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Breast density was quantified on one screening mammogram per participant using Volpara Density Grade 4th and 5th edition, Quantra Density Assessment Software, and iCAD Density Assessment. Agreement between categorical outputs was assessed via weighted kappa statistics. Breast cancer cases were identified through linkage with the Netherlands Cancer Registry (follow-up until December 2022). Associations with five-year breast cancer risk were analyzed using Cox proportional hazards models. Discriminative ability was evaluated using the area under the receiver operating characteristic curve (AUC).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Good agreement was observed between algorithms (kappa: 0.725\u20130.826); the strongest across-vendor agreement was between Volpara Density Grade 5th edition and Quantra Density Assessment Software (kappa\u2009=\u20090.795). However, the proportion of women classified with extremely dense breasts (category D) varied (3.1\u20138.4%). During a median follow-up of 4.2 years, 777 breast cancer cases were identified. Both breast cancer risk and interval cancer risk increased with higher density, with similar magnitudes across algorithms. Categorical measures yielded similar AUCs to distinguish between women with and without breast cancer (0.53\u20130.55) and between screen-detected and interval cancers (0.57\u20130.60).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Three automated density assessment algorithms show strong agreement and comparable associations with breast cancer risk. However, differences in categorization\u2014particularly the proportions classified as entirely fatty or extremely dense\u2014should be considered in personalized screening strategies, as these may influence the capacity needed for potential supplemental screening.<\/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>How do three automated breast density measurement algorithms compare in terms of agreement and their association with breast cancer risk in a large screening cohort<\/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 three algorithms showed strong agreement (kappa<\/jats:italic>\n                      \u2009=\u2009\n                      <jats:italic>0.725\u20130.826) and similar associations with breast cancer risk. Proportions of mammograms classified as density categories A or D varied<\/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>The strong agreement and similar associations with breast cancer risk support algorithm interchangeability, but variations in the proportion of mammograms classified as entirely fatty (category A) or extremely dense (category D) may impact capacity requirements for personalized screening<\/jats:italic>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00330-026-12681-7","type":"journal-article","created":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T14:02:35Z","timestamp":1781100155000},"page":"8347-8358","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Comparison of three algorithms to measure breast density on mammograms in a population-based screening cohort"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6115-9678","authenticated-orcid":false,"given":"Jim","family":"Peters","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dani\u00eblle","family":"van der Waal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carla H.","family":"van Gils","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mireille J. 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C.H.v.G. is the principal investigator of the DENSE trial, financially supported by the University Medical Center Utrecht (UMC Utrecht, UMCU DENSE), the Netherlands Organization for Health Research and Development (ZonMw ZONMW-200320002-UMCU, ZonMW Preventie 50\u201353125\u201398\u2013014), KWF Kankerbestrijding (DCS-UU-2009\u20134348, UU-2014\u20136859, UU-2014\u20137151), the Dutch Pink Ribbon\/A Sister\u2019s Hope (Pink Ribbon-10074), Bayer AG Pharmaceuticals, Radiology (project number: BSP-DENSE), and Stichting Kankerpreventie Midden-West. C.H.v.G. is also the principal investigator of the DENSE-2 trial, financially supported by the Dutch Organisation for Knowledge and Innovation in Health, Healthcare and Well-being (ZonMw) (project number: 05550412310002), Bayer, Hologic, and Siemens Healthineers. C.H.v.G. is the director of the Dutch Cancer Society. M.J.M.B. received grants or has grants pending to the institution from ScreenPoint Medical, Sectra Benelux, Hologic, Volpara Solutions, Lunit, and iCAD. M.J.M.B. received speaker fees and travel support from Hologic and Siemens Healthcare.","order":2,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"All authors have significant statistical expertise.","order":3,"name":"Ethics","label":"Statistics and biometry","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"Written informed consent was obtained from all subjects (patients) in this study.","order":4,"name":"Ethics","label":"Informed consent","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"Institutional Review Board approval was obtained. All scientific content, data interpretation, and conclusions were developed and verified solely by the authors.","order":5,"name":"Ethics","label":"Ethical approval","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"Not applicable.","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"}}]}}