Dr. Anne Marie McCarthy Profile

Dr. Anne Marie McCarthy

at Univ. of Pennsylvania

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Proceedings Article | 13 July 2022 Paper

External validation of an AI-driven breast cancer risk prediction model in a racially diverse cohort of women undergoing mammographic screening

Aimilia Gastounioti, Mikael Eriksson, Eric Cohen, Walter Mankowski, Lauren Pantalone, Anne Marie McCarthy, Despina Kontos, Per Hall, Emily Conant

Proceedings Volume 12286, 1228617 (2022) https://doi.org/10.1117/12.2627140

KEYWORDS: Breast cancer, Artificial intelligence, Breast, Digital mammography, Machine learning

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The aim of this retrospective case-cohort study was to perform additional validation of an artificial intelligence (AI)-driven breast cancer risk model in a racially diverse cohort of women undergoing screening. We included 176 breast cancer cases with non-actionable mammographic screening exams 3 months to 2 years prior to cancer diagnosis and a random sample of 4,963 controls from women with non-actionable mammographic screening exams and at least one-year of negative follow-up (Hospital University Pennsylvania, PA, USA; 9/1/2010-1/6/2015). A risk score for each woman was extracted from full-field digital mammography (FFDM) images via an AI risk prediction model, previously developed and validated in a Swedish screening cohort. The performance of the AI risk model was assessed via age-adjusted area under the ROC curve (AUC) for the entire cohort, as well as for the two largest racial subgroups (White and Black). The performance of the Gail 5-year risk model was also evaluated for comparison purposes. The AI risk model demonstrated an AUC for all women = 0.68 95% CIs [0.64, 0.72]; for White = 0.67 [0.61, 0.72]; for Black = 0.70 [0.65, 0.76]. The AI risk model significantly outperformed the Gail risk model for all women (AUC = 0.68 vs AUC = 0.55, p<0.01) and for Black women (AUC = 0.71 vs AUC = 0.48, p<0.01), but not for White women (AUC = 0.66 vs AUC = 0.61, p=0.38). Preliminary findings in an independent dataset suggest a promising performance of the AI risk prediction model in a racially diverse breast cancer screening cohort.

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