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.