The benefits and potential harms of mammography-based screening for breast cancer are often a matter of debate. Here, I discuss the promises and limitations of a recent study that tested an artificial intelligence-based tool for the detection of breast cancer in digital mammograms in a large, prospective screening setting.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
References
Yala, A. et al. Multi-institutional validation of a mammography-based breast cancer risk model. J. Clin. Oncol. 40, 1732–1740 (2022).
Lehman, C. D. et al. Diagnostic accuracy of digital screening mammography with and without computer-aided detection. JAMA Intern. Med. 175, 1828–1837 (2015).
Nishikawa, R. M., Schmidt, R. A. & Metz, C. E. Computer-aided screening mammography. N. Engl. J. Med. 357, 84 (2007).
Lång, K. et al. Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial (MASAI): a clinical safety analysis of a randomised, controlled, non-inferiority, single-blinded, screening accuracy study. Lancet Oncol. 24, 936–944 (2023).
Yala, A., Lehman, C., Schuster, T., Portnoi, T. & Barzilay, R. A deep learning mammography-based model for improved breast cancer risk prediction. Radiology 292, 60–66 (2019).
Ng, A. Y. et al. Prospective implementation of AI-assisted screen reading to improve early detection of breast cancer. Nat. Med. 29, 3044–3049 (2023).
Sharma, N. et al. Multi-vendor evaluation of artificial intelligence as an independent reader for double reading in breast cancer screening on 275,900 mammograms. BMC Cancer 23, 460 (2023).
Conant, E. F. et al. Mammographic screening in routine practice: multisite study of digital breast tomosynthesis and digital mammography screenings. Radiology 307, e221571 (2023).
Zuckerman, S. P., Sprague, B. L., Weaver, D. L., Herschorn, S. D. & Conant, E. F. Multicenter evaluation of breast cancer screening with digital breast tomosynthesis in combination with synthetic versus digital mammography. Radiology 297, 545–553 (2020).
Yoon, J. H. et al. Standalone AI for breast cancer detection at screening digital mammography and digital breast tomosynthesis: a systematic review and meta-analysis. Radiology 307, e222639 (2023).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
D.K. has received honoraria for speaker roles at Memorial Sloan Kettering Cancer Center, Society of Breast Imaging, SPIE Medical Imaging Symposium, Stanford University and University of Hawaii, and her institution receives research funding from Calico, GenMab and iCAD.
Rights and permissions
About this article
Cite this article
Kontos, D. The promise of AI in personalized breast cancer screening: are we there yet?. Nat Rev Clin Oncol 21, 403–404 (2024). https://doi.org/10.1038/s41571-024-00877-z
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41571-024-00877-z