MIRAI-MRI: Comparing Screening MRI for Patients at High Risk for Breast Cancer Identified by Mirai and Tyrer-Cuzick

Trial Title: MIRAI-MRI: Comparing Screening MRI for Patients at High Risk for Breast Cancer Identified by Mirai and Tyrer-Cuzick

NCT ID: NCT05968157

Condition: Breast Cancer

Conditions: Official terms:
Breast Neoplasms

Conditions: Keywords:
Breast Cancer
Screening
Artificial Intelligence

Study type: Interventional

Study phase: N/A

Overall status: Recruiting

Study design:

Allocation: N/A

Intervention model: Single Group Assignment

Primary purpose: Screening

Masking: None (Open Label)

Intervention:

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

Summary: 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.

Criteria for eligibility:
Criteria:
Inclusion Criteria: - Women who were identified as high risk on the retrospective study (dating from 2017-2023) using MIRAI will be recruited and consented for the prospective study - Women over 40 years of age identified as high risk according to traditional guidelines will also be potentially eligible for this study - Following consent and enrollment in the study, a participant will subsequently receive the following: 1. These patients will be invited to receive a supplemental MRI examination currently considered the most sensitive test for breast cancer detection. 2. Any positive diagnosis on MRI will be followed by biopsy to confirm 'truth" of diagnosis. - To be selected, a given record must include the following: 1. A report of a routine screening mammogram or diagnostic mammogram, and availability of the DICOM images from that report with the PACS system. 2. Reports of all follow up screening and diagnostic studies documented on PACS. 3. Some may have interventional procedures (as long as all of these are done at one of Umass sites) and documentation of these biopsy results in the hospitals EHR. Exclusion Criteria: - Under age 40. Women under 40 years are not routinely xrayed with a mammogram. - Xray breast cancer screening imaging study that has artifacts, corruption, or other image quality degradation. - Pregnant patients because they do not routinely receive screening mammogram - Adult male patients with breast cancer

Gender: Female

Minimum age: 40 Years

Maximum age: N/A

Healthy volunteers: No

Locations:

Facility:
Name: UMass Medical School

Address:
City: Worcester
Zip: 01655
Country: United States

Status: Recruiting

Contact:
Last name: Mohammed Shazeeb, PhD

Phone: 508-856-4255
Email: mohammed.shazeeb@umassmed.edu

Start date: February 16, 2024

Completion date: January 1, 2025

Lead sponsor:
Agency: University of Massachusetts, Worcester
Agency class: Other

Source: University of Massachusetts, Worcester

Record processing date: ClinicalTrials.gov processed this data on November 12, 2024

Source: ClinicalTrials.gov page: https://clinicaltrials.gov/ct2/show/NCT05968157