|1R01CA275074-01A1 Interpret this number
|Mayo Clinic Rochester
|Evaluation of Novel Tomosynthesis Density Measures in Breast Cancer Risk Prediction
Breast screening has rapidly transitioned in the US to digital breast tomosynthesis (DBT), an x-ray technology
in which 3-D images are reconstructed from a limited number of low-dose x-ray source projections. DBT offers
superior tissue visualization allowing for the direct measurement of the actual volume of dense tissue, rather
than an estimated percent or volume (from a 2-D mammogram). Since breast density is a strong predictor of
masking and risk, DBT volumetric density measures, including our recently developed and first of its kind, fully
automated 3-D measure, have the potential to improve individualized breast cancer (BC) risk prediction. No
studies to date have evaluated DBT volumetric density measures in large, diverse cohorts or subpopulations to
understand the impact of these measures to improve prediction of masking and risk in order to tailor prevention
and screening approaches. Our goal is to comprehensively examine DBT volumetric density measures
as risk factors for invasive, interval and advanced BC, and evaluate their impact on clinically relevant
BC risk models and artificial intelligence (AI) algorithms across multiple racial groups. We propose this
research in three large breast screening cohorts that perform routine DBT, each with comprehensive clinical
risk factors, multiple DBT per woman, follow-up and BC outcomes. Specifically, we will establish a nested
case-control study of over 3,000 invasive BC cases and 9,000 controls matched on facility, age, race, ethnicity,
date of enrollment DBT and follow-up time and estimate novel research and commercial DBT volumetric
measures as well as ascertain clinical BI-RADS density from DBT screening exams from enrollment up to 6
months prior to diagnosis (or corresponding follow-up for controls). In Aim 1, we will evaluate DBT volumetric
density measures and their combinations as risk factors for invasive, interval and advanced BC, at enrollment
DBT exam, as well as DBT exams within five years of the cancer (or follow-up for controls), using state of the
art commercial and research algorithms. We will also assess differences in associations by time of DBT exam,
age, race, menopausal status and body mass index. In Aim 2, we will evaluate the contribution of DBT
volumetric density measures to clinical BC risk models, including the BCSC 5-year risk model, the novel BCSC
6-year cumulative risk model for advanced cancer, and secondarily, the Tyrer-Cuzick model for both 5 and 10-
year risk. Using these results, we will determine the impact of DBT density measures on high-risk thresholds
for preventative therapy and tailored imaging. Finally, in Aim 3, we will evaluate the contribution of DBT
volumetric density measures to three novel AI algorithms developed for BC risk and detection, with risk of
invasive, advanced and interval BC in the short- and longer-term. Our innovative study will be the largest to
inform how novel DBT volumetric density measures can augment BC risk-stratification and prediction
across multiple races and build a diverse resource to evaluate new DBT measures and risk models as
they evolve. These findings will build an evidence base to inform personalized prevention approaches.
Impact of Artificial Intelligence System and Volumetric Density on Risk Prediction of Interval, Screen-Detected, and Advanced Breast Cancer.
, Scott C.G.
, Norman A.D.
, Khanani S.A.
, Jensen M.R.
, Hruska C.B.
, Brandt K.R.
, Winham S.J.
, Kerlikowske K.
Journal of clinical oncology : official journal of the American Society of Clinical Oncology, 2023-06-10; 41(17), p. 3172-3183.