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 F. Conant

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publication16th International Workshop on Breast Imaging, IWBI 2022
EditorsHilde Bosmans, Nicholas Marshall, Chantal Van Ongeval
PublisherSPIE
ISBN (Electronic)9781510655843
DOIs
StatePublished - 2022
Event16th International Workshop on Breast Imaging, IWBI 2022 - Leuven, Belgium
Duration: May 22 2022May 25 2022

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume12286
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference16th International Workshop on Breast Imaging, IWBI 2022
Country/TerritoryBelgium
CityLeuven
Period05/22/2205/25/22

Keywords

  • artificial intelligence
  • Breast cancer risk
  • breast density
  • digital mammography
  • supplemental screening

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