Artificial intelligence may be capable of identifying subtle mammographic signs of breast cancer years before conventional diagnosis, according to a new study published in Radiology. Researchers from Sweden found that three commercially available AI algorithms for mammography screening generated elevated cancer scores as early as 10 years before diagnosis, with detection signals strengthening as diagnosis approached.
Predicting breast cancer risk offers the prospect not only of detecting cancer earlier, but also of tailoring mammography screening to women most likely to benefit from it.
- Clinical risk calculators like Tyrer-Cuzick and breast density analysis are available, but AI-based algorithms are showing promise by predicting risk from screening mammograms.
In the new study, researchers analyzed 89k mammograms from 31.4k women collected over a 10-year period, drawn from Sweden’s national screening program, where women aged 40-74 undergo biennial mammography interpreted by two radiologists.
- During the study period, 12.1k women (39%) were ultimately diagnosed with breast cancer. Three commercially available AI algorithms were used to generate risk scores (Vara AI from Vara, Lunit Insight MMG from Lunit, and MammoScreen from Therapixel). (It’s worth noting all three were originally designed for cancer detection rather than risk prediction.)
AI scores increased progressively over time in women who later developed cancer, while remaining relatively stable among cancer-free participants…
- At 90% specificity, AI systems flagged 19%-20% of future breast cancer cases six years before diagnosis.
- Detection increased to 23%-25% at four years before diagnosis.
- Performance rose further to 35%-39% at two years before diagnosis.
- Even 10 years before diagnosis, the systems identified 13%-17% of future cancers.
- Across all pre-diagnostic examinations, AI achieved AUC values of 0.63-0.67, outperforming mammographic density alone (AUC = 0.57).
The findings suggest that AI tools developed for cancer detection may also have value as early-alert systems for identifying women who could benefit from closer surveillance or supplemental imaging.
- While prospective validation is still needed, sequential AI scoring may ultimately help identify women who would benefit from supplemental imaging, closer surveillance, or earlier intervention.
The Takeaway
The study adds to growing evidence that mammography AI can extend beyond cancer detection to long-term risk stratification. By identifying subtle imaging patterns years before diagnosis, AI-derived detection scores could provide an additional layer of longitudinal risk monitoring and help guide more personalized screening strategies.
