{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:23:01Z","timestamp":1792790581504,"version":"4.5.0"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T00:00:00Z","timestamp":1786579200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T00:00:00Z","timestamp":1786579200000},"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"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Radiol"],"abstract":"<jats:title>\n                    <jats:bold>Abstract<\/jats:bold>\n                  <\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>The sensitivity of mammographic screening is lower for women with mammographically dense vs fatty breasts. We aimed to explore automated mammographic breast density and malignancy risk scores generated by an artificial intelligence (AI) model for breast cancer detection, stratified by mammography vendor.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>This retrospective study included information from 200,000 examinations within BreastScreen Norway. An automated volumetric breast density (VBD) assessment and risk scores were obtained from a commercial AI model. The continuous VBD output from the AI model was categorized into four groups, VBD 1-4, with 15%, 40%, 40%, and 5% of the examinations in each group for exam level and for right and left breast. Area under the receiver operating characteristic curve (AUC) for breast cancer detection was calculated using a continuous AI risk score. Analyses were performed for exam-level and breast-level measures.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>At exam level, the difference in AUC estimates between the VBD groups was statistically\u00a0significant, with the highest AUC observed for VBD 1 (0.955, 95% CI: 0.937-0.992) and lowest for VBD 4 (0.857, 95% CI: 0.803-0.910). Stratified by vendor, 3.3% and 6.7% were classified as VBD 4 for A vs B. At breast level, 18.5% of the examinations were classified with different VBD groups for the right and left breast.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Despite high AUC estimates across all VBD groups and the potential advantages of an automated density assessment, several factors, such as vendor and breast vs exam level, must be carefully considered before implementing automated density\u00a0measures in screening programs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Question<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>What are the benefits and drawbacks of mammographic density assessment using an AI model for breast cancer detection<\/jats:italic>\n                      ?\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Findings<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>The AI model\u2019s performance for cancer detection was promising but decreased with increasing density category. Density distribution varied by equipment vendor and examination level<\/jats:italic>\n                      .\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Clinical relevance<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>AI risk assessment at the current examination was promising despite the variation by mammographic density. However, discrepancies in density by equipment vendors and examination level highlight the need for careful consideration before use in risk-stratified screening.<\/jats:italic>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00330-026-12786-z","type":"journal-article","created":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T12:37:21Z","timestamp":1786624641000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Mammographic density assessment by an artificial intelligence model for breast cancer detection in BreastScreen Norway"],"prefix":"10.1007","author":[{"given":"Marthe","family":"Larsen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nataliia","family":"Moshina","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karl \u00d8yvind","family":"Mikalsen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Solveig Roth","family":"Hoff","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"H\u00e5kon","family":"Lund-Hanssen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marit A.","family":"Martiniussen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marko","family":"Silberhorn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marte M.","family":"Schulze","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":[[2026,8,13]]},"reference":[{"key":"12786_CR1","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1002\/ijc.31043","volume":"142","author":"P Basu","year":"2018","unstructured":"Basu P, Ponti A, Anttila A et al (2018) Status of implementation and organization of cancer screening in the European Union Member States-Summary results from the second European screening report. 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The data were disclosed with a legal basis in the Cancer Registry Regulations Sections\u00a03\u20131 and the Personal Health Filing System Act.","order":4,"name":"Ethics","label":"Informed consent","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"Institutional Review Board approval was obtained, i.e., the study was approved by the Regional Committee for Medical and Health Research Ethics (2018\/13294).","order":5,"name":"Ethics","label":"Ethical approval","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"Overall performance for Transpara version 2.1 has been accepted for publication in European Radiology Larsen M, Lee CI, Bergan MB et al (2026) Performance across different versions of an AI model for screen-reading of mammograms.\n                      Eur Radiol\n                      .\u00a036:4422-4433\u00a0\n                      \n                      . There are some cohorts that overlap with the study sample from two breast centers using Siemens equipment.","order":6,"name":"Ethics","label":"Study subjects or cohorts overlap","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"order":7,"name":"Ethics","label":"Methodology","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}}]}}