Clinical trial NCT05968157: MIRAI-MRI: Comparing Screening MRI for Patients at High Risk for Breast Cancer Identified by Mirai and Tyrer-Cuzick
Official title: MIRAI-MRI: Comparing Screening MRI for Patients at High Risk for Breast Cancer Identified by Mirai and Tyrer-Cuzick
Study type: INTERVENTIONAL
Overall status: RECRUITING
Phase: NA
Conditions: Breast Cancer
Keywords: Breast Cancer
Screening
Artificial Intelligence
Lead sponsor: University of Massachusetts, Worcester
Sponsor class: OTHER
Collaborators: name: Massachusetts Institute of Technology
name: Breast Cancer Research Foundation
First posted: 2023-08-01
Enrollment: 200
Sex: FEMALE
Minimum age (years): 40
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NON_RANDOMIZED
Intervention model: PARALLEL
Primary purpose: SCREENING

Brief 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.


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

Primary outcomes:
measure: CDR Mirai Assessment versus CDR Traditional High Risk Screening
timeFrame: 1.5 years (duration of patient recruitment and outcome data collection)
Secondary outcomes:
measure: Cancer development within study population versus general population of average risk women
timeFrame: 1.5 years (duration of patient recruitment and outcome data collection)
Other outcomes:
Locations:
city: Worcester
country: United States
facility: UMass Medical School
state: Massachusetts
Study officials:
affiliation: UMass Chan Medical School
name: Mohammed Shazeeb, PhD
role: PRINCIPAL_INVESTIGATOR
References: