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Research Articles Hannah Oh, Lusine Yaghjyan, Rebecca J. Austin-Datta, Yujing J. Heng, Gabrielle M. Baker, Korsuk Sirinukunwattana, Adithya D. Vellal, Laura C. Collins, Divya Murthy, A. Heather Eliassen, Bernard A. Rosner and Rulla M. Tamimi DOI: 10.1158/1055-9965.EPI-20-1348 Published April 2021 Abstract Background: Early-life and adult anthropometrics are associated with breast density and breast cancer risk.
Summary We developed a deep learning model that uses full-field mammograms and traditional risk factors, and found that our model was more accurate than the Tyrer-Cusick model (version 8), a current clinical standard. Key Points ■ A deep learning (DL) mammography-based model identified women at high risk for breast cancer and placed 31% of all patients with future breast cancer in the top risk decile compared with only 18% by the Tyrer-Cuzick model (version 8).
Summary A deep learning algorithm was used to assess mammographic breast density at the level of an experienced mammographer during routine clinical practice. Implications for Patient Care ■ A deep learning algorithm was used to reliably and accurately assess mammographic breast density in a large clinical practice.
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