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DOI 10.1093/eurheartj/ehag128

Медицинская библиотека · European heart journal · 2026

Artificial intelligence-based quantification of breast arterial calcifications to predict cardiovascular morbidity and mortality.

Dapamede Theodorus, Urooj Aisha, Joshi Vedant, Gershon Gabrielle, Li Frank, Chavoshi Mohammadreza, Brown-Mulry Beatrice, Isaac Rohan Satya, Mansuri Aawez, Robichaux Chad, Ayoub Chadi, Arsanjani Reza, Sperling Laurence, Gichoya Judy, van Assen Marly, O'Neill W Charles, Banerjee Imon, Trivedi Hari

СтатьяМногоцентровое исследованиеПеревод в очереди

Аннотация

BACKGROUND AND AIMS: Women are underdiagnosed and undertreated for cardiovascular disease (CVD). Automatic quantification of breast arterial calcification (BAC) on screening mammography can identify women at risk for CVD. This study aimed to determine whether artificial intelligence-based automatic quantification of BAC from screening mammograms predicts CVD and mortality beyond PREVENT scores in a large, racially diverse, multi-institutional population.

METHODS

This retrospective cohort study included 123 762 women from two healthcare systems who had screening mammograms. Breast arterial calcification was quantified using a transformer-based neural network for segmentation. Breast arterial calcification severity was categorized as zero (0 mm2), mild (>0-10 mm2), moderate (>10-25 mm2), and severe (>25 mm2). Kaplan-Meier analysis, Cox proportional hazards, and Fine-Gray competing event models were used to examine the association between BAC and major adverse cardiovascular events (MACE).

RESULTS

Breast arterial calcification was detected in 16.1% (internal cohort) and 20.6% (external cohort) of women and provided significant prognostic value incremental to the PREVENT score. In PREVENT adjusted models, a clear dose-response was observed. Compared with zero BAC, mild [internal: hazard ratio (HR) 1.32, 95% confidence interval (CI) 1.10-1.59; external: HR 1.28, 95% CI 1.17-1.39], moderate (internal: HR 1.75, 95% CI 1.23-2.50; external: HR 1.79, 95% CI 1.55-2.06), and severe BAC (internal: HR 3.29, 95% CI 2.15-5.05; external: HR 2.80, 95% CI 2.36-3.32) were all prognostic for any MACE. Each 1 mm2 increase in BAC conferred an additional 2%-3% risk for MACE (P < .001).

CONCLUSIONS

Automatically quantified BAC is an independent predictor of MACE and mortality, adding prognostic value to the PREVENT score. This approach may provide an opportunistic cardiovascular risk assessment during routine mammography screening without additional radiation exposure to guide earlier and more effective preventive care for women.

Для цитирования: Dapamede Theodorus, Urooj Aisha, Joshi Vedant, Gershon Gabrielle, Li Frank, Chavoshi Mohammadreza, Brown-Mulry Beatrice, Isaac Rohan Satya, Mansuri Aawez, Robichaux Chad, Ayoub Chadi, Arsanjani Reza, Sperling Laurence, Gichoya Judy, van Assen Marly, O'Neill W Charles, Banerjee Imon, Trivedi Hari. Artificial intelligence-based quantification of breast arterial calcifications to predict cardiovascular morbidity and mortality.. European heart journal. 2026. DOI: 10.1093/eurheartj/ehag128.

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