Artificial Intelligence-Based Computer-Aided Detection in Breast Cancer Diagnosis: Variation by Breast Density, Imaging Feature, and Tumor Characteristics.

RATIONALE AND OBJECTIVES: To determine whether an FDA-approved artificial intelligence computer-aided detection and diagnosis (AI-CAD) system assigns varying case scores based on imaging features, tumor characteristics, and breast density. MATERIALS AND METHODS: This retrospective multisite study included patients undergoing biopsy after abnormal screening tomosynthesis across four U.S. states in 2021. A commercially available AI-CAD tool generated case-level scores (range 0-100). Imaging features were classified as calcifications, mass, mass with calcifications, architectural distortion/asymmetry, or nodal-only metastasis. Breast density was dichotomized as dense or non-dense. Tumor size, grade and pathology were also collected. Associations were assessed using univariable and multivariable linear regression. RESULTS: Breast cancer was diagnosed in 735/3899 patients. The mean age and case score for patients with breast cancer were 65.3±11.7 years and 78.1±22.9, respectively, both significantly higher than those of non-malignant cases (58.9±11.1 years and 31.8±25.9; p<0.001). Scores were highest for calcifications (91.6±15.2) and mass with calcifications (90.5±16.3), both significantly higher than mass alone (79.1±23.6; p<0.01). In univariable analysis, ductal carcinoma in-situ (DCIS) had higher scores than invasive cancers (p<0.05). However, in multivariable analysis, higher scores were associated with non-dense breasts, higher tumor grade, larger tumor size, older age, and specific imaging features, such as calcifications, whereas the association with pathologic subtype was attenuated. CONCLUSION: Our findings suggest AI-CAD scores differ by breast density, imaging presentation, and tumor characteristics. AI outputs may reflect a combination of imaging features and associated tumor characteristics, highlighting the importance of context-informed interpretation of AI outputs in clinical practice.