chicago Comprehensive Cancer Center
    Chicago Illinois 60637 United States
  • University of Massachusetts Memorial Medical Center
    Worcester Massachusetts 01655 United States

Lead Scientist at UCSF

  • Maggie Chung, MD
    Maggie Chung, MD is an Assistant Professor in the Department of Radiology and Biomedical Imaging at the University of California, San Francisco, specializing in breast imaging.

Details

Status
not yet accepting patients
Start Date
Completion Date
(estimated)
Sponsor
University of California, San Francisco
ID
NCT07121972
Study Type
Interventional
Participants
Expecting 400 study participants
Last Updated