DBT-DINO: Toward Foundation Model-Based Analysis of Digital Breast Tomosynthesis.

Background Foundation models show promise in medical imaging but remain underexplored in three-dimensional modalities. Despite the adoption of digital breast tomosynthesis (DBT) in breast cancer screening, no dedicated foundation model currently exists for this modality. Purpose To develop and evaluate a foundation model for DBT (DBT-DINO) and assess the impact of domain-specific pretraining across multiple clinical tasks. Materials and Methods This retrospective study used DBT images from Mass General Brigham acquired between March 2011 and February 2024. Self-supervised pretraining was performed using Meta AI's DINOv2 methodology on more than 25 million two-dimensional sections from 487 975 DBT volumes from 27 990 patients. Three downstream tasks were evaluated: (a) breast density classification using 5000 screening examinations, (b) 5-year risk of developing biopsy-proven breast cancer using 106 417 screening examinations, and (c) lesion detection using 393 annotated volumes. The performance of DBT-DINO was compared with that of ImageNet-pretrained DINOv2 baselines using McNemar and DeLong tests. Results A total of 4981 patients (mean age, 57.76 years ± 11.40 [SD]; 4855 female) were included for density classification, 31 561 patients (mean age, 60.09 years ± 10.47; 31 559 female) were included for risk prediction, and 199 female patients were included for lesion detection. For breast density classification, DBT-DINO achieved 79% (786 of 997 examinations) accuracy, outperforming the DINOv2 baseline (73% [728 of 997 examinations]; P < .001). For 5-year breast cancer risk prediction, DBT-DINO had an area under the receiver operating characteristic curve (AUC) of 0.78 and DINOv2 had an AUC of 0.76 (P = .057), showing no evidence of a difference. In lesion detection, DINOv2 had an average sensitivity of 67% (91 of 136 lesions), whereas DBT-DINO had a sensitivity of 62% (84 of 136 lesions) (P = .60), again with no evidence of a difference. Conclusion DBT-DINO demonstrated strong performance in breast density classification; however, there was no evidence of a difference compared with the ImageNet baseline in 5-year breast cancer risk prediction or lesion detection, suggesting that domain-specific pretraining for localized detection tasks required further refinement. © RSNA, 2026 Supplemental material is available for this article. See also the editorial by Wu in this issue.