Digital screening mammograms: current status and future prospects.
Authors
Affiliations (5)
Affiliations (5)
- School of Medicine, University of California, San Francisco, Ca, USA.
- Department of Radiology, University of Washington School of Medicine, Seattle, Wa, USA.
- Fred Hutchinson Cancer Center, Seattle, Wa, USA.
- Departments of Medicine and Epidemiology, University of California, San Francisco, Ca, USA.
- General Internal Medicine Section, Department of Veterans Affairs, San Francisco, Ca, USA.
Abstract
Mammography has been a cornerstone of breast cancer screening for decades, demonstrating population-level reductions in breast cancer mortality. Limitations of mammography include reduced sensitivity in dense breast tissue, false-positive recalls and biopsies, and possible overdiagnosis. Emerging mammography technologies, including digital breast tomosynthesis (DBT), contrast-enhanced mammography (CEM), and artificial intelligence (AI) applications, seek to address these limitations and improve screening outcomes. In this review, we discuss endpoints for assessment of emerging mammography technologies, including cancer detection, interval cancer, and advanced cancer rates. We review clinical trial data for DBT, highlighting studies reporting interval and advanced cancer rates, and contrast performance in average-risk and high-risk populations. We review CEM as a new modality for supplemental screening in women with dense breasts. Lastly, we cover the rapidly expanding space of AI-supported mammography tools, including those for lesion detection, exam triage, breast density assessment, and risk prediction. Emerging technologies in digital screening mammography have demonstrated improvements in surrogate endpoints like cancer detection, however, their effect on clinically meaningful outcomes such as interval cancer and advanced cancer reduction remains unproven. To ensure that innovations translate into tangible patient benefit, future research should prioritize clinically relevant endpoints and real-world evaluation.