{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T21:22:58Z","timestamp":1791667378837,"version":"4.3.4"},"reference-count":30,"publisher":"Elsevier BV","issue":"6","license":[{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"}],"funder":[{"DOI":"10.13039\/501100004332","name":"Seoul National University Hospital","doi-asserted-by":"publisher","award":["04-2020-2290"],"award-info":[{"award-number":["04-2020-2290"]}],"id":[{"id":"10.13039\/501100004332","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.fr","clinicalkey.jp","clinicalkey.es","clinicalkey.com.au","academicradiology.org","clinicalkey.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Academic Radiology"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1016\/j.acra.2023.12.006","type":"journal-article","created":{"date-parts":[[2024,1,11]],"date-time":"2024-01-11T20:49:29Z","timestamp":1705006169000},"page":"2239-2247","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":28,"title":["Artificial Intelligence for Breast Cancer Detection on Mammography: Factors Related to Cancer Detection"],"prefix":"10.1016","volume":"31","author":[{"given":"Heera","family":"Yoen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Myoung-jin","family":"Jang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ann","family":"Yi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Woo Kyung","family":"Moon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5726-9797","authenticated-orcid":false,"given":"Jung Min","family":"Chang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"13","key":"10.1016\/j.acra.2023.12.006_bib1","doi-asserted-by":"crossref","first-page":"2971","DOI":"10.1002\/cncr.32859","article-title":"Mammography screening reduces rates of advanced and fatal breast cancers: results in 549,091 women","volume":"126","author":"Duffy","year":"2020","journal-title":"Cancer"},{"issue":"4","key":"10.1016\/j.acra.2023.12.006_bib2","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1002\/cncr.31840","article-title":"The incidence of fatal breast cancer measures the increased effectiveness of therapy in women participating in mammography screening","volume":"125","author":"Tab\u00e1r","year":"2019","journal-title":"Cancer"},{"issue":"1","key":"10.1016\/j.acra.2023.12.006_bib3","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1111\/tbj.12354","article-title":"Insights from the breast cancer screening trials: how screening affects the natural history of breast cancer and implications for evaluating service screening programs","volume":"21","author":"Tab\u00e1r","year":"2015","journal-title":"Breast\u00a0J"},{"issue":"15","key":"10.1016\/j.acra.2023.12.006_bib4","doi-asserted-by":"crossref","first-page":"1615","DOI":"10.1001\/jama.2015.13183","article-title":"Benefits and harms of breast cancer screening: a systematic review","volume":"314","author":"Myers","year":"2015","journal-title":"JAMA"},{"issue":"13","key":"10.1016\/j.acra.2023.12.006_bib5","doi-asserted-by":"crossref","first-page":"1081","DOI":"10.1093\/jnci\/92.13.1081","article-title":"Breast density as a predictor of mammographic detection: comparison of interval-and screen-detected cancers","volume":"92","author":"Mandelson","year":"2000","journal-title":"J\u00a0Natl Cancer Inst"},{"issue":"24","key":"10.1016\/j.acra.2023.12.006_bib6","doi-asserted-by":"crossref","first-page":"2353","DOI":"10.1056\/NEJMsr1504363","article-title":"Breast-cancer screening\u2014viewpoint of the IARC Working Group","volume":"372","author":"Lauby-Secretan","year":"2015","journal-title":"N\u00a0Engl J\u00a0Med"},{"issue":"2","key":"10.1016\/j.acra.2023.12.006_bib7","first-page":"316","article-title":"Screening guidelines update for average-risk and high-risk women","volume":"214","author":"Lee","year":"2020","journal-title":"Am\u00a0J\u00a0Roentgenol"},{"issue":"6","key":"10.1016\/j.acra.2023.12.006_bib8","first-page":"4036","article-title":"Breast cancer screening in women with extremely dense breasts recommendations of the European Society of Breast Imaging (EUSOBI)","volume":"32","author":"Mann","year":"2022","journal-title":"Eur\u00a0Radiol"},{"issue":"1103","key":"10.1016\/j.acra.2023.12.006_bib9","article-title":"Breast cancer screening in average-risk women: towards personalized screening","volume":"92","author":"Bitencourt","year":"2019","journal-title":"Brit J\u00a0Radiol"},{"issue":"4","key":"10.1016\/j.acra.2023.12.006_bib10","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1148\/rg.234025083","article-title":"Missed breast carcinoma: pitfalls and pearls","volume":"23","author":"Majid","year":"2003","journal-title":"Radiographics"},{"issue":"1","key":"10.1016\/j.acra.2023.12.006_bib11","first-page":"16","article-title":"Imaging features of breast cancer molecular subtypes: state of the art","volume":"55","author":"Cho","year":"2021","journal-title":"J\u00a0Pathol\u00a0Transl\u00a0Med"},{"issue":"4","key":"10.1016\/j.acra.2023.12.006_bib12","doi-asserted-by":"crossref","first-page":"941","DOI":"10.1148\/rg.2020190090","article-title":"Missed breast cancer: effects of subconscious bias and lesion characteristics","volume":"40","author":"Lamb","year":"2020","journal-title":"Radiographics"},{"issue":"2","key":"10.1016\/j.acra.2023.12.006_bib13","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1148\/rg.352140106","article-title":"Mammographic breast density: impact on breast cancer risk and implications for screening","volume":"35","author":"Freer","year":"2015","journal-title":"Radiographics"},{"issue":"9","key":"10.1016\/j.acra.2023.12.006_bib14","doi-asserted-by":"crossref","first-page":"916","DOI":"10.1093\/jnci\/djy222","article-title":"Stand-alone artificial intelligence for breast cancer detection in mammography: comparison with 101 radiologists","volume":"111","author":"Rodriguez-Ruiz","year":"2019","journal-title":"J\u00a0Natl Cancer Inst"},{"issue":"3","key":"10.1016\/j.acra.2023.12.006_bib15","doi-asserted-by":"crossref","first-page":"e138","DOI":"10.1016\/S2589-7500(20)30003-0","article-title":"Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader study","volume":"2","author":"Kim","year":"2020","journal-title":"Lancet Digit\u00a0Health"},{"issue":"6","key":"10.1016\/j.acra.2023.12.006_bib16","article-title":"Improving breast cancer detection accuracy of mammography with the concurrent use of an artificial intelligence tool","volume":"2","author":"Pacil\u00e8","year":"2020","journal-title":"Radiol: Artif\u00a0Intell"},{"issue":"8","key":"10.1016\/j.acra.2023.12.006_bib17","doi-asserted-by":"crossref","first-page":"1225","DOI":"10.3348\/kjr.2020.1210","article-title":"Deep learning-based artificial intelligence for mammography","volume":"22","author":"Yoon","year":"2021","journal-title":"Korean J Radiol"},{"issue":"11","key":"10.1016\/j.acra.2023.12.006_bib18","first-page":"1828","article-title":"Diagnostic accuracy of digital screening mammography with and without computer-aided detection","volume":"175","author":"Lehman","year":"2015","journal-title":"JAMA\u00a0Intern\u00a0Med"},{"issue":"2","key":"10.1016\/j.acra.2023.12.006_bib19","doi-asserted-by":"crossref","first-page":"293","DOI":"10.2214\/AJR.18.20532","article-title":"Artificial intelligence in breast imaging: potentials and limitations","volume":"212","author":"Mendelson","year":"2019","journal-title":"AJR Am J Roentgenol"},{"issue":"3","key":"10.1016\/j.acra.2023.12.006_bib20","first-page":"369","article-title":"Artificial intelligence (AI) for screening mammography, from the AJR Special Series on AI Applications","volume":"219","author":"Lamb","year":"2022","journal-title":"Am\u00a0J\u00a0Roentgenol"},{"issue":"11","key":"10.1016\/j.acra.2023.12.006_bib21","first-page":"7400","article-title":"Depiction of breast cancers on digital mammograms by artificial intelligence-based computer-assisted diagnosis according to cancer characteristics","volume":"32","author":"Lee","year":"2022","journal-title":"Eur\u00a0Radiol"},{"issue":"5","key":"10.1016\/j.acra.2023.12.006_bib22","doi-asserted-by":"crossref","first-page":"504","DOI":"10.4048\/jbc.2023.26.e39","article-title":"Artificial intelligence improves detection of supplemental screening ultrasound-detected breast cancers in mammography","volume":"26","author":"Yoen","year":"2023","journal-title":"J Breast Cancer"},{"issue":"3","key":"10.1016\/j.acra.2023.12.006_bib23","first-page":"798","article-title":"Trends in participation rates for the National Cancer Screening Program in Korea, 2002-2012","volume":"49","author":"Suh","year":"2017","journal-title":"Cancer\u00a0Res\u00a0Treat: Off\u00a0J\u00a0Korean Cancer Assoc"},{"key":"10.1016\/j.acra.2023.12.006_bib24","unstructured":"Sickles E.A., D\u2019Orsi C.J.Bassett L.W. et al., CR BI-RADS\u00ae mammography.in: ACR BI-RADS\u00ae Atlas, In: Breast Imaging Reporting and Data System, American College of Radiology; Reston, VA, 2013."},{"issue":"5","key":"10.1016\/j.acra.2023.12.006_bib25","doi-asserted-by":"crossref","DOI":"10.1148\/radiol.222639","article-title":"Standalone AI for breast cancer detection at screening digital mammography and digital breast tomosynthesis: a systematic review and meta-analysis","volume":"307","author":"Yoon","year":"2023","journal-title":"Radiology"},{"issue":"10","key":"10.1016\/j.acra.2023.12.006_bib26","first-page":"1581","article-title":"External evaluation of 3 commercial artificial intelligence algorithms for independent assessment of screening mammograms","volume":"6","author":"Salim","year":"2020","journal-title":"JAMA\u00a0Oncol"},{"issue":"1","key":"10.1016\/j.acra.2023.12.006_bib27","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1186\/s13244-022-01183-x","article-title":"Mammographically occult breast cancers detected with AI-based diagnosis supporting software: clinical and histopathologic characteristics","volume":"13","author":"Kim","year":"2022","journal-title":"Insights Imaging"},{"issue":"12","key":"10.1016\/j.acra.2023.12.006_bib28","doi-asserted-by":"crossref","first-page":"1241","DOI":"10.3348\/kjr.2022.0263","article-title":"Use of artificial intelligence for reducing unnecessary recalls at screening mammography: a simulation study","volume":"23","author":"Kim","year":"2022","journal-title":"Korean J\u00a0Radiol"},{"issue":"2","key":"10.1016\/j.acra.2023.12.006_bib29","doi-asserted-by":"crossref","first-page":"387","DOI":"10.3390\/diagnostics12020387","article-title":"Retrospective review of missed cancer detection and its mammography findings with artificial-intelligence-based, computer-aided diagnosis","volume":"12","author":"Park","year":"2022","journal-title":"Diagnostics"},{"issue":"9","key":"10.1016\/j.acra.2023.12.006_bib30","doi-asserted-by":"crossref","first-page":"e468","DOI":"10.1016\/S2589-7500(20)30185-0","article-title":"Effect of artificial intelligence-based triaging of breast cancer screening mammograms on cancer detection and radiologist workload: a retrospective simulation study","volume":"2","author":"Dembrower","year":"2020","journal-title":"Lancet Digit\u00a0Health"}],"container-title":["Academic Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1076633223006797?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1076633223006797?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T02:07:17Z","timestamp":1729217237000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1076633223006797"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6]]},"references-count":30,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["S1076633223006797"],"URL":"https:\/\/doi.org\/10.1016\/j.acra.2023.12.006","relation":{},"ISSN":["1076-6332"],"issn-type":[{"value":"1076-6332","type":"print"}],"subject":[],"published":{"date-parts":[[2024,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Artificial Intelligence for Breast Cancer Detection on Mammography: Factors Related to Cancer Detection","name":"articletitle","label":"Article Title"},{"value":"Academic Radiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.acra.2023.12.006","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}]}}