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

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 Online supplemental material is available for this article. See also the editorial by Sitek and Wolfe in this issue.

Deep Learning Model to Assess Cancer Risk on the Basis of a Breast MR Image Alone.

OBJECTIVE. The purpose of this study is to develop an image-based deep learning (DL) model to predict the 5-year risk of breast cancer on the basis of a single breast MR image from a screening examination. MATERIALS AND METHODS. We collected 1656 consecutive breast MR images from screening examinations performed for 1183 high-risk women from January 2011 to June 2013, to predict the risk of cancer developing within 5 years of the screening. Women who lacked a 5-year screening follow-up examination and women who had cancer other than primary breast cancer develop in their breast were excluded from the study. We developed a logistic regression model based on traditional risk factors (the risk factor logistic regression [RF-LR] model) and a DL model based on the MR image alone (the Image-DL model). Examinations occurring within 6 months of a cancer diagnosis were excluded from the testing sets in each fold of cross-validation. We compared our models against the Tyrer-Cuzick (TC) model. All models were evaluated using mean (± SD) AUC values and observed-to-expected (OE) ratios across 10-fold cross-validation. RESULTS. The RF-LR and Image-DL models achieved mean AUC values of 0.558 ± 0.108 and 0.638 ± 0.094, respectively. In contrast, the TC model achieved an AUC value of 0.493 ± 0.092. The Image-DL and RF-LR models achieved mean OE ratios of 0.993 ± 0.658 and 0.828 ± 0.181, compared with the mean OE ratio of 1.091 ± 0.255 obtained using the TC model. CONCLUSION. Our DL model can assess the 5-year cancer risk on the basis of a breast MR image alone, and it showed improved individual risk discrimination when compared with a state-of-the-art risk assessment model. These results offer promising preliminary data regarding the potential of image-based risk assessment models to support more personalized care.