Screening mammography performance according to breast density: a comparison between radiologists versus standalone intelligence detection
Breast Cancer Research, 2026
Abstract
Introduction: Artificial intelligence (AI) algorithms for the independent assessment of screening mammograms have not been well established in a large screening cohort of Asian women. We compared the performance of screening digital mammography considering breast density, between radiologists and AI standalone detection among Korean women.
Methods: We retrospectively included 89,855 Korean women who underwent their initial screening digital mammography from 2009 to 2020. Breast cancer within 12 months of the screening mammography was the reference standard, according to the National Cancer Registry. Lunit software was used to determine the probability of malignancy scores, with a cutoff of 10% for breast cancer detection. The AI's performance was compared with that of the final Breast Imaging Reporting and Data System category, as recorded by breast radiologists. Breast density was classified into four categories (A–D) based on the radiologist and AI-based assessments.
Results: Mean participant age was 43.5 ± 8.7 years; 143 breast cancer cases were identified within 12 months. The CDRs (1.1/1000 examination) and sensitivity showed no significant differences between radiologist and AI-based results (69.9% vs. 67.1%). However, AI demonstrated significantly better specificity (93.0% vs. 77.6%), PPV (1.5% vs. 0.5%), recall rate (7.1% vs. 22.5%), and AUC (0.8 vs. 0.74). Radiologist and AI-based results showed best performance in the non-dense category; the CDR and sensitivity were higher for radiologists in the heterogeneously dense category. However, specificity, PPV, and recall rate consistently favored AI across all categories, including the extremely dense category.
Conclusion: AI-based software showed slightly lower sensitivity, although the difference was not statistically significant. However, it outperformed radiologists in recall rate, specificity, PPV, and AUC, with disparities most prominent in extremely dense breast tissue.
