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

Abstract

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.

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.

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.

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.

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.