European radiology · 2026
Mammographic density assessment by an artificial intelligence model for breast cancer detection in BreastScreen Norway.
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Larsen Marthe, Moshina Nataliia, Mikalsen Karl Øyvind, Hoff Solveig Roth, Lund-Hanssen Håkon, Martiniussen Marit A, Silberhorn Marko, Schulze Marte M, Hofvind Solveig
Аннотация
OBJECTIVES: The sensitivity of mammographic screening is lower for women with mammographically dense vs fatty breasts. We aimed to explore automated mammographic breast density and malignancy risk scores generated by an artificial intelligence (AI) model for breast cancer detection, stratified by mammography vendor.
MATERIALS AND METHODS: This retrospective study included information from 200,000 examinations within BreastScreen Norway. An automated volumetric breast density (VBD) assessment and risk scores were obtained from a commercial AI model. The continuous VBD output from the AI model was categorized into four groups, VBD 1-4, with 15%, 40%, 40%, and 5% of the examinations in each group for exam level and for right and left breast. Area under the receiver operating characteristic curve (AUC) for breast cancer detection was calculated using a continuous AI risk score. Analyses were performed for exam-level and breast-level measures.
RESULTS: At exam level, the difference in AUC estimates between the VBD groups was statistically significant, with the highest AUC observed for VBD 1 (0.955, 95% CI: 0.937-0.992) and lowest for VBD 4 (0.857, 95% CI: 0.803-0.910). Stratified by vendor, 3.3% and 6.7% were classified as VBD 4 for A vs B. At breast level, 18.5% of the examinations were classified with different VBD groups for the right and left breast.
CONCLUSION: Despite high AUC estimates across all VBD groups and the potential advantages of an automated density assessment, several factors, such as vendor and breast vs exam level, must be carefully considered before implementing automated density measures in screening programs.
KEY POINTS: Question What are the benefits and drawbacks of mammographic density assessment using an AI model for breast cancer detection? Findings The AI model's performance for cancer detection was promising but decreased with increasing density category. Density distribution varied by equipment vendor and examination level. Clinical relevance AI risk assessment at the current examination was promising despite the variation by mammographic density. However, discrepancies in density by equipment vendors and examination level highlight the need for careful consideration before use in risk-stratified screening.
MATERIALS AND METHODS: This retrospective study included information from 200,000 examinations within BreastScreen Norway. An automated volumetric breast density (VBD) assessment and risk scores were obtained from a commercial AI model. The continuous VBD output from the AI model was categorized into four groups, VBD 1-4, with 15%, 40%, 40%, and 5% of the examinations in each group for exam level and for right and left breast. Area under the receiver operating characteristic curve (AUC) for breast cancer detection was calculated using a continuous AI risk score. Analyses were performed for exam-level and breast-level measures.
RESULTS: At exam level, the difference in AUC estimates between the VBD groups was statistically significant, with the highest AUC observed for VBD 1 (0.955, 95% CI: 0.937-0.992) and lowest for VBD 4 (0.857, 95% CI: 0.803-0.910). Stratified by vendor, 3.3% and 6.7% were classified as VBD 4 for A vs B. At breast level, 18.5% of the examinations were classified with different VBD groups for the right and left breast.
CONCLUSION: Despite high AUC estimates across all VBD groups and the potential advantages of an automated density assessment, several factors, such as vendor and breast vs exam level, must be carefully considered before implementing automated density measures in screening programs.
KEY POINTS: Question What are the benefits and drawbacks of mammographic density assessment using an AI model for breast cancer detection? Findings The AI model's performance for cancer detection was promising but decreased with increasing density category. Density distribution varied by equipment vendor and examination level. Clinical relevance AI risk assessment at the current examination was promising despite the variation by mammographic density. However, discrepancies in density by equipment vendors and examination level highlight the need for careful consideration before use in risk-stratified screening.