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.
Study Type
INTERVENTIONAL
Allocation
NON_RANDOMIZED
Purpose
SCREENING
Masking
NONE
Enrollment
145
Supplemental MRI (in addition to standard of care MRI).
Artificial intelligence software
UMass Medical School
Worcester, Massachusetts, United States
CDR Mirai Assessment versus CDR Traditional High Risk Screening
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.
Time frame: 1.5 years (duration of patient recruitment and outcome data collection)
Cancer development within study population versus general population of average risk women
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.
Time frame: 1.5 years (duration of patient recruitment and outcome data collection)
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