{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T03:51:55Z","timestamp":1792813915958,"version":"4.5.0"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2024,3,25]],"date-time":"2024-03-25T00:00:00Z","timestamp":1711324800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,3,25]],"date-time":"2024-03-25T00:00:00Z","timestamp":1711324800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100008730","name":"Kreftforeningen","doi-asserted-by":"publisher","award":["214931"],"award-info":[{"award-number":["214931"]}],"id":[{"id":"10.13039\/100008730","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100021078","name":"Norwegian Institute of Public Health","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100021078","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Radiol"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To explore the ability of artificial intelligence (AI) to classify breast cancer by mammographic density in an organized screening program.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and method<\/jats:title>\n                    <jats:p>We included information about 99,489 examinations from 74,941 women who participated in BreastScreen Norway, 2013\u20132019. All examinations were analyzed with an AI system that assigned a malignancy risk score (AI score) from 1 (lowest) to 10 (highest) for each examination. Mammographic density was classified into Volpara density grade (VDG), VDG1\u20134; VDG1 indicated fatty and VDG4 extremely dense breasts. Screen-detected and interval cancers with an AI score of 1\u201310 were stratified by VDG.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We found 10,406 (10.5% of the total) examinations to have an AI risk score of 10, of which 6.7% (704\/10,406) was breast cancer. The cancers represented 89.7% (617\/688) of the screen-detected and 44.6% (87\/195) of the interval cancers. 20.3% (20,178\/99,489)\u00a0of the examinations were classified as VDG1 and 6.1% (6047\/99,489)\u00a0as VDG4. For screen-detected cancers, 84.0% (68\/81, 95% CI, 74.1\u201391.2) had an AI score of 10 for VDG1, 88.9% (328\/369, 95% CI, 85.2\u201391.9) for VDG2, 92.5% (185\/200, 95% CI, 87.9\u201395.7) for VDG3, and 94.7% (36\/38, 95% CI, 82.3\u201399.4) for VDG4. For interval cancers, the percentages with an AI score of 10 were 33.3% (3\/9, 95% CI, 7.5\u201370.1) for VDG1 and 48.0% (12\/25, 95% CI, 27.8\u201368.7) for VDG4.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The tested AI system performed well according to cancer detection across all density categories, especially for extremely dense breasts. The highest proportion of screen-detected cancers with an AI score of 10 was observed for women classified as VDG4.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Clinical relevance statement<\/jats:title>\n                    <jats:p>Our study demonstrates that AI can correctly classify the majority of screen-detected and about half of the interval breast cancers, regardless of breast density.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:italic>\u2022 Mammographic density is important to consider in the evaluation of artificial intelligence in mammographic screening.<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:italic>\u2022 Given a threshold representing about 10% of those with the highest malignancy risk score by an AI system, we found an increasing percentage of cancers with increasing mammographic density.<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:italic>\u2022 Artificial intelligence risk score and mammographic density combined may help triage examinations to reduce workload for radiologists.<\/jats:italic>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00330-024-10681-z","type":"journal-article","created":{"date-parts":[[2024,3,25]],"date-time":"2024-03-25T17:40:13Z","timestamp":1711388413000},"page":"6298-6308","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["AI performance by mammographic density in a retrospective cohort study of 99,489 participants in BreastScreen Norway"],"prefix":"10.1007","volume":"34","author":[{"given":"Marie Burns","family":"Bergan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marthe","family":"Larsen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nataliia","family":"Moshina","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hauke","family":"Bartsch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Henrik Wethe","family":"Koch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hildegunn Siv","family":"Aase","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhanbolat","family":"Satybaldinov","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ingfrid Helene Salvesen","family":"Haldorsen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christoph I.","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0178-8939","authenticated-orcid":false,"given":"Solveig","family":"Hofvind","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,3,25]]},"reference":[{"issue":"24","key":"10681_CR1","doi-asserted-by":"publisher","first-page":"2353","DOI":"10.1056\/NEJMsr1504363","volume":"372","author":"B Lauby-Secretan","year":"2015","unstructured":"Lauby-Secretan B, Scoccianti C, Loomis D et al (2015) Breast-cancer screening\u2013viewpoint of the IARC Working Group. N Engl J Med 372(24):2353\u20132358","journal-title":"N Engl J Med"},{"key":"10681_CR2","unstructured":"European Commission initiative on breast cancer (2021) Manual for Breast Cancer Services. European Quality Assurance Scheme for Breast Cancer Services. Cited February 2024: https:\/\/cancer-screening-and-care.jrc.ec.europa.eu\/en\/ecibc\/breast-quality-assurance-scheme"},{"key":"10681_CR3","unstructured":"Bj\u00f8rnson EW, Holen \u00c5S, Sagstad S et al (2022) BreastScreen Norway: 25 years of organized screening. Oslo: Cancer Registry of Norway. Cited February, 2024. Avaliable from: https:\/\/www.kreftregisteret.no\/Generelt\/Rapporter\/Mammografiprogrammet\/25-arsrapport-mammografiprogrammet\/"},{"issue":"7","key":"10681_CR4","doi-asserted-by":"publisher","first-page":"2737","DOI":"10.1007\/s00330-016-4612-z","volume":"27","author":"F Sardanelli","year":"2017","unstructured":"Sardanelli F, Aase HS, \u00c1lvarez M et al (2017) Position paper on screening for breast cancer by the European Society of Breast Imaging (EUSOBI) and 30 national breast radiology bodies from Austria, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Israel, Lithuania, Moldova, The Netherlands, Norway, Poland, Portugal, Romania, Serbia, Slovakia, Spain, Sweden Switzerland and Turkey. Eur Radiol 27(7):2737\u20132743","journal-title":"Eur Radiol"},{"key":"10681_CR5","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.bpobgyn.2019.11.005","volume":"65","author":"EF Harkness","year":"2020","unstructured":"Harkness EF, Astley SM, Evans DG (2020) Risk-based breast cancer screening strategies in women. Best Pract Res Clin Obstet Gynaecol 65:3\u201317","journal-title":"Best Pract Res Clin Obstet Gynaecol"},{"issue":"4","key":"10681_CR6","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1186\/s13058-014-0417-7","volume":"16","author":"NF Boyd","year":"2014","unstructured":"Boyd NF, Huszti E, Melnichouk O et al (2014) Mammographic features associated with interval breast cancers in screening programs. Breast Cancer Res 16(4):417","journal-title":"Breast Cancer Res"},{"issue":"6","key":"10681_CR7","doi-asserted-by":"publisher","first-page":"1159","DOI":"10.1158\/1055-9965.EPI-06-0034","volume":"15","author":"VA McCormack","year":"2006","unstructured":"McCormack VA, dos Santos SI (2006) Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis. Cancer Epidemiol Biomarkers Prev 15(6):1159\u20131169","journal-title":"Cancer Epidemiol Biomarkers Prev"},{"issue":"2","key":"10681_CR8","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1148\/rg.352140106","volume":"35","author":"PE Freer","year":"2015","unstructured":"Freer PE (2015) Mammographic breast density: impact on breast cancer risk and implications for screening. Radiographics 35(2):302\u2013315","journal-title":"Radiographics"},{"issue":"6","key":"10681_CR9","doi-asserted-by":"publisher","first-page":"4036","DOI":"10.1007\/s00330-022-08617-6","volume":"32","author":"RM Mann","year":"2022","unstructured":"Mann RM, Athanasiou A, Baltzer PAT et al (2022) Breast cancer screening in women with extremely dense breasts recommendations of the European Society of Breast Imaging (EUSOBI). Eur Radiol 32(6):4036\u20134045","journal-title":"Eur Radiol"},{"key":"10681_CR10","unstructured":"SAPEA, Science Advice for Policy by European Academies (2022) Improving cancer screening in the European Union. Berlin: SAPEA"},{"key":"10681_CR11","unstructured":"Council Recommendation on cancer screening (update). Cited February 2024. Available from: https:\/\/www.europarl.europa.eu\/legislative-train\/theme-promoting-our-european-way-of-life\/file-cancer-screening?sid=7701"},{"issue":"1","key":"10681_CR12","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.ajog.2014.06.048","volume":"212","author":"JR Scheel","year":"2015","unstructured":"Scheel JR, Lee JM, Sprague BL, Lee CI, Lehman CD (2015) Screening ultrasound as an adjunct to mammography in women with mammographically dense breasts. Am J Obstet Gynecol 212(1):9\u201317","journal-title":"Am J Obstet Gynecol"},{"issue":"5","key":"10681_CR13","doi-asserted-by":"publisher","first-page":"3735","DOI":"10.1007\/s00330-023-09461-y","volume":"33","author":"HW Koch","year":"2023","unstructured":"Koch HW, Larsen M, Bartsch H, Kurz KD, Hofvind S (2023) Artificial intelligence in BreastScreen Norway: a retrospective analysis of a cancer-enriched sample including 1254 breast cancer cases. Eur Radiol 33(5):3735\u20133743","journal-title":"Eur Radiol"},{"issue":"1","key":"10681_CR14","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1177\/08465371211027958","volume":"73","author":"JM Seely","year":"2022","unstructured":"Seely JM, Peddle SE, Yang H et al (2022) Breast density and risk of interval cancers: the effect of annual versus biennial screening mammography policies in Canada. Can Assoc Radiol J 73(1):90\u2013100","journal-title":"Can Assoc Radiol J"},{"key":"10681_CR15","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1016\/j.breast.2023.03.010","volume":"69","author":"M Larsen","year":"2023","unstructured":"Larsen M, Lynge E, Lee CI, L\u00e5ng K, Hofvind S (2023) Mammographic density and interval cancers in mammographic screening: moving towards more personalized screening. Breast 69:306\u2013311","journal-title":"Breast"},{"issue":"8","key":"10681_CR16","doi-asserted-by":"publisher","first-page":"5940","DOI":"10.1007\/s00330-021-07686-3","volume":"31","author":"K L\u00e5ng","year":"2021","unstructured":"L\u00e5ng K, Hofvind S, Rodr\u00edguez-Ruiz A, Andersson I (2021) Can artificial intelligence reduce the interval cancer rate in mammography screening? Eur Radiol 31(8):5940\u20135947","journal-title":"Eur Radiol"},{"issue":"9","key":"10681_CR17","doi-asserted-by":"publisher","first-page":"4825","DOI":"10.1007\/s00330-019-06186-9","volume":"29","author":"A Rodriguez-Ruiz","year":"2019","unstructured":"Rodriguez-Ruiz A, L\u00e5ng K, Gubern-Merida A et al (2019) Can we reduce the workload of mammographic screening by automatic identification of normal exams with artificial intelligence? A feasibility study. Eur Radiol 29(9):4825\u20134832","journal-title":"Eur Radiol"},{"issue":"9","key":"10681_CR18","doi-asserted-by":"publisher","first-page":"916","DOI":"10.1093\/jnci\/djy222","volume":"111","author":"A Rodriguez-Ruiz","year":"2019","unstructured":"Rodriguez-Ruiz A, L\u00e5ng K, Gubern-Merida A et al (2019) Stand-alone artificial intelligence for breast cancer detection in mammography: comparison with 101 radiologists. J Natl Cancer Inst 111(9):916\u2013922","journal-title":"J Natl Cancer Inst"},{"issue":"10","key":"10681_CR19","doi-asserted-by":"publisher","first-page":"1581","DOI":"10.1001\/jamaoncol.2020.3321","volume":"6","author":"M Salim","year":"2020","unstructured":"Salim M, W\u00e5hlin E, Dembrower K et al (2020) External evaluation of 3 commercial artificial intelligence algorithms for independent assessment of screening mammograms. JAMA Oncol 6(10):1581\u20131588","journal-title":"JAMA Oncol"},{"issue":"7788","key":"10681_CR20","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1038\/s41586-019-1799-6","volume":"577","author":"SM McKinney","year":"2020","unstructured":"McKinney SM, Sieniek M, Godbole V et al (2020) International evaluation of an AI system for breast cancer screening. Nature 577(7788):89\u201394","journal-title":"Nature"},{"issue":"3","key":"10681_CR21","doi-asserted-by":"publisher","first-page":"e200265","DOI":"10.1001\/jamanetworkopen.2020.0265","volume":"3","author":"T Schaffter","year":"2020","unstructured":"Schaffter T, Buist DSM, Lee CI et al (2020) Evaluation of combined artificial intelligence and radiologist assessment to interpret screening mammograms. JAMA Netw Open 3(3):e200265","journal-title":"JAMA Netw Open"},{"issue":"4","key":"10681_CR22","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1093\/jbi\/wbaa033","volume":"2","author":"M Bahl","year":"2020","unstructured":"Bahl M (2020) Artificial intelligence: a primer for breast imaging radiologists. J Breast Imaging 2(4):304\u2013314","journal-title":"J Breast Imaging"},{"key":"10681_CR23","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.semcancer.2020.06.002","volume":"72","author":"I Sechopoulos","year":"2021","unstructured":"Sechopoulos I, Teuwen J, Mann R (2021) Artificial intelligence for breast cancer detection in mammography and digital breast tomosynthesis: state of the art. Semin Cancer Biol 72:214\u2013225","journal-title":"Semin Cancer Biol"},{"issue":"1","key":"10681_CR24","first-page":"20200063","volume":"3","author":"G Johansson","year":"2021","unstructured":"Johansson G, Olsson C, Smith F, Edegran M, Bj\u00f6rk-Eriksson T (2021) AI-aided detection of malignant lesions in mammography screening - evaluation of a program in clinical practice. BJR Open 3(1):20200063","journal-title":"BJR Open"},{"issue":"3","key":"10681_CR25","doi-asserted-by":"publisher","first-page":"502","DOI":"10.1148\/radiol.212381","volume":"303","author":"M Larsen","year":"2022","unstructured":"Larsen M, Aglen CF, Lee CI et al (2022) Artificial intelligence evaluation of 122 969 mammography examinations from a population-based screening program. Radiology 303(3):502\u2013511","journal-title":"Radiology"},{"issue":"8","key":"10681_CR26","doi-asserted-by":"publisher","first-page":"936","DOI":"10.1016\/S1470-2045(23)00298-X","volume":"24","author":"K L\u00e5ng","year":"2023","unstructured":"L\u00e5ng K, Josefsson V, Larsson AM et al (2023) Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial (MASAI): a clinical safety analysis of a randomised, controlled, non-inferiority, single-blinded, screening accuracy study. Lancet Oncol 24(8):936\u2013944","journal-title":"Lancet Oncol"},{"key":"10681_CR27","unstructured":"Lovdata. Kreftregisterforskriften. 2001. Cited February 2024. Available from: https:\/\/lovdata.no\/dokument\/SF\/forskrift\/2001-12-21-1477"},{"key":"10681_CR28","unstructured":"Lov om helseregistre og behandling av helseopplysninger (helseregisterloven) [21.04.2023]. Cited February 2024. Available from: https:\/\/lovdata.no\/dokument\/NL\/lov\/2014-06-20-43"},{"issue":"6","key":"10681_CR29","doi-asserted-by":"publisher","first-page":"795","DOI":"10.1016\/S1470-2045(19)30161-5","volume":"20","author":"S Hofvind","year":"2019","unstructured":"Hofvind S, Holen \u00c5S, Aase HS et al (2019) Two-view digital breast tomosynthesis versus digital mammography in a population-based breast cancer screening programme (To-Be): a randomised, controlled trial. Lancet Oncol 20(6):795\u2013805","journal-title":"Lancet Oncol"},{"key":"10681_CR30","unstructured":"https:\/\/screenpoint-medical.com\/ Cited February 2024"},{"key":"10681_CR31","unstructured":"https:\/\/www.volparahealth.com\/ Cited February 2024"},{"issue":"2","key":"10681_CR32","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1158\/1055-9965.EPI-09-1059","volume":"19","author":"Z Aitken","year":"2010","unstructured":"Aitken Z, McCormack VA, Highnam RP et al (2010) Screen-film mammographic density and breast cancer risk: a comparison of the volumetric standard mammogram form and the interactive threshold measurement methods. Cancer Epidemiol Biomarkers Prev 19(2):418\u2013428","journal-title":"Cancer Epidemiol Biomarkers Prev"},{"issue":"24","key":"10681_CR33","doi-asserted-by":"publisher","first-page":"8244","DOI":"10.1158\/1078-0432.CCR-08-0939","volume":"14","author":"RL Balleine","year":"2008","unstructured":"Balleine RL, Webster LR, Davis S et al (2008) Molecular grading of ductal carcinoma in situ of the breast. Clin Cancer Res 14(24):8244\u20138252","journal-title":"Clin Cancer Res"},{"issue":"6797","key":"10681_CR34","doi-asserted-by":"publisher","first-page":"747","DOI":"10.1038\/35021093","volume":"406","author":"CM Perou","year":"2000","unstructured":"Perou CM, S\u00f8rlie T, Eisen MB et al (2000) Molecular portraits of human breast tumours. Nature 406(6797):747\u201352","journal-title":"Nature"},{"issue":"1","key":"10681_CR35","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1186\/s12885-023-10890-7","volume":"23","author":"N Sharma","year":"2023","unstructured":"Sharma N, Ng AY, James JJ et al (2023) Multi-vendor evaluation of artificial intelligence as an independent reader for double reading in breast cancer screening on 275,900 mammograms. BMC Cancer 23(1):460","journal-title":"BMC Cancer"},{"key":"10681_CR36","doi-asserted-by":"publisher","unstructured":"Koch HW, Larsen M, Bartsch H et al (2024) How do AI-markings on screening mammograms correspond to cancer location? An informed review of 270 breast cancer cases in BreastScreen Norway. Eur Radiol.\u00a0https:\/\/doi.org\/10.1007\/s00330-024-10662-2","DOI":"10.1007\/s00330-024-10662-2"},{"key":"10681_CR37","doi-asserted-by":"crossref","unstructured":"Houssami N, Hunter K (2017) The epidemiology, radiology and biological characteristics of interval breast cancers in population mammography screening. NPJ Breast Cancer 3:12","DOI":"10.1038\/s41523-017-0014-x"},{"key":"10681_CR38","doi-asserted-by":"publisher","first-page":"S180","DOI":"10.1016\/j.acra.2021.03.022","volume":"1","author":"T Hovda","year":"2022","unstructured":"Hovda T, Hoff SR, Larsen M, Romundstad L, Sahlberg KK, Hofvind S (2022) True and missed interval cancer in organized mammographic screening: a retrospective review study of diagnostic and prior screening mammograms. Acad Radiol Suppl 1:S180\u2013S191. https:\/\/doi.org\/10.1016\/j.acra.2021.03.022","journal-title":"Acad Radiol Suppl"},{"issue":"9","key":"10681_CR39","doi-asserted-by":"publisher","first-page":"5974","DOI":"10.1007\/s00330-022-08711-9","volume":"32","author":"MA Martiniussen","year":"2022","unstructured":"Martiniussen MA, Sagstad S, Larsen M et al (2022) Screen-detected and interval breast cancer after concordant and discordant interpretations in a population based screening program using independent double reading. Eur Radiol 32(9):5974\u20135985","journal-title":"Eur Radiol"},{"issue":"3","key":"10681_CR40","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1148\/radiol.2021204432","volume":"300","author":"MC Pinto","year":"2021","unstructured":"Pinto MC, Rodriguez-Ruiz A, Pedersen K et al (2021) Impact of artificial intelligence decision support using deep learning on breast cancer screening interpretation with single-view wide-angle digital breast tomosynthesis. Radiology 300(3):529\u2013536","journal-title":"Radiology"},{"issue":"Suppl 2","key":"10681_CR41","first-page":"S22405","volume":"10","author":"K Dembrower","year":"2023","unstructured":"Dembrower K, Salim M, Eklund M, Lindholm P, Strand F (2023) Implications for downstream workload based on calibrating an artificial intelligence detection algorithm by standalone-reader or combined-reader sensitivity matching. J Med Imaging (Bellingham) 10(Suppl 2):S22405","journal-title":"J Med Imaging (Bellingham)"}],"container-title":["European Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-024-10681-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00330-024-10681-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-024-10681-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T13:12:28Z","timestamp":1726233148000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00330-024-10681-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,25]]},"references-count":41,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["10681"],"URL":"https:\/\/doi.org\/10.1007\/s00330-024-10681-z","relation":{},"ISSN":["1432-1084"],"issn-type":[{"value":"1432-1084","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,25]]},"assertion":[{"value":"25 September 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 January 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 February 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 March 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The scientific guarantor of this publication is Solveig Hofvind.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Guarantor"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"One of the authors (ML) has significant statistical expertise, but no complex statistical methods were necessary for this paper.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Statistics and biometry"}},{"value":"The study was approved by the Regional Committees for Medical and Health Research Ethics (#13294) and had a legal basis in accordance with Articles 6 (1) (e) and 9 (2) (j) of the GDPR. Pursuant to Section\u00a035 of the Health Research Act, the Regional Committees for Medical Research Ethics has granted the project exemption from the requirement of consent.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Institutional review board approval was obtained. The study was approved by the Regional Committee for Medical and Health Research Ethics (#2018\/2574).","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"A retrospective study using a cancer-enriched sample from Rogaland, 2010\u20132018, has been published (Koch HW, Larsen M, Bartsch H, Kurz KD, Hofvind S (2023) Artificial intelligence in BreastScreen Norway: a retrospective analysis of a cancer-enriched sample including 1254 breast cancer cases. Eur Radiol 33:3735\u20133743). This study uses data from a regular screening setting in Rogaland, 2017\u20132019, so there are some overlapping examinations from 2017 and 2018.","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Study subjects or cohorts overlap"}},{"value":"\u2022 retrospective\u2022 registry study\/observational study\u2022 performed at two breast centers","order":8,"name":"Ethics","group":{"name":"EthicsHeading","label":"Methodology"}}]}}