{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T13:47:40Z","timestamp":1791380860859,"version":"4.3.3"},"reference-count":16,"publisher":"Elsevier BV","issue":"3","license":[{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["cell.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Trends in Cancer"],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1016\/j.trecan.2024.10.007","type":"journal-article","created":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T09:32:03Z","timestamp":1733995923000},"page":"188-191","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":9,"title":["Artificial intelligence improves mammography-based breast cancer risk prediction"],"prefix":"10.1016","volume":"11","author":[{"given":"Wendy V.","family":"Ingman","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kara L.","family":"Britt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jennifer","family":"Stone","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tuong L.","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John L.","family":"Hopper","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9723-4924","authenticated-orcid":false,"given":"Erik W.","family":"Thompson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.trecan.2024.10.007_bb0005","doi-asserted-by":"crossref","first-page":"2486","DOI":"10.1002\/1097-0142(197605)37:5<2486::AID-CNCR2820370542>3.0.CO;2-8","article-title":"Risk for breast cancer development determined by mammographic parenchymal pattern","volume":"37","author":"Wolfe","year":"1976","journal-title":"Cancer"},{"key":"10.1016\/j.trecan.2024.10.007_bb0010","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1007\/s10549-014-2901-2","article-title":"Mammographic density-a review on the current understanding of its association with breast cancer","volume":"144","author":"Huo","year":"2014","journal-title":"Breast Cancer Res. Treat."},{"key":"10.1016\/j.trecan.2024.10.007_bb0015","series-title":"BI-RADS\u00ae Atlas","author":"D\u2019Orsi","year":"2013"},{"key":"10.1016\/j.trecan.2024.10.007_bb0020","doi-asserted-by":"crossref","first-page":"1159","DOI":"10.1158\/1055-9965.EPI-06-0034","article-title":"Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis","volume":"15","author":"McCormack","year":"2006","journal-title":"Cancer Epidemiol. Biomarkers Prev."},{"key":"10.1016\/j.trecan.2024.10.007_bb0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejrad.2022.110416","article-title":"Examining the effectiveness of supplementary imaging modalities for breast cancer screening in women with dense breasts: a systematic review and meta-analysis","volume":"154","author":"Mizzi","year":"2022","journal-title":"Eur. J. Radiol."},{"key":"10.1016\/j.trecan.2024.10.007_bb0030","doi-asserted-by":"crossref","first-page":"681","DOI":"10.3390\/jcm9030681","article-title":"Breast density notification: an Australian perspective","volume":"9","author":"Ingman","year":"2020","journal-title":"J. Clin. Med."},{"key":"10.1016\/j.trecan.2024.10.007_bb0035","doi-asserted-by":"crossref","DOI":"10.1148\/radiol.222575","article-title":"Beyond breast density: risk measures for breast cancer in multiple imaging modalities","volume":"306","author":"Acciavatti","year":"2023","journal-title":"Radiology"},{"key":"10.1016\/j.trecan.2024.10.007_bb0040","article-title":"Braix risk score: an automated mammogram-based biomarker for breast cancer created by applying artificial intelligence","author":"Hopper","year":"2024","journal-title":"Lancet"},{"key":"10.1016\/j.trecan.2024.10.007_bb0045","doi-asserted-by":"crossref","first-page":"2623","DOI":"10.1038\/s41591-024-03093-5","article-title":"AI-based selection of individuals for supplemental MRI in population-based breast cancer screening: the randomized ScreenTrustMRI trial","volume":"30","author":"Salim","year":"2024","journal-title":"Nat. Med."},{"key":"10.1016\/j.trecan.2024.10.007_bb0050","doi-asserted-by":"crossref","first-page":"7525","DOI":"10.1038\/s41467-024-51725-8","article-title":"Comparison of AI-integrated pathways with human-AI interaction in population mammographic screening for breast cancer","volume":"15","author":"Frazer","year":"2024","journal-title":"Nat. Commun."},{"key":"10.1016\/j.trecan.2024.10.007_bb0055","doi-asserted-by":"crossref","DOI":"10.1148\/radiol.230227","article-title":"Assessing breast cancer risk by combining AI for lesion detection and mammographic texture","volume":"308","author":"Lauritzen","year":"2023","journal-title":"Radiology"},{"key":"10.1016\/j.trecan.2024.10.007_bb0060","doi-asserted-by":"crossref","first-page":"5721","DOI":"10.1172\/JCI129249","article-title":"Stiff stroma increases breast cancer risk by inducing the oncogene ZNF217","volume":"130","author":"Northey","year":"2020","journal-title":"J. Clin. Invest."},{"key":"10.1016\/j.trecan.2024.10.007_bb0065","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1186\/s13058-018-1010-2","article-title":"High mammographic density in women is associated with protumor inflammation","volume":"20","author":"Huo","year":"2018","journal-title":"Breast Cancer Res."},{"key":"10.1016\/j.trecan.2024.10.007_bb0070","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1186\/s13058-016-0767-4","article-title":"Mammographically dense human breast tissue stimulates MCF10DCIS.com progression to invasive lesions and metastasis","volume":"18","author":"Huo","year":"2016","journal-title":"Breast Cancer Res."},{"key":"10.1016\/j.trecan.2024.10.007_bb0075","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/s13058-016-0796-z","article-title":"CCL2-driven inflammation increases mammary gland stromal density and cancer susceptibility in a transgenic mouse model","volume":"19","author":"Sun","year":"2017","journal-title":"Breast Cancer Res."},{"key":"10.1016\/j.trecan.2024.10.007_bb0080","doi-asserted-by":"crossref","first-page":"5578","DOI":"10.18632\/oncotarget.13484","article-title":"Mammographic density: a potential monitoring biomarker for adjuvant and preventative breast cancer endocrine therapies","volume":"8","author":"Shawky","year":"2017","journal-title":"Oncotarget"}],"container-title":["Trends in Cancer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2405803324002267?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2405803324002267?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T11:30:15Z","timestamp":1741692615000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2405803324002267"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3]]},"references-count":16,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["S2405803324002267"],"URL":"https:\/\/doi.org\/10.1016\/j.trecan.2024.10.007","relation":{},"ISSN":["2405-8033"],"issn-type":[{"value":"2405-8033","type":"print"}],"subject":[],"published":{"date-parts":[[2025,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Artificial intelligence improves mammography-based breast cancer risk prediction","name":"articletitle","label":"Article Title"},{"value":"Trends in Cancer","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.trecan.2024.10.007","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Published by Elsevier Inc.","name":"copyright","label":"Copyright"}]}}