Prospective Evaluation of AI Risk Stratification for Triaging Expedited Screening Mammogram Interpretation
Chung, M.; Davis, E.; Greenwood, H.; Hayward, J.; Chou, S.-H.; Joe, B.; Strachowski, L.; Kelil, T.; Freimanis, R.; Price, E.; Ray, K.; Lee, A.; Yala, A.
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PURPOSETo prospectively evaluate the feasibility and performance of expedited screening mammogram interpretation for women identified as high-risk by a deep learning risk model. METHODS AND MATERIALSThis HIPAA-compliant, IRB-approved prospective controlled study was conducted at an urban safety-net facility. The Mirai breast cancer risk model was retrospectively validated on 114,229 local mammograms (2006-2023) to identify the top 10% of 1-year breast cancer risk threshold. During the prospective study (12/2024-6/2025), Mirai 1-year risk scores were generated in real-time. On enrollment days, high-risk women were consented and offered immediate screening mammogram interpretation. Patients assessed as BI-RADS 0 were offered same-day diagnostic evaluation when feasible. Outcomes included feasibility of immediate interpretation, time to screening result (Ts), diagnostic evaluation (Td), and biopsy (Tb), as well as cancer detection rate (CDR). Comparisons were made with high-risk controls on non-enrollment days. RESULTSAmong 4,145 screening mammograms, Mirai flagged 525 (12.7%) as high-risk; 973 (23.5%) were performed on enrollment days with 115 (11.8%) flagged as high-risk. Of 100 women who consented, 94% received immediate reads. Thirty-one were assessed as BI-RADS 0; 30 underwent diagnostic imaging (26 same-day). Thirteen biopsies yielded 6 malignant, 2 high-risk, and 5 benign lesions. The CDR in high-risk expedited women was 60/1,000 (95% CI, 22.3-126.0) compared with 2.3/1,000 (95% CI, 0.3-8.4) in non-high-risk women (odds ratio 27.1; p<0.001). Median Ts, Td, and Tb were significantly shorter in expedited patients vs. high-risk controls (13.0 min vs. 191.9 min; 1.3 hrs vs. 852.8 hrs; 20.1 vs. 59.0 days; all p<0.001). For screen-detected cancers, expedited interpretation reduced mean Ts, Td, and Tb by 99.1%, 99.1%, and 87.2%, respectively. CONCLUSIONIntegrating an AI risk model into mammography workflow is feasible and enables same-day evaluation for high-risk women. This approach markedly shortens time to diagnostic imaging and biopsy for timely breast cancer care.
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