A Deep Learning Breast Cancer Risk Model for Precise Supplemental Screening

Can a deep learning (DL) model applied to screening mammograms more accurately identify patients at risk for future breast cancer and false-negative screening results than breast density?In a multisite cohort study of 123 091 consecutive screening mammograms in 67 019 patients, the DL model showed greater accuracy than breast density in estimating future breast cancer. False-negative rates were stratified across DL risk groups and were highest in high-risk patients.Findings of this study suggest that DL risk models could offer a more precise and equitable alternative to breast density as a policy criterion for determining access to supplemental breast imaging.

Corresponding Author: Leslie R. Lamb, MD, MSc, Department of Radiology, Massachusetts General Hospital, 55 Fruit St, Wang 240, Boston, MA 02114-2696 (lrlamb@mgh.harvard.edu).

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