A Deep Learning Mammography-based Model for Improved Breast Cancer Risk Prediction
Adam Yala , Constance Lehman, Tal Schuster, Tally Portnoi, Regina Barzilay
Author Affiliations
From the Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 32 Vassar St, 32-G484, Cambridge, MA 02139 (A.Y., T.S., T.P., R.B.); and Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (C.L.).
Address correspondence to A.Y. (e-mail: adamyala@csail.mit.edu).
Published Online:May 7 2019https://doi.org/10.1148/radiol.2019182716
See editorial byArkadiusz Sitek
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Abstract
Background
Mammographic density improves the accuracy of breast cancer risk models. However, the use of breast density is limited by subjective assessment, variation across radiologists, and restricted data. A mammography-based deep learning (DL) model may provide more accurate risk prediction.

Purpose
To develop a mammography-based DL breast cancer risk model that is more accurate than established clinical breast cancer risk models.

Materials and Methods
This retrospective study included 88 994 consecutive screening mammograms in 39 571 women between January 1, 2009, and December 31, 2012. For each patient, all examinations were assigned to either training, validation, or test sets, resulting in 71 689, 8554, and 8751 examinations, respectively. Cancer outcomes were obtained through linkage to a regional tumor registry. By using risk factor information from patient questionnaires and electronic medical records review, three models were developed to assess breast cancer risk within 5 years: a risk-factor-based logistic regression model (RF-LR) that used traditional risk factors, a DL model (image-only DL) that used mammograms alone, and a hybrid DL model that used both traditional risk factors and mammograms. Comparisons were made to an established breast cancer risk model that included breast density (Tyrer-Cuzick model, version 8 [TC]). Model performance was compared by using areas under the receiver operating characteristic curve (AUCs) with DeLong test (P < .05).

Results
The test set included 3937 women, aged 56.20 years ± 10.04. Hybrid DL and image-only DL showed AUCs of 0.70 (95% confidence interval [CI]: 0.66, 0.75) and 0.68 (95% CI: 0.64, 0.73), respectively. RF-LR and TC showed AUCs of 0.67 (95% CI: 0.62, 0.72) and 0.62 (95% CI: 0.57, 0.66), respectively. Hybrid DL showed a significantly higher AUC (0.70) than TC (0.62; P < .001) and RF-LR (0.67; P = .01).

Conclusion
Deep learning models that use full-field mammograms yield substantially improved risk discrimination compared with the Tyrer-Cuzick (version 8) model.

© RSNA, 2019