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A Deep Learning Mammography-based Model for Improved Breast Cancer Risk Prediction

Adam Yala, Constance Lehman, Tal Schuster, Tally Portnoi, Regina Barzilay

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).
  • Our hybrid DL model is equally accurate for white and African American women (area under the receiver operating characteristic curve [AUC], 0.71 for both ethnicities) whereas the Tyrer-Cuzick model AUC was 0.62 and 0.45 for women who were white and African American, respectively; the AUC improvement was significant for women who were white (P, .001) and African American (P, .01).
  • When our hybrid DL model was compared with breast density, we found that patients with nondense breasts and model-assessed high risk had 3.9 times the cancer incidence of patients with dense breasts and model-assessed low risk.

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