TY - JOUR
T1 - Impact of a Deep Learning Model for Predicting Mammographic Breast Density in Routine Clinical Practice
AU - Dontchos, Brian N.
AU - Cavallo-Hom, Katherine
AU - Lamb, Leslie R.
AU - Mercaldo, Sarah F.
AU - Eklund, Martin
AU - Dang, Pragya
AU - Lehman, Constance D.
N1 - Publisher Copyright:
© 2022 American College of Radiology
PY - 2022/9
Y1 - 2022/9
N2 - Objective: Legislation in 38 states requires patient notification of dense mammographic breast tissue because increased density is a marker of breast cancer risk and can limit mammographic sensitivity. Because radiologist density assessments vary widely, our objective was to implement and measure the impact of a deep learning (DL) model on mammographic breast density assessments in clinical practice. Methods: This institutional review board–approved prospective study identified consecutive screening mammograms performed across three clinical sites over two periods: 2017 period (January 1, 2017, through September 30, 2017) and 2019 period (January 1, 2019, through September 30, 2019). The DL model was implemented at sites A (academic practice) and B (community practice) in 2018 for all screening mammograms. Site C (community practice) was never exposed to the DL model. Prospective densities were evaluated, and multivariable logistic regression models evaluated the odds of a dense mammogram classification as a function of time and site. Results: We identified 85,124 consecutive screening mammograms across the three sites. Across time intervals, odds of a dense classification decreased at sites exposed to the DL model, site A (adjusted odds ratio [aOR], 0.93; 95% confidence interval [CI], 0.86-0.99; P = .024) and site B (aOR, 0.81 [95% CI, 0.70-0.93]; P = .003), and odds increased at the site unexposed to the model (site C) (aOR, 1.13 [95% CI, 1.01-1.27]; P = .033). Discussion: A DL model reduces the odds of screening mammograms categorized as dense. Accurate density assessments could help health care systems more appropriately use limited supplemental screening resources and help better inform traditional clinical risk models.
AB - Objective: Legislation in 38 states requires patient notification of dense mammographic breast tissue because increased density is a marker of breast cancer risk and can limit mammographic sensitivity. Because radiologist density assessments vary widely, our objective was to implement and measure the impact of a deep learning (DL) model on mammographic breast density assessments in clinical practice. Methods: This institutional review board–approved prospective study identified consecutive screening mammograms performed across three clinical sites over two periods: 2017 period (January 1, 2017, through September 30, 2017) and 2019 period (January 1, 2019, through September 30, 2019). The DL model was implemented at sites A (academic practice) and B (community practice) in 2018 for all screening mammograms. Site C (community practice) was never exposed to the DL model. Prospective densities were evaluated, and multivariable logistic regression models evaluated the odds of a dense mammogram classification as a function of time and site. Results: We identified 85,124 consecutive screening mammograms across the three sites. Across time intervals, odds of a dense classification decreased at sites exposed to the DL model, site A (adjusted odds ratio [aOR], 0.93; 95% confidence interval [CI], 0.86-0.99; P = .024) and site B (aOR, 0.81 [95% CI, 0.70-0.93]; P = .003), and odds increased at the site unexposed to the model (site C) (aOR, 1.13 [95% CI, 1.01-1.27]; P = .033). Discussion: A DL model reduces the odds of screening mammograms categorized as dense. Accurate density assessments could help health care systems more appropriately use limited supplemental screening resources and help better inform traditional clinical risk models.
KW - Breast density
KW - risk assessment
KW - screening mammography
UR - http://www.scopus.com/inward/record.url?scp=85131508054&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85131508054&partnerID=8YFLogxK
U2 - 10.1016/j.jacr.2022.04.001
DO - 10.1016/j.jacr.2022.04.001
M3 - Article
C2 - 35618002
AN - SCOPUS:85131508054
SN - 1546-1440
VL - 19
SP - 1021
EP - 1030
JO - Journal of the American College of Radiology
JF - Journal of the American College of Radiology
IS - 9
ER -