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A paired case–control study tested whether algorithms that perform well for cancer detection also perform well for risk assessment, and whether detection and risk performances correlate. Risk was assessed using the Mirai deep-learning model alongside three open-source detection algorithms, with discrimination measured by matched AUC.",{"@graph":69,"@context":122},[70,84,105],{"@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":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/breast-cancer-research-are-better-ai-algorithms-for-breast-cancer-detection-also-better-at-predicting-risk/331836/",{"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-cancer-research-are-better-ai-algorithms-for-breast-cancer-detection-also-better-at-predicting-risk/331836.png","ImageObject",300,407,{"name":92,"@type":93},"Himbo","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-22","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":29},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What hypothesis did the study test about AI algorithms?","Question",{"text":112,"@type":113},"The study hypothesized that AI algorithms previously shown to work well for breast cancer detection would also work well for breast cancer risk assessment, and that detection and risk performances would be correlated.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were cases and controls selected for the paired case–control design?",{"text":117,"@type":113},"Cases had invasive breast cancer or ductal carcinoma in situ and included paired mammograms from the diagnosis and the prior screening visit when no cancer was detected. Controls without cancer were matched 1:1 by age, screening site, and mammography machine type.",{"name":119,"@type":110,"acceptedAnswer":120},"Which algorithm performed best for both detection and risk assessment?",{"text":121,"@type":113},"Mirai performed best overall for risk assessment and detection, maintaining the same algorithm ranking across the paired mammograms.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},331836,1790812214,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":29,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":34,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144},687197100911,"https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697","Santeramo etal. Breast Cancer Research (2024) 26:25  \n[https://doi.org/10.1186/s13058-024-01775-z](https://doi.org/10.1186/s13058-024-01775-z)  \nBreast Cancer Research  \n RESEARCH Open Access  \nAre better AI algorithms for breast cancer  \ndetection also better at predicting risk? A paired case–control study  \nRuggiero Santeramo1,2*, Celeste Damiani 1,3, Jiefei Wei4, Giovanni Montana2,4* and Adam R. Brentnall 1*  \nAbstract  \nBackground There is increasing evidence that artificial intelligence (AI) breast cancer risk evaluation tools using digital mammograms are highly informative for 1–6 years following a negative screening examination. We hypothesized that algorithms that have previously been shown to work well for cancer detection will also work well for risk assessment and that performance of algorithms for detection and risk assessment is correlated.  \nMethods To evaluate our hypothesis, we designed a case-control study using paired mammograms at diagnosis and at the previous screening visit. The study included n = 3386 women from the OPTIMAM registry, that includes mammograms from women diagnosed with breast cancer in the English breast screening program 2010–2019.  \nCases were diagnosed with invasive breast cancer or ductal carcinoma in situ at screening and were selected if they had a mammogram available at the screening examination that led to detection, and a paired mammogram at their previous screening visit 3y prior to detection when no cancer was detected. Controls without cancer were matched 1:1 to cases based on age (year), screening site, and mammography machine type. Risk assessment was conducted using a deep-learning model designed for breast cancer risk assessment (Mirai), and three open-source deep-learning algorithms designed for breast cancer detection. Discrimination was assessed using a matched area under the curve (AUC) statistic.  \nResults Overall performance using the paired mammograms followed the same order by algorithm for risk assessment (AUC range 0 .59–0. 67) and detection (AUC 0 .81–0. 89), with Mirai performing best for both. There was also a correlation in performance for risk and detection within algorithms by cancer size, with much greater accuracy for large cancers (30 mm+, detection AUC: 0 .88–0. 92; risk AUC: 0 .64–0. 74) than smaller cancers (0 to \u003C 10 mm, detection AUC: 0.73–0. 86, risk AUC: 0 .54–0. 64) . Mirai was relatively strong for risk assessment of smaller cancers (0 to \u003C 10 mm, risk, Mirai AUC: 0.64 (95% CI 0.57 to 0 . 70); other algorithms AUC 0 .54–0. 56) .  \nConclusions Improvements in risk assessment could stem from enhancing cancer detection capabilities of smaller cancers. Other state-of-the-art AI detection algorithms with high performance for smaller cancers might achieve relatively high performance for risk assessment.  \nKeywords Breast cancer, Risk assessment, AI, Deep learning, Medical imaging  \n*Correspondence:  \nRuggiero Santeramo [ruggiero.santeramo.ai@gmail.com](ruggiero.santeramo.ai@gmail.com)[ ](ruggiero.santeramo.ai@gmail.com)Giovanni Montana [g.montana@warwick.ac.uk](g.montana@warwick.ac.uk)[ ](g.montana@warwick.ac.uk)Adam R. Brentnall[a.brentnall@qmul.ac.uk](a.brentnall@qmul.ac.uk)  \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 us","cbCaiitQA40urzzB","https://ap.wps.com/l/cbCaiitQA40urzzB","pdf",1050946,"English","# Abstract\n## Background\n## Methods\n## Results\n## Conclusions\n# Introduction","[{\"question\":\"What hypothesis did the study test about AI algorithms?\",\"answer\":\"The study hypothesized that AI algorithms previously shown to work well for breast cancer detection would also work well for breast cancer risk assessment, and that detection and risk performances would be correlated.\"},{\"question\":\"How were cases and controls selected for the paired case–control design?\",\"answer\":\"Cases had invasive breast cancer or ductal carcinoma in situ and included paired mammograms from the diagnosis and the prior screening visit when no cancer was detected. Controls without cancer were matched 1:1 by age, screening site, and mammography machine type.\"},{\"question\":\"Which algorithm performed best for both detection and risk assessment?\",\"answer\":\"Mirai performed best overall for risk assessment and detection, maintaining the same algorithm ranking across the paired mammograms.\"}]","Breast Cancer Research - Are better AI algorithms for breast cancer detection also better at predicting risk? | PDF",1789995413,18]