European journal of radiology · 2026
Breast area affects the performance of a commercial artificial intelligence algorithm assessment of negative digital breast tomosynthesis exams.
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Barre Emily C, Ren Yinhao, Nguyen Derek L, Lo Joseph Y, Grimm Lars J
Аннотация
OBJECTIVE: To understand whether cancer-neutral image attributes (breast area and number of slices) impact an AI algorithm assessment of negative digital breast tomosynthesis (DBT) screening exams.
METHODS: This retrospective cohort study included women from a single institution whose screening mammogram was interpreted as negative between 2016 and 2019. All patients had at least 2 years follow-up without evidence of malignancy. Primary outcome measures were AI-calculated assessment of present and future likelihood of malignancy, quantified as a case and risk score. A multivariable linear regression model evaluated the relationship between patient demographics (age, race/ethnicity), image size (breast area, number of slices), and AI algorithm outputs (breast density, case score, risk score).
RESULTS: There were 4842 female patients included in the study (mean age 55.0 ± 10.6 years). For case score, there was a positive association with breast area (p < 0.0001), as well as older age, breast density (scattered vs fatty), and race (White vs Asian and Black vs White, all p < 0.05). For risk score, there was also a positive association with breast area (p < 0.001), as well as older age, breast density (scattered vs fatty, heterogeneously dense vs scattered, extremely dense vs heterogeneously dense), and race (White vs Asian, all p < 0.05). Number of DBT slices was not significantly associated with either case or risk scores.
CONCLUSION: Known breast cancer risk factors and one neutral characteristic (breast area), significantly impacted an AI algorithm's assessment of present and future likelihood of malignancy.
METHODS: This retrospective cohort study included women from a single institution whose screening mammogram was interpreted as negative between 2016 and 2019. All patients had at least 2 years follow-up without evidence of malignancy. Primary outcome measures were AI-calculated assessment of present and future likelihood of malignancy, quantified as a case and risk score. A multivariable linear regression model evaluated the relationship between patient demographics (age, race/ethnicity), image size (breast area, number of slices), and AI algorithm outputs (breast density, case score, risk score).
RESULTS: There were 4842 female patients included in the study (mean age 55.0 ± 10.6 years). For case score, there was a positive association with breast area (p < 0.0001), as well as older age, breast density (scattered vs fatty), and race (White vs Asian and Black vs White, all p < 0.05). For risk score, there was also a positive association with breast area (p < 0.001), as well as older age, breast density (scattered vs fatty, heterogeneously dense vs scattered, extremely dense vs heterogeneously dense), and race (White vs Asian, all p < 0.05). Number of DBT slices was not significantly associated with either case or risk scores.
CONCLUSION: Known breast cancer risk factors and one neutral characteristic (breast area), significantly impacted an AI algorithm's assessment of present and future likelihood of malignancy.