{"id":1467,"date":"2023-04-27T20:11:07","date_gmt":"2023-04-27T20:11:07","guid":{"rendered":"https:\/\/www.lunit.io\/publication\/impact-of-artificial-intelligence-system-and-volumetric-density-on-risk-prediction-of-interval-screen-detected-and-advanced-breast-cancer\/"},"modified":"2025-11-02T13:08:24","modified_gmt":"2025-11-02T13:08:24","slug":"impact-of-artificial-intelligence-system-and-volumetric-density-on-risk-prediction-of-interval-screen-detected-and-advanced-breast-cancer","status":"publish","type":"publication","link":"https:\/\/www.lunit.io\/en\/publication\/impact-of-artificial-intelligence-system-and-volumetric-density-on-risk-prediction-of-interval-screen-detected-and-advanced-breast-cancer\/","title":{"rendered":"Impact of artificial intelligence system and volumetric density on risk prediction of interval, screen-detected, and advanced breast cancer"},"content":{"rendered":"

Impact of Artificial Intelligence System and Volumetric Density on Risk Prediction of Interval, Screen-Detected, and Advanced Breast Cancer<\/h3>\n

C. M. Vachon; C. G. Scott; A. D. Norman; S. A. Khanani; M. R. Jensen; C. B. Hruska; K. R. Brandt; S. J. Winham; K. Kerlikowske<\/p>\n

Journal of Clinical Oncology, 2023<\/strong><\/p>\n

Abstract<\/strong>
\nPurpose<\/strong>
\nArtificial intelligence (AI) algorithms improve breast cancer detection on mammography, but their contribution to long-term risk prediction for advanced and interval cancers is unknown.<\/p>\n

Methods<\/strong>
\nWe identified 2,412 women with invasive breast cancer and 4,995 controls matched on age, race, and date of mammogram, from two US mammography cohorts, who had two-dimensional full-field digital mammograms performed 2-5.5 years before cancer diagnosis. We assessed Breast Imaging Reporting and Data System density, an AI malignancy score (1-10), and volumetric density measures. We used conditional logistic regression to estimate odds ratios (ORs), 95% CIs, adjusted for age and BMI, and C-statistics (AUC) to describe the association of AI score with invasive cancer and its contribution to models with breast density measures. Likelihood ratio tests (LRTs) and bootstrapping methods were used to compare model performance.<\/p>\n

Results<\/strong>
\nOn mammograms between 2-5.5 years prior to cancer, a one unit increase in AI score was associated with 20% greater odds of invasive breast cancer (OR, 1.20; 95% CI, 1.17 to 1.22; AUC, 0.63; 95% CI, 0.62 to 0.64) and was similarly predictive of interval (OR, 1.20; 95% CI, 1.13 to 1.27; AUC, 0.63) and advanced cancers (OR, 1.23; 95% CI, 1.16 to 1.31; AUC, 0.64) and in dense (OR, 1.18; 95% CI, 1.15 to 1.22; AUC, 0.66) breasts. AI score improved prediction of all cancer types in models with density measures (PLRT values < .001); discrimination improved for advanced cancer (ie, AUC for dense volume increased from 0.624 to 0.679, \u0394 AUC 0.065, P = .01) but did not reach statistical significance for interval cancer.<\/p>\n

Conclusion<\/strong>
\nAI imaging algorithms coupled with breast density independently contribute to long-term risk prediction of invasive breast cancers, in particular, advanced cancer.<\/p>\n

Read the full paper<\/strong><\/a><\/p>\n","protected":false},"featured_media":0,"template":"","publication-oncology":[],"publication-region":[91],"publication-type":[],"radiology":[122,121],"class_list":["post-1467","publication","type-publication","status-publish","hentry","publication-region-north-america","radiology-breast-cancer-risk-and-density-volpara","radiology-volpara"],"acf":[],"yoast_head":"\nImpact of artificial intelligence system and volumetric density on risk prediction of interval, screen-detected, and advanced breast cancer - 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\/impact-of-artificial-intelligence-system-and-volumetric-density-on-risk-prediction-of-interval-screen-detected-and-advanced-breast-cancer\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Impact of artificial intelligence system and volumetric density on risk prediction of interval, screen-detected, and advanced breast cancer - Lunit\" \/>\n<meta property=\"og:description\" content=\"Impact of Artificial Intelligence System and Volumetric Density on Risk Prediction of Interval, Screen-Detected, and Advanced Breast Cancer C. M. Vachon; C. G. Scott; A. D. Norman; S. A. Khanani; M. R. Jensen; C. B. Hruska; K. R. Brandt; S. J. Winham; K. 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