Artificial Intelligence for Digital Breast Tomosynthesis Screening with and without Prior Examinations in BreastScreen Norway.

Abstract

Purpose To evaluate the performance of an artificial intelligence (AI) model for digital breast tomosynthesis (DBT) with and without prior screening examinations by using independent double reading by radiologists as the reference standard. Materials and Methods This retrospective study included women screened in BreastScreen Norway, 2018-2019. The AI model provided continuous malignancy risk and categorical scores (ie, 1-10) with and without the inclusion of prior examinations for 24 315 women with prior examinations. The area under the receiver operating characteristic curve (AUC) was calculated for AI using screening-detected and interval cancers as true-positives. The positive predictive value for a score of 10 was defined as the proportion of screening-detected cancers among all examinations scored 10. Results Among 30 724 DBT examinations of women (mean age ± SD, 61.3 years ± 5.0), the sensitivity of radiologists was 86.0% (95% CI: 81.3, 89.7). Among comparisons that included prior examinations, the AUC of the AI model was 0.93 (95% CI: 0.91, 0.95), with 81.0% (95% CI: 75.8, 85.3) scored 10 (screening-detected: 88.3% [95% CI: 83.4, 91.9] vs interval cancers: 36.1% [95% CI: 22.5, 52.4]). The positive predictive value was 10.2% (95% CI: 8.9, 11.6). Among cases without prior examinations, the AUC was 0.93 (95% CI: 0.91, 0.95; P = .13), with 82.2% (95% CI: 77.0, 86.4) of all cancer cases scored 10 (screening-detected: 88.7% [95% CI: 83.9, 92.3] vs interval cancers: 41.7% [95% CI: 27.1, 57.8]). The positive predictive value was 8.9% (95% CI: 7.8, 10.1). Conclusion The performance of the AI model was similar to that of double reading by radiologists. Although prior examinations provided limited additional benefit, they offered slightly improved specificities. Keywords: Digital Breast Tomosynthesis, Mammography, Breast Cancer, Screening Supplemental material is available for this article. © RSNA, 2026 See also commentary by Yao and Chae in this issue.