Research ArticleCancer

Toward robust mammography-based models for breast cancer risk

See allHide authors and affiliations

Science Translational Medicine  27 Jan 2021:
Vol. 13, Issue 578, eaba4373
DOI: 10.1126/scitranslmed.aba4373

You are currently viewing the abstract.

View Full Text

Log in to view the full text

Log in through your institution

Log in through your institution

Don’t risk it

Mammograms are a common but imperfect way of assessing breast cancer risk. Current U.S. breast cancer screening guidelines all use a component of cancer risk assessment to inform clinical course. Yala et al. developed a machine learning model called “Mirai” to predict breast cancer risk based on traditional mammograms. The authors’ risk model performed better than Tyrer-Cuzick and previous deep learning models at identifying both 5-year breast cancer risk and high-risk patients across multiple international cohorts. Mirai also performed similarly across race and ethnicity categories, suggesting the potential for improvement in patient care across the board.

Abstract

Improved breast cancer risk models enable targeted screening strategies that achieve earlier detection and less screening harm than existing guidelines. To bring deep learning risk models to clinical practice, we need to further refine their accuracy, validate them across diverse populations, and demonstrate their potential to improve clinical workflows. We developed Mirai, a mammography-based deep learning model designed to predict risk at multiple timepoints, leverage potentially missing risk factor information, and produce predictions that are consistent across mammography machines. Mirai was trained on a large dataset from Massachusetts General Hospital (MGH) in the United States and tested on held-out test sets from MGH, Karolinska University Hospital in Sweden, and Chang Gung Memorial Hospital (CGMH) in Taiwan, obtaining C-indices of 0.76 (95% confidence interval, 0.74 to 0.80), 0.81 (0.79 to 0.82), and 0.79 (0.79 to 0.83), respectively. Mirai obtained significantly higher 5-year ROC AUCs than the Tyrer-Cuzick model (P < 0.001) and prior deep learning models Hybrid DL (P < 0.001) and Image-Only DL (P < 0.001), trained on the same dataset. Mirai more accurately identified high-risk patients than prior methods across all datasets. On the MGH test set, 41.5% (34.4 to 48.5) of patients who would develop cancer within 5 years were identified as high risk, compared with 36.1% (29.1 to 42.9) by Hybrid DL (P = 0.02) and 22.9% (15.9 to 29.6) by the Tyrer-Cuzick model (P < 0.001).

View Full Text

Stay Connected to Science Translational Medicine