Breast Cancers Detected and Missed by AI-CAD: Results from the AI-STREAM Trial

Abstract

Purpose To evaluate the characteristics of breast cancers detected and missed by artificial intelligence-based computer-assisted diagnosis (AI-CAD) during screening mammography. Materials and Methods This retrospective secondary analysis was conducted using data from the Artificial Intelligence for Breast Cancer Screening in Mammography trial (ClinicalTrials.gov: NCT05024591), a prospective, multicenter cohort study performed from 2021 to 2022. AI-CAD results were categorized into nine subgroups based on abnormality scores (in 10% increments). Positive predictive values of recall (PPV1s) were calculated for each subgroup and by breast density, and AI-CAD scores were compared with mammographic and pathologic features. Results A total of 24 543 women (mean age ± SD, 59.8 years ± 11.2), including two with bilateral cancer, were included; 148 cancers were confirmed by pathologic evaluation after 1 year of follow-up. AI-CAD results were negative in 23 010 cases (93.8%) and positive in 1535 (6.2%). The overall PPV1 was 8.7% (133 of 1535), with a sensitivity of 89.9% and specificity of 94.3%; PPV1 increased with higher abnormality scores but remained below 3% in groups 1 and 3 for dense breasts. AI-CAD detected 3.4% (five of 148) of cancers missed by radiologists but missed 8.1% (12 of 148) that were detected at radiologist recall. Abnormality scores were lower in patients presenting with mammographic asymmetry (<i>P</i> = .001) and luminal A subtype (<i>P</i> = .032). Conclusion AI-CAD shows potential to improve breast cancer detection in screening programs and to support radiologists in mammogram interpretation. Understanding the imaging and pathologic features of cancers detected or missed by AI-CAD may enhance its effective clinical application. <b>Keywords:</b> Breast Cancer, Mammography, AI CAD Clinical trial registration no. NCT05024591 © RSNA, 2025 See also commentary by Do and Bahl in this issue.

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