Breast Cancer
Conditions
Keywords
Breast Cancer, Screening, Artificial Intelligence
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.
Interventions
DIAGNOSTIC_TESTBreast MRI
Supplemental MRI (in addition to standard of care MRI).
DEVICEMIRAI
Artificial intelligence software
Sponsors
Massachusetts Institute of Technology
Breast Cancer Research Foundation
University of Massachusetts, Worcester
Study design
Allocation
NON_RANDOMIZED
Intervention model
PARALLEL
Primary purpose
SCREENING
Masking
NONE
Eligibility
Sex/Gender
FEMALE
Age
40 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Women who were identified as high risk on the retrospective study (dating from 2017-2025) 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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| CDR Mirai Assessment versus CDR Traditional High Risk Screening | 1.5 years (duration of patient recruitment and outcome data collection) | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Cancer development within study population versus general population of average risk women | 1.5 years (duration of patient recruitment and outcome data collection) | 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. |
Countries
United States
Contacts
Primary ContactSara Schiller, MPH
Outcome results
None listed