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The study includes 99,489 examinations from 74,941 women in BreastScreen Norway (2013–2019). An AI system assigns a malignancy risk score (1–10), and cases are stratified by mammographic density using Volpara density grade (VDG1–4). Results report a strong concentration of highest AI scores across density categories, with the highest proportion among extremely dense breasts. ",{"@graph":69,"@context":127},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/breast-ai-performance-by-mammographic-density-in-a-retrospective-cohort-study/333773/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/breast-ai-performance-by-mammographic-density-in-a-retrospective-cohort-study/333773.png","ImageObject",300,407,{"name":92,"@type":93},"Theodore","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119,123],{"name":110,"@type":111,"acceptedAnswer":112},"What is the study objective regarding AI and mammographic density?","Question",{"text":113,"@type":114},"The study explores whether an AI system can classify breast cancer by mammographic density in an organized screening program.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How were participants and examinations selected for analysis?",{"text":118,"@type":114},"The study included 99,489 examinations from 74,941 women who participated in BreastScreen Norway between 2013 and 2019.",{"name":120,"@type":111,"acceptedAnswer":121},"What approach did the AI system use for each examination?",{"text":122,"@type":114},"For each examination, the AI system assigned a malignancy risk score (AI score) from 1 (lowest) to 10 (highest).",{"name":124,"@type":111,"acceptedAnswer":125},"What did the study conclude about performance across density categories?",{"text":126,"@type":114},"The AI system performed well for cancer detection across all density categories, with particularly strong results for extremely dense breasts (VDG4).","https://schema.org",{"og:url":83,"og:type":129,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":131,"canonical":83},"index,follow",{"doc_id":133,"site_id":62},333773,1790232388,{"code":4,"msg":5,"data":136},{"doc_id":133,"user_id":137,"nickname":92,"user_avatar":138,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":139,"file_id":140,"file_url":141,"file_type":142,"file_size":143,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":105,"language":144,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":145,"faqs":146,"seo_title":147,"seo_description":67,"update_tm":148,"read_time":149,"preview_page_count":4},7971461740886,"https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2","Bergan et al. European Radiology (2024) 34:6298-6308  \n[https://doi.org/10.1007/s00330-024-10681-z](https://doi.org/10.1007/s00330-024-10681-z)  \nBREAST  \nAI performance by mammographic density   in a retrospective cohort study of 99,489 participants in BreastScreen Norway  \nMarie Burns Bergan 1, Marthe Larsen 1, Nataliia Moshina 1, Hauke Bartsch2, Henrik Wethe Koch3,4, Hildegunn Siv Aase5, Zhanbolat Satybaldinov2, Ingfrid Helene Salvesen Haldorsen2,6, Christoph I. Lee7,8 and Solveig Hofvind 1,9*  \nAbstract  \nObjective To explore the ability of artificial intelligence (AI) to classify breast cancer by mammographic density in an organized screening program.  \nMaterials and method We included information about 99,489 examinations from 74,941 women who participated in BreastScreen Norway, 2013–2019. 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), VDG1–4; VDG1 indicated fatty andVDG4 extremely dense breasts. Screen-detected and interval cancers with an AI score of 1–10 were stratified by VDG.  \nResults 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) of the examinations were classified as VDG1 and 6 . 1%(6047/99,489) as VDG4 . For screen-detected cancers, 84 .0%(68/81, 95% CI, 74 .1–91.2) had an AI score of 10 forVDG1, 88 .9%(328/369, 95% CI, 85 .2–91.9) forVDG2, 92.5%(185/200, 95% CI, 87 .9–95. 7) forVDG3, and 94 . 7%(36/38, 95% CI, 82 .3–99.4) forVDG4 . For interval cancers, the percentages with an AI score of 10 were 33 .3%(3/9, 95% CI, 7 .5–70. 1) forVDG1 and 48 .0%(12/25, 95% CI, 27 .8–68. 7) forVDG4 . Conclusion 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 .  \nClinical relevance statement 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.  \nKey Points  \n• Mammographic density is important to consider in the evaluation of artificial intelligence in mammographic screening.  \n• Given a threshold representing about 10% of those with the highest malignancy risk score by anAI system, we found an increasing percentage of cancers with increasing mammographic density.  \n• Artificial intelligence risk score and mammographic density combined may help triage examinations to reduce workload for radiologists.  \n*Correspondence: Solveig Hofvind[sshh@kreftregisteret. no](sshh@kreftregisteret. no)  \nFull list of author information is available at the end of the article  \n© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/.)[.](http://creativecommons.org/licenses/by/4.0/.)  \nBergan et al. European Radiol","cbCaignVDLogxjVA","https://ap.wps.com/l/cbCaignVDLogxjVA","pdf",1388426,"English","# Abstract\n# Objective\n# Materials and method\n# Results\n# Conclusion\n# Clinical relevance statement\n# Key Points\n# Introduction","[{\"question\":\"What is the study objective regarding AI and mammographic density?\",\"answer\":\"The study explores whether an AI system can classify breast cancer by mammographic density in an organized screening program.\"},{\"question\":\"How were participants and examinations selected for analysis?\",\"answer\":\"The study included 99,489 examinations from 74,941 women who participated in BreastScreen Norway between 2013 and 2019.\"},{\"question\":\"What approach did the AI system use for each examination?\",\"answer\":\"For each examination, the AI system assigned a malignancy risk score (AI score) from 1 (lowest) to 10 (highest).\"},{\"question\":\"What did the study conclude about performance across density categories?\",\"answer\":\"The AI system performed well for cancer detection across all density categories, with particularly strong results for extremely dense breasts (VDG4).\"}]","Breast - AI performance by mammographic density in a retrospective cohort study | PDF",1790004008,28]