Selecting Women for Supplemental Breast Imaging using AI Biomarkers of Cancer Signs, Masking, and Risk

• Background: Traditional mammographic density aids in determining the need for supplemental imagingby MRI or ultrasound. However, AI image analysis, considering more subtle and complex image features,may enable a more effective identification of women requiring supplemental imaging.Purpose: To assess if AISmartDensity, an AI-based score considering cancer signs, masking, and risk,surpasses traditional mammographic density in identifying women for supplemental imaging after negativescreening mammography.Methods: This retrospective study included randomly selected breast cancer patients and healthy controlsat Karolinska University Hospital between 2008 and 2015. Bootstrapping simulated a 0.2% interval cancerrate. We included previous exams for diagnosed women and all exams for controls. AISmartDensity hadbeen developed using random mammograms from a population non-overlapping with the current studypopulation. We evaluated AISmartDensity to, based on negative screening mammograms, identify womenwith interval cancer and next-round screen-detected cancer. It was compared to age and density models, withsensitivity and PPV calculated for women with the top 8% scores, mimicking the proportion of BIRADS“extremely dense” category. Statistical significance was determined using the Student’s t-test.Results: The study involved 2043 women, 258 with breast cancer diagnosed within 3 years of a negativemammogram, and 1785 healthy controls. Diagnosed women had a median age of 57 years (IQR 16) versus53 years (IQR 15) for controls (p < .001). At the 92nd percentile, AISmartDenstiy identified 87 (33.67%)future cancers with PPV 1.68%, whereas mammographic density identified 34 (13.18%) with PPV 0.66%(p < .001). AISmartDensity identified 32% interval and 36% next-round cancers, versus mammographicdensity’s 16% and 10%. The combined mammographic density and age model yielded an AUC of 0.60,significantly lower than AISmartDensity’s 0.73 (p < .001).Conclusions: AISmartDensity, integrating cancer signs, masking, and risk, more effectively identifiedwomen for additional breast imaging than traditional age and density models. 

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