{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,10]],"date-time":"2026-11-10T02:42:13Z","timestamp":1794278533057,"version":"4.9.0"},"reference-count":40,"publisher":"Oxford University Press (OUP)","issue":"18","license":[{"start":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T00:00:00Z","timestamp":1773014400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000050","name":"NHLBI","doi-asserted-by":"publisher","award":["R01HL167811"],"award-info":[{"award-number":["R01HL167811"]}],"id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Mayo Clinic Research Committee","award":["1R01HL174650-01"],"award-info":[{"award-number":["1R01HL174650-01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,5,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background and Aims<\/jats:title>\n                    <jats:p>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\u2013based automatic quantification of BAC from screening mammograms predicts CVD and mortality beyond PREVENT scores in a large, racially diverse, multi-institutional population.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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 (&amp;gt;0\u201310 mm2), moderate (&amp;gt;10\u201325 mm2), and severe (&amp;gt;25 mm2). Kaplan\u2013Meier analysis, Cox proportional hazards, and Fine\u2013Gray competing event models were used to examine the association between BAC and major adverse cardiovascular events (MACE).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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\u2013response was observed. Compared with zero BAC, mild [internal: hazard ratio (HR) 1.32, 95% confidence interval (CI) 1.10\u20131.59; external: HR 1.28, 95% CI 1.17\u20131.39], moderate (internal: HR 1.75, 95% CI 1.23\u20132.50; external: HR 1.79, 95% CI 1.55\u20132.06), and severe BAC (internal: HR 3.29, 95% CI 2.15\u20135.05; external: HR 2.80, 95% CI 2.36\u20133.32) were all prognostic for any MACE. Each 1 mm2 increase in BAC conferred an additional 2%\u20133% risk for MACE (P &amp;lt; .001).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/eurheartj\/ehag128","type":"journal-article","created":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T12:30:30Z","timestamp":1770726630000},"page":"2206-2220","source":"Crossref","is-referenced-by-count":17,"title":["Artificial intelligence\u2013based quantification of breast arterial calcifications to predict cardiovascular morbidity and mortality"],"prefix":"10.1093","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6264-2282","authenticated-orcid":false,"given":"Theodorus","family":"Dapamede","sequence":"first","affiliation":[{"name":"Department of Radiology, Emory University , Atlanta, GA 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