{"id":1508,"date":"2024-04-10T20:11:14","date_gmt":"2024-04-10T20:11:14","guid":{"rendered":"https:\/\/www.lunit.io\/publication\/performance-of-an-ai-system-for-breast-cancer-detection-on-screening-mammograms-from-breastscreen-norway\/"},"modified":"2025-11-02T12:35:22","modified_gmt":"2025-11-02T12:35:22","slug":"performance-of-an-ai-system-for-breast-cancer-detection-on-screening-mammograms-from-breastscreen-norway","status":"publish","type":"publication","link":"https:\/\/www.lunit.io\/en\/publication\/performance-of-an-ai-system-for-breast-cancer-detection-on-screening-mammograms-from-breastscreen-norway\/","title":{"rendered":"Performance of an AI System for Breast Cancer Detection on Screening Mammograms from BreastScreen Norway"},"content":{"rendered":"

Performance of an Artificial Intelligence System for Breast Cancer Detection on Screening Mammograms from BreastScreen Norway<\/h3>\n

Marthe Larsen, Camilla F. Olstad, Christoph I. Lee, Tone Hovda, Solveig R. Hoff, Marit A. Martiniussen, Karl \u00d8yvind Mikalsen, H\u00e5kon Lund-Hanssen, Helene S. Solli, Marko Silberhorn, \u00c5se \u00d8. Sulheim, Steinar Auensen, Jan F. Nyg\u00e5rd, Solveig Hofvind<\/p>\n

Radiology: Artificial Intelligence, 2024<\/strong><\/p>\n

Abstract<\/strong>
\nPurpose<\/strong>
\nTo explore the stand-alone breast cancer detection performance, at different risk score thresholds, of a commercially available artificial intelligence (AI) system.<\/p>\n

Materials and Methods<\/strong>
\nThis retrospective study included information from 661\u2009695 digital mammographic examinations performed among 242\u2009629 female individuals screened as a part of BreastScreen Norway, 2004\u20132018. The study sample included 3807 screen-detected cancers and 1110 interval breast cancers. A continuous examination-level risk score by the AI system was used to measure performance as the area under the receiver operating characteristic curve (AUC) with 95% CIs and cancer detection at different AI risk score thresholds.<\/p>\n

Results<\/strong>
\nThe AUC of the AI system was 0.93 (95% CI: 0.92, 0.93) for screen-detected cancers and interval breast cancers combined and 0.97 (95% CI: 0.97, 0.97) for screen-detected cancers. In a setting where 10% of the examinations with the highest AI risk scores were defined as positive and 90% with the lowest scores as negative, 92.0% (3502 of 3807) of the screen-detected cancers and 44.6% (495 of 1110) of the interval breast cancers were identified with AI. In this scenario, 68.5% (10\u2009987 of 16\u2009040) of false-positive screening results (negative recall assessment) were considered negative by AI. When 50% was used as the cutoff, 99.3% (3781 of 3807) of the screen-detected cancers and 85.2% (946 of 1110) of the interval breast cancers were identified as positive by AI, whereas 17.0% (2725 of 16\u2009040) of the false-positive results were considered negative.<\/p>\n

Conclusion<\/strong>
\nThe AI system showed high performance in detecting breast cancers within 2 years of screening mammography and a potential for use to triage low-risk mammograms to reduce radiologist workload.<\/p>\n

Read the full paper<\/strong><\/a><\/p>\n","protected":false},"featured_media":0,"template":"","publication-oncology":[],"publication-region":[89],"publication-type":[],"radiology":[97,99,96],"class_list":["post-1508","publication","type-publication","status-publish","hentry","publication-region-europe","radiology-breast","radiology-earlier-detection-including-interval-cancers","radiology-lunit-insight"],"acf":[],"yoast_head":"\nPerformance of an AI System for Breast Cancer Detection on Screening Mammograms from BreastScreen Norway - Lunit<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.lunit.io\/en\/publication\/performance-of-an-ai-system-for-breast-cancer-detection-on-screening-mammograms-from-breastscreen-norway\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Performance of an AI System for Breast Cancer Detection on Screening Mammograms from BreastScreen Norway - Lunit\" \/>\n<meta property=\"og:description\" content=\"Performance of an Artificial Intelligence System for Breast Cancer Detection on Screening Mammograms from BreastScreen Norway Marthe Larsen, Camilla F. 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